P(VDF TrFE) piezoelectric sensor for Internet of … · Chow et al., 2018 43 Figure 3 Electrical properties: Voltage output at resonance frequency. 3. RESULTS AND DISCUSSION After

Proceedings of Mechanical Engineering Research Day 2018, pp. 42-43, May 2018

__________

© Centre for Advanced Research on Energy

P(VDF-TrFE) piezoelectric sensor for Internet of Things application Khoon-Keat Chow1,2,*, Swee-Leong Kok2,*, Kok-Tee Lau3, Ali Mohammed Abdal-Kadhim2

1) Department of Electrical Engineering, Politeknik Ungku Omar,

Jalan Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia. 2) Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 3) Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia.

*Corresponding e-mail: [email protected]; [email protected]

Keywords: piezoelectric sensor; P(VDF-TrFE); Internet of Things (IoT)

ABSTRACT – This paper presents the conversion of

mechanical vibration into electrical energy using

P(VDF-TrFE) piezoelectric sensor. The sensor consisted

of three materials with two layers of electrode in

between of P(VDF-TrFE) layer on top of the flexible

substrate as PET. The 2D model was conducted by

using COMSOL Multiphysics 5.1. Results of simulation

shown maximum of displacement and stress were

obtained 1.01×107 N/m2 and 0.3mm at resonance

frequency of 131Hz under acceleration of 1g with

maximum output voltage of 20.2mV. This P(VDF-TrFE)

piezoelectric sensor can be used for the evolution of

internet of things (IoT) where IoT system can be

connected with number of sensors.

1. INTRODUCTION

Nowadays, the greatest challenge faced by

advances technology in wireless sensor networks and

internet of things application is energy. Most of these

devices and sensor are portable and powered by

conventional battery but their life span of battery is

short. Furthermore, the process of replacing battery is

complicated task because these electronic systems could

shut down at any time and some devices can be placed

in remote area such as under bridge or structural sensor

[1-2].

To overcome the battery replacement issue, energy

harvesting is an alternative solution to harvest energy

from ambient environment from mechanical vibration

into electrical energy using piezoelectric materials such

as lead zirconate titanate (PZT), polyvinylidene fluoride

(PVDF), poly(vinylidene fluoride) trifluoroethylene

P(VDF-TrFE) and aluminium nitride (AIN) [3-4]. Due

to the flexible structure application, polymer

piezoelectric materials like PVDF and P(VDF-TrFe) are

the option to be used in energy harvesting application

usage. Previous researcher harvested 0.95 mV PVDF

wafer active sensor [5]. In this study demonstrates the

finite element method (FEM) simulation for P(VDF-

TrFE) piezoelectric as a solution of battery-less for

sensor used in internet of things application. The stress

generation, displacement, resonance frequency and

voltage output have been studied to optimize the

physical parameters of this piezoelectric sensor.

2. METHODOLOGY

A 2D model was designed and simulated using

Comsol Multiphysics 5.1 to analyse the electrical and

mechanical properties. This P(VDF-TrFE) piezoelectric

sensor was simulated based on electrostatics and

structural mechanics interface. The 2D model was

meshed using quadrilateral elements with 1050 fine

elements and minimum meshing size of 0.012, as shown

in the Figure 1.

Figure 1 2D meshed geometry.

Figure 2 Mechanical properties: Resonant frequency for

displacement and von Mises stress contour.

In the mechanical properties, the maximum

displacement was 0.3mm and the maximum von Mises

stress generated about 1.01×107 N/m2 at the resonance

frequency of 131 Hz which its shown in the Figure 2.

Then, frequency domain analysis was able to generate

voltage output about 20.2mV at 131Hz as shown in

Figure 3.

Chow et al., 2018

43

Figure 3 Electrical properties: Voltage output at

resonance frequency.

3. RESULTS AND DISCUSSION

After the COMSOL simulation results, the P(VDF-

TrFE) piezoelectric sensor was fabricated and the

process of fabrication steps have been reported in

previous research paper [6]. Then, it was demonstrated

by using IoT application as shown in the Figure 4 and

the prototype system of P(VDF-TrFE) piezoelectric

sensor was able to generate output voltage about 743mV

when a force of 5N from index finger.

Figure 4 An image and block diagram of the system.

4. CONCLUSION

In this finite element simulation, the electrical and

mechanical properties of P(VDF-TrFE) piezoelectric is

successfully performed. The simulation results shown

maximum stress and displacement were obtained

1.01×107 N/m2 and 0.3mm. It’s also generates maximum

output about 20.2 mV where under acceleration of 1g at

at resonance frequency of 131 Hz. This study shows that

P(VDF-TrFE) piezoelectric can be used as a sensor by

using IoT application without using battery source for

sensor application.

ACKNOWLEDGEMENT

The authors would like to thank the Ministry of

Higher Education of Malaysia for the research grant of

and research grant of PRGS/1/2016/TK10/FKEKK-

CETRI/02/T00016 and UTeM-Industry Matching

GLUAR/IMPRESSIVE/2017/FKEKK-CETRI/I00024

and the support facility provided by Advanced Sensors

and Embedded Control Systems Research Group

(ASECs), UTeM.

REFERENCES

[1] Chuang, C. H., Lee, D. H., Chang, W. J., Weng, W.

C., Shaikh, M. O., & Huang, C. L. (2017). Real-

time monitoring via patch-type piezoelectric force

sensors for Internet of Things based

logistics. IEEE Sensors Journal, 17(8), 2498-2506.

[2] Seah, W. K., Eu, Z. A., & Tan, H. P. (2009, May).

Wireless sensor networks powered by ambient

energy harvesting (WSN-HEAP)-Survey and

challenges. In Wireless Communication, Vehicular

Technology, Information Theory and Aerospace &

Electronic Systems Technology, 2009. Wireless

VITAE 2009. 1st International Conference on, 1-5.

[3] Vatansever, D., Hadimani, R. L., Shah, T., &

Siores, E. (2011). An investigation of energy

harvesting from renewable sources with PVDF and

PZT. Smart Materials and Structures, 20(5), 1-6.

[4] Lavrik, N. V., Sepaniak, M. J., & Datskos, P. G.

(2004). Cantilever transducers as a platform for

chemical and biological sensors. Review of

Scientific Instruments, 75(7), 2229-2253.

[5] Lin, B., & Giurgiutiu, V. (2006). Modeling and

testing of PZT and PVDF piezoelectric wafer

active sensors. Smart Materials and

Structures, 15(4), 1085-1093.

[6] Chow, K. K., Kok, S. L., & Lau, K. T. (2017).

Fabrication and characterization of Piezoelectric P

(VDF-TrFE) thick film on Flexible

Substrate. APRN Journal of Engineering and

Applied Sciences, 12(10), 3347-3351.

Figure 5 An image of the prototype.


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Study of milling parameter effect of AISI 4340 alloy steel using FEM simulation

Afifah Juri, Shalina Sheik Muhamad, Jaharah A. Ghani*, Che Hassan Che Haron

Department of Mechnical and Materals Engneering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.

*Corresponding e-mail: [email protected]

Keywords: AISI 4340; thirdwave advantedge; cutting force; cutting Temperature

ABSTRACT – Nowadays, there is significant progress

in the development of simulation and modeling for

machining operations. The aim of this paper is to

investigate the effect of milling parameters in terms of

cutting temperature and cutting force by using Finite

Element Method (FEM). Simulations were performed

using Thirdwave AdvantEdge of the AISI 4340 steel

using uncoated carbide cutting tool. The milling

parameters used were cutting speed of 180- 220 m/min,

feed rate of 0.1- 0.2 mm/tooth and depth of cut at 0.3-

0.7 mm under dry condition. Analysis of variance

(ANOVA) was executed to find a significant parameter

that affects the machining responses. The analysis of the

result shows that cutting speed is the most significant

parameter affecting the cutting temperature and cutting

force on the end milling process.

1. INTRODUCTION

Finite element method is a very useful tool to

study the cutting process of the material. Thirdwave

AdvantEdge software has been used for machining

process simulation. It is a typical program written

specifically for machining simulations. It can generate

simulation results such as temperatures in the tool- chip

interface, as well as cutting forces; during the chip

formation much faster than using costly and time

consuming experiments [1]. Several studies have been

done on modeling and simulation using Thirdwave

AdvantEdge software in analyzing the cutting

temperature and cutting force during the machining

process. Kadirgama et al., [2] used Thirdwave

AdvantEdge software when machining Hastelloy C-

22HS with carbide coated cutting tool to determine

temperature and heat generated at the tool tip. Qasim et

al. [3] used FEM to find optimal parameters of the

orthogonal cutting process of AISI 1045 steel in order to

reduce the cutting forces. It is shown from ANOVA that

the depth of cut and feed rate are the most significant

factor affecting the cutting force.

The problems during machining are due to heat

generation and temperature associated with it. In metal

cutting, the magnitude of the temperature and friction

occur at the tool-chip interface is a function of the

cutting parameters. The study of cutting temperature

generated and cutting force are important due to their

effect on the machining responses. Therefore, this study

is to determine the temperatures at the tool and chip

interface and cutting force to determine the optimum the

cutting condition.

2. METHODOLOGY

The commercial FEM software of Third Wave

AdvantEdge (v6.4) was used to simulate the milling

process in a two dimensional (2D). Figure 1 shows the

schematic of an orthogonal cutting condition model.

Figure 1 Simulation model.

The cutting parameter for this study is shown in

Table 1. Nine sets of simulation combinations were

generated by using Taguchi design of experiment. By

applying Taguchi, the S/N ratios were calculated from

temperature and resultant force using larger is better and

smaller is better respectively.

Table 1 Cutting parameter and their levels.

Cutting parameters Level 1 Level 2 Level 3

Cutting speed

(m/min) 180 200 220

Feed (mm/tooth) 0.1 0.15 0.2

Axial depth of cut

(mm) 0.3 0.5 0.7

The workpiece material was high strength low

alloy steel AISI 4340. The operation is simulated using

insert carbide of DNMA432 type that has a nose angle

of 55 deg and without the use of coolant. The tool was

defined to be a rigid body which considers thermal

transfer for modeling the cutting temperature field. The

workpiece was meshed for a maximum number of

24000 nodes. The maximum and minimum element

sizes for both workpiece and insert were set at 0.1 mm

and 0.02 mm, respectively. The mesh refinement factor

was set at the value of 2, and the coarsening factor was

set at 6.

Juri et al., 2018

45


3.1 Cutting temperature

The isothermal contours of the temperature

distributions for dry condition are shown in Figure 2

(Vc: 180 m/min, fz: 0.1 mm/tooth, ap: 0.3 mm). Table 2

shows the cutting temperature obtained by simulations.

Figure 2 Simulation of cutting temperatures at Vc: 180

m/min, fz: 0.1 mm/tooth, ap: 0.3 mm.

Table 2 Cutting temperature obtained by simulation.

Vc

m/min

fz

mm/tooth

ap

mm

Cutting

temperature (0C)

180 0.10 0.3 959.99

180 0.15 0.5 975.748

180 0.20 0.7 948.033

200 0.10 0.5 953.346

200 0.15 0.7 962.458

200 0.20 0.3 978.136

220 0.10 0.7 969.642

220 0.15 0.3 1010.93

220 0.20 0.5 982.21

Table 2 shows the highest temperature is generated

at cutting speed of 220 m/min, feed rate 0.15 mm/tooth,

and depth of cut 0.3 mm. While the lowest temperature

generated is at cutting speed 180 m/min, feed rate 0.2

mm/ tooth, and depth of cut 0.7 mm. In order to

minimise force generates are at the combination of low

speed (180 m/min), low feed (0.1 mm/tooth) and high

depth of cut (0.7 mm). Anova analysis found that the

cutting speed contributes 43.55 %, followed by depth

of cut of 28.08% and feed rate of 26.25% to the

temperature generated. Therefore, cutting speed has the

major effect on temperature generated, which is similar

finding with Das et al. [4].

3.2 Cutting force

Table 3 shows the cutting force in Fx and Fy

obtained from the simulations and calculated resultant

force FR.

From Table 3, the highest cutting force FR is

generated at cutting speed of 220 m/min, feed rate 0.15

mm/tooth, and depth of cut 0.3 mm. While the lowest

cutting force is generated at cutting speed 180 m/min,

feed rate 0.2 mm/ tooth, and depth of cut 0.7 mm. In

order for maximise temperature generates during the

milling the variables are the combination of high speed

(220 m/min), medium feed rate (0.15 mm/tooth) and

low depth of cut (0.3 mm). ANOVA analysis shown that

the cutting speed contributes 75.73%, followed by feed

rate of 12.71% and depth of cut of 11.18 % of the

cutting force generated. Therefore, the cutting speed is

the most influential effect affecting the cutting force.

Table 3 Cutting force obtained by simulation.

Vc

m/min

fz

mm/tooth

ap

mm

Fx

N

Fy

N

FR

N

180 0.10 0.3 444.99 816.09 972.23

180 0.15 0.5 528.72 862.12 972.98

180 0.20 0.7 539.79 878.76 967.44

200 0.10 0.5 490.66 840.21 975.74

200 0.15 0.7 532.21 871.94 994.34

200 0.20 0.3 489.63 839.94 1002.50

220 0.10 0.7 558.93 889.29 997.74

220 0.15 0.3 493.42 845.78 1028.17

220 0.20 0.5 577.11 926.93 1013.37

4. CONCLUSION

From this study, it can be concluded that the FEM

simulation is able to help the researcher to evaluate the

machinability of a particular material such as AISI

4340. From the results obtained it can be concluded that

the cutting speed is the most significant parameter

affecting the cutting temperature and cutting force.

ACKNOWLEDGEMENT

The authors would like to thank the Government

of Malaysia and Universiti Kebangsaan Malaysia for

their financial support under a

FRGS/1/2016/TK03/UKM/01/1 and GUP-2017-048

Grants.

REFERENCES

[1] Maňková, I., Kovac, P., Kundrak, J., & Beňo, J.

(2011). Finite element analysis of hardened steel

cutting. Journal of Production Engineering, 14, 7-

10.

[2] Kadirgama, K., Noor, M. M., & Rahman, M. M.

(2010). Heat Distribution Analysis In End-Milling.

National Conference in Mechanical Engineering

Research, 326-335.

[3] Qasim, A., Nisar, S., Shah, A., Khalid, M. S., &

Sheikh, M. A. (2015). Optimization of process

parameters for machining of AISI-1045 steel using

Taguchi design and ANOVA. Simulation

Modelling Practice and Theory, 59, 36-51.

[4] Das, S. R., Nayak, R. P., & Dhupal, D. (2012).

Optimization of cutting parameters on tool wear

and workpiece surface temperature in turning of

AISI D2 steel. International Journal of Lean

Thinking, 3(2), 140-156.


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Constrained MPC based Laguerre network to control IPA positioning system

Siti Fatimah Sulaiman1,2,*, M.F. Rahmat1, A.A.M. Faudzi1,3, Khairuddin Osman2, A.R. Azira2

1) Faculty of Electrical Engineering, Universiti Teknologi Malaysia,

81310 UTM Johor Bahru, Johor Bahru, Malaysia. 2) Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka,


3) Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia,

81310 UTM Johor Bahru, Johor Bahru, Malaysia.


Keywords: Constrained model predictive control; pneumatic system; position control

ABSTRACT – This study proposed a Model Predictive

Control (MPC) based Laguerre network as a strategy to

control the Intelligent Pneumatic Actuator (IPA)

positioning system. This study mainly aimed to

investigate the effect of considering constraints of IPA in

MPC algorithm towards the performance of IPA

positioning system. Simulation and experimental test

using MATLAB/Simulink demonstrated that the

proposed strategy was feasible, where it managed to

control the IPA positioning system well. The most

noticeable result from this study was the inclusion of

constraints in the controller algorithm effectively reduced

the overshoot in the system response, however, makes the

system relatively slower.

1. INTRODUCTION

Over the past few years, predictive control has

attracted increasing attention in controlling particular

systems due to its ability to predict the future behaviour

of the system. The suitability of predictive control to

control the IPA positioning system used in this study were

also reported in [1-3]. However, all the controllers

reported in those studies did not take into account the

prescribed limitations of the IPA system. Considering the

limitation of the IPA system when developing controller

was very crucial as it can affect the overall performance

of the system, and non-compliance with the prescribed

limits may also cause damage to the IPA system and its

components, especially when implementing the system

in real-time environment. MPC is the only controller in

the predictive control family which has an advantage of

considering system’s constraints in its algorithm. Wakasa

et al. [4] and Daepp [5] in their studies have revealed that

including the system’s constraint in MPC algorithm

significantly improved the performance of the respective

systems. Therefore, based on these facts, this study

proposes the development of constraint MPC as a new

strategy for controlling the IPA positioning system. The

main purpose of this study is to investigate the effects of

considering constraints in the MPC algorithm towards

the performance of the IPA positioning system.

2. METHODOLOGY

In this study, the dynamics of IPA system was

modeled using system identification approach and

procedures involved during modeling the system was

similar as described in [6-7]. Figure 1 illustrates the

schematic diagram of the IPA system used in this study.

The cylinder stroke movements were manipulated by the

duty-cycle of Pulse-Width Modulator (PWM) signals to

drive the valves. If the PWM model receives a positive

signal from the controller, it converts the signal into

equivalent PWM signal and sends that signal to Valve 1

to perform extension. If the PWM model receives

negative signal, the model sends the signal to Valve 2 to

perform retraction.

Chamber 1Chamber 2

Valve 1

Valve 2

Pressure

sensor

Cylinder

0.6 MPa

Exhaust

Optical

sensorExtend

Retract

Stroke

Figure 1 IPA system schematic diagram.

The MPC control law based Laguerre network

which consider the constraints on the valves control

signals of IPA system can be realized as:

−255 ≤ 𝑀𝜂 + 𝑢(𝑘 − 1) ≤ +255 (1)

Meanwhile, 𝑀 in Equation (1) can be represented as:

𝑀

=

[ ∑ 𝐿1(𝑖)

𝑇𝑘−1

𝑖=002

𝑇 ⋯ 0𝑚𝑇

01𝑇 ∑ 𝐿2(𝑖)

𝑇𝑘−1

𝑖=0⋯ 0𝑚

𝑇

⋮ ⋮ ⋮ ⋮

01𝑇 02

𝑇 ⋯ ∑ 𝐿𝑚(𝑖)𝑇𝑘−1

𝑖=0 ]

Note that 0𝑘𝑇 is a row vector with dimension as in

𝐿𝑘(0)𝑇 . +255 and −255 are the limits on the control

signals of Valve 1 and Valve 2, respectively, 𝜂 is the

Sulaiman et al., 2018

47

optimal solution based on minimization of the cost

function, and 𝑢(𝑘 − 1) is the previous control signal. In

this study, 𝑀𝜂 + 𝑢(𝑘 − 1) represents the controller

signal and can be denoted as 𝑢𝑚𝑝𝑐 . Figure 2 illustrates the

block diagram of the IPA positioning system with the

proposed control strategies.

Reference

(mm)Input valves

Linear

dynamic

MPC

(unconstrained/

constrained)

Stroke

position

(mm)+

-

IPA system

Umpc

Figure 2 IPA positioning system block diagram.


The performances of MPC with and without

considering valve’s constraints in their control strategies

were presented in Table 1. In this study, the performances

of both controllers were observed based on their ability

to control the IPA positioning system at the actuator mid-

stroke position (100 mm).

Table 1 Performance comparison of UMPC and CMPC

Method Criterion UMPC CMPC

Simulation

(sim)

𝒕𝒓(s) 0.141 0.533

𝑶𝑺(%) 5.219 1.031

𝒆𝒔𝒔(mm) 0 0

Experiment

(exp)

𝒕𝒓(s) 0.669 0.972

𝑶𝑺(%) 12.535 0.420

𝒆𝒔𝒔(mm) 0.040 0.010

*UMPC is unconstrained MPC, CMPC is constrained

MPC, 𝑡𝑟 is rise time, 𝑂𝑆 is overshoot, and 𝑒𝑠𝑠 is steady-

state error.

The results in Table 1 is in the lines of earlier

literature [1-3] that found a predictive control is very

suitable to be used to control the IPA positioning system,

where it successfully provides a highly accurate

positioning control (𝑒𝑠𝑠(𝑠𝑖𝑚) = 0 and 𝑒𝑠𝑠(𝑒𝑥𝑝) ≈ 0).

From the results also, it is apparent that including

constraints in the MPC algorithm affect the 𝑡𝑟 and 𝑂𝑆 of

the system performance. Figure 3 illustrates a

comparative close-up view of the positioning system step

response based on the results in Table 1.

Figure 3 UMPC and CMPC responses at midstroke

position.

A comparison of the closed-loop responses of

UMPC and CMPC in Figure 3 shows that the 𝑂𝑆 in the

system response reduced with the inclusion of constraints

in the control algorithm. However, the system

performance becomes slower with the inclusion of

constraint in the algorithm as it would require more

computational effort to optimize the cost function

compared to the UMPC, consistent with the findings as

reported in [4-5].

4. CONCLUSION

This study was devoted to assess the capability and

effect of adding constraints in the MPC algorithm on the

IPA system’s position. Simulation and experimental

results demonstrated that the proposed control strategy

was feasible to be implemented and was found to be an

effective technique to reduce the overshoot in the

system’s transient response. The overshoot decreased

with the inclusion of constraints in the controller

algorithm. However, the proposed method resulted in a

less aggressive positioning response of IPA system as it

requires more computational effort to optimize the cost

function, compared to the unconstrained case. Further

study will investigate a suitable method to improve the

speed response of the system.

ACKNOWLEDGEMENT

The authors would like to acknowledge Universiti

Teknikal Malaysia Melaka (UTeM), Universiti Teknologi

Malaysia (UTM) and Ministry of Higher Education

(MOHE) of Malaysia for their support.

REFERENCES

[1] Faudzi, A. A. M., Mustafa, N. D., Osman, K.,

Azman, M. A., & Suzumori, K. (2012). GPC

controller design for an intelligent pneumatic

actuator. Procedia Engineering, 41(2012), 657-663.

[2] Faudzi, A. A. M., Mustafa, N. D., Azman, M. A., &

Osman, K. (2014). Position tracking of pneumatic

actuator with loads by using predictive and fuzzy

logic controller. Applied Mechanics and

Materials, 529(2014), 259-266.

[3] Osman, K., Faudzi, A. A. M., Rahmat, M. F.,

Hikmat, O. F., & Suzumori, K. (2015). Predictive

functional control with observer (PFC-O) design

and loading effects performance for a pneumatic

system. Arabian Journal for Science and

Engineering, 40(2), 633-643.

[4] Wakasa, Y., Sasaki, R., Tanaka, K., & Akashi, T.

(2007). Servo control of pneumatic systems

considering input and output constraints. IEEE

International Conference on Control Applications,

2007, 765-770.

[5] Daepp, H. G. (2016). Constrained model predictive

control for compliant position tracking of

pneumatic systems. Georgia Institute of

Technology.

[6] Sulaiman, S. F., Rahmat, M. F., Faudzi, A. A. M., &

Osman, K. (2016). Linear and nonlinear ARX

model for intelligent pneumatic actuator system.

Jurnal Teknologi, 78(6), 21-28.

[7] Sulaiman, S. F., Rahmat, M. F., Faudzi, A. A. M.,

Osman, K., Sunar, N. H., & Salim, S. N. S. (2017).

Hammerstein model based RLS algorithm for

modeling the intelligent pneumatic actuator (IPA)

system. International Journal on Advanced

Science, Engineering and Information

Technology, 7(4), 1457-1463.


__________


Stepwise regression for kenaf reinforced polypropylene composite M. Noryani1,2, S.M. Sapuan1,*, M.T. Mastura3, M.Y.M. Zuhri1, E.S. Zainudin1

1) Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia,

43400 UPM Serdang, Selangor, Malaysia.

2) Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka,




Keywords: Kenaf reinforced polypropylene composite; stepwise regression

ABSTRACT – Stepwise regression is an alternative in

statistical modelling. This paper discusses the parameters

that influence the performance score (PS) of kenaf

reinforced polypropylene composite (KRPC). It was

found the tensile strength, Young’s modulus and flexural

strength are the parameters that influence the materials

performance of KRPC. The model adequacy checking

was done by plotted the normality and regression

probability standardized residual.

1. INTRODUCTION

The demand of natural fibre as a plastics additive

with the polymer in automotive applications is driving

increasing year by year. The increment by 60% of natural

fibre demand was confirm by natural fibre industry [1].

Kenaf fibre was a favorite fibre in automotive application

due to the availability, ability and flexibility of them. A

good agreement with a better performance on mechanical

and thermal properties can be produce by the composite

compare to single material [2]. There are various

methodologies that can be used to analyze the material

performance and there are several parameters could

influence the material performance. The design engineer

should identify the significant parameters to optimize the

cost and time doing testing the function of desire

automotive component design. In this study, the best

statistical modelling is introduced with significant

parameters to predict the performance of KRPC using a

novel statistical approach.

2. METHODOLOGY

Secondary data of KRPC on strength and flexural

mechanical properties is referred. Table 1 shows the

tensile strength (TS), Young’s modulus (YM), flexural

strength (FS) and flexural modulus (FM). These

independent variables are the regressors to the dependent

variables in this study which is performance score (PS)

that can be calculated by using Equation (1). Where xi is

the mechanical properties of KRPC while n is the number

of samples. The best finding based on material

decomposition such as fibre loading and types of fibre

with polypropylene (PP) are considered. Statistical

package for social sciences (SPSS) software is used to

analyze the data. Statistical measurement such as

coefficient of correlation (r), determination of coefficient

(R2), adjusted determination of coefficient (AdjR2) and

analysis of variance (ANOVA) are used to identify the

significant parameters that influence PS.

1

n

ijiPS x

== 1,2,...,i n= (1)

Table 1 The data of KRPC [1], [3-6].

Mechanical Properties

TS (MPa) YM (GPa) FS (MPa) FM (GPa)

32 1.2 58 2.3

58 1.3 71 3.6

46 2.1 58 4.0

62 6.5 58 3.3

28 6.3 61 3.3

46 6.9 61 3.3

44 4.1 61 3.3

54 4.8 61 3.3


The best statistical model from possible fifteen

models is selected based on the highest value of

coefficient of correlation and adjusted determination of

coefficient while the P-value of ANOVA testing should

less than 0.05. The best model of KRPC is selected with

three significant mechanical properties which are tensile

strength, Young’s modulus and flexural strength as

shown in Equation (2). This model shows the highest

value of Adj R2 indicate 99.8% the variation of the PS of

the KRPC is explained by tensile strength, Young’s

modulus and flexural strength. There is significant

difference in the model since the P-value of ANOVA is

less than standard error (α=0.05). The effect of

mechanical properties as a single and multiple parameter

in the possible constructed statistical model is shown in

Table 2.

ˆ 1.220 1.015 1.023 1.021y TS YM FS= + + + (2)

Equation (2) can be used as an inferential statistic

from the sample to predict the PS with the information of

TS, YM and FS. This model can benefit to design engineer

in manufacturing automotive component application

especially in composite industry.

mailto:[email protected]

Noryani et al., 2018

49

Table 2 Fifteen possible statistical model of KRPC.

Parameter r R2/ Adj

R2

ANOVA

(P-value)

TS 0.958 0.918 0.000

YM 0.130 0.017 0.579

FS 0.530 0.281 0.177

FM 0.503 0.253 0.204

TS, YM 0.961 0.894 0.002

TS, FS 0.987 0.963 0.000

TS, FM 0.962 0.896 0.002

YM, FS 0.608 0.117 0.316

YM, FM 0.511 0.035 0.469

FS, FM 0.647 0.186 0.257

TS, YM, FS 0.999 0.998 0.000

TS, YM, FM 0.965 0.878 0.000

TS, FS, FM 0.988 0.957 0.001

YM, FS, FM 0.689 0.080 0.416

TS, YM, FS, FM 1.00 0.997 0.20

The adequacy checking was done on this final

model of KRPC shows in Figure 1 and Figure 2. The

normality plot of the observed and expected data display

a good dispersion. There is no positive and negative

skewed in the data. The residual is plotted lie near exactly

along a straight line. The observed value is approximate

to the expected value. There is no obvious model defect

or nonlinearity pattern. The estimation process by using

this model can increase the trustworthy to the user and it

can reduce the uncertainties and error.

4. CONCLUSION

The stepwise regression is used to identify the best

model represent the performance score of KRPC in this

study. Tensile strength, Young’s modulus and flexural

strength are the significant mechanical properties that

influence the performance score of KRPC. The best fit

model with highest r, Adj R2 and significant P-value can

be used to others to estimate the performance score of

KRPC.

ACKNOWLEDGEMENT

The authors would like to thank Universiti Putra

Malaysia for the opportunity doing this study as well as

Universiti Teknikal Malaysia Melaka and Ministry of

Education of Malaysia for providing the scholarship

award and grant scheme Hi-COE (6369107) to the

principal author in this project.

REFERENCES

[1] Akil H. M, Omar M. F., Mazuki A. A. M., Safiee S.,

Ishak Z. A. M., & Abu Bakar A. (2011). Kenaf fiber

reinforced composites: A review. Mater. Des., 32(8–

9), 4107–4121.

[2] Radzi A. M., Sapuan S. M., Jawaid M., & Mansor

M. R. (2017). Influence of fibre contents on

mechanical and thermal properties of roselle fibre

reinforced polyurethane composites. Fibers Polym.,

18(7), 1353–1358.

[3] Ku H., Wang H., Pattarachaiyakoop N., & Trada M.

A review on the tensile properties of natural fiber

reinforced polymer composites. Compos. Part B,

42, 856–873.

[4] Mansor M. R., Sapuan S. M., Zainudin E. S.,

Nuraini A. A., & Hambali A. (2014). Rigidity

Analysis of Kenaf Thermoplastic Composites

Using Halpin-Tsai Equation. Appl. Mech. Mater.,

548, 29–33.

[5] Asumani O. M. L., Reid R. G., & Paskaramoorthy

R. (2012). The effects of alkali-silane treatment on

the tensile and flexural properties of short fibre non-

woven kenaf reinforced polypropylene composites.

Compos. Part A Appl. Sci. Manuf., 43(9), 431–

1440.

[6] Zampaloni M., Pourboghrat F., Yankovich S. A.,

Rodgers B. N., Moodre J., Drzal L. T., Mohanty A.

K. & Misra M.. (2007). Kenaf natural fiber

reinforced polypropylene composites: A discussion

on manufacturing problems and solutions. Compos.

Part A Appl. Sci. Manuf., 38(6), 1569–1580.

Figure 1 Normality plot of KRPC.

Figure 2 Normal probability plot of regression

standardized residual.


__________


Simulation of tool flute geometry influences the micro-end milling operation

N.A. Norrdin1,*, J.B. Saedon2, M.S. Kasim3

1)Faculty of Mechanical Engineering, Universiti Teknologi MARA Cawangan Pulau Pinang,

Jalan Permatang Pauh, 13500 Permatang Pauh, Pulau Pinang Malaysia. 2) Faculty of Mechanical Engineering, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.

3) Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia.


Keywords: 3D FEM; micro-end milling; tool flute geometry

ABSTRACT – This paper presents a 3D FE model for

micro-end milling operation on hardened AISI D2 cold

work tool steel in order to investigate the effect of

different tool geometry of cutting flute numbers (two,

four, six and eight flutes) on the Von-Mises stress

distribution and cutting force analysis in three directions

Fx, Fy and Fz. The machining parameters used in the

simulation work was the cutting speed of 50m/min, depth

of cut of 55 µm, feed per tooth is of 2 µm /tooth. The

predicted cutting forces were compared with the result

trends from the literature to verify the accuracy of 3D FE

model. The results obtained indicate that tool flute

geometry had a slightly impact to cutting forces.

1. INTRODUCTION

Micro-end milling is a tool-based material removal

process; hence it very much relies on the performance of

the micro-milling tools. Chatelain and Zaghbani [1] used

different combinations of three cutters with different

geometry in order to find commonly stable cutting

condition. Common defects related with micro end mills

are the geometric deviations, poor determination of the

tool cutting edge and also the cutting force. This problem

is encountered when scaling down the tool. The micro-

tools are normally fabricated with an edge radius of 1-5

μm [2]. The tool edge radius need to be smaller than the

chip thickness dimension[3]. Additionally, Fang et al. [4]

studied various type of micro tool geometries in order to

obtain the lowest cutting force and higher rigidity during

machining. This paper introduces a 3D FEM model for

micro-end milling to study on the Von-Mises stress

distribution and cutting force. Nevertheless, based on the

previous studies [1-4], it reveals that the tool geometry

can become as an obstacle that can limit the capability in

micro-machining. Therefore, the aim of this paper is

investigate the effect of cutting tool geometry (two, four,

six and eight flutes) on the Von-Mises stress distribution

and cutting force analysis in micro-end milling process.

2. METHODOLOGY

The 3D model is a dynamic thermo-mechanical

FEA using explicit integration in Abaqus/Explicit. The

tool rotates around the Z-axis and the workpiece feed in

the cutting direction at the same time, as shown in Figure

1(a). The workpiece moves in the direction X and fixed

in the direction Y. The workpiece is set as deformed body

and rigid constraints are applied to the tool. The mesh

type of the workpiece is C3D8R which defined as a

hexahedral with eight node coupled bricks and first order

linear interpolation solid element and the tool is C3D4

which defined as a tetrahedron with four nodes linear

interpolation solid element, as shown in Figure 1(b). The

arbitrary Lagrangian Eulerian (ALE) formulation has

been adopted for the workpiece to reduce distortions

during simulations. The cutting tool used in this study

was 0.5mm of diameter with varying geometry in terms

of number of flutes and helix angle as listed in Table 1.

Figure 1 Top view of FEM setting (a) boundary

conditions and (b) meshing of the workpiece and tool.

Table 1 Tool geometry criteria.

Tool Criteria

T1 2-Flute, Diameter = Ø 0.5 mm, Helix angle =

15°, Rake angle = 0°







3. RESULT AND DISCUSSION

3.1 Von-Mises stress distribution

It was noted that all the simulated maximum value

of stress developed at the shear zone at the tool-chip

interface for all cutting tools geometry however the

values are dissimilar. A primary shear zone was able to

be seen on Von-Mises contours as shown in the Figure 2.

As expected, the stress distribution contours show that

the lower value of Von-Mises stress occurs on the

machined surface and increasing towards the cutting

Norrdin et al., 2018

51

edge of the tool. This is happened due to when the cutting

tool edge in the shear zone, it involves higher force to

create chip formation.

Figure 2 Predicted Von-Mises stress contour for T1 tool

in orthogonal view.

Figure 3 shows the decreasing of Von-Mises stress

values was identified when adding the number of cutting

flute to the cutting tool. Nevertheless, the stress

distributions are still in the identical average. This finding

is consistent with the finding of Pratap and Patra [5] in

their FEM model to analyze the Von-Mises stress

distribution and cutting force for Copper material

workpiece.

Figure 3 Predicted Von-Mises stress magnitude on T1,

T2, T3 and T4.

3.2 Cutting force predictions

The cutting forces provide a good indication of

cutting tool performance. It can be summarized the

simulated cutting force variations in the steady state for

each cutting tool geometry of T1, T2, T3 and T4, as

shown in Figure 4. When comparing the cutting force

trends among all the cutting tool geometry, it showed that

the forces generated from the small number of flutes

higher compared to the high number of flutes, giving the

change of 15%, 19% and 10% for Fx, Fy and Fz,

respectively. This is due to fact that when cutting with

higher number of flutes, the tools become more rigid and

feed faster. Since they make less change, it can be said

that the forces of three directions are not sensitive to the

change of number of tool flute since their functions are

to cut the workpiece during the roughing and finishing

process. The addition of tooth at the cutting tool

smoothers the force profile making the fluctuation less

sudden (T4 compare to T1). This is because when adding

the number flutes in tool geometry is most probably due

of the reduction of the high contact surface of cutter tooth

in the cutting zone [6]. Therefore, this can have led to

lower rate of material removal at each tooth, which

significantly decreased the amplitude of the cutting

forces. The comparisons were performed with the finding

of Fang et al. [4] and Davounedinejad et al. [6] which

reveals globally similar trends for Fx and Fy components

in terms of the curve shapes. However, some

discrepancies in the magnitude of forces were observed,

due to different workpiece material.

Figure 4 Simulated cutting forces in three directions, Fx,

Fy and Fz on tool geometry effect,

4. CONCLUSION

The following observation can be concluded on the

influence of tool flute geometry on the simulated Von-

Mises stress and cutting force; (i) the Von-Mises stress

distribution is not significantly influenced by the change

of number of flutes in the cutting tool. (ii) a slightly

reduction in cutting forces was observed as the number

of flute increases. This model will be used as a platform

system which can access by various users for further

studies.

REFERENCES

[1] Chatelain, J. F., & Zaghbani, I. (2011, September).

Effect of tool geometry special features on cutting

forces of multilayered CFRP laminates.

In Proceedings of the 4th International Conference

on Manufacturing Engineering, Quality and

Production Systems (MEQAPS’11), 15-17.

[2] Bissacco, G., Hansen, H. N., & Slunsky, J. (2008).

Modelling the cutting-edge radius size effect for

force prediction in micro milling. CIRP Annals-

Manufacturing Technology, 57(1), 113-116.

[3] Jin, X., & Altintas, Y. (2012). Prediction of micro-

milling forces with finite element method. Journal

of Materials Processing Technology, 212(3), 542-

552.

[4] Fang, F. Z., Wu, H., Liu, X. D., Liu, Y. C., & Ng, S.

T. (2003). Tool geometry study in

micromachining. Journal of Micromechanics and

Microengineering, 13(5), 726-731.

[5] Pratap, T., & Patra, K. (2014). Modeling and

analysis of cutting forces in micro end milling. In

5th International & 26th All India Manufacturing

Technology, Design and Research Conference

(AIMTDR 2014), 1-5.

[6] Davoudinejad, A., Tosello, G., Parenti, P., &

Annoni, M. (2017). 3D Finite Element Simulation

of Micro End-Milling by Considering the Effect of

Tool Run-Out. Micromachines, 8(6), 187-206.

3229

3209 3207

3171

3140

3150

3160

3170

3180

3190

3200

3210

3220

3230

3240

T1 T2 T3 T4

Vo

n M

ises

str

ess

(M

Pa

)

Tool

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

T1 T2 T3 T4

Fo

rce, (N

)

Tool

Fx Fy Fz


__________


Influence of dimple textured surface on hydrodynamic pressure distribution via computational fluid dynamic

Haniff Abdul Rahman*, Jaharah A. Ghani, Wan Mohd Faizal Wan Mahmood, Rasidi Rasani

Department of Mechanical and Material Engineering, Faculty of Engineering and Built Environment,

Universiti Kebangsaan Malaysia, Bangi, Malaysia.


Keywords: Fluid mechanic; surface texturing; hydrodynamic pressure

ABSTRACT – Surface texturing has been widely used

to reduce friction between sliding component due to its

ability to trap lubricant, generating hydrodynamic

pressure and increasing load capacity to reduce friction.

In this paper, Finite Volume Method has been used to

study the influence of dimple surface textured via turning

process on pressure distribution for future piston skirt

application. Three cases were simulated: flow along

minor dimple axis, flow along major dimple axis and

flow along untextured surface. Dimple with flow along

major axis was observed to provide the largest positive-

pressure distribution throughout the model compared to

two other cases.

1. INTRODUCTION

In order to improve engine efficiency and reducing

fuel consumption, friction reduction proved to be a vital

step. Approximately 25% of fuel consumption are due to

friction loss in ICE by which piston/cylinder system

contributed to half of it [1]. Surface texturing is an

effective method to counter the friction loss in sliding

component. Earliest work has been done by Hamilton et

al. [2] in regard of mechanical seal. They found that

micro asperity could generate hydrodynamic pressure

and lift, thus creating load carrying capacity. In another

study, using a pin on disk test, Kovalchenko et al. [3] also

observed a friction reduction trend by adding dimples

even at the lowest sliding speed. Similar trend was also

seen by [4]. They stated that textured surface delayed the

transition from hydrodynamic to mixed and boundary

lubrication, for all speed tested. Recently, numerical

method has been implemented by researchers to give in

depth view on a fluid mechanics of the textured surface.

Han et al. [5] modelled a single spherical cap between

two parallel surfaces using CFD. Pressure curve was

found to be asymmetric throughout the dimple, which

results in net pressure build up. Similar result was

obtained by Menon et al. [6] using hemispherical and

semi-ellipsoidal dimple. It was concluded that divergent-

convergent portion of textured surface able to generate a

pressure gradient that helped in supporting load thus

reducing friction.

2. METHODOLOGY

In this study, Ansys-CFX software has been used to

simulate the pressure distribution between smooth top

surface and dimple textured bottom surface. Modelling

method was inspired by [6]. Dimple's parameter was

based on previous study by Dali et al. [7] using turning

process for further used in piston application. The dimple

width is 1.6308𝜇𝑚 on major axis (a) and 0.1984𝜇𝑚 on

minor axis (b). The depth is 63.43𝜇𝑚. Figure 1 shows the

model geometry of dimple in this study.

Figure 1 Geometry with flow along minor dimple axis.

Periodic boundary condition was assigned at the

inlet and outlet. Top plate is smooth with wall velocity U

= 0.39m/s at y coordinate, and bottom plate is static with

texture. Fluid properties are according to real engine

lubricant. The condition is set to isothermal,

incompressible, laminar and steady. Cavitation

phenomenon is not yet considered.

In this study, three cases were simulated, case 1:

flow along minor dimple axis, case 2: flow along major

dimple axis and case 3: flow along minor dimple axis.

Both case 1 and 2 was simulated using area ratio of 9.5%

for comparison purposes. Grid independence test was

done on case 1, by which grid size of 580000 element

was sufficient for simulation due to error with regard to

higher element is less than 5%.


3.1 Pressure distribution

Figure 2 shows the pressure distribution on top mid

plane wall for three different cases in which pressure is

plotted against dimensionless distance x.

From Figure 2, sudden rise is seen for all three cases

since the beginning of the plate as it starts moving.

Passing around the x=10 mark, all three cases show a

declination in pressure. This is following the Bernoulli

principle by which as velocity increases, pressure

decreases. Case 1 appears to experience the most drop-in

pressure at this mark followed by case 2 and case 3. This

is due to the flow already reached the divergent part of

the dimple, causing a significant drop in pressure as

Outlet

Inlet

U

Rahman et al., 2018

53

stated by [8]. However, passing the x=40-mark, case 2

shows the largest positive pressure built up throughout

the plate followed by case 2. While for case 1, further

reduction in pressure is exhibited thus providing no load

carrying capacity at this stage. The finding from the

graph is in accordance with study by Menon et al. [6] in

which dimple with flow along major axis provides the

maximum positive pressure compared to minor axis thus

providing highest load capacity and lift force.

Figure 2 Pressure distribution for three different cases.

3.2 Pressure contour

Figure 3 shows the pressure contour on the bottom

plate whereby the dimple is located for case 1 and 2.

(a)

(b)

Figure 3 Pressure distribution for (a) flow along minor

dimple axis and (b) flow along major dimple axis.

From Figure 3, it is observed that negative pressure

drop occurs at the diverging part of both dimples.

Gradual increase in pressure is then seen along the

dimple. This is in agreement with the study by Yu et al.

[9] that found a positive pressure accumulated towards

the converging part for a single dimple. For flow along

minor axis, faster transition from negative to positive

pressure is seen, compared to major axis. However, as the

flow passes the dimple region, the pressure decreases

earlier compared to the major axis with longer width. The

increase in dimple width that the fluid flow through,

provide a larger positive-pressure distribution throughout

the plane, thus results in net load capacity. This provides

an insight on how dimple size is important in generating

hydrodynamic pressure.

4. CONCLUSION

In conclusion, the dimple structure produced via

turning process on the sliding surface able to increase the

hydrodynamic pressure, which results in increasing load

capacity, to help reducing contact and friction between

plate. The orientation of dimple has an effect towards

pressure distribution, as flow parallel to the major axis

provides the largest pressure rise to this case. However,

the present study only acknowledged a single dimple

case, while for future piston skirt application, surface

area is larger. Thus, a study of the area ratio needs to be

done in the future to further optimize the effect of this

dimple on pressure distribution.

ACKNOWLEDGEMENT

This project is supported by the Government of

Malaysia and Universiti Kebangsaan Malaysia under

FRGS/1/2016/TK03/UKM/01/1 and GUP-2017-048

Grants.

REFERENCES

[1] Ryk, G., Kligerman, Y., Etsion, I., & Shinkarenko,

A. (2005). Experimental investigation of partial

laser surface texturing for piston-ring friction

reduction. Tribology Transactions, 48(4), 583-588.

[2] Hamilton, D. B., Walowit, J. A., & Allen, C. M.

(1966). A theory of lubrication by

microirregularities. Journal of Basic

Engineering, 88(1), 177-185.

[3] Kovalchenko, A., Ajayi, O., Erdemir, A., Fenske,

G., & Etsion, I. (2005). The effect of laser surface

texturing on transitions in lubrication regimes

during unidirectional sliding contact. Tribology

International, 38(3), 219-225.

[4] Borghi, A., Gualtieri, E., Marchetto, D., Moretti, L.,

& Valeri, S. (2008). Tribological effects of surface

texturing on nitriding steel for high-performance

engine applications. Wear, 265(7-8), 1046-1051.

[5] Han, J., Fang, L., Sun, J., & Ge, S. (2010).

Hydrodynamic lubrication of microdimple textured

surface using three-dimensional CFD. Tribology

transactions, 53(6), 860-870.

[6] Menon, D. P., Anil, P. M., & Kulkarni, P. S. (2015).

An analysis on the influence of oil pocket shape and

distribution on the reduction of friction in

hydrodynamic lubrication. In Proceedings of

the17th Annual CFD Symposium, Bangalore.

[7] Mohd Dali, M. N. A., Ghani, J. A., Che Haron, C.

H., & Hassan, S. (2017). Fabrication of dimple

-4

-3

-2

-1

0

1

2

3

4

5

0 20 40 60 80 100

PR

ESSU

RE

(PA

)

DIMENSIONLESS Y

Minor Axis Major Axis No Dimple

Rahman et al., 2018

54

structured surface of A390 Al-Si alloy using turning

process. Industrial Lubrication and

Tribology, 69(3), 348-354.

[8] Gropper, D., Wang, L., & Harvey, T. J. (2016).

Hydrodynamic lubrication of textured surfaces: A

review of modeling techniques and key

findings. Tribology International, 94, 509-529.

[9] Yu, H., Wang, X., & Zhou, F. (2010). Geometric

shape effects of surface texture on the generation of

hydrodynamic pressure between conformal

contacting surfaces. Tribology Letters, 37(2), 123-

130.


__________


The role of stiffener in resisting buckling of externally pressurized cone-cylinder intersection

M.S. Ismail1,3*, O. Ifayefunmi2, S.H.S.M. Fadzullah1


Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia. 2) Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia. 3) Excellence and Professional Division, Polytechnic Education Department, 62100, Putrajaya, Malaysia.


Keywords: Cone-cylinder intersection; external pressure; shell Buckling

ABSTRACT – This paper aims to examine the

influence of stiffener position on the buckling

performance of externally pressurized ring-stiffened

cone-cylinder intersection of B/t = 5. Three different

stiffener locations are analyzed, they are: (i) cone-

cylinder junction, (ii) cone mid-section, and (iii)

cylinder mid-section. Buckling strength of the stiffened

cone-cylinder intersections were obtained with the aid

of FE analyses for all the cases above. Cone-cylinder

intersections are modelled as elastic-perfectly plastic of

Hiduminium alloy (HE-15). Bifurcation study shows

that the intersection area is much stronger than any

other parts of the assembly. Shell reinforced with

stiffener produces stronger structure by 23% of

increment.

1. INTRODUCTION

Shells buckling failure is a common event in the

industrial field and continuously attracts numerous

engineers/designers in finding the appropriate solution

to prevent it. Cylinders with conical end are widely used

in many engineering industries. Their application can be

found in submersibles, missiles, reducer and silo as well

as nuclear industries. Commonly, cylinders with conical

end are combined with a simple weld joint that is known

as intersection. In designing this kind of intersection, it

is necessary to take into account the structural capability

in resisting the buckling occurrence caused by the

excessive load during operation. This buckling

occurrence indeed can be catastrophic. In general,

cylindrical shell with conical ends, subject to external

pressure, may collapse in two difference ways. First, the

shell will locally collapse within either or both the

cylindrical or conical portions. Secondly, the cylindrical

and conical parts may collapse simultaneously.

In the early 1970s, tremendous amount of works

have been done in investigating the buckling

performance of externally pressurize cylinder shells

with conical end closure. Aylward et al. [1] has

presented some experimental and theoretical results of

nearly perfect steel cone-cylinder intersection.

Theoretical results were obtained by using BOSOR 3. In

another study by Galletly et al. [2], the buckling

behaviour of cone-cylinder combinations subjected to

uniform external pressure was examined through

experimental and numerical approach.

Generally, it is believed that shell component can

be further strengthened through reinforcement with

additional stiffener. The stiffener may be positioned

either vertically (stringer) or circumferentially (ring)

along the shell’s axis line. Stiffener often appears with

numerous profiles, this include; flat, angle, tee, box and

many more. However, flat stiffener is frequently used in

engineering design and application. It seems that for

cone-cylinder intersection, the stiffener (ring-type) is

positioned at the cone-cylinder junction, as the shell

junction is assumed to be the weakest part of the entire

assembly. Nonetheless, little work has been done on the

buckling characteristic of stiffened cone-cylinder

intersection. To-date, most recent work referred to the

study of internally pressurized ring-stiffened cone-

cylinder intersections by Teng and Ma [3].

This paper contributes on the understanding of the

influence of stiffener location on the buckling

performance of cone-cylinder intersection with a ring-

stiffener of B/t = 5. The present work is reported in

terms of numerical modelling analysis using the

ABAQUS software package, with the aim to

compliment and validate the experimental results

reported by Galletly et al. [2].

2. METHODOLOGY

First, preliminary calculations were carried out to

validate the experimental data presented in Galletly et

al. [3]. Two types of analysis were employed: (i)

bifurcation eigenvalue analysis, and (ii) nonlinear static

RIKS analysis. The shells consist of vertex cone angle,

α = 45○, 60○ and 75○ with series of L/D = 0.5, as

illustrated in Figure 1 (a). Specimens were assumed to

be made from Hiduminium Alloy (HE-15) with material

properties of E = 75.152 GPa, σyield = 434.37 MPa and υ

= 0.32, with the shell detail information as given in

Table 1. Next, to examine the role of stiffener at

different shell location, three different stiffener locations

are analysed, they are: (i) cone-cylinder junction, (ii)

cone mid-section, and (iii) cylinder mid-section. The

ring-stiffener of B/t = 5, as given in Figure 1 (b) is used

in the present analysis. The above shells were subjected

to external pressurize. All the analyses were carried by

using ABAQUS finite element software package.


3.1 Validation study

Figure 2 illustrates the validation results of tested

Ismail et al., 2018

56

shells. The agreement between experiment and FE

analysis are very satisfactory. It may be summarizing

that the nonlinear static, RIKS analysis is found to be

within 1% - 6%. Somehow, the bifurcation eigenvalue

analysis shows that the discrepancy is in the range of

5% - 10%. It is also demonstrated that the bifurcation

analyses overestimate the shells buckling strength by

1% - 5% in comparison to the nonlinear static, RIKS.

However, model G3 was found to be only marginally

affected by both analyses.

Table 1 Detail of tested models.

Model r

[mm]

t

[mm] α [○]

B

[mm]

tstiff

[mm]

G1

68.58 1.3716

45

5 1 G2 60

G3 75

Figure 1 Dimension of (a) stiffened cone-cylinder

intersection and (b) stiffener.

Figure 2 Series of shells validation results.

3.2 The role of stiffener at different shells locations

Figure 3 and 4 illustrate the role of stiffener at

different shell locations for bifurcation eigenvalue and

nonlinear static RIKS analyses. The bifurcation analysis

indicates that the eigenmode formation take place at the

cone region for all tested models. Stiffened the conical

area (Case 2) produces stronger shells with a maximum

increment of 23% obtained, as presented in Figure 3.

Nonetheless, it appears that Case 1 and 2 (stiffener at

intersection and cone mid-section) calculate almost

identical pressure load for model G3.

In contrast, the nonlinear static RIKS analysis,

Case 1 (stiffener at shells intersection) proves to be

more effective in strengthening the shells load bearing

capability (Figure 4). The improvement of the shells

strength is calculated to be in the range of 1% - 6% for

all cases. Apparently, the role of stiffener seems to be

fairly insignificant for all models under Case 3 (stiffener

at cylinder mid-section). This insignificant is probably

due to the fact that the conical part of the assembly is

strongly affected by the buckling formation (similar to

the bifurcation study). Thus, stiffened the cylinder does

not seems to make any difference in improving the

shells load bearing capability.

Figure 3 Results of bifurcation eigenvalue analysis for

each case.

Figure 4 Results of nonlinear static, RIKS analysis for

each case.

4. CONCLUSION

The correlation obtained in the validation study is

very satisfactory as the results agreement is within 10%.

The role of stiffener appears to have a large effect on

cone-cylinder shells buckling strength, as it strengthen

up the shells to an increase of 23%. In contrast, the

bifurcation study shows that the conical part is much

weaker than the junction of cone-cylinder. This finding

can be further verified through experimental study.

5. REFERENCES

[1] Aylward, R. W., Galletly, G. D., & Moffat, D. G.

(1975). Buckling under external pressure of

cylinders with toriconical or pierced torispherical

ends: a comparison of experiment with

theory. Journal of Mechanical Engineering

Science, 17(1), 11-18.

[2] Galletly, G. D., Aylward, R. W., & Bushnell, D.

(1974). An experimental and theoretical

investigation of elastic and elastic-plastic

asymmetric buckling of cylinder-cone

combinations subjected to uniform external

pressure. Ingenieur-Archiv, 43(6), 345–358.

[3] Teng, J. G., & Ma, H. W. (1999). Elastic buckling

of ring-stiffened cone-cylinder intersections under

internal pressure. International Journal of

Mechanical Sciences, 41(11), 1357–1383.


__________


Impact behaviour of lightweight metal component repair using aluminium particles with high pressure cold spray process and low-

pressure cold spray process A. Manap1,*, Siti Nurul Akmal Yusof1, N.F. Afandi1, Savisha Mahalingam2

1) College of Engineering, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia.

2) Institute of Sustainable Energy, Universiti Tenaga Nasional, 43000 Kajang, Selangor, Malaysia.


Keywords: Aluminium; high pressure cold spray; low pressure cold spray

ABSTRACT – This study focuses on impact behaviour

of lightweight metal repair using aluminium powder by

high pressure cold spray process (HPCS) and low-

pressure cold spray process (LPCS). The aluminium

particles impacting on aluminium substrates using

LPCS process deformed slightly with the smallest

flattening ratio that leads to less pore formation between

the particles and resulted in good coating quality.

Moreover, LPCS deposition experiences compressive

stress that ensures longer component lifetime due to its

positive effect on the fatigue life and wear resistance

application. The overall results denote that LPCS

process is better for repairing lightweight metal

component than HPCS process.

1. INTRODUCTION

In the past decades, the world is facing the

challenge of global warming caused by the emission of

greenhouse gases such as carbon dioxide (CO2) through

human activities. Lightweight metal such as aluminium

(Al) applied tremendously in transportation can be used

to reduce CO2 emissions. However, defects may occur

in Al components such as crack, corrosion and wear.

Thus, repairing the components is an effective way to

lessen the global impact on the environment that can

save energy consumption and cost where, repairing the

components costs less than by replacing with new ones

[1]. Cold spray (CS) technique is a new approach to

repair all defects in lightweight materials. There are two

types of CS techniques which are high pressure cold

spray (HPCS) and low pressure cold spray (LPCS).

However, HPCS has limitation with lightweight

components. One of the common problem include

dimensional error. Al coating deposited using LPCS has

higher hardness because of peening effect [2]. Thus

LPCS is better for lightweight metal deposition. This

work aims on the study of impact behaviour of Al

particles impacting on different lightweight substrates

such as Al, titanium (Ti) and magnesium (Mg) by

smoothed particle hydrodynamics (SPH) simulation.

2. METHODOLOGY

The SPH modeling of the CS process was

performed using an in-house research program

developed in FORTRAN. The impact of Al powder on

Al, Mg, and Ti substrate is simulated using SPH. The

Johnson-Cook parameters and Gruneisen equation of

state are presented in Table 1.

Table 1 Material properties of Al, Mg, Ti.

Properties (Unit) Al Mg Ti

Density, ρ (g/m3) 2710 1.738 4520

Shear Modulus (GPa) 68.9 17 116

Heat capacity (J/kg/K) 904 1020 528

Reference temperature, T0

(K) 300 300 300

Melting temperature, Tm

(K) 916 923 1650

JC parameter, A, B, C, n,

m (MPa)

148.4

345.5

0.001

0.183

0.895

532

229

0.0294

0.032

1.00

806.6

481

0.319

0.019

0.655

Gruneisen parameter 2 1.07 1.23

Intercept Us-Up curve c

(m/s) 5386 5920 4573

Slope Us-Up curve, S 1.338 1.38 1.536


3.1 Impact behavior of Al on Al

Figure 1 and 2 shows the deformation behavior of

Al multiple particle impact on the Al substrate using

LPCS and HPCS processes, respectively.

Figure 1 Deformation behaviour of Al multiple particle

impact on Al substrate by LPCS process.

The low velocity from LPCS process caused less

intensive deformation in lower particle and leads to a

good bond formation. On the other hand, the HPCS

process caused intensive deformation and created more

Manap et al., 2018

58

pores between the particles and forms poor coating.

Figure 3 shows the residual stress of Al on Al by

using LPCS and HPCS. Stress turns to compressive

stress at greater depth due to the large peening effect of

the Al impacting on the substrate that leads to good

coating deposition [4].


impact on Al substrate by HPCS process.

Figure 3 Residual stress of Al multiple particle impact

on Al substrate by LPCS and HPCS processes.

3.2 Impact behavior of Al on other substrates

Figure 4 and 5 shows the deformation behavior of

Al multiple particle impact on the Ti and Mg substrates

using LPCS and HPCS processes, respectively. Since,

Mg is lighter and less hard than Al, more deformation

was formed in the substrate. On the other hand, more

deformation formed on particles on Ti substrate than Mg

due to almost all kinetic energy dissipated into Al

particle than in the Ti substrate.


impact on (a) Ti (b) Mg substrates by LPCS process.

Figure 6 shows the residual stress of Al on Ti and

Mg by using LPCS and HPCS. From Figure 6, LPCS

experiences compressive stress that ensures longer

component lifetime.


impact on (a) Ti (b) Mg substrates by HPCS process.

Figure 6 Residual stress of Al multiple particle impact

on Ti and Mg substrates by LPCS and HPCS processes.

4. CONCLUSION

In conclusion, Al particles impacting on Al

substrate using HPCS process deformed intensively that

leads to greater porosity formation between the particles

by experiencing tensile stress and resulted in poor

coating quality. Meanwhile, Al on Al created denser

coating with better bond formation during impact using

LPCS. Therefore, LPCS process is better for repairing

aluminium component than HPCS process.

ACKNOWLEDGEMENT

The authors acknowledge the financial supports by

the Malaysian Ministry of Higher Education (Grant

number: FRGS20160105).

REFERENCES

[1] Cobden, R., & Banbury, A. (1994). Aluminium:

Physical Properties, Characteristics and Alloys.

Training in Aluminium Application Technologies

Lecture 1501. European Aluminium Association.

[2] Lee, J. C., Kang, H. J., Chu, W. S., & Ahn, S. H.

(2007). Repair of damaged mold surface by cold-

spray method. CIRP Annals - Manufacturing

Technology, 56(1), 577-580.

[3] Manap, A., Okabe, T., & Ogawa, K. (2011).

Computer simulation of cold sprayed deposition

using smoothed particle hydrodynamics. Procedia

Engineering, 10, 1145-1150.

[4] Lee, H., Shin, H., Lee, S., & Ko, K. (2008). Effect

of gas pressure on Al coatings by cold gas dynamic

spray. Materials Letters, 62(10-11), 1579-1581.


__________


Computational modelling for autonomous vehicle navigation using stereo vision sensor

Rostam Affendi Hamzah1,*, Melvin Gan Yeou Wei2, Nik Syahrim Nik Anwar2, Ahmad Fauzan Kadmin1,

Shamsul Fakhar Abd Gani1, Mohd Saad Hamid1, Saifullah Salam1, Nadzrie Mohamood1

1) Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka,



Keywords: Autonomous vehicle; computational modelling; stereo vision

ABSTRACT – This paper proposes a new computational

modelling for autonomous vehicle navigation (AVN)

using a stereo vision sensor. The modelling process

requires four important stages which are matching cost

computation, cost aggregation, optimization and

refinement stage. The result will be a depth map which

will be used for the AVN. This map contains the depth

information and obstacle locations. Based on the standard

benchmarking evaluation system from the KITTI, the

proposed work produces an accurate result and very

competitive with recent published works in the KITTI

database.

1. INTRODUCTION

In recent years, the vision system has been widely

used for the autonomous vehicle navigation (AVN). In

AVN technology, the stereo vision system is a

trustworthy perception of real world obstacle detection

system for dynamic environments [1]. In order to achieve

good performance, the AVN requires accurate depth

information and detection to make the system more

reliable and safe to be used. Hence, the important part of

the computational modelling needs to be very precise and

robust against the complex structure of real environment

and weather conditions [2]. There were several reliable

methods such as SGM [3], DWBF [4] and SED [5].

These computational modelling produces a map that

contains the depth information and objects detection.

2. METHODOLOGY

Figure 1 shows a framework of the proposed work.

It starts with input stereo images, the main framework

and the output of the objects detection. The four main

stages which are explained as follows:

2.1 Matching cost computation (MCC) – Stage 1

The first stage of the propose work is using

normalised cross correlation (NCC). The NCC

effectively reduces the preliminary mismatched errors.

This stage produces preliminary differences data between

the right and left images.

NCC(𝑥, 𝑦, 𝑑) =∑ 𝐼𝑙(𝑥,𝑦).𝐼𝑟(𝑥,𝑦−𝑑)(𝑥,𝑦)∈𝑤

√∑ 𝐼𝑙2(𝑥,𝑦).∑ 𝐼𝑟

2(𝑥,𝑦−𝑑)(𝑥,𝑦)∈𝑤(𝑥,𝑦)∈𝑤

(1)

Where {Il,Ir,x,y,d,w} denote {left image, right image, x-

coordinate, y-coordinate, depth data, window size}.

Figure 1 The proposed framework.

2.2 Cost aggregation (CA) – Stage 2

The CA is the second stage of the propose

framework. This stage reduces noise from the MCC data

to make the results more consistent. The propose work in

this article uses the guided filter (GF). The GF was

developed by He et al. [6] which was capable to reduce

the noise and preserve the edges of objects matching.

The GF kernel is given by Equation 2.

𝐾(𝑝,𝑞)𝐺𝐹 (𝐼) =

1

𝑤2∑ (1 +

(𝐼𝑝−𝜇𝑐)(𝐼𝑞−𝜇𝑐)

𝜎𝑐2+𝜀

)(𝑝,𝑞)∈𝑤𝑐 (2)

Where {w,p,q,c,I,µ,σ,ε} represented by {window support

size, coordinates of (x,y), neighbouring coordinates,

center pixel of w, reference image (left input image),

mean value, variance value, constant parameter}. The GF

is used in this article due to fast processing which relies

on the image pixels and better filtering effect near the

object edges. The final equation of this stage is given by

Equation 3.

Stage 1

Normalized

Cross Correlation (NCC)

Stage 2

Guided Filter

(GF)

Input

Stage 3

Winner-takes-all

(WTA)

Stage 4

Median Filter

(MF)

Output

Depth information /

objects detection

Hamzah et al., 2018

60

𝐶𝐴(𝑥, 𝑦, 𝑑) = 𝑁𝐶𝐶(𝑥, 𝑦, 𝑑)𝐾(𝑝,𝑞)𝐺𝐹 (𝐼) (3)

where 𝑁𝐶𝐶(𝑥, 𝑦, 𝑑) is the input from MCC stage and

𝐾(𝑝,𝑞)𝐺𝐹 (𝐼) represents the GF kernel with left image as a

reference image in this article.

2.3 Depth optimization (DO) – Stage 3

The third stage of the framework is the DO which

minimizes the data selection on a location and

represented it with disparity or depth value. Generally,

the local based stereo video matching algorithm is using

Winner-Takes-All (WTA) strategy [11]. The WTA uses

the minimum value of 𝐶𝐴(𝑥, 𝑦, 𝑑) and represented the

same location with the depth value. The DO stage is

given by Equation 4:

𝑑(𝑥, 𝑦) = argmin𝑑∈𝐷𝐶𝐴(𝑥, 𝑦, 𝑑) (4)

2.4 Refinement (RF) – Stage 4

The RF is the last stage of the modelling framework.

This stage is also known as depth refinement stage which

reduces the noise and invalid depth values on the results

from the DO. Fundamentally, the used of second filtering

process at this stage is to increase the efficiency and

accuracy of the depth map. The proposed work in this

article is using the median filter which works as a second

filtering process to increase the accuracy. The final depth

result is determined from the Equation 5:

𝑑𝑓𝑖𝑛𝑎𝑙 = 𝑚𝑒𝑑 ∑ 𝑑(𝑥, 𝑦)𝑤∈(𝑖,𝑓) (5)

Where the 𝑑𝑓𝑖𝑛𝑎𝑙 is the final depth value at the coordinate

of (x,y), med denotes as a median filter and 𝑑(𝑥, 𝑦) is the

depth value from DO.


This section presents the experimental results and

performance of the proposed work. All of the analysis

were implemented using a computational platform with

the features of CPU i7-5500, 8G RAM and GPU

GTX550. The standard benchmarking dataset have been

used from the KITTI Vision Benchmark. This dataset was

developed by Menze and Geiger [7] which consists of

200 training images. The stereo images were recorded

from real environment of autonomous vehicle navigation

using a stereo vision system. Hence, it contains very

complex and challenging images. The performance is

measured based on the bad pixel percentage of the

nonocc and all error attributes. These attributes contain

the background (bg) and foreground (fg) accuracy of

objects detection. The parameters {wncc,wgf,ε,wmed} were

used in this article with the values of

{7×7,9×9,0.0001,11×11}. Table 1 displays the

quantitative results of the proposed work and other

published methods based on the KITTI dataset for

accuracy comparison. The proposed work produces the

lowest average error with 8.71% and 19.95% for nonocc-

bg and nonocc-fg respectively. Additionally, the all-bg

and all-fg are also the lowest average error with 8.98%

and 20.04% respectively. It shows that the proposed

work in this article is accurate and competitive with other

established methods.

Table 1 The average results based on the KITTI.

Method Nonocc (%) All (%)

bg fg bg fg

Proposed 8.71 19.95 8.98 20.04

SGM [3] 11.12 18.84 11.93 20.57

iGF [4] 17.76 20.14 18.61 21.69

SED [5] 24.67 39.95 25.01 40.43

4. CONCLUSION

In conclusion, the proposed work in this article

produces accurate results based on the standard

benchmarking evaluation system. It also shows the

competitiveness of the proposed computational

modelling with other methods in Table 1. Hence, the

proposed framework in this article can be used as a

complete model for the AVN.

ACKNOWLEDGEMENT

This project is supported by Universiti Teknikal

Malaysia Melaka. (grant number:

PJP/2018/FTK(13C)/S01632).

REFERENCES

[1] Freundlich, C., Zhang, Y., Zhu, A.Z., Mordohai, P.

and Zavlanos, M.M. (2017). Controlling a robotic

stereo camera under image quantization noise. The

International Journal of Robotics Research, 36(12),

1268-1285.

[2] McGuire, K., de Croon, G., De Wagter, C., Tuyls,

K. and Kappen, H. (2017). Efficient optical flow

and stereo vision for velocity estimation and

obstacle avoidance on an autonomous pocket drone.

IEEE Robotics and Automation Letters, 2(2), 1070-

1076.

[3] Schuster, R., Bailer, C., Wasenmüller, O. and

Stricker, D. (2018). Combining stereo disparity and

optical flow for basic scene flow. arXiv preprint

arXiv:1801.04720, 1-10.

[4] Hamzah, R. A., Kadmin, A. F., Hamid, M. S., A

Ghani, S. F. and Ibrahim, H. (2018). Improvement

of stereo matching algorithm for 3D surface

reconstruction. Signal Processing: Image

Communication, 65, 165-172.

[5] Peña, D. and Sutherland, A. (2016). Disparity

Estimation by Simultaneous Edge Drawing.

Proceedings of Asian Conference on Computer

Vision, 124-135.

[6] He, K., Sun, J. and Tang, X. (2013). Guided image

filtering. IEEE Transactions on Pattern Analysis

and Machine Intelligence, 35(6), 1397-1409. ook

[7] Menze, M. and Geiger, A. (2015). Object scene flow

for autonomous vehicles. Proceedings of the IEEE

Conference on Computer Vision and Pattern

Recognition, 3061-3070.


__________


Characterising durability for solid and honeycomb plate under constant loading using Finite Element Analysis

N.M.A. Arifin*, S. Abdullah, S.S.K. Singh, A.H. Azman

Centre for Integrated Design for Advanced Mechanical System (PRISMA),

Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.


Keywords: Durability; Finite Element Analysis; fatigue life

ABSTRACT – This paper presents the durability

characteristics for solid and honeycomb cantilever

plates under constant loading using the Finite Element

Analysis (FEA). The aim is to investigate the impact on

fatigue life. A geometrical model for the plates of solid

and honeycomb was developed using durability

assessments aided by a computer software. A

comparison of these simulations was done following the

FEA of fatigue life.

1. INTRODUCTION

Durability is an item's ability to withhold the

intended use for an appropriate timeframe. In the

automotive industry, the FEA method is often used for

evaluating durability. Determining the material

durability is an important factor in selecting engineering

material before designing a product. Fatigue is a major

failure in the structure of a material, pertaining to the

cracks in components that occur due to repeated load

cycles. Assessments of the mechanical durability of

mechanical products rendered estimations of the S-N

stress life or the ε-N strain life based on the mean values

of the cavity load fatigue test [1].

2. METHODOLOGY

The objective of this paper is to investigate how

‘part geometry’ can influence the maximum stress found

at critical points for plates and understand how this

influences the strain concentration factor. The FEA

programs eliminate the conventional method, which

takes a long time to solve deflections and high stress

locations in complex parts [2]. Table 1 shows the

properties of the material used for comparative

simulation [3] between the solid plate and the

honeycomb plate. A fatigue assessment using the strain

life durability approaches is performed. LMS Virtual

Lab Durability is a finite element software for analysing

parameter generation [4]. In durability analysis, using

this finite element software, the load is charged at

4000N on the material of the geometrical model for

plates of solid and honeycomb (as shown in Figure 1).

The results are scheduled for maximum life expectancy

until failure against the number of cycles.

The second simulation is used to analyse fatigue

and determine the durability parameters performed with

the same structural state but a different load of 2350N.

The schematic diagram of the finite element base

durability analysis is show in Figure 2.

Table 1 Properties of SAE1045

Material Properties Value

Modulus Young (GPa) 207

Poisson Ratio 0.3

Yield Strength (MPa) 1515

Ultimate tensile strength (MPa) 1584

Figure 1 Geomertical model for plates.

Figure 2 Final element-based durability analysis.

Arifin et al., 2018

62

Fatigue analysis can be carried out using one of the

three basic methodologies: stress life, strain life and

crack growth. However, for this study, the analysis is

made based on strain life, for material damage cycle D

is 1 / Nf, where Nf is the number of cycles corresponding

to the median fatigue life. Damage to the number of n

cycles is nD = n/N and is shown in Equation (1).

(1)

Where D is the fatigue damage, ni the number of load

cycles and Ni the number of cycles until failure at the

load level.


Figure 3 and Figure 4 show the simulation results,

i.e. the difference between 2 plates: solid and

honeycomb. As observed in table 2, the fatigue life for

solid plate is 2.14 x 105 cycles, while that for the

honeycomb plane is 2.19 x 105 cycles. For a honeycomb

plate, the damage caused by a reduced impact is

difficult to be obtained because the deformation in

honeycombs is in the form of bending of the cell walls

[5]. This result clearly indicates that the solid plate can

withstand a larger load than the honeycomb plate.

(a)

(b)

Figure 3 Fatigue life assessments for (a) solid 4000N

and (b) honeycomb 2350N

4. CONCLUSION

The performance of plates was estimated for

various dimensions of the solid and honeycomb plates.

On comparing the fatigue life gained, the cantilever

plate deflections with a force on the free end of the solid

and honeycomb plates were determined as 11.4mm and

6.64mm, respectively.

(a)

(b)

Figure 4 Translational displacements for (a) solid

4000N and (b) honeycomb 2350N.

Table 2 Comparison of life and damage characteristic

for solid and honeycomb plate.

Plate Solid Honeycomb

Loading (N) 4000 2350 4000 2350

Fatigue

damage 4.15 x 10-6 - 5.57x 10-4 4.56 x 10-6

Fatigue life 2.14 x 105 - 1.79 x 103 2.19 x 105

Translational

displacement

(mm)

11.4 6.69 11.3 6.64

Von Mises

Stress (MPa) 674 396 1440 848

REFERENCES

[1] Gates, N. R., & Fatemi, A. (2018). Multiaxial

variable amplitude fatigue life analysis using the

critical plane approach, Part II: Notched specimen

experiments and life estimations. International

Journal of Fatigue, 106, 56-69.

[2] Smith, J., Medar, P., & MR, I. A. (2014). Finite

Element Analysis and Fatigue Life Estimation of

Plate with Different Stress Levels. International

Journal of Advance Research and Innovation, 2(3),

613-617.

[3] Karthik, J. P., Chaitanya, K. L., & Sasanka, C. T.

(2012). Fatigue life prediction of a parabolic spring

under non-constant amplitude proportional loading

using finite element method. International Journal

of Advanced Science and Technology, 46, 143-156.

[4] Łukasiewicz, M., Kałaczyński, T., Liss, M., &

Kanigowski, J. (2014). The LMS Virtual. Lab

application in machines technical state

analysis. Journal of Polish CIMAC, 9(3), 59-66.

Griese, D., Summers, J. D., & Thompson, L.

(2015). The effect of honeycomb core geometry on

the sound transmission performance of sandwich

panels. Journal of Vibration and Acoustics, 137(2),

1-11.


__________


Conceptual design and computational analysis of new school desk Muhammad Ikman Ishak*, Nurul Syifa’ Ahmad Zohri, Wan Nur A’tiqah Wan Draman, Suhaimi Shahrin,

A.H.M. Haidiezul, A.K. Mohamad Syafiq, Norsyahadah Yeop Wasir, Noor Diyana Dahlan, Bakri Bakar

Faculty of Engineering Technology, Universiti Malaysia Perlis, Level 1, Block S2, UniCITI Alam Campus,

Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia.


Keywords: Conceptual design; finite element analysis; school desk

ABSTRACT – This study aims to design and analyse a

new school desk which may reduce the number of the

unattended desks by transforming them into a blocking

panel to separate one family area to another owing to

privacy reason during the flood season. A series of

design development and evaluation phases were

undertaken. The results depicted that the proposed

design promoted convincing stress and displacement

distributions. This could be due to the presence of

highly stable placement and position of the legs as well

as the configuration of the desk top to bear the applied

load.

1. INTRODUCTION

Malaysia regularly experiences natural disasters

which commonly cause widespread destruction and loss

of life[1, 2]. One of the frequent natural disasters

attacking Malaysia is floods and it has usually forced

the affected communities to leave their home for a safer

place. Schools are commonly used as temporary relief

centre in flood-stricken states to accommodate the

victims. It is however a significant privacy issue raised

in performing personal activities due to lack of enclosed

area available. As the victims will stay at the relief

centre for a period of times, there is a huge necessity to

have panels which separating one family area to another

and this could be achieved by the use of the unattended

school desks. Most of the student learning desks are put

left aside to provide large spaces for comfort. The

conventional school desks used nowadays are heavy in

weight which makes them hard to be lifted and moved

besides consuming large spaces for storage due to

limited downsizing mechanism. Therefore, it is an

essential for the present study to develop a new design

of school desk and analyse it via three-dimensional (3-

D) finite element analysis (FEA).

2. METHODOLOGY

There were two ethnographic studies performed at

a few selected schools to determine the user needs and

product design specifications. The findings of the first

study revealed that those schools used the same type of

desks which are mainly made of rubber wood.

Moreover, some of the desks were in poor condition

with incomplete and broken parts. Whilst, the second

study was conducted to specifically investigate the real

situations faced by the flood victims at the selected

relief centres. A thorough observation was made on the

arrangement of classroom furniture especially the desks.

The placement of the desks inside or outside of the

classroom had consumed large spaces which may

considerably be wasted.

Several interview sessions and questionnaires

circulation were also performed with relevant personnel.

Among the vital responses obtained were the desk

design must be ergonomic, durable and long-life span

materials used, attractive colour, reasonable weight,

adjustable height, stable, and simple downsizing

mechanism.

All the information collected were used in the

design development stages which are starting from

constructing List of Metrics, followed by Needs-Metrics

Mapping, Competitor Benchmarking Information,

Target Specifications, and Final Specifications. Thus,

the finalised needs for the desk design are as the

following: multipurpose, adjustable height, light-weight,

portable, having compartments, low maintenance, safety

features, durable, and attractive colour.

The determination of target product specifications

has led to the next main phase to be undertaken which is

Functional Analysis. Consequently, there were three

different concepts of desk had been successfully created

as shown in Figure 1.

Figure 1 Three-dimensional model of (a) Concept A, (b)

Concept B, and (c) Concept C.

The desk design in Concept A was developed to be

70 cm in height, 70 cm in length, and 51 cm in width. It

is completed with a flip drawer which installed using

hinges to obtain a gentle closing mechanism. All the

four legs are detachable from the base of the desk and

they may also be adjusted to set into different heights.

Whilst, Concept B shares the same main dimensions

with Concept A, however, the drawer implies the

mechanism of pull-and-push where it is embedded with

Ishak et al., 2018

64

roller and trail. For the leg, it can be flipped for a

convenient storage. Concept C, on the other hand,

having similar leg design and overall dimensions with

Concept A, and completed with an opened drawer.

Based on the results of concept evaluation process,

Concept A had recorded the highest score compared

with another two. The structural performance of

Concept A was then verified via computational 3-D FEA

in terms of stress and displacement distributions. All

FEA models were assumed to be isotropic,

homogeneous, static, and linearly elastic throughout the

analysis[3].A vertical load of 981 N was applied on the

top surface of the desk and all bottom flat surfaces of

the legs were fixed.


It was clearly observed that Concept A has more

superior features than those of Concept B and Concept

C. The legs in Concept A may easily be adjusted to set

the desk in a few heights. The users have to pull out the

adjustable part of the leg at the bottom to increase the

length. The original height of the desk is 70 cm and it

could be increased up to 75 cm by using the adjustable

legs. It also was exhibited that Concept A promotes the

most convenient way to detach the legs from the base in

order to transform the desk into a panel by merely

rotating them and stored in the inner drawer slots. There

is a low tendency of the legs to fall out from the drawer

due to tight closing attachment provided by the hinges.

As the proposed product is intended to tackle the

privacy issue raised at the relief centres, thus, Figure 2

illustrates the steps of downsizing the desk in

transforming it into a series of blocking panel.

Figure 2 (a) Detachment of the legs. (b) Storage of the

legs. (c) Development of the blocking panel.

The results of analysis showed that the highest

stress value within the desk structure was generated in

Leg 3 with 2.22 MPa as depicted in Figure 3a. A similar

pattern of stress distribution was found for the other legs

where the top connecting parts seemed to sustain a

greater stress level.It was also clearly shown that a

wider stress concentration region developed in the

frontal legs (Leg 1 and 2) as compared to the back legs

(Leg 3 and Leg 4). The greatest stress value was

recorded within the leg body could probably be due to

high carbon steel modulus of elasticity of 200 GPa used

as compared to the wood. Moreover, the maximum

stress level generated within the legs has no tendency to

the part failure as carbon steel is known can tolerate

stresses up to 900 MPa[4]. The displacement results of

the desk structure were in contrast with the stress

outcome where the desk top part produced the greatest

displacement value (30.83 mm downwards) in

comparison to other parts as exhibited in Figure 3b.

Figure 3 The (a) stress and (b) displacement dispersions.

4. CONCLUSION

It is suggested that the new design of school desk

comprises one desk top part which completed with a

tightly-closed drawer and four easy-detachable legs for

the transformation of the desk into blocking panel. The

structural performances of the proposed desk model

were found to satisfactorily withstand the applied load

in terms of stress and displacement values and

dispersions.

ACKNOWLEDGEMENT

Appreciation is given to Faculty of Engineering

Technology, Universiti Malaysia Perlis.

REFERENCES

[1] AlBattat, A. R. & MatSom, A. P. (2014).

Emergency planning and disaster recovery in

Malaysian hospitality industry. Procedia - Social

and Behavioral Sciences, 144, 45-53.

[2] Janius, R., Abdan, K., & Zulkaflli, Z. A. (2017).

Development of a disaster action plan for hospitals

in Malaysia pertaining to critical engineering

infrastructure risk analysis. International Journal

of Disaster Risk Reduction, 21, 168-175.

[3] Ishak, M. I., Khor, C. Y., Jamalludin, M. R., Rosli,

M. U., Shahrin, S., Yeop Wasir, N., Zakaria, M. S.,

Yamin, A. F. M., Dahlan, N. D., & Wan Draman,

W. N. A. (2017). Conceptual design of automotive

compressor for integrated portable air conditioning

system. MATEC Web Conf., 97, 01040.

[4] Odusote, J. K., Ajiboye, T. K., & Rabiu, A. B.

(2012). Evaluation of mechanical properties of

medium carbon steel quenched in water and oil.

Journal of Minerals and Materials

Characterization and Engineering, 11, 859-862.


__________


Simulation of aluminum cylindrical cup in deep drawing process A.F.M. Yamin*, A.S. Abdullah, N.S. Abdullah, H. Ghafar, S.N.A.M. Halidi, H. Yusoff

Faculty of Mechanical Engineering, Universiti Teknologi MARA (UiTM) Penang,

13500 Permatang Pauh, Penang, Malaysia.


Keywords: Deep drawing; finite element analysis; Johnson-cook

ABSTRACT – This study investigates a mechanic

behavior of cylindrical cup during deep drawing process.

The assembly consists of 78 mm diameter of Aluminum

blank with 1 mm thickness, 40 mm diameter of punch

and 42 mm cavity of conical die and blank holder. The

travel displacement of the punch is limited to 35 mm. The

inelastic strain behavior of the cup is predicted using a

quasi-static Johnson-Cook model. The response of the

simulation was compared with the experimental

methodology to address the capability of numerical

technique. Results show that good agreement in terms of

punch forces and displacements between experimental

work and numerical simulation. At 26.5 mm punch travel

displacement, the critical location of the cylindrical cup

is located near to the fillet radius of the conical die. Such

condition resulting maximum stress and inelastic strain

values which are 308 MPa and 0.7024 respectively.

1. INTRODUCTION

Deep drawing is one of the widely used sheet metal

forming process. The successful rate of drawing product

depends on several factors such as blank material, tools

geometry, blank holder force etc. Most of the industries

relies on empirical methodology to find the best process

condition in which time consuming and costly.

Alternatively, numerical technique such as finite element

(FE) simulation can be adopted as it is more cost-efficient

and reliable.

In order to analyse the dynamic process experienced

by conical cup, FE simulation can be done. The FE model

was developed according to experimental works done by

Moshksar et al. [1]. Results of the simulation and

experiment were then compared in term of punch load

and displacement. Better prediction of FE simulation was

expected. Further results in term of stress and inelastic

strain distribution were then discussed to describe the

mechanic behaviour of the conical cup during this

process.

2. MATERIAL MODEL

Since, the blank sheet experienced high and

complex deformation [2], the capability of inelastic

model to predict the plastic response is crucial. A

Johnson-Cook model was used to estimate the inelastic

strain under such condition as describe in Equation 1.

𝜎𝑦 = 𝐴 + 𝐵𝜀𝑝𝑙𝑛 (1)

where A, B and n are Johnson-Cook material constants,

εpl is equivalent plastic strain and σy is equivalent yield

stress. Material properties and Johnson-Cook parameters

of the Aluminium alloy used in this study is summarized

in Table 1. The material properties and parameters were

found by curve fitting the hardening Equation (1) with

tensile test data done by Moshksar et al [1].

Table 1 Material properties and Johnson-cook

parameters of aluminium alloy [1].

Property / Parameter Value

Elastic Modulus, E (GPa) 114

Poisson Ratio, ν 0.33

Parameters of Johnson-Cook plasticity model

Quasi-static yield stress, A (MPa) 33.56

Strain hardening modulus, B (MPa) 177.1

Strain hardening exponent, n 0.4368

3. FINITE ELEMENT MODEL OF DEEP

DRAWING PROCESS

The FE model of deep drawing process of

cylindrical cup was based on an experimental works done

by Moshksar et al [1]. The model consisted of 78 mm

diameter of Aluminium blank with 1 mm thickness, 40

mm diameter of rigid punch and 42 mm cavity of rigid

conical die and rigid blank holder. For this analysis, the

punch and conical die nose radius were set to 6 mm and

4 mm respectively.

Figure 1 Axis-symmetry model of deep drawing process

of cylindrical cup.

Due to symmetry of the geometry, loading and

boundary conditions, the model was simplified to axis-

symmetry model as illustrated in Figure 1. Throughout

the simulation, all contact friction between surfaces were

included in the model and assumed to be 0.3 for friction

coefficient. The rigid punch travel from 0 mm (top

surface of the blank) to 35 mm at negative-2 direction.

Yamin et al., 2018

66

Fixed boundary condition (U1 = U2 = UR3 = 0) was

imposed at the conical die. The blank-holder force was

set to 1 kN to prevent wrinkling of the cup in the

simulation.

4. RESULTS AND DISCUSSIONS

Results of FE simulation are presented and

discussed in terms of force-displacement response of the

punch and distribution of stress and inelastic strain in

cylindrical cup.

4.1 Punch force-displacement

Figure 2 shows the force-displacement curves

between experiment [1] and current work. The predicted

model correlated well with the experiment until 28 mm

punch displacement. However, after 28 mm tool stroke,

the numerical value of the punch force is under predicted

compared to the experiment. This is likely due to some

portion in cylindrical cup starts to experience damage and

consequently degrading the load carrying capacity of the

material. Since, the material model used in this study was

not considering the damage behaviour of the cup, the cup

will keep hardened even beyond the fracture limit.

Figure 2 Punch force-displacement curves between

experiment and simulation during deep drawing

process.

4.2 Stress and inelastic strain distribution

The effective stress and inelastic distribution of the

cylindrical cup during the deep drawing process is

illustrated in Figure 3. The stress and inelastic strain

ranges from 0 to 310 MPa and 0 to 0.75, respectively.

Due to complex deformation of the cup, the critical

location is different depending on the punch stroke. At

the beginning of the simulation, only a minor stress

occurs at the blank due to engagement between conical

die and rigid blank holder. After punch displaces, a

significant change of stress and inelastic strain

distribution across the cylindrical cup.

It is observed that, the highest stress and inelastic

strain distribution of the blank is located closely to the

die nose radius. At 26.5 mm punch displacement, the

highest value of stress and inelastic strain are predicted

which are 308 MPa and 0.7024 respectively. Notice that,

the minimum stress and inelastic strain distribution of the

cup is located near to the contact surface of the punch up

to the punch fillet radius. Since less plastic deformation

happens at this area, less chance of crack will be initiated

and subsequently crack growth.

Figure 3 Evolution of stress and inelastic strain

distribution of the blank during deep drawing process.

5. SUMMARY

The response of a cylindrical cup during deep

drawing process has been examined using FE simulation.

Results show that;

a) Good agreement between experiment and simulation

in terms of punch force and displacement,

b) At 26.5 mm punch displacement, the maximum

stress and inelastic strain at the critical point are 86

MPa and 0.032 respectively,

c) The highest stress and inelastic strain distribution of

the cylindrical cup is located near to the die nose

radius,

d) The bottom depth of the cup is the minimum stress

and inelastic strain distribution predicted in the

simulation.

REFERENCES

[1] Moshksar, M. M., & Zamanian, A. (1997).

Optimization of the tool geometry in the deep

drawing of aluminium. Journal of Materials

Processing Technology, 72(3), 363-370.

[2] Raju, S., Ganesan, G. & Karthikeyan, R. (2010).

Influence of variables in deep drawing of AA 6061

sheet. Transaction of Nonferrous Metals Society of

China 20, 2010, 1856-1862.

Punch Displacement

(mm)

Stress (MPa)

Inelastic Strain

0.0

10.0

17.5

26.5

35.0


__________


Initial validation of RULA-Kinect system – Comparing assessment results between system and human assessors

Radin Zaid Radin Umar1,*, Chai Fong Ling1, Nadiah Ahmad1, Isa Halim1, Fatin Ayuni Mohd Azli Lee1,

Nazreen Abdullasim2

1) Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka,



Keywords: Ergonomics; Rapid Upper Limb Assessment; RULA – Kinect system

ABSTRACT – Automated smart system has been an

emerging trend in Industry 4.0. A RULA-Kinect system

has been developed to automate the traditional Rapid

Upper Limb Assessment (RULA). A validation study was

conducted to compare RULA assessment between the

system and human assessors. T-Test results showed that

the RULA scores of 9 tasks are comparable (no statistical

significant differences at α = 0.05) between developed

system and 10 novice human assessors. The promising

initial results demonstrated the potential to automate

RULA process, simplifying and improve efficiency of

assessment, and thereby is in line with the direction on

Industry 4.0.

1. INTRODUCTION

Smart integrated system, which is one of the

emerging trends in Industry 4.0, has been used in many

industries such as manufacturing and healthcare [1-2].

Sensitive, cheap, and easily accessible sensors have

played a big role as an enabler of systems’ integration and

automation. Smart system results in a more efficient

management of big data, and consequently enhance the

ability to measure the overall workplace improvement

programs. Human Factors and Ergonomics, which

focuses on interactions between human and workplace

system may benefit from application of Industry 4.0.

Postural assessment of workers has been one of the

core responsibilities for occupational ergonomists.

Ergonomic assessment tools have conventionally relied

on video, paper and pencil. One of the most common

assessment tools is Rapid Upper Limb Assessment

(RULA), developed by McAtemney and Corlett [3].

RULA has been widely used for assessing work posture

in different industries and countries [4-6]. Digitalization

and automation of RULA can simplify the assessment

process and improve the assessment efficiency, which

directly aligned with the direction of Industry 4.0.

A RULA-Kinect system has been developed by the

research team to automate RULA assessment at

workplaces. The hardware consisted of Kinect Xbox 360

camera and computer, while software system consisted of

customized programming algorithm developed using

Microsoft Visual Studio. The system is sensitive enough

to capture real time postural angle data, at a maximum

rate of 30 frames per second. The algorithm calculates the

RULA scoring for postural data of each frame. Muscle

and force / load data can be directly input in the system’s

Graphical User Interface (GUI), before producing RULA

scores. The computerized calculation of the scoring

system helps to simplify the calculation task and

minimize manual computing errors. This manuscript is

aimed to describe the initial validation process of the

system, through comparison of RULA scores between the

system and the traditional method.

2. METHODOLOGY

This initial validation study compared generated

RULA scores between the RULA-Kinect system and the

conventional method on 9 different tasks from actual

workplace activities. In preparing for the validation

process, RULA-Kinect system was used to capture

RULA scores of each task at actual worksite. At the same

time, videos of each task were separately captured for

traditional RULA to be conducted by human assessors at

later times using video, paper and pencil technique.

The traditional RULA assessments were then

conducted among 10 novice human assessors with

technical and engineering background. Each assessor

received training on RULA and were given practical

exercises as well as an examination prior to being asked

to conduct actual RULA evaluations. The video of each

task captured was shown to each human assessor. An

assessor would choose specific work posture to evaluate

by pausing the video, before calculating the RULA scores

using the traditional RULA form. As a result, each RULA

assessment conducted by human assessors is independent

of each other.

The RULA scores from specific posture chosen by

the human assessors were directly compared to RULA

scores of similar posture assessed by the RULA-Kinect

system. To minimize bias, each human assessor did not

have access to RULA scores generated by the RULA-

Kinect system. Descriptive analysis was conducted to

compare RULA scores between traditional assessment

and Kinect-RULA system. In addition, two samples T-

test was conducted using SPSS statistical software

package to compare between the results.


In general, the RULA scores between the system

and assessors are comparable across the different tasks.

Figure 1 shows an example of the posture assessed in one

of the tasks by one of the human assessors using the

traditional RULA vs. RULA-Kinect system. Example of

RULA scores from traditional and system across subjects

for one of the tasks is demonstrated in Table 1.

Radin Umar et al., 2018

68

Figure 1 Posture of technician operating lathe machine

assessed using traditional RULA (left) and RULA-

Kinect system (right) for Subject 4 (S4).

Table 1 Example of RULA scores between Traditional

and Kinect-RULA system of lathe machine operation

between human assessor and Kinect-RULA system.

Subjects

RULA score

Manual

assessment

Kinect-RULA

assessment

S1 4 4

S2 4 3

S3 3 4

S4 4 3

S5 2 3

S6 2 3

S7 2 4

S8 4 4

S9 3 3

S10 2 3

The average T-Test analysis showed no significant

differences (at α = 0.05) in RULA scores between human

assessors and RULA-Kinect system, as shown in Table 2.

This indicates the developed system is capable to produce

results of RULA assessment similar to the results

produced by human assessors. The findings showed the

same trend of potentials with similar system developed

by other researchers [7-8].

4. CONCLUSION

This study provides an early validation of the

developed RULA-Kinect system. The results

demonstrated the early potential of the system to

automate RULA assessment. However, it should be

noted that the study only involves novice human

assessors. Follow up system validation requires

assessment comparison between the system and expert

human assessors.

ACKNOWLEDGEMENT


Malaysia Melaka and Ministry of Education Malaysia

(grant number: PJP/2016/FKP-AMC/S01501).

REFERENCES

[1] Brettel, M., Friederichsen, N., Keller, M., &

Rosenberg, M. (2014). How virtualization,

decentralization and network building change the

manufacturing landscape: An Industry 4.0

Perspective. International Journal of Mechanical,

Industrial Science and Engineering, 8(1), 37-44.

[2] Bates, D. W., Saria, S., Ohno-Machado, L., Shah,

A., & Escobar, G. (2014). Big data in health care:

using analytics to identify and manage high-risk and

high-cost patients. Health Affairs, 33(7), 1123-

1131.

[3] McAtamney, L., & Corlett, E. N. (1993). RULA: a

survey method for the investigation of work-related

upper limb disorders. Applied Ergonomics, 24(2),

91-99.

[4] Gandavadi, A., Ramsay, J. R. E., & Burke, F. J. T.

(2007). Assessment of dental student posture in two

seating conditions using RULA methodology–a

pilot study. British Dental Journal, 203(10), 601-

605.

[5] Moghaddam, S. R., Khanjani, N., & Hasheminejad,

N. (2012). Evaluating risk factors of work-related

musculoskeletal disorders in assembly workers of

Nishabur, Iran using rapid upper limb assessment.

Journal of Health and Development, 1(3), 227-236.

[6] Chyuan, J. Y. (2007). Ergonomic assessment of

musculoskeletal discomfort among commissary

foodservice workers in Taiwan. Journal of

Foodservice Business Research, 10(3), 73-86.

[7] Plantard, P., Shum, H. P., Le Pierres, A. S., &

Multon, F. (2017). Validation of an ergonomic

assessment method using Kinect data in real

workplace conditions. Applied Ergonomics, 65,

562-569.

[8] Nahavandi, D., & Hossny, M. (2017). Skeleton-free

RULA ergonomic assessment using Kinect sensors.

Intelligent Decision Technologies, 11(3), 275-284.

Table 2 T-test analysis to compare RULA scores

between two methods among novice assessors.

Tasks Method Mean SD P-

value

1. Copying

documents

TR 3.6 0.56 0.074

KRS 3.2 0.42

2. Stacking

shelves

TR 5.9 1.29 0.060

KRS 6.8 0.42

3. Operating

milling

machine

TR 4.8 1.75 0.133

KRS 3.8 0.02

4. Operating

lathe machine

TR 2.8 1.23 0.180

KRS 3.4 0.52

5. Replacing

parts

underside of

vehicle

TR 5.6 1.17

0.318 KRS 5.1 0.99

6. Maintaining

engine gasket

TR 3.2 0.63 0.057

KRS 3.2 0.42

7. Printing

silkscreen

TR 3.2 0.63 1.000

KRS 3.2 0.42

8. Operating

perspex

machine

TR 4.5 0.85 0.285

KRS 4.0 1.15

9. Labelling

part

TR 4.7 0.82 0.148

KRS 5.3 0.95

Note: Sample size (n) = 10.

TR = Traditional RULA. KRS = Kinect RULA system.


__________


Yaw angle effect on the aerodynamic performance of hatchback vehicle fitted with combo-type spoiler

Kwang Yhee Chin1, Cheng See Yuan1,2,*


Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Centre for Advanced Research on Energy, Universiti Teknikal Malaysia Melaka,



Keywords: Yaw angle; spoiler; aerodynamics

ABSTRACT – This study investigated the aerodynamic

performance of the combo-type spoiler in yawing

conditions using RANS-based CFD method. To date, a

majority of the tests performed on spoiler were done in

a straight-ahead driving condition. However, the effect

of spoiler is most demanded during cornering for

stability reason. The results show the spoiler is the main

contributor to the overall downforce generated on the

vehicle, but its performance deteriorates with increasing

yaw angles.

1. INTRODUCTION

Basically, rear spoiler is an aerodynamic device

added externally to the trailing edge of the roof or trunk

of a vehicle to alter the air movement around the

vehicle. Ever since its practicability on racing car has

been proven, a variety of spoiler types has been

investigated extensively in previous studies [1-2].

Generally, there are two types of rear spoilers, namely

strip-type and wing-type spoilers. Besides, there are

spoilers in the market nowadays featuring the

combination of the two configurations. This type of

spoiler will be designated as combo-type spoiler for

convenient in the discussions in this study.

The effectiveness of spoilers has been the interest

in various studies due to fuel and energy consumption,

vehicle stability, and racing speed concerns. However,

publication on the effectiveness of combo-type spoiler

made available to the public was scarce. Despite the

scarcity, there was a study on this type of spoiler

reported by Gerhardt and Kramer [3] on the

aerodynamic optimization of a Group-5 racing car.

There is a 30s reduction of lap time on Nürburgring

racing track. The results reported are produced using

wind tunnel experimentation and practical experience.

Hence there was no specific data recorded on how the

spoiler contributed to the reduced lap time.

Moreover, despite yaw angle affects the vehicles’

aerodynamic performance, as shown in previous studies

[4-5], majority of the studies on rear spoilers did not

reported on this influences. To fill in the gap, the present

study investigated the aerodynamic performance of the

combo-type spoiler in yawing conditions.

2. METHODOLOGY

2.1 Model

In order to conduct this study, the wing spoiler is

utilizing airfoil profile of NACA 0018 combine with a

strip spoiler. The angle of attack of both spoilers is 5°. This spoiler is modeled and mounted on simplified road

vehicle geometry, namely Ahmed model [6] as shown in

Figure 1. The slant angle for this model is 35°, which is

a typical angle for most hatchback vehicle. The wing

spoiler has a chord length of 69mm, which is the result

of the scale ratio of 6.61% of the length of Ahmed

model, resembling the length of wing spoiler in reality.

The yaw angles investigated are from 0° to 12°, at 4° increments.

Figure 1 Ahmed model with combo-type spoiler.

2.2 Meshing

The model was meshed with the computational

domain being discretized into unstructured and

prismatic cells. The result of grid convergence study

indicates that the mesh is sufficiently refined at around

2337141 cells. The first prismatic cell layer thickness

around the model surface was 0.5 mm. The

corresponding y+ ranges from around 1.2 to 80.

2.3 CFD setting

The effect of yaw angle on the aerodynamic forces

of Ahmed model with a wing spoiler was investigated

using numerical simulation method. The commercial

finite-volume solver, ANSYS Fluent 16 was used to

calculate all the results obtained in this study using the

Reynolds-averaged Navier-Strokes (RANS) approach.

The boundary condition for inlet was set to be

uniform flow with inlet velocity of 40 m/s. The

Reynolds number was 2.7 x 106 corresponding to the

model’s length. On the other hand, the boundary

condition for outlet was set to be having zero gauge

pressure. The walls on top and at both sides of the

domain were set as symmetry boundary condition, while

the ground and model surfaces were set as no-slip wall.

Validation of the numerical method was done

through comparing the drag coefficient Cd of the Ahmed

model at increasing yaw angle obtained by the present

CFD method and the experimental work by Bello-

Chin et al., 2018

70

Millan et al. [4]. The two curves produced are in very

good agreement (with the maximum difference of 5.2%

at 20° yaw angle). The Cd values by each method are

normalized by their respective Cd values at 0° yaw.


Table 1 shows the force coefficients simulated in

the present study (i.e. with spoiler) compared with the

results published by Meile et. al. [7] (i.e. without

spoiler). Hence, when Ahmed model was fitted with a

combo spoiler, there was a 1% and 600% of reduction

for Cd and Cl respectively.

Table 1 Cd and Cl of Ahmed model with and without the

combo spoiler at 0˚ yaw for 𝑈∞=40 m/s.

Figure 3 shows the effect of yaw angle on the total

Cd and Cl of the model. Both aerodynamic force

coefficients increased with increasing yaw angle. Note

that the body axis system adopted by this study is such

that Cd is defined as the aerodynamic force component

parallel to the longitudinal axis of the model. The graph

demonstrated that when the vehicle is no longer

travelling in a straight path, its aerodynamic

performances deteriorate.

Figure 3 Effect of yaw angle on Cd and Cl of Ahmed

model with wing-strip-combo-type spoiler.

Furthermore, Figure 4 shows the graph of Cd and

Cl against yaw angle, indicating the effect of yaw angles

on aerodynamic forces of the spoiler. It shows favorable

trend of decreasing for Cl with a decrease of 27.7%. In

contrary, Cd showed the opposite trend with an increase

of 6.1% at 8˚ yaw and 2.29% at 12˚ yaw. The values

recorded for the aerodynamic force coefficients proved

that the spoiler does contribute to lower the lift

coefficients for vehicles, even during non-zero-yaw

conditions but failed to do the same for the drag

coefficients.

In regard to Cd, the contribution of the spoiler to

the overall Cd just accounted to at most 6.39% at 0˚ yaw

and diminished to 4.24% at 12˚ yaw. Hence, its effect to

the overall Cd was insignificant. However, as for the Cl

values recorded, the proportion contribution of the

wing-strip-combo-type spoiler to the overall Cl recorded

at least 49.5% at 12˚ yaw and at most 52.6% at 0˚ yaw.

In addition, the spoiler is the only component

contributed to creating downforce. Hence, the spoiler

was reason for the negativity of the overall lift

coefficients.

However, despite the Cl of spoiler decreases with

yaw angle (see Figure 4), the overall Cl increases

instead. This may due to the fact that other than the

spoiler, all other components of the model, especially

the roof, increasingly contributed to positive lift.

Figure 4 Effect of yaw angle on Cd and Cl of wing-strip-

combo-type spoiler.

4. CONCLUSION

This study investigated the effect of combo-type

spoiler fitted onto simplified vehicle geometry in

different yaw angles using CFD simulations with RANS

approach. The results show that adding spoiler onto

vehicle did reduce both drag and lift coefficients.

Besides, not much yaw angle effect was found on Cd,

but for Cl, the spoiler actually provides favorable

influence. However, the overall Cl still increase with

yaw angle which could be due to increasing contribution

on positive lift from other components of the vehicle

model.

ACKNOWLEDGEMENT


Malaysia Melaka (UTeM) and Ministry of Higher

Education under FRGS/1/2015/TK03/FKM/02/F00273.

REFERENCES

[1] Cheng, S. Y., & Mansor, S. (2017). Rear-roof

spoiler effect on the aerodynamic drag

performance of a simplified hatchback model.

Journal of Physics: Conference Series, 822(1), 1-6.

[2] Kodali, S. P., & Bezavada, S. R. I. N. I. V. A. S.

(2012). Numerical simulation of air flow over a

passenger car and the Influence of rear spoiler

using CFD. International Journal of Advanced

Transport Phenomena, 1(1), 6-13.

[3] Gerhardt, H. J., Kramer, C., AmmerschlÄger, T., &

Fuhrmann, R. (1981). Aerodynamic optimization

of a group-5 racing car. Journal of Wind

Engineering and Industrial Aerodynamics, 9(1-2),

155-165.

[4] Bello-Millán, F. J., Mäkelä, T., Parras, L., Del

Pino, C., & Ferrera, C. (2016). Experimental study

on Ahmed's body drag coefficient for different yaw

angles. Journal of Wind Engineering and

Industrial Aerodynamics, 157, 140-144.

[5] Meile, W., Ladinek, T., Brenn, G., Reppenhagen,

A., & Fuchs, A. (2016). Non-symmetric bi-stable

flow around the Ahmed body. International

Journal of Heat and Fluid Flow, 57, 34-47.

[6] Ahmed, S. R., Ramm, G., & Faltin, G. (1984).

Some salient features of the time-averaged ground

vehicle wake. SAE Transactions, 473-503.

[7] Meile, W., Brenn, G., Reppenhagen, A., & Fuchs,

A. (2011). Experiments and numerical simulations

on the aerodynamics of the Ahmed body. CFD

letters, 3(1), 32-39.

-0.20

0.00

0.20

0.40

0 4 8 12

Cd

an

d C

l [-]

Yaw angle, Ψ [°]

Total Cl Total Cd

-0.10

-0.05

0.00

0.05

0 4 8 12

Cd

an

d C

l [-]

Yaw angle, Ψ [°]

Cl Cd

Force Coefficients Without spoiler With spoiler

Cd 0.276 0.273

Cl 0.013 -0.065


__________


Impact of roof shape on the wind pressure difference between the Windward and Leeward Façades of a building

Zhongyu Goh2, Cheng See Yuan1,2*

1) Centre for Advanced Research on Energy, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Faculty of Mechanical Engineering, Universiti Teknikal Malaysia Melaka,



Keywords: Cross-ventilation; CFD; roof shape

ABSTRACT – The present study investigated the impact

of different roof shapes on the natural ventilation

performance of an isolated low-rise building by using

Computational Fluid Dynamics (CFD). The Gable,

Pyramid and Shed roof were chosen for the study. The

Realizable 𝒌 − 𝜺 turbulent model was adopted in the

CFD simulations. The wind which obeyed power law

equation was set to approach the building model at an

angle perpendicular to the front building surface.

1. INTRODUCTION

Adequate air ventilation provide thermal comfort in

a building and at the same time reduce the possibility of

Sick Building Syndrome (SBS) among the residents as

stated in previous study [1]. According to an estimation

by Spiru and Simona [2], people in urban areas tend to

spend up to 90% of their time in indoor environments

especially work place. A previous study [3] mentioned

that over reliance on mechanical ventilation on a global

scale will cause enormous amount of burden towards the

environment and energy suppliers. According to Schulze

and Eicker [4], several studies had showed that natural

ventilation was able to save 17% of energy consumption

by mechanical ventilation in a targeted building at Meiji

University, Tokyo. Previous study [5] shows that the

formation of natural ventilation relies heavily on air

velocity and air flow pressure difference. For cases where

only insignificant indoor and outdoor temperature

difference occur, air flow pressure difference determines

the performance of natural ventilation as stated in

previous study [6]. Hence, the objective of this study is

to investigate the impact of roof shape on the natural

ventilation performance of a Building.

2. METHODOLOGY

In this study, ANSYS FLUENT 16.0 commercial

software was used to perform simulations. The building

model length, L, width, W and height, He was fixed at

6.6m, 6.6m and 6m respectively while maximum roof

height for all roof shapes was 7.65m. The computational

domain was 126 m (Length), 54 m (Width) and 54 m

(Height). The distance between the front façade of the

building model and domain inlet was 42m. The domain

was discretized into 1.4 million tetrahedral elements with

4 hundred thousand nodes and finer elements were

adopted on regions around the building model.

Realizable 𝑘 − 𝜀 turbulent model was used and all

the transport equation were discretized using a second-

order upwind scheme. The COUPLED algorithm was

used for pressure-velocity coupling. At the inlet, the

mean streamwise velocity of the approaching flow

obeyed a power law with an exponent of 0.25 as shown

in Equation 1.

UPL(z) = Un (𝑍

𝑍𝑛)𝛼 (1)

Where, Zn is reference height, Un is reference velocity at

reference height and 𝛼 is power-law index.

Zero static pressure was applied for the domain

outlet. Both of the sides and the top of the domain were

applied symmetry boundary conditions. The wall

functions on the ground were altered for roughness

height, ks of 1.0mm and roughness constant, Cs of 1.0.

Grid Independent Test was done to identify the optimum

grid quality for simulation. Gable, Pyramid and Shed roof

were simulated with wind approach angles perpendicular

to the building model front façade. The velocity profile

for each roof shapes were plotted. The wind pressure

difference between two points, P1 and P2 which situated

on windward and leeward façade respectively, 1.5m from

the ground were compared for each roof shape to identify

the best natural ventilation performance potential.


Figure 1 shows three dimensional modelling of the

building models with each roof shape: Gable, Pyramid

and Shed.

3.1 Wind velocity profile

Figure 2 shows the velocity profile of the mean

velocity, U1 of each roof shape generated by CFD. All

roof shapes generated significant backflow at the region

around leeward façade as seen in wind velocity profile at

x/He = 1.0 in Figure 2. Overall wind velocity profile for

Gable configuration was the largest and it indicated the

wind had the least resistance flowing over the building

surface. On the windward side, Shed configuration

created smallest wind velocity profile which shows the

airflow around the building went through highest

resistance. While for the leeward side, Pyramid

configuration generated the largest airflow resistance

around the building due to the smallest wind velocity

profile.

.

Goh et al., 2018

72

Figure 1 Gable, Pyramid and Shed Three Dimensional

Modelling.

Figure 2 Gable, Pyramid and Shed Wind Velocity

Profile Comparison.

3.2 Wind pressure difference

The average wind pressure data were extracted from

the windward and leeward surface of the each building

model. The wind pressure data obtained were tabulated

in Table 1 to calculate the wind pressure difference. Both

Gable and Shed roof produced similar wind pressure

measurements on the windward façade. It was suggested

that same roof cross sectional area contributed to the

similarity. Meanwhile, the Shed roof had 43% wind

pressure difference with the Pyramid roof on the

windward facade. On the leeward facade, the wind

pressure difference between the Gable, Pyramid and

Shed roof did not exceed 9%. At the end of the result,

Shed roof came up with highest wind pressure difference

with Gable roof in second and Pyramid roof in the last

place. The wind pressure difference between windward

and leeward which the windows shall be located plays an

important role in encouraging natural ventilation in the

interior of the building. Thus, by comparing the wind

pressure difference, it is possible to analyse the natural

ventilation performance impact by roof shape.

Table 1 Wind Pressure Data for Each Roof Shapes.

Windward

Facade

(Pa)

Leeward

Facade

(Pa)

Pressure

Difference,

∆P (Pa)

Gable 1.9795 -1.2909 3.2704

Pyramid 1.0822 -1.2894 2.3716

Shed 1.9242 -1.3964 3.3206

4. CONCLUSION

As a conclusion, the roof shape significantly affect

the pressure difference between the windward and

leeward facades of a building. The percentage of

difference is up to about 40% between Shed and Pyramid

configurations. The results suggest that the Shed roof has

the highest natural ventilation performance potential,

followed by Gable roof and lastly, Pyramid roof.

ACKNOWLEDGEMENT


Malaysia Melaka (UTeM) and Ministry of Higher

Education under FRGS/1/2015/TK03/FKM/02/F00273.

REFERENCES

[1] Norhidayah, A., Chia-Kuang, L., Azhar, M. K., &

Nurulwahida, S. (2013). Indoor air quality and sick

building syndrome in three selected

buildings. Procedia Engineering, 53, 93-98.

[2] Spiru, P., & Simona, P. L. (2017). A review on

interactions between energy performance of the

buildings, outdoor air pollution and the indoor air

quality. Energy Procedia, 128, 179-186.

[3] Omrani, S., Garcia-Hansen, V., Capra, B. R., &

Drogemuller, R. (2017). Effect of natural

ventilation mode on thermal comfort and

ventilation performance: Full-scale

measurement. Energy and Buildings, 156, 1-16.

[4] Schulze, T., & Eicker, U. (2013). Controlled natural

ventilation for energy efficient buildings. Energy

and Buildings, 56, 221-232.

[5] Burnett, J., Bojić, M., & Yik, F. (2005). Wind-

induced pressure at external surfaces of a high-rise

residential building in Hong Kong. Building and

environment, 40(6), 765-777.

[6] Yuan, C. S. (2007). The effect of building shape

modification on wind pressure differences for cross-

ventilation of a low-rise building. International

Journal of Ventilation, 6(2), 167-176.


__________


Development of machining simulation application using visual basic programming in NX CAM system environment

Mohamad Hafiz Mohamad, Muhammed Nafis Osman Zahid*

Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, 26660, Pekan, Pahang, Malaysia.


Keywords: Visual Basic programming (VB); Computer-Aided Manufacturing (CAM); simulation

ABSTRACT – This paper presents the integration of

visual basic programming in NX Computer-Aided

Manufacturing (CAM) system with 4th axis milling

simulations as machining routines. A customized

graphical user interface (GUI) was developed to

simplify the simulation process planning and reduce the

dependency on user’s experience while developing the

machining program in NX CAM system. The simulation

operation construction code was recorded by using

journaling tool that available in NX CAM. Then the

code is modified in visual basic program to build

custom machining simulation applications. The results

indicate that the developed programs are capable to

optimize 4th axis machining simulation by reducing the

processing steps and time with minimum process

planning tasks.

1. INTRODUCTION

Simulation in manufacturing is defined as the

imitation routines of the selected operation in real

processes for pre-evaluation purposes. The behavior of

machining processes and response parameter is studied

by developing a simulation model for cutting operation

before proceed into real machining. The simulation is

carried out to identify the issue or problem at early stage

of machining [1]. It is important to investigate the

machining processes by simulating the operation to

ensure the result is similar as expected. Simulation can

be carried out in CAM software or direct on the

machine control panel. Besides that, simulation analysis

also permits the user to identify the effect of changes

and act as a design tool to develop a new system [2]. A

part of that, it is also can be used to analyze different

machining scenarios, not only rapidly but also without

any risk, damage and waste of workpiece. In Computer-

Aided Manufacturing (CAM), the efficiency of planning

task and process execution are crucial factors to develop

machining routines for simulation purpose. Process

planning in CNC machining is directly influence the

processing time, procedure, operator skill and operation

cost [3].

2. METHODOLOGY

In this study, a visual basic programming language

was used as a basis for graphical user interfaces (GUI)

development and machine code customization. The

developed GUI was embedded with journaling code

generated from NX CAM system. Journaling is a tool

that available inside NX CAM where it allows user to

record, edit and replay back all the interaction during

NX sessions [7]. The instruction tasks during machining

program developments are recorded separately with

different parameter setup for each operation. The

recorded codes are translated into visual basic script

files. Then it was modified to remove the code

stickiness. The modification allows user to input certain

parameters such as, cutting orientation, cutting

parameter (spindle speed, feed rate, and depth-of-cut),

tool diameter and workpiece diameter. Two different

GUI programs were developed to handles different

simulations and operation analysis. Roughing operation

GUI used to build roughing machining operation and

aims to remove large amounts of material rapidly from

the workpiece to produce part geometry close to the

desired shape. Finishing operation GUI construct

finishing machining operation and the purpose is to

achieve final geometry of the machined parts with a

good surface finish. In order to illustrate the overview of

simulation operation, the differences in process planning

between conventional and proposed approaches can be

seen as shown in Figure 1. Manual approach is

conventional methods that are typically used to build

machining programs and requires a significant user

intervention and efforts to execute the repetitive

processes [8]. Some parameters and settings for each

operation need to be changes in order to run simulations

with a few constant parameters. In this study, certain

level of automation is expected to be embedded in the

operation build-up routines. The proposed approach is

an improvised method by developing a custom

application to build a NX CAM program with the

addition of several automation elements.

Figure 1 Comparison of simulation approaches.

Mohamad and Osman Zahid, 2018

74

The simulation will run continuously without

requiring user intervention between the geometry in 4th

axis machining operation. Consequently, if there has

much geometry in one operation, the program will

automatically loop the simulation to the next operation

efficiently. Journaling program codes are recorded

through the tool in NX CAM starts from “Create

Geometry” (level-2) to “Create Operation” (level-5).

Some parameters values that need to be set in each level

have been simplified and grouped in GUI program

window. Through this method, the proposed approach

has managed to reduce the processing step from 7 steps

to just 4 steps.


The proposed simulation application was validated

by machining several 3D CAD models as shown in

Figure 2. Table 1 reveals the results of the proposed

approach in assisting the process planning of machining

program developments in NX CAM systems.

Figure 2 3D CAD simulation models.

Table 1 Results of processing time required to construct

a machining operation programs using conventional and

proposed approach.

No. Total

operations

Processing time

(min) Impro

vemen

t

rate

(%)

Conventio

nal

approach

(min)

Proposed

approach

(min)

1 4 Roughing

2 Finishing 16.78 2.98 82.2%

2 4 Roughing

2 Finishing 14.48 2.38 83.5%

3 4 Roughing

2 Finishing 16.27 2.45 84.9%

4 4 Roughing

2 Finishing 15.82 2.80 82.3%

4. CONCLUSION

This paper has discussed the integration of visual

basic programming in NX Computer-Aided

Manufacturing (CAM) system for the application of 4th

axis machining. From the study, the developed

applications managed to execute, control and develop

machining simulation programs efficiently with

minimum processing steps. The results show that

proposed approach successfully reduces processing time

up to 84.9% of improvement rate.

ACKNOWLEDGEMENT

We acknowledge with gratitude to Ministry of

Higher Education Malaysia for providing a financial

support under Research Acculturation Grant Scheme

(RDU151406), which realize this research project.

REFERENCES

[1] Anderberg, S. (2009). A study of process planning

for metal cutting (Doctoral dissertation, Chalmers

Reproservice).

[2] Banks, J., Carson, J. S., & Nelson, B. L. DM

Nicol.(2010). Discrete-Event System Simulation.

5th ed., Prentice Hall, 2010.

[3] Frank, M. C. (2007). Implementing rapid

prototyping using CNC machining (CNC-RP)

through a CAD/CAM interface. Proc. Solid Free.

Fabr. 112–123.

[4] Osman Zahid, M. N., Case, K., & Watts, D. M.

(2017). Rapid process planning in CNC machining

for rapid manufacturing applications. Int. J. Mech.

Eng. Robot. Res., 6(2), 118–121.

[5] Moi, M. B. (2013). Web Based Customized

Design (Master's thesis, Institutt for

produktutvikling og materialer).

[6] Zhao, J., Zhang, D. H., & Chang, Z. Y. (2011). 3D

model based machining process planning.

Advanced Materials Research, 301, 534-544.

[7] Siemens. (2014). Getting Started with SNAP, no.

October. Siemens Product Lifecycle Management

Software Inc.

[8] Turley, S. P., Diederich, D. M., Jayanthi, B. K.,

Datar, A., Ligetti, C. B., Finke, D. A., ... & Joshi,

S. (2014, January). Automated process planning

and CNC-Code generation. In IIE Annual

Conference. Proceedings (p. 2138). Institute of

Industrial and Systems Engineers (IISE).


__________


Study the electromyography (EMG) technique for rehabilitation purpose Nurul Muthmainnah Mohd Noor1,*, Mohamad Saddam Mohamad Baharudin2

1) Faculty of Mechanical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang,

Kampus Permatang Pauh, 13500 Pulau Pinang, Malaysia. 2) Faculty of Mechanical Engineering, Universiti Teknologi MARA Shah Alam, 40500 Selangor, Malaysia.


Keywords: Electromyography; rehabilitation; applications; sensor; muscle

ABSTRACT – Electromyography (EMG) is a

technique for evaluating and recording the electrical

produced by skeletal muscles. This method can be used

as a communication tool between human and machine.

The detection, processing and classification of EMG are

very desirable because it allows a more standardized

and precise evaluation of the neuropsychological,

rehabilitation and assistive technological findings. This

paper aims to study and analyze the EMG from arm

muscle by using the shield EMG-EKG circuit. The

EMG signal was acted as an input, and then the data

was acquired by interfaced with MATLAB software.

There are five subjects who involved in the experiment

with 2 males and 3 females. These EMG signals will be

used as an algorithm to the user for any rehabilitation

purpose.

1. INTRODUCTION

Electromyography is the discipline that deals with

the detection, analysis, and use of the electrical signal

that emanates from contracting muscles [1]. It is a

technique for evaluating and recording the electrical

activity produced by skeletal muscles and study the

muscular function through the generated electrical

signal that produce when muscle has any activity such

as contraction or movement of muscle. The small

electrical currents are generated by muscle fibers prior

to the production of muscle force. These currents are

generated by the exchange of ions across muscle fiber

membranes, a part of the signaling process for the

muscle fibers to contract. EMG can be achieved by

using a highly and precision technology device is called

as electromyograph and produce the EMG signal it

called as an electromyogram. The electromyograph

purpose is to detect the electrical potential that

generated by the muscle cells when these cells are

electrically or neurologically activated [2]. The potential

difference that obtained in the muscle fiber can be

registered in the surface of the human body through

surface electrodes due to the biological tissues

conducting properties [3]. It is the electrical expression

caused by neuromuscular activation during muscular

contraction, depicting the current detected at the specific

location that is produced by the ionic flow that across

the muscle fibre membranes and transmitted through

intervening tissues. The motor unit is the most

elementary functional unit of a muscle, generating a

motor unit action potential (MUAP) when activated.

Repeated continuous activation of motor units generates

motor unit action potential trains (MUAPT) that are

superimposed to form the EMG signal [4]. Collecting

EMG signals emanated from the human body using

electrodes has become a routine procedure both in

rehabilitation engineering and medical research.

2. METHODOLOGY

Figure 1 shows the flowchart in collecting the

EMG signals. The EMG data was collected using Shield

EMG-EKG by Olimex (Olimexino-328). This board

was interfaced with the Arduino IDE software.

Therefore, the result of signal was displayed through

MATLAB software. By using this circuit, it is also

already built-in with high voltage protection, filter and

rectifier and smoothing to avoid the noise signal. The

placements of three Ag/AgCl electrodes were attached

to the arm as shown in Figure 2, where at point A, point

B (as a reference) and point C for ground. The selection

this electrode because it is common used in detection of

EMG signals. It has electrolyte for conducting the

electrical signal produce by skeleton muscle.

Figure 1 Flowchart of collecting EMG signals.

2.1 Experiment setup

For this project, the experiment was setup as

shown in Figure 3. The three electrodes were attached to

Muthmainnah and Mohammad Baharudin, 2018

76

the user’s arm. The EMG signal was reflected at point A

meanwhile at point B and C for reference only. Then the

activity of muscle signal either in moving or resting was

recorded by EMG-EKG circuit board and the signal was

displayed on the scope on the MATLAB software. There

are 5 readings were taken for each subject.

Figure 2 Placements of electrodes.

Figure 3 Experiment setup for collecting EMG data.


3.1 EMG signal

Figure 4 shows the EMG signal that was displayed

on the scope in MATLAB software. This result shows

the muscles when doing activity and without activity (at

rest). The value of EMG was measured in mV scale

unit. When muscle in rest condition, the EMG signal

was shown the smooth line compared to the muscle has

doing activity. The applied pressure on the muscle or

movement of muscle also will increase the electrical

potential difference, so that the value of EMG signal

was varied but in constant.

Figure 4 The EMG signal data for muscle activity.

In this study, there are two experiments were

carried out. i) Using the hand gripped (the mass ~

21kg). In this experiment, each the subject should grip

the gripper for 1 s and rest and grip again for another 1

s. ii) Using the 3kg mass. For the second experiment,

the subject should make the movement of muscle with

the mass of 3 kg in 2 s. Figure 5 and Figure 6 show the

reading of EMG signals for five subjects. Subject 1 and

Subject 2 are female and the rest are male. From the

figures, the reading for both experiment are slightly

same for each other. In this experiment, each subject

was needed to grip the gripper for 100 s. Then rest for

100 s and continued with other 100s. Therefore from the

graph, the highest value for each subject was same.

From this result, it can develop the algorithm for

controlling any external devices such as prosthetic arm

or powered wheelchair.

Figure 5 The EMG signal data for muscle activity-hand

gripped.

Figure 6 The EMG signal data for muscle activity –

mass 3kg.

4. CONCLUSION

In a conclusion, the main objective for this study

has been successfully developed and achieved in order

to collect the data from arm muscle using the EMG

circuit board by Shield EMG-EKG and Arduino

Olimexino-328. The result of EMG signal that obtained

has been compared with the different moving activities

of muscles. Most of each data signals have their own

pattern of waveform of the EMG signals. The type these

patterns of the EMG signal with several subjects is

important to formulate the algorithm for controlling

external devices.

REFERENCES

[1] De Luca, C.J. (2006) Electromyography

Encyclopaedia of Medical Devices and

Instrumentation, John Wiley Publisher, 98-109.

[2] David, M. Blake, Procedure Offered for Lexington

Neurology General Services. Lexington, KY.

[3] De la Rosa, R., Alonso, A., Carrera, A., Durán, R.,

& Fernández, P. (2010). Man-machine interface

system for neuromuscular training and evaluation

based on EMG and MMG signals. Sensors, 10(12),

11100-11125.

[4] Soderberg, G. L., & Cook, T. M. (1984).

Electromyography in biomechanics. Physical

Therapy, 64(12), 1813-1820.


__________


Preliminary study of future lightweight aircraft structure during survivable crash event

A.S. Abdullah1,2,*, A.F.M. Yamin1, H. Ghafar1, H. Yusoff1, R. Othman1, S.N.A.M Halidi1, N.S. Abdullah1, H. Sharudin1

1) Faculty of Mechanical Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang,

Kampus Permatang Pauh, 13500 Pulau Pinang, Malaysia. 2) ARTeC, Universiti Teknologi MARA, Cawangan Pulau Pinang,

Kampus Permatang Pauh, 13500 Pulau Pinang, Malaysia.


Keywords: ABAQUS/Explicit; aircraft crashwothiness; 7075-T6

ABSTRACT – The objective of the study was to develop

a reliable finite element model (FEM) of the main

structure of fuselage during survivable crash event using

ABAQUS/Explicit. For validation, the Johnson-Cook

material model for the aluminum alloy 7075-T6 was

compared to the experimental stress-strain curves. The

results of the crash simulation indicated that this

preliminary FEM development can be reliably used for

further crash simulation that involves advanced material

as the main structure of the fuselage.

1. INTRODUCTION

New type of advanced materials namely fiber metal

laminates (FML) has started to be used in the high-

performance lightweight aircraft and plenty of

commercial aircrafts have already incorporated high

strength-to-weight ratio composite in the main structure

[1,2]. As new materials being introduced, the structural

integrity and crashworthiness of the aircraft during

survivable crash must not be compromised. To simulate

the crash of such a complex structure that consisted of

many structural parts, one can start with a preliminary

simulation on the crashworthiness of the main structure,

namely fuselage frames which carries the main load of

the aircraft. This study focused on establishing a reliable

finite element (FE) model of crash simulation of the

fuselage frame using ABAQUS/Explicit.

2. FINITE ELEMENT MODEL

The frame structure in this paper is based on the

previous study done by Abdullah [3]. In that paper, the

preliminary study was on semi-monocoque frame of

Boeing 737 but with the absence of the passenger’s floor

as shown in Figure 1a. The absence of the floor

underestimated the integrity of the structure. Figure 1b

shows the fuselage frame with passenger’s floor being

studied in this work. The upper outer radius of the frame

is 1.88 meter meanwhile the lower outer radius is 1.80

meter. Boeing Company website and Niu provided the

details of the frame’s geometry. [1,4].

The fuselage structure was discretized by shell

element, S4R with hourglass reduced integration. The

connection between floor and frame was modelled as tie

connection which represented rigid connection without

damage model. Eight mass elements were modelled on

the passenger’s floor at the location of the seat tracks to

represent the loading due to the weight of passengers and

seats. The mass element was 53 kg at each point. The

impact velocity with downward direction of the fuselage

was set as 9 m/s. The vertical component of the impact

velocity represents the vertical impact speed during a

survivable crash scenario [5]. With this impact velocity,

18 kJ of impact energy was generated during the crash

event. Penalty contact method was applied to define

contact between fuselage structures to the rigid surface

that represent the ground. The same contact method

defines the contact between the fuselage structures

themselves.

Figure 1 Fuselage frame.

2.1 Material model

Both frame and passenger’s floor of the fuselage

were made of aluminium alloy (AA) 7075-T6. Johnson-

Cook plasticity and damage models were used to model

AA 7075-T6 for this simulation. In order to ensure

reliable plastic model, the Johnson-Cook plasticity model

was compared with the experimental stress-strain curve

as shown in Figure 3 [6]. Table 1 provides the material

properties, plastic parameters and damage parameters of

AA 7075-T6.


The approximation of plastic hardening using

Johnson-Cook plastic model was verified by the

experimental stress-strain curves as illustrated in Figure

2. This indicates that the material model used is reliable

to simulate the crash as material properties play a big role

in determining how the structure deform, fail and absorb

the impact energy.

Figure 3 illustrates the crash of the fuselage frame.

It started with high stress concentration at the lower part

of the semi-monocoque frame as in Figure 3a in which

immediately followed by buckling at that area as shown

in Figure 3b. The buckling progresses at various spots as

shown in Figure 3c, d and then followed by large

structural displacements and rotations which causes the

fuselage frame to sustain an amount of crush magnitude.

At 194 ms, it is observed that the fuselage starts to

rebound as the elastic strain energy is released from the

(a) (b)

Abdullah et al., 2018

78

fuselage structure. The crushing distance observed is

1.71 meter which is almost half of the original height of

the fuselage as shown in Figure 4. Important note is that

the crushing stopped at the height of the passenger’s

floor. Figure 5 illustrates the energy balance of the

fuselage during crash.

Figure 2 Validation of Johnson-Cook approximation by

experimental data [3].

Figure 3 (a) t = 2.5 ms; high stress concentration at few

spots, (b) t = 3.75 ms; buckling initiated at high stress

concentration spots, (c) t = 10 ms; buckling progress.

(d) 32.5 ms, buckling progress at various spots, (e) t =

125 ms; further crushing, (f) t = 194 ms, fuselage

rebound.

Figure 4 Crushing of fuselage frame.

Figure 5 Energy balance of the fuselage frame.

Table 1 Material properties and related Johnson-Cook

plastic and damage parameters [6]. Material properties

Density [kg/m3] 2934

Young’s modulus [GPa] 59.8

Poisson’s ratio 0.33

Parameter Notation

Hardening parameters

Static yield stress [MPa] A 473

Strain hardening modulus [MPa] B 210

Strain hardening exponent n 0.3813

Strain rate coefficient C 0.033

Thermal softening exponent m 1.56 Melting temperature [K] θmelt 750

Damage parameters

d1 0.3714

d2 -0.1233 d3 -1.9354

d4 0.0101

4. SUMMARY

(a) The crushing distance is 1.71 meter

(b) During crash, few spots within the fuselage

frame experience high-stress concentration

then followed by buckling. Then the buckling

progresses and caused large structural

deformation and rotation.

(c) The crushing stopped just before the

passenger’s floor indicating that the safety

envelop for the occupants has not been

penetrated.

REFERENCES

[1] 2016 Boeing, “The 787 Dreamliner family,” 1995.

[Online]. Accessed: May. 1, 2015.

[2] A. S, “Technology | Airbus, a leading aircraft

manufacturer,” airbus. [Online]. Accessed: May 1,

2015.

[3] Abdullah, A. S., Yamin, A. F. M., Ghafar, H., Halidi,

S. N. A. M., Ab Hamid Pahmi, M. A., & Ismail, N.

I. (2017). Structural integrity of aluminum alloy

7075-T6 fuselage frame during crash event.

Proceedings of Mechanical Engineering Research

Day, 2017, 84-85.

[4] Niu, C. (1988). Airframe structural design:

practical design information and data on aircraft

structures. Conmilit Press.

[5] Abromowitz, A., Smith, T. G., & Vu, T. (2000).

Vertical drop test of a narrow-body transport

fuselage section with a conformable auxiliary fuel

tank onboard. 2000.

[6] Zhang, D. N., Shangguan, Q. Q., Xie, C. J., & Liu,

F. (2015). A modified Johnson–Cook model of

dynamic tensile behaviors for 7075-T6 aluminum

alloy. Journal of Alloys and Compounds, 619, 186-

194.

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100

200

300

400

500

600

700

0.01 0.03 0.05 0.07 0.09 0.11 0.13

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__________


Fluid structure interaction simulation of large deformation and added-mass effect using OpenFOAM

Mohamad Shukri Zakaria1,2,3,*, Haslina Abdullah5, Kamarul Arifin Ahmad3,4,*



Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 3) Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia. 4) Mechanical Engineering Department, College of Engineering, King Saud University,

P.O. Box 800, Riyadh 11421, Saudi Arabia. 5)Faculty of Mechanical & Manufacturing Engineering, Universiti Tun Hussein Onn,

Parit Raja, 86400 Parit Raja, Johor, Malaysia.


Keywords: Fluid structure interaction (FSI); Arbitrary Lagrangian Eulerian (ALE), OpenFOAM, simulation

ABSTRACT – Large deformation and added mass

effect (i.e., closed density between vessel and blood)

dominate the Fluid Structure Interaction (FSI)

simulation instability on the numerical algorithm.

Therefore, in this article, we provide numerical study of

such problem using FSI partition coupling approach.

The FSI solver were develop using CFD Open source

solver library OpenFOAM, in which Arbitrary

Lagrangian Eulerian (ALE) Finite Volume Method

(FVM) solver for fluid with automatic mesh motion and

updated lagrangian FVM solver is used for elastic solid.

The robustness of the solver as well as its accuracy is

compared to the monolithic solution of classical FSI

benchmark test case of flapping flag attaches on the

back of cylinder. Finally, implications of the results and

future research directions on efficient FSI simulation

especially in heart valve applications are also presented.

1. INTRODUCTION

Stability of the numerical algorithm in FSI for

elastic structure is challenging. It required either strong

coupling or the use of a monolithic FSI model due to the

onset of a stability problem known as added mass effect.

This term is normally applied in literature to indicate the

instability of the FSI algorithm when the density of the

solid is almost similar to that of the surrounding

viscous, incompressible fluid. This phenomenon is not

observed in other branches of engineering problems,

such as aero-elasticity, but this issue is prominent in

biomechanics applications, such as heart valves. In this

scenario, the density of the leaflets is almost similar to

that of blood [1-2]. Furthermore, the very low moment

of inertia of the valve leaflets, owing to the added mass

effect, induces numerical instabilities in the fluid-

structure interaction algorithm that compromises its

stability and prevents it from converging [3].

Conventional partition approaches cannot be

employed when added mass effect is significant, even

under strong coupling algorithm, the coupling is not

stable. However, with the aid of Aitken relaxation

technique, the number of iteration needed for

convergence per time step could be significantly

reduced [3]. Alternatively, the monolithic approach

must be applied to prevent numerical instability.

Therefore, in this paper, the monolithic ALE method is

using to simultaneously solve the governing equations

of the flow and structure with a single solver together

with Aitken relaxation algorithm

2. METHODOLOGY

2.1 Numerical method

In this article, continuity and momentum equation

for incompressible laminar flow is solved using in

ALE formulation. The large solid deformation solid is

govern by the Piola-Kirchoff stress-strain formulation. It

is important to noted that the largest solid movement is

adjacent to the moving boundary, potentially leading to

local deterioration in mesh quality. Ideally, largest

deformation should be confined to the internal part of

the mesh, where it causes less distortion. This can be

achieved by prescribing variable diffusivity in the

Laplacian. To compute this grid velocity while

considering the conservation principle and avoiding the

loss of mass and momentum, the space conservation law

(SCL) is applied.

The collocated 2nd order finite volume method on

a deforming mesh is used for the space discretization of

fluid flow equation, while 2nd order backward scheme

is used for temporal discretization. The solution

procedure make use of segregated procedure on a

moving mesh with Pressure-implicit with splitting of

operators (PISO) pressure-velocity coupling algorithm

used in fluid flows.

Number of mesh used in this study is 20940 with

time step of 0.001s. This setup yield CFL < 0.2 since we

have noted that larger stability parameters while

increasing proportionally the time step size also

increased the initial error of the fluid- structure

interaction algorithm that needed more iterations to

converge; this resulted in an overall increase of the CPU

time.

3. NUMERICAL RESULTS

The following test case extends the previously

detected classical stationary flow around cylinders in

Zakaria et al., 2018

80

CFD to an FSI test by attaching an elastic flag to the

back of the cylinder. This FSI benchmark problem was

posed by Turek and Hron [4] and is challenging because

two main issues are involved that require fully coupled

FSI, namely, the finite deformation of a beam structure

during fluid interaction and the added mass effect

attributed to densities of solid and fluid, which are close

to each other.

The sketch problem is depicted in Fig. 3. The

prescribed parabolic velocity profile at the inlet channel

is derived as follows:

Where is average inflow velocity. Three test cases were

considered, namely, FSI1, FSI2, and FSI3, as shown in

Table 1. Each case has its own challenge: FSI1 is

subject to added mass effect; FSI2 possesses a large

deformed structure; and FSI3 combines both features.

However, for feasibility of current method, only the

most complex case, namely FSI3 will be tested.

Figure 3 Geometry of the test case for the flow around a

cylinder with elastic flap. The dimensions applied are

those used in [4].

Table 1. Parameter setting for FSI test cases.

FSI1 FSI2 FSI3

Solid density 1000 1000 1000

Poisson coefficient 0.4 0.4 0.4

Young modulus 1.4e-6 1.4e-6 1.4e-6

Fluid density 1000 1000 1000

Fluid viscosity 1 1 1

Average inflow velocity 0.2 1 2

Reynolds number 20 100 200

(a) (b)

(c) (d)

Figure 4. Fluid-structure simulation with large

deformations in the solid and added mass effect for (a)

1s (b) 1.5s (c) 2.075s and (d) 2.152s.

As we can see on result despite in Fig. 4, the solid

undergoes nonlinear and severe deformation, which in

turn affect the fluid flow. This simulation is well agreed

by the experimental work of Turek and Hron [4]. Both

fluid velocity and stress distribution is clearly seen. One

also can easily obtain the displacement of the tip of the

solid.

4. CONCLUSION

Numerical results are presented which proved

strong implicit coupling approach can handle large

deformation and added mass effect problem which is

inherent in FSI systems such as tissue heart valves. The

used of single solver implement in OpenFOAM is

feasible for this type of problem. Although the test case

used is somewhat trivial with heart valve model, it could

easily have extended as long as the method is validated.

As expected, moving mesh in ALE method is

suffered in term of computational time. The CPU time

taken is 20hrs for only 2.54s real time. However, the

result could be used as an additional benchmarking of

current author for further code development such as

using fix grid method.

5. REFERENCES

[1] Zakaria, M. S., Ismail, F., Tamagawa, M., Aziz, A.

F. A., Wiriadidjaja, S., Basri, A. A., & Ahmad, K.

A. (2017). Review of numerical methods for

simulation of mechanical heart valves and the

potential for blood clotting. Medical & Biological

Engineering & Computing, 55(9), 1519-1548.

[2] Zakaria, M. S., Ismail, F., Wiriadidjaja, S., Aziz,

M. F. A., Tamagawa, M., Basri, A. A., & Ahmad,

K. A. (2017). Numerical simulation of mechanical

heart valve with coherent vortex shedding in

OpenFOAM. Proceedings of Mechanical

Engineering Research Day, 2017, 68-69.

[3] Borazjani, I., Ge, L., & Sotiropoulos, F. (2008).

Curvilinear immersed boundary method for

simulating fluid structure interaction with complex

3D rigid bodies. Journal of Computational

Physics, 227(16), 7587-7620.

[4] Turek, S., & Hron, J. (2006). Proposal for

numerical benchmarking of fluid-structure

interaction between an elastic object and laminar

incompressible flow. Fluid-Structure

Interaction (pp. 371-385). Springer, Berlin,

Heidelberg.


__________


Preliminary results of numerical simulation in pre-combustion chamber (PCC) engine

M.N. Norizan1,*, S.I. Sazman1, M.I.M. Zainudin1, F.A. Munir1,2,*, A.R. Saleman1,2, F. Idral1,2, M.S. Yob1,2


Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2) Centre for Advanced Research on Energy, Universiti Teknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia


Keywords: Numerical simulation; pre-combustion chamber

ABSTRACT – There are two combustion chambers in

the spark ignition engine. First, the main combustion

chamber and another one is pre-combustion chamber

(PCC). This setting is utilized in Compressed Natural Gas

(CNG) engine to improve the combustion efficiency.

Generally, the size of PCC is much smaller than the main

combustion chamber. The air is going through the main

chamber during compression stroke and enters the PCC.

At the same time, mixture of gaseous is being directed

into combustion chamber and ignited using the spark

plug. In this research, concept designs of PCC were

developed in Design Modeler in ANSYS Fluent Version

16.0. The simulation of combustion of compressed

natural gas is performed by utilizing the similar software.

Each design of the PCC was analyzed for their flow

behavior for the velocity vector, kinetic rate of reactions

and static temperature.

1. INTRODUCTION

The focus alternative fuel in this study is natural gas

with add pre-combustion chamber in internal combustion

engine. Natural gas is also gained from fossil fuel, which

is similar to liquid fuel and diesel. However, natural gas

can be considered as renewable energy due to the recycle

of methane gas [1-2]. However, the power obtained from

natural gas is not as high as liquid fuel. Hence, that is why

pre-combustion chamber must be added in internal

combustion engine to increase engine power [3].

The main substance inside the natural gas is

methane. Methane can be considered as renewable fuel

and apply in internal combustion engine. The

implementation of natural gas in internal combustion

engine produces low emission but at the main problems

is that it produced lower performance [4, 5]. This study

focused on the numerical simulations of different design

of PCC for a single cylinder engine.

A PCC air-fuel ratio (A/F) was performed at each

engine operating condition by varying the fuel supply

pressure to the PCC. An analytical model was developed

to estimate the mass flow between the PCC and main

cylinder, and mass flow out of the PCC through a sample

extraction apparatus [6].

More specifically, a novel staged pre-chamber

yielded significant reductions in NOx and total

hydrocarbon emissions by promoting stable pre-chamber

and main chamber ignition under fuel-lean conditions [7].

Precise fuel control was also critical when balancing low

emissions and engine efficiency.

2. RESEARCH METHODOLOGY

Figure 1 shows the CFD simulation step of this

study.

Figure 1 CFD simulation step.

2.1 Meshing

Meshing is probably the most important part in any

of the computer simulations because it can show drastic

changes in obtained results. Meshing is defined as giving

the geometry multiple nodes or grid generation. Meshing

is performed with a variety of tools & options available

in the software. Figure 2 shows the completed meshing

on the geometry. The results are calculated by solving the

relevant governing equations that are continuity,

momentum, energy and species equations. The pattern

and relative positioning of the nodes affects the

computational time.

Figure 2 Geometry meshing.

2.2 Solver setup: Boundary condition

There are several boundary conditions that need to

be set up. This boundary condition is important because

the fluid flows need to be defined with parameters desired

with certain magnitude. Three boundary conditions are

set which are the velocity inlet, pressure outlet and

combustor wall.

2.3 Solver setup: Post processing

The final simulation step is the post-processing.

Post-processing step enables the handling of the

Norizan et al., 2018

82

information that is obtained from the computed solution

and also observations and examinations of the outcome

can be simply conducted. After finishing the computation,

the ‘Result’ tab from the workbench window is selected

where the CFD-Post window appeared. There a lot of

data that can be sort out from the simulation


The velocity vector of the flow at each of designs of

pre-combustion chamber were analysed to determine the

maximum velocity vector of the flow as depicted in

Figure 3.

Figure 3 Velocity vector.

This kinetic rate of reaction as shown in Figure 4

helps the combustion rate to be faster and at the right

place. From Fluent, the flame propagation can be seen

clearly and the position of flame propagation started can

be found during analyzation of kinetic rate of reaction

contours.

Figure 4 Kinetic rate of reaction.

From ANSYS post-processing module, static

temperature contour can be extracted as depicted in

Figure 5. and analysed by researcher which one has the

highest static temperature.

Figure 5 Static temperature.

4. CONCLUSIONS

When the inlet velocity magnitude has the highest

velocity vector, then the combustion will be faster and

better performance of spark ignition engine can be

obtained. Apart from that, the kinetic rate of reaction is

related to the flame propagation. From the result, the

starting point of flame propagation which is the ignition

point can be discovered. The higher the air inlet velocity

magnitude, the longer distance of flame propagation

happened from inlet wall.

REFERENCES

[1] Alvarez, C. E. C., Couto, G. E., Roso, V. R., Thiriet,

A. B., & Valle, R. M. (2017). A review of

prechamber ignition systems as lean combustion

technology for SI engines. Applied Thermal

Engineering, 128, 107-120.

[2] Korakianitis, T., Namasivayam, A. M., & Crookes,

R. J. (2011). Natural-gas fueled spark-ignition (SI)

and compression-ignition (CI) engine performance

and emissions. Progress in energy and combustion

science, 37(1), 89-112.

[3] Esfahanian, V., Salahi, M. M., Gharehghani, A., &

Mirsalim, M. (2017). Extending the lean operating

range of a premixed charged compression ignition

natural gas engine using a pre-

chamber. Energy, 119, 1181-1194.

[4] Tahir, M. M., Ali, M. S., Salim, M. A., Bakar, R. A.,

Fudhail, A. M., Hassan, M. Z., & Muhaimin, M. A.

(2015). Performance analysis of a spark ignition

engine using compressed natural gas (CNG) as

fuel. Energy Procedia, 68, 355-362.

[5] Barzegar, R., Shafee, S., & Khalilarya, S. (2013).

Computational fluid dynamics simulation of the

combustion process, emission formation and the

flow field in an in-direct injection diesel

engine. Thermal Science, 17(1), 11-23.

[6] Gingrich, J. W., Olsen, D. B., Puzinauskas, P., &

Willson, B. D. (2006). Precombustion chamber

NOx emission contribution to an industrial natural

gas engine. International Journal of Engine

Research, 7(1), 41-49.

[7] Crane, M. E., & King, S. R. (1992). Emission

reductions through precombustion chamber design

in a natural gas, lean burn engine. Journal of

Engineering for Gas Turbines and Power, 114(3),

466-474.


__________


Thermal-stress analysis of the corrugated metal gasket under high temperature load

W.S. Widodo1,2,*, M.A. Choiron2, Hambali Arep@Ariff1, Mohd Shukor Salleh1


Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Faculty of Engineering, University of Brawijaya University, Jalan Veteran, Ketawanggede, Lowokwaru,

Ketawanggede, Kec. Lowokwaru, Kota Malang, Jawa Timur 65145, Indonesia.


Keywords: Corrugated metal gasket; thermal stress; finite element

ABSTRACT – The paper presents thermal stress

simulation and analysis of the corrugated metal gasket

under high temperature environment (200oC – 400oC).

High temperature condition resulting thermal stresses

and contact stresses which affects sealing performance of

the gasket. Sealing performance of the gasket depends on

the contact stress and contact length between the gasket

and flanges. The study consists of development of CAD

model and simulation of the finite element model in

ANSYS software to obtain deformation, temperature and

stress distribution of the gasket. The simulations show

that contact stresses and contact length between the

gasket and flanges increase when temperature increases.

1. INTRODUCTION

Gaskets as seal elements play an important role in

piping connection in some industries such as food

industries, automotive, oil and gas industries, as well as

in pressure vessel piping systems and distillation column

installations. Saeed et al. [1] studied about corrugated

metal gaskets named Super Seal Gasket using Finite

Element (FE) simulation and Taguchi method to optimize

the geometric model and the dimensions of the gasket.

Choiron et al. [2] focused on design optimization based

on contact stresses of the corrugated metal gasket.

Choiron et al. [3] also evaluated the effects of contact

area to leak performance of the corrugated metal gasket

based on FE simulation and helium leak test experiment.

Ushijima et al. [4] examined the deformation model of

the corrugated metal gasket and put highlight on

geometrical transformation on flat area and convex area

of the gasket. Nurhadianto et al. [5] investigated the

effects of forming process during manufacturing phase to

contact stresses of the gasket.

The research investigated the effect of high

temperature (200oC – 400oC) to the contact stress and

contact length between the corrugated metal gasket and

flanges. Previous researches [1-3] proved that sealing

performance of a gasket mostly affected by contact

stresses and contact length between a gasket and flanges,

higher contact stresses and contact length will improve

sealing performance of a gasket. Investigation of this

problem consists of several steps. Firstly, the governing

equation were developed to obtain the correlation

between thermal loads and stresses in the gasket and its

effects to the gasket deformations. Secondly, the physical

model of the gasket and flanges were recreated in CAD

software. Lastly, the CAD model was transferred as a FE

model in ANSYS software. By applying appropriate

boundary conditions, mechanical behavior of the gasket

as response to bolts pretension and thermal load can be

simulated and investigated.

2. METHODOLOGY

In this study, a corrugated metal gasket is assembled

in the bolted flanges piping connection where a hot fluid

flows, as shown in the Figure 1.

Figure 1 General gasket installation.

The temperature of the fluid is simulated within the

range of 200oC – 400oC. The gasket and flanges are made

of stainless steel SUS304 with material properties as

shown in Table 1.

Table 1 Properties of SUS304.

Tensile strength, σty (N/mm2) 398.5

*Modulus of elasticity, E (N/mm2) 210000

*Conductivity, k (W/m.K) 16.2

*Coeff of thermal expansion, α (μm/m.K) 17.3

*ANSYS database.

2.1 Finite element model

The CAD model was designed in Solidwork and

imported into ANSYS workbench as a 2D model to

reduce the memory space and time required to perform

the simulation. The meshing/discretization process for

the gasket-flanges model was done by using ANSYS

Mechanical workbench software. The finite element

model for the gasket-flanges consists of 459 nodes and

325 (PLANE 77) elements. The boundary condition is

shown in Figure 2.

Widodo et al., 2018

84


3.1 Bolt-pretension loading

Figure 3 shows the deformed model and stress

distribution of the gasket during tightening the bolt of the

gasket-flange assembly. The deformed model obtained

from this simulation then was transformed as a new

model for the steady-state heat transfer simulation

process.

Figure 2 Boundary condition for FE model.

Figure 3 Gasket deformation and stress distribution

under bolt pretension loading.

3.2 Thermal loading

Figure 4 shows temperature distribution of the

gasket-flange assembly. The temperature distribution

from this simulation then be used as thermal loading and

be converted as structural loading in the gasket.

Figure 4 Temperature distribution at gasket-flanges.

3.2 Thermal stress

Figure 5 shows the gasket deformation as an effect

of thermal load at 200oC. Temperature for the model was

varied from 200oC–400oC to obtain behaviour of gasket

deformation when temperature increased.

Figure 5 Deformation of the gasket at T= 200 oC.

Table 2 presents measured contact length and

maximum stress at three regions of gasket (A, B, C)

obtained from the simulation with temperatures varies

from 200oC to 400oC.

Table 2 Contact length and contact stress at different

temperatures.

Temp (oC) Contact length (mm) Contact stress (MPa)

A B C A B C

200 0.998 1.085 1.002 386.3 301.4 298.7

250 1.065 1.092 1.013 393.6 304.7 301.5

300 1.069 1.107 1.015 394.8 309.2 303.9

350 1.073 1.112 1.019 399.6 311.5 307.2

400 1.081 1.124 1.026 404.6 314.7 309.8

It can be seen from here that the region B

experienced the highest contact length while region A

experienced the highest stress. The stress even exceeds

the ultimate strength of the material thus indicate that the

material starts to fail/crack, but this only occurred in the

very small area, whereas the others area experienced

much lower stress.

4. CONCLUSION

Simulation results show that contact length and

contact stress between the gasket and flanges increase as

temperature increases. Previous researches proved that

higher contact length and contact stress improve the

sealing performance of the gasket. This implies that

higher temperature will increase the sealing performance

of the gasket. On the other hand, higher temperature also

produces higher stress which exceeds the tensile strength

of the material, as a result at certain temperature the

gasket will be crack and the sealing performance will be

fail.

ACKNOWLEDGEMENT

The authors would like to thank Universiti

Teknikal Malaysia Melaka for the continuous support on

this research project.

REFERENCES

[1] Saeed, H. A., Izumi, S., Sakai, S., Haruyama, S.,

Nagawa, M., & Noda, H. (2008). Development of

new metallic gasket and its optimum design for

leakage performance. Journal of Solid Mechanics

and Materials Engineering, 2(1), 105-114.

[2] Choiron, M. A., Haruyama, S., & Kaminishi, K.

(2011). Optimum Design of New 25A-size Metal

Gasket Considering Plastic Contact

Stress. International Journal of Modeling and

Optimization, 1(2), 146-150.

[3] Choiron, M. A., Haruyama, S., & Kaminishi, K.

(2011). Simulation and experimentation on the

contact width of new metal gasket for asbestos

substitution. International Journal of Aerospace

and Mechanical Engineering, 5(4), 283-287.

[4] Ushijima K., Haruyama S., Kaminishi K., Chen

Dai-Heng (2011). Study on deformed mode of thin

metal gasket based on experimental and FEM.

Proceeding of the 8th International Conference on

Innovation and Management, 302-306.

[5] Nurhadiyanto, D., Choiron, M. A., Haruyama, S., &

Kaminishi, K. (2011). Contact Width Evaluation of

New 25A-size Metal Gasket Considering Forming

Effect. 8th International Conference on Innovation

and Management, 296-301.


__________


A significant improvement of vehicle body responses using limited state feedback controller for active suspension system

Mohd Hanif Harun1,3,*, Ridhwan Jumaidin2,3, Adzni Md Saad1,3, Fauzi Ahmad1,3, Mohd Zakaria Mohamad Nasir2,3






Keywords: Vehicle suspension; quarter vehicle model; limited state feedback controller

ABSTRACT – This paper investigates the performance

of passive and active vehicle suspension. It introduces

two degrees of freedom (2-DOF) mathematical

equations of a quarter vehicle mode which was

governed based on Newton’s second law. Two

difference control strategies were applied on this quarter

vehicle suspension namely Proportional-Integrated-

Derivative (PID) controller and Limited-State-Feedback

(LSF) controllers. A 5 cm road bump is considered as a

road disturbance in this study. The results show that

both controllers were able to reduce unwanted vehicle

body motions in terms of vehicle body acceleration and

suspension travel. From the study, it can be concluded

that the performances of both controllers in improving

ride quality of vehicle body are much better compared

to the passive system.

1. INTRODUCTION

Comfortable is one of hundred important criteria to

be considered by all manufacturer around the world in

designing a vehicle. The functions of vehicle suspension

is to minimize the vehicle body vibration caused by

road surface, to support a vehicle body and for vehicle

handling. Many researchers and academicians nowadays

doing a research on vehicle suspension focusing on

improving vehicle ride and handling qualities [1-3].

Due to lack of attitude control and high cost in

implementing active suspension on vehicle, it inspires

and motivates the researchers to consider the use of low

cost of active suspension system by simplifying an

outer-loop controller of vehicle suspension. According

to Kumar [4], the electronically controlled active

suspension system can potentially improve ride comfort

as well as stability of the vehicle. This paper I organized

as follow: The first section is an introduction on vehicle

suspension system, followed by quarter vehicle

modelling and control structure of active suspension in

second and third sections. The forth section presents the

performance evaluations of active suspension system

and the last section contain some conclusion.

2. QUARTER VEHICLE MODEL

The quarter vehicle model of the passenger vehicle

consists of a 1/4 sprung mass (vehicle body) mass

connected to unsprung mass and presented as a 2-DOF

system. The sprung mass is represented as a plane and

allowed to displace in vertical direction, while the

unsprung mass is allowed to bounce vertically with

respect to the sprung mass. Figure 1 shows the hematic

diagram of 2-DOF quarter vehicle model and Table 1

shows the parameters of vehicle suspension.

Figure 1 Schematic diagram of a 2-DOF quarter vehicle

model.

Table 1 Suspension parameters.

Definition Value

Sprung mass ( )sm 466.5 kg

Unsprung mass ( )um 49.8 kg

Spring stiffness ( )sk 5,700 N/m

Damping coefficient ( )dc 850 Ns/m

Wheel spring stiffness ( )tk 192,000 N/m

Wheel damping coefficient ( )tc 12,000 Ns/m

Force balance analysis on sprung and unsprung masses

can be written as:

asudsuss Fxxcxxkzmb

−−+−= )()(....

(1)

)()()(....

utdsusu xwkxxcxxkzmsub

−+−−−−=

ad Fxwc

u+−+ )(

.

(2)

Where uuusss xxxxxx ,,,,, are the sprung and

unsprung mass displacements, velocities and

accelerations, while w is the road input disturbance.

Harun et al., 2018

86

3. CONTROL STRUCTURE

3.1 PID controller

Figure 2 below shows the structure of the

controller connected to the plant. Plant is a system that

is going to be controlled and the controller is the

selected control method that provides the excitation for

the plant.

Figure 2 Basic controller design of PID.

An error signal (e), also known as the different

between desired input and actual output will be sent to

the controller.

3.2 LSF controller

LSF is a control scheme that use two simple gains

control the vehicle body displacement and damper

displacement. There is an advantage of using this

controller such as it can control the vehicle body and

damper displacement simultaneously.

Figure 3 Basic controller design of LSF.


4.1 Vehicle body acceleration

Figure 4 is the time response of vehicle body

vehicle acceleration. From the figure, it shows that the

PID and LSF control can reduce the magnitudes with

the best performance as an active suspension system

with an active system to become stable is faster than

passive with 3.1 m/s2 for LSF and 3.9 m/s2 for PID

controller against 4.5 m/s2 for passive system.

4.2 Suspension travel

It can be clearly seen that the suspension travel of

active suspension with PID and LSF controller are much

higher than the passive suspension system as shown in

Figure 5. This is due to the quick force applied by the

actuator in response to the signal from the controller, but

the vibrations become stable are faster than the passive

system due to the initial stage of transient vibration

which increasing of displacement amplitude [4].

Figure 4 Vehicle body acceleration.

Figure 5 Suspension travel.

5. CONCLUSION

In conclusion, the performance of LSF strategy

shows the superiority over PID scheme and passive

system in a quarter car model. This is due to an ability

of LSF controller to control both body and damper

displacement. Both controllers are able to reduce or

eliminate both amplitude and settling time of unwanted

body motions.

ACKNOWLEDGMENT


Teknikal Malaysia Melaka for the financial support

provided through Journal Publication Incentive Grant

(project number JURNAL/2018/FTK/J00004).

REFERENCES

[1] Emam, A. S. (2015). Fuzzy Self Tuning of PID

controller for active suspension system. Advances

in Powertrains and Automotives, 1(1), 34-41.

[2] Kashem, S. B. A., Roy, S., & Mukharjee, R. (2014,

May). A modified skyhook control system (SKDT)

to improve suspension control strategy of vehicles.

Informatics, Electronics & Vision (ICIEV), 2014,

1-8.

[3] Ahmad, F., Hudha, K., & Harun, M. H. (2009).

Pneumatically actuated active suspension system

for reducing vehicle dive and squat. Jurnal

mekanikal, 28(1), 85-114.

[4] Kumar, M. S., & Vijayarangan, S. (2007).

Analytical and experimental studies on active

suspension system of light passenger vehicle to

improve ride comfort. Mechanics, 65(3), 34-41.

0 1 2 3 4 5 6 7 8 9 10-4

-3

-2

-1

0

1

2

3

4

5

time (sec)

Vehic

le B

ody A

ccele

ration (

m/s

2)

Body Acceleration of Peugeot 206 with different type of Suspension System

Passive

PID

LSF

0 1 2 3 4 5 6 7 8 9 10-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

time (sec)

Suspensio

n T

ravel (m

)

Suspension Travel of Peugeot 206 with different type of SUspension System

Passive

PID

LSF


__________


The effect of spin and friction on reaction forces in a soccer ball impact: A computational study

Mohd Hasnun Arif Hassan*, Zahari Taha

Innovative Manufacturing, Mechatronics & Sports Lab (iMAMS), Faculty of Manufacturing Engineering,

Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia.


Keywords: Soccer ball; finite element; ball spin; coefficient of friction

ABSTRACT – This study investigates the effect of spin

and coefficient of friction on the reaction forces in a

soccer ball impact. The analysis was conducted by means

of finite element (FE) method. A validated soccer ball FE

model was used. Angular ball velocity and the coefficient

of friction between the ball and the impacted surface were

varied in the simulations. The normal and friction forces

were the output parameters of the simulations. The results

show that the normal force is neither influenced by the

coefficient of friction nor the angular velocity, while the

friction force is influenced by all impact variables. This

study shows that a soccer ball impact can be influenced

by several external factors.

1. INTRODUCTION

Soccer is one of the most popular sports in the

world. The total number of people who are actively

involved in soccer are about 4% of the total population of

the world [1]. This is a contact sport, which involves

many contacts between players and also the ball. One of

the popular manoeuvre in soccer is ball heading. The

player uses his/her head to hit the ball to another

teammate or hit the ball into the net to score a goal.

Several studies have suggested that soccer heading might

cause traumatic brain injury [2-5]. Measurement of brain

motion experimentally is very difficult, thus a

computational method like the FE analysis is a very

useful tool in studying such case.

During the game, the ball impacts various types of

surface. Simulation studies for instance by means of FE

method have been conducted to study and understand the

mechanics of the ball impact [6-8]. The output of the

simulations are the ball impact characteristics, namely

the contact time, rebound velocity and maximum

longitudinal deformation. The input of the simulation, on

the other hand, is the inbound velocity. Nonetheless,

these studies employed only linear ball velocity without

any spin, which is

The objective of this study is to quantify the

reaction forces due to the ball impact using the FE

method. In the abovementioned studies, the only input

was the inbound velocity. In this study, we introduce a

spin to the ball by defining both linear and angular

velocities. This study serves as an initial study before

simulating the soccer heading manoeuvre of a spinning

ball. Several parameters, namely the inbound velocity,

the ball spin and the coefficient of friction were varied to

investigate the influence of each parameter on the

reaction forces. The following sections detail the

methodology of the study and the results obtained.

2. METHOD

This investigation was conducted computationally

using a validated finite element model of soccer ball. The

model was developed by Taha and Hassan [8], which

utilises a composite sphere shell geometry. The soccer

ball model comprises of two layers, namely the inner

rubber bladder and an outer composite panel. The

material properties of each layer were obtained from

Price et al. [6].

The model was validated by Taha and Hassan [8]

through a dynamic impact test and a drop test on a force

platform that measures the reaction force upon impact.

Nonetheless, their simulation was performed without any

ball spin. Thus, this study aims to extend the work of

Taha and Hassan [8] by introducing spin to the ball before

it impacts the rigid surface as shown in Figure 1.

Figure 1 Soccer ball impacting rigid floor with spin.

To define the velocity of the ball, a reference point

was created at its centre of mass. The reference point was

coupled to the ball’s surface using the structural coupling

method. The structural coupling method couples the

translation and rotation of the reference node to the

translation and the rotation motion of the coupling nodes.

By doing this, the motion of the ball is governed by the

motion of the reference point. Therefore, the velocity and

the angular velocity of the ball were defined at the

reference point, which in turn will cause the ball to have

the same motion. The velocity of the ball was varied from

9, 12, 15 and 18 m/s, while the angular velocity was

varied from 5, 10, 25 and 50 rad/s.

The impact between the ball and the rigid surface

involves a contact. Thus, a general contact was defined.

The friction between the contact surfaces was defined

using penalty method. This method permits some relative

motion of the surfaces when they should be sticking. The

coefficient of friction was then defined as 0 (frictionless),

0.3, 0.6, and 0.9, and varied one at a time for the

parametric analysis.


Figure 2 and Figure 3 show the magnitude of total

forces due to frictional stress for different ball spins and

Hassan and Taha, 2018

88

coefficients of friction. It is seen that the friction force is

influenced by all parameters.

Figure 2 Friction forces with respect to the ball spins.

Figure 3 Friction forces with respect to the coefficient of

friction.

The larger the ball spin and coefficient of friction,

the larger the friction force. In Figure 2, it is observed that

the 5 and 10 rad/s spins have different friction forces

pattern compared to that of 25 and 50 rad/s spin.

Nonetheless, the contact time is almost the same in all

cases, regardless of the magnitude of the angular velocity.

Figure 4 Normal forces with respect to the ball spins.

Figure 5 Normal forces with respect to the coefficient of

friction.

Further, Figure 4 and Figure 5 depict the magnitude

of total forces due to contact pressure for different ball

spins and coefficients of friction. It was found that the

ball spin and coefficient of friction were found to not

influence the normal forces as shown in both figures.

4. CONCLUSIONS

This study uses the FE method to investigate the

influence of the ball spin and coefficient of friction on the

friction force and normal force. The friction force was

found to be influenced by all parameters. The larger the

magnitude of these parameters, the larger the resulting

friction force. The magnitude of normal force, as

obtained from the simulations, was not influenced by the

ball spin and coefficient of friction. This study provides

an initial insight of the impact characteristics of a

spinning ball. Future study will be looking into the effect

of ball spin on the head motion in a soccer heading

manoeuvre.

ACKNOWLEDGEMENT

This study was supported by the Universiti

Malaysia Pahang (UMP) internal grant RDU1603106.

REFERENCES

[1] FIFA.com. (2006). Big Count - FIFA.com.

https://www.fifa.com/mm/document/fifafacts/bcof

fsurv/bigcount.statspackage_7024.pdf

[2] Lipton, M. L., Kim, N., Zimmerman, M. E., Kim,

M., Stewart, W. F., Branch, C. A., & Lipton, R. B.

(2013). Soccer heading is associated with white

matter microstructural and cognitive

abnormalities. Radiology, 268(3), 850-857.

[3] Matser, J. T., Kessels, G., Lezak, M. D., & Troost,

J. (2001). A dose-response relation of headers and

concussions with cognitive impairment in

professional soccer players. Journal of Clinical and

Experimental Neuropsychology, 23(6), 770–774.

[4] Naunheim, R., Bayly, P., Standeven, J., Neubauer,

J., Lewis, L., & Genin, G. (2003). Linear and

angular head accelerations during heading of a

soccer ball. Medicine and Science in Sports and

Exercise, 35(8), 1406–1412.

[5] Zhang, M. R., Red, S. D., Lin, A. H., Patel, S. S., &

Sereno, A. B. (2013). Evidence of cognitive

dysfunction after soccer playing with ball heading

using a novel tablet-based approach. PloS One,

8(2), 1–4.

[6] Price, D., Jones, R., & Harland, A. (2006).

Computational modelling of manually stitched

soccer balls. Proceedings of the Institution of

Mechanical Engineers, Part L: Journal of

Materials Design and Applications, 220(4), 259–

268.

[7] Price, D., Jones, R., & Harland, A. (2007).

Advanced finite-element moxzddelling of a 32-

panel soccer ball. Proceedings of the Institution of

Mechanical Engineers, Part C: Journal of

Mechanical Engineering Science, 221(11), 1309–

1319.

[8] Taha, Z., & Hassan, M. H. A. (2017). A reaction-

force-validated soccer ball finite element

model. Proceedings of the Institution of

Mechanical Engineers, Part P: Journal of Sports

Engineering and Technology, 231(1), 43-49.


__________


Statistical process control as a traceability tools for industry 4.0 Norazlin Nasir1,2,*, Ahmad Yusairi Bani Hashim1, Mohamad Hafidz Fazly Md. Fauadi1, Teruaki Ito2


Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Institute of Technology and Science Tokushima University,

Minami-Jyousanjima 2-1, Tokushima-shi, Tokushima, 770-8506, Japan.


Keywords: Industry 4.0; statistical process control; traceability

ABSTRACT – Industry 4.0 brings integrative concepts

where all the manufacturing processes including

resources are connected and automatically information

exchange. The main issues are controlling and

traceability the original sources to ensure the data gain

is eminently trustable. In manufacturing applications,

statistical process control seems fit to be implemented

by showing the process trend. Based on that, engineers

are able to evaluate the process control for just-in-time

and the process change can be done for improvement.

Eventually, at the end of this paper the SPC as a

Traceability tool is proposed which may ease and

expedite the decision-making process in production.

1. INTRODUCTION

Internet of things (IoT) becomes a pillar of

Industry 4.0 (I4). In IoT environment, trust is a best

practice to overcome the data leakage issues. The

trackback of file-centric approached has increased the

data traceability, real-time record, accountability and

transparency in IoT environment [1].

Traceability is an information path between two

different domain platform that allows changes, decision

and eases the maintenance. Traceability also a

transformation model that enables to keep track the

information evolvement and its original source by

providing the trackback record in different-time [2]. The

traceability tool makes the man/operator to expedite the

decision-making without any delay. Traceability i.e for

the residential user may not be a concerned instead of

the high-quality timing source. However, when

manufacturing required automation process that worked

under internet network to transmit the information

between synchronized distinct and devices that correlate

with time, the real-time source is necessary [3].

2. STATISTICAL PROCESS CONTROL AS A

TRACEABILITY TOOL

Since the statistical process control (SPC) is

typically used in industry and it’s also become a reason

to be explored deeply in this study. The p-chart is one of

the others charts in SPC which represented by a ratio of

the number of non-conforming units to the total number

of units in the sample set. The probability having the

non-conforming units in the set of 𝑛 is known as 𝑝 and

the experimental data can be evaluated from Equation 1

where in the fraction of the non-confirming units, 𝑝𝑖 in

𝑖𝑡ℎ sample set, 𝑘 is known as the number of the sample

set.

=

=

k

i

ipk

p

1

1 (1)

The p-chart can be prepared with constant n and

graphically represented the confidence limits with: pCL =

(2)

( )

−−=

n

pppLCL

13 (3)

( )

−+=

n

pppUCL

13

(4)

2.1 Boundaries condition

The graphical p-chart representation also able to

show the lack of control scenarios which may occur

under various circumstances that evidenced of the un-

behavior trend. ASTM International suggested nine

conditions of un-behavior trend that can be detected if

one or more points for un-behavior trend are violated

[4]. However, only five conditions are considered for

further investigation. The conditions are:

a) Any single point, 𝛽 or more those beyond 3𝜎

which is upper control limit (UCL), or lower

control limit (LCL), 𝛽 ± 3𝜎.

b) Two consecutive points, 2𝛽 beyond 2𝜎 from the

mean or centreline either above or below, 2(𝛽 ±𝜎).

c) Four out of five consecutive points, 4𝛽 beyond 1𝜎

from the mean or centreline either above or below,

4𝛽 ± 1𝜎.

d) Six or more points, 6𝛽 that consecutive higher or

lower with no change in direction, 6𝛽 ± �̅�.

e) Eight or more points, 8𝛽 in a row on one side of

the centerline, 8𝛽 ± �̅�.

3. METHODOLOGY

The pseudocode as shown in Table 1, is chosen as

a unified language to share the procedure in SPC chart

development. n≤100 as a set of data input for the SPC

that needed to be analyzed. The size of data must not

more than 100 for optimal evaluation. To plot the SPC

chart, the value of LCL must be less than 0. β is any

single point of data plotted in SPC chart where the value

is bigger than mean, �̅� but less than LCL and UCL to

show that the process is under control. If the value of β

is similar to �̅� means that the process is under control, return (x,y)=(0,0).

Nasir et al., 2018

90

Table 1. The pseudocode for SPR chart development.

Pseudocode: SPR Chart

�̅� =∑ 𝑝

∑ 𝑛, 𝐶𝐿 = �̅�, 𝐿𝐶𝐿 = �̅� − 3 (

�̅�(1−�̅�)

𝑛), 𝑈𝐶𝐿 = �̅� + 3 (

�̅�(1−�̅�)

𝑛),

1𝜎 = 𝐶𝐿 +(𝑈𝐶𝐿−𝐶𝐿)

3, 2𝜎 = 1𝜎 +

(𝑈𝐶𝐿−𝐶𝐿)

3

if LCL<0 then

plot CL, UCL, LCL, 1𝜎 and 2𝜎 else

if β<100 then

return (x,y)=(0,0)

end

end

4. A CASE STUDY

4.1 Ceramic valve

Company XYZ producing the ceramic valve with

the excellent surface. Most of the ceramic valve is used

for sliding component, potentiometers, isolators and

faucet valves. Generally, the ceramic valve

manufacturing processes begin with ceramic powder

pressing. No quality activities for powder pressing and

the output measurement details are relied on machine

setting and a second process which is firing/curing.

After the firing process, the product sample needs to be

measured to ensure the machine setting is correctly

done.

Based on the pattern shown in Figure 1, some

adjustment would be done for improvement when the

uncertainty is found. The SPC help in showing the

powder press machine pattern. Even the pattern showed

the process is under control, but the process needs to be

monitored carefully. It is proven by Batch 3 in figure 2

where most of the point is consecutive higher compare

with Batch 1 and 2.

The next process after firing is grinding and

lapping. For grinding process, the thickness does not

give the quality issues in the production process.

Lapping is a process to give the flat surface with

roughness up to 0.0001 in precision. The lapping relies

on two factors which are flatness level for diamond plat

and the material handling during process input. Those

factors are related with working knowledge and skill.

The result obtained shows that the surface roughness for

Batch 1, Batch 2 and Batch 3 is under control. However,

there are some uncertainty that may contribute to defect

if without any monitoring and control action taken.

Figure 1 The uncertainty found in Batch 1 and Batch 2.

Even the pressing process is under control but without

trigger action, it may contribute to defect.

Figure 2 The consecutive points are higher compared to

Batch 1 and Batch 2.

Figure 3: The lapping result for ceramic valve where the

worker knowledge and skill play a major role in quality

of surface roughness.

4.2 Discussion

Company XYZ was chosen is because of they still

implement the manual production as for production

type. Both process firing and lapping are critical in

quality factors. Furthermore, company XYZ still does

not implement any Industry 4.0 tools which able to help

them in ease the monitoring process. By having the SPC

as a Traceability tool, it will expedite the analysis

process for decision making and minimize the cost of

defects.

5. CONCLUSIONS

In this paper, the SPC as a Traceability tool is

emphasized where the real data from manual production

type is used for validation. The SPC is aimed to be

operated through the graphical user interface as a

service where the visual graph may ease on analyzing

the production process instead of bulky data and paper.

For the future work, the data from semi-auto and fully

automated production is required for validation purpose.

In order to recognize the SPC pattern just-in-time, the

Hidden Markov Model is emphasized by considering

the boundaries conditions.

REFERENCES

[1] Qin, J., Liu, Y., & Grosvenor, R. (2016). A

categorical framework of manufacturing for

industry 4.0 and beyond. Procedia Cirp, 52, 173-

178.

[2] Bhatt, G. D. (2000). An empirical examination of

the effects of information systems integration on

business process improvement. International

Journal of Operations & Production

Management, 20(11), 1331-1359.

[3] Herter, J., & Ovtcharova, J. (2016). A Model based

Visualization Framework for Cross Discipline

Collaboration in Industry 4.0 Scenarios. Procedia

CIRP, 57, 398-403.

[4] Neubauer, D. V. (2010). Manual on Presentation of

data and control chart analysis. ASTM

International.


__________


Real time object customization in cad software via visual basic programming

Zainal Fahmi Zainol Abidin, Muhammed Nafis Osman Zahid*

Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia.

Corresponding e-mail: [email protected]

Keywords: 3D CAD modelling; object customization; graphical user interface (GUI)

ABSTRACT – This paper outlines a development of

graphical user interface (GUI) for object customization

in computer aided design (CAD) by utilizing Visual

Basic (VB) programming language in NX10 CAD

software. Major works involve development of the object

customization tool in the form of new GUI which

provides a set of editable parameters section. A

customized graphical user interface (GUI) was developed

to simplify the manual customization process. Initially,

customization work is translated into programming codes

via advanced tool available in the NX10. The recorded

codes translated into visual basic script files and then is

modified to create a functional GUI. The results revealed

that the developed programs are capable to simplify

drawing work in CAD by reducing the drawing steps and

time.

1. INTRODUCTION

In conventional approach, customization process

for a model designed in CAD requires user to modify the

parameter manually. User needs to reopen the file, select

the area that need to be customized and make the editing

process by key-in the desired input. For recurring work

towards the same model, it consumes a lot of steps

needed to complete the sketch. Other than that, in current

industrial environment, production time becomes a

crucial factor. Considering overall product development

cycle, 80% of the production time is wasted in design

process [1]. Based on the previous studies [2-7], time

saving can be greatly reduced during the designing stage

of the product. In this project, real time object

customization has been developed by integration of

Visual Basic Programming studio and Unigraphics NX

10 software. NX 10 has a smart feature called NX Flow,

and NX Open Common Application Programming

Interface (API) that allows the integration of custom

software applications.

2. METHODOLOGY

In this study, object customization was executed by

using Microsoft Visual Basic 2010 (VB). Integration

between NX 10 and VB was possible due to NX Open. It

works through the Common API of NX 10. NX Open

provides all programming languages to assist the

development of software to improve automation and

fusion of the tasks. The customization work involves four

main steps that need to be executed. The block diagram

for this process is shown in Figure 1.

Figure 1 Block diagram for object customization

process.

2.1 Journaling

A function inside NX 10 called Journaling allowed

a series of actions performed on the interface being

recorded. A file of .net language code was automatically

produced by NX 10 describing what had been carried out

in programming language (visual basic, JAVA or C++) as

shown in figure 2. Based on that, it produces a scripted

file from an interactive session of NX which can be run

and replay again later. These sessions will be edited and

enhanced with distinct programming instruction for

example to construct graphical user interface component.

Figure 2 Sample of VB language programming recorded

by NX10 Journaling tool.

2.2 Visual basic studio

Figure 3 below shows the GUI that has been created

for the object customization. The GUI has been

developed in Visual Basic Studio using windows form

application. Visual basic language programming that has

Recording

journalling in

NX 10

3D part

modelling

GUI design in

VB

Executing .exe

file in NX10

Zainol Abidin and Osman Zahid, 2018

92

been recorded by NX10 Journaling tool will be open in

the Visual Basic software and then the designing process

was executed.

Figure 3 Object customization graphical user interface

(GUI) created using visual basic studio.


Experiment was conducted using developed

program and are classify into two levels which is

classified as expert and beginner. Expert user represented

by worker with more than 3 years of CAD experience

while beginner user represented by worker with below

than 3 years of experience in CAD software. Object

selected for this customization process is guitar model

sketched in NX-10. The guitar’s curve surface will be

modified using GUI that has been developed. The curve

surface will be adjusted to the most comfortable curve

that will fit user body. For this guitar shape, the

customization is made on B-spline curve on the guitar

model. Developed GUI allow user to customize the B-

spline point that has been plotted for the curve shape as

shown in Figure 4.

Figure 4 Developed GUI is activated in NX10 system.

Figure 5 Customization on the B-spline control point.

From Table 4, object customization manages to

increase 93% time saving for common user and 50% time

saving for daily user in editing a guitar model when using

real-time customization method. Time saving efficiency

has been measured by dividing time recorded using GUI

and without using GUI.

Table 1 Experimental results.

Guitar

model

Time

recorded

without using

GUI (sec)

Time

recorded with

using GUI

(sec)

Time

saving

(%)

Expert 40 20 50

Beginner 300 20 93

4. CONCLUSION

This approach is considered as an alternative way

from editing the sketch manually into real time

customization that allows user to view real time changes

on the object while manipulating the GUI. As conclusion,

the developed program managed to improve the

efficiency of object customization and shorten the design

processing time in CAD system.

ACKNOWLEDGEMENT

We acknowledge with gratitude to Ministry of

Higher Education Malaysia for providing a financial

support under Research Acculturation Grant Scheme

(RDU160130), which realize this research project.

REFERENCES

[1] Koli, P. S., & Patil, S. K. (2017). Customization of

3D CAD model for piston fixture using nx software.

International Journal of Scientific & Engineering

Research, 8(4), 221-228.

[2] Alsop, L. (2010). Intelligent 3D cad modelling of a

diseased carotid. Thesis.

[3] Wade, B. (2011). Automated solution to the

CADMAT project. Thesis.

[4] Camba, J. D., & Contero, M. (2016). Parametric

CAD modeling: An analysis of strategies for design

reusability. Computer-Aided Design, 74, 18-31.

[5] Monedero, J. (2000). Parametric design: a review

and some experiences. Automation in

Construction, 9(4), 369-377.

[6] Mok, H. S., Kim, C. H., & Kim, C. B. (2011).

Automation of mold designs with the reuse of

standard parts. Expert Systems with

Applications, 38(10), 12537-12547.

[7] Bodein, Y., Rose, B., & Caillaud, E. (2014). Explicit

reference modeling methodology in parametric

CAD system. Computers in Industry, 65(1), 136-

147.


__________


Power spectral density-based analysis of secondary suspension parameters effect on railway vehicle ride quality

Mohd Hanif Harun1,3*, Ridhwan Jumaidin2,3, Adzni Md Saad1,3, Fauzi Ahmad1,3, Faizul Akmar Abdul Kadir1,3






Keywords: Railway vehicle suspension; frequency response analysis; power spectral density

ABSTRACT – The objective of this paper is to study

the effects of suspension parameters on ride quality of

railway vehicle in frequency response analysis.

Seventeen degrees of freedom (17-DOF) railway

vehicle lateral model was used and it governed based on

Newton’s second law. The value of spring stiffness and

damping coefficient for secondary suspension are varied

in order to realize which suspension elements give more

effect to vehicle ride comfort. All simulation analysis

was made using MATLAB/Simulink software. The

analysis shows that the secondary lateral damper was

able to reduce unwanted motion and improve comfort

level simultaneously.

1. INTRODUCTION

The function of suspension system in railway

vehicle can be divided into two categories; first is for

wheel-set guidance and stability (primary suspension),

and another is for ride quality and passenger comfort

(secondary suspension). A comfort level can be analysed

by means of the response of railway vehicle body in

terms of body acceleration. It can be measured in both

time response and frequency responses analysis. This

paper presents the analysis of simulation results of

railway vehicle body vibration in frequency domain.

There are three criteria’s considered in the frequency

response analysis which are body lateral, roll and yaw

acceleration as proposed by [1].

There are two levels in railway vehicle suspension

system and can be categorized based on the location of

the suspension component namely primary and

secondary suspension [2]. The function of primary

suspension is to connect wheel-sets to the bogie and

enhance bogie stability. The secondary suspension is

located between bogie and vehicle body. The effect of

secondary suspension systems on ride quality of railway

vehicle will be studied in this paper. The study is

important in order to examine which suspension

element gives more effect on ride quality in the railway

vehicle.

2. 17-DOF RAILWAY VEHICLE MODEL

2.1 Mathematical Model

A mathematical model of 17-DOF railway vehicle

model was developed based on Newton’ second law as

described in previous study [3]. A schematic diagram of

the suspension model can be seen in Figure 1 and 2

below.

Figure 1 Top view of suspension system.

Figure 2 Front view of suspension system.


Figure 3 shows the frequency responses of railway

vehicle body accelerations for all kind of suspension

parameters. Based on the figure, the lateral and roll

body accelerations of railway vehicle have two

resonance peaks, except for yaw acceleration which has

three peaks. For lateral acceleration, when the spring

stiffness is decreased, the lateral acceleration is also

decrease at the first peak, however at the second peak

the response is the same with others. Just like the

response of the roll acceleration, it also has two peaks

which the first peak is looks like the suspension with

lower spring stiffness still has lower vehicle body

response. Moreover, there are three peaks of the railway

vehicle body response for the yaw acceleration. The

system with lower spring stiffness has a better

Harun et al., 2018

94

attenuation ability especially at the second peak but for

almost the whole frequency range, the responses are

almost the same for all different suspension parameters.

From the figure, it can be concluded that the ability

of the secondary spring is not superior in improving ride

quality of railway vehicle body since the main function

of this component is to support the vehicle body.

Nowadays most of the modern railway vehicle

technology is not fitted with the secondary spring and it

is replaced with an air-bag suspension system.

Figure 3 PSD of the body acceleration after varying the

secondary spring stiffness: (a) lateral acceleration,

(b) roll acceleration, and (c) yaw acceleration.

Figure 4 depicts the PSD graph of the railway

vehicle body lateral, roll and yaw accelerations. As

discussed in time response analysis, by increasing the

value of the damping coefficient at the secondary level,

significant improvement occurred on ride quality of

railway vehicle. For all vehicle body accelerations, the

system with higher damping coefficient has three peaks,

which the first and second peaks are the natural

frequency of the system and it is occurred below 1 Hz.

The third resonance peak of the system which is

occurred at the frequency of 2.3 Hz shows that the

system has minimum response of vehicle body

accelerations.

Figure 4 PSD of the body acceleration after varying the

secondary damping coefficient: (a) lateral acceleration,

(b) roll acceleration, and (c) yaw acceleration.

4. SUMMARY

The simulation test was done by varying the lateral

suspension parameters which consists of secondary

springs and dampers. From the result analysis, the

secondary damper gives more effect on ride quality of

railway vehicle body compared to the secondary spring

element.

ACKNOWLEDGMENT


Teknikal Malaysia Melaka for the financial support

provided through Journal Publication Incentive Grant

(project number JURNAL/2018/FTK/J00004).

REFERENCES

[1] Wang, D. H., & Lao, W. H. (2009) Semi-active

suspension systems for railway vehicles using

magnetorheological dampers. Part II: Simulation

and analysis. Vehicle System Dynamics, 47(1),

1439-1471.

[2] Goodall, R. M., & Mei, T. X. (2006) Active

Suspensions. in Handbook of Railway Vehicle

Dynamics, S. Iwnicki, Ed., ed London: CRC Press

Taylor & Francis. 328-357.

[3] Harun, M. H., Jamaluddin, H., Rahman, R. A.,

Hudha, K., & Wan Abdullah, W. M. Z. (2014).

Analysis of primary and secondary lateral

suspension of railway vehicle system. Journal of

Mechanical Engineering, 11(1) 19-40.

10-1

100

101

102

0

0.01

0.02

0.03

0.04

0.05

0.06

Frequency (Hz)(a)

PS

D [(m

/s2)2

/Hz]

Decrease by 50% of k2y


k2y = 2800 N/m

Increase by 25% of k2y


10-1

100

101

102

0

0.01

0.02

0.03

0.04

0.05

Frequency (Hz)(b)

PS

D [(r

ad

/s2)2

/Hz]



k2y = 2800 N/m



10-1

100

101

102

0

1

2

3

4

5

6x 10

-4

Frequency (Hz)(c)

PS

D [(r

ad

/s2)2

/Hz]



k2y = 2800 N/m



10-1

100

101

102

0

0.02

0.04

0.06

0.08

0.1

0.12

Frequency (Hz)(a)

PS

D [(m

/s2)2

/Hz]

Decrease by 50% of c2y


c2y = 2800 Ns/m

Increase by 25% of c2y


10-1

100

101

102

0

0.02

0.04

0.06

0.08

0.1

Frequency (Hz)(b)

PS

D [(r

ad

/s2)2

/Hz]



c2y = 2800 Ns/m



10-1

100

101

102

0

1

2

3

4

5

6

7

8x 10

-4

Frequency (Hz)(c)

PS

D [(r

ad

/s2)2

/Hz]



c2y = 2800 Ns/m




__________


Preliminary thermal simulation analysis of building via IES<VE> software

Afiqah Ngah Nasaruddin1,3,*, Tee Boon Tuan1,2, Musthafah Mohd Tahir1,2, Teruaki Ito3

1) Faculty of Mechanical Engineering, UniversitiTeknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 2) Centre for Advanced Research on Energy, UniversitiTeknikal Malaysia Melaka,

Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia. 3)Department of Mechanical Engineering, Graduate School of Technology, Industrial and Social Sciences,

Tokushima University, 2-1 Minamijosanjima-cho, Tokushima-shi, 770-0855, Japan.


Keywords: Thermal environment; building information modeling; computational fluid dynamics

ABSTRACT – Attaining thermal environment was a

severe issue to be considered in building design.

Besides that, in the case of existing building, thermal

environment is crucial to be monitored for the purpose

of optimization. With that in mind, IES<VE> software

is selected as a platform to simulate the thermal

condition. This paper aims to present the steps involved

prior to implementation of thermal analysis. From the

simulation parameters such as radiance level, solar gain,

air temperature, moisture content and relative humidity

was identified accordingly. The value of building

predicted chiller load in condition described in the

simulation study is 81.7139 kW.

1. INTRODUCTION

Building simulation is intentionally governed as a

tool for prediction corresponding to several what-if

scenarios for both indoor and outdoor environment [1].

Though researchers are having the unalike parameter of

interest and focus output. Their output somehow will

lead to precise building’s thermal characteristic profile

development and possibility for optimization based on

the data mining respectively [2]. Thermal analysis that

is being conducted on existing building aims to obtain a

baseline. In contrary, if it is conducted during the design

stage, the purpose is to enable early prediction based on

several possibilities of phenomena [3].

Existence of multiple software platforms that

support architectural model development along with

building performance analysis are the outcome of the

advancement in the computer aided design (CAD) [4].

Numerous software are available that support thermal

simulations analysis such as EnergyPlus, TRNSYS,

Ecotect, Green Building Studio and many more [5].

IES<VE> is applied to conduct thermal simulation and

analysis of the building for this case study. In general,

IES<VE> provides user with numbers of modules that

serve for major element in building simulation such as

HVAC, daylighting and flow dynamic. This paper

provides the information related to workflow of

preliminary analysis of thermal simulation on selected

building model which is library building.

2. METHODOLOGY

The research consists of several steps which

generally described to exist between pre-simulation and

post-simulation stages. For instance, it is essential to go

through data gathering steps in pre-simulation stages to

developed thermal profile of studied building and

considering any relevant parameter that having a great

influence on the simulation. The workflow for the

simulation designed, and analysis are shown in Figure 1.

Figure 1 Thermal analysis simulation workflow

The information required in running thermal

analysis simulation comprises of building layout,

detailed on surface volume any opening such as air vent,

window and door. In addition, considering the internal

load contributor such as the daylighting, occupant and

weather condition contribute to much accurate analysis

[6]. Initially, the building profile was developed by

gathering the information as in Table 1.

Table 1 Building descriptions: Library building.

Properties Description

Total Area 10,063.68 m2

level 4

Roof Reinforced-concrete metal roof

Window Uncoated single glazing ¼ inch thick

Wall 230mm thick brickwall plaster on both side

Table 2 describes the cooling capacity of water cooled

screw chiller model that currently operated at library

building as for 2017. In term of operation all three

chillers are scheduled to rotate their duty consecutively.

With only two chillers will be operating at one time

while the other will serve as a backup and provide a

long-lasting lifetime.

mailto:[email protected]

Ngah Nasaruddin et al., 2018

96

Table 2 Chiller cooling capacity and input power (2017)

Model Cooling capacity Input

power COP

TR kW

YEWS

100SA50D 102 359 68.8 5.21

YEWS

130SA50D 170 598 108 5.53

YEWS

210SA50D 210 738 134 5.51

The building model is developed accordingly as

shown in Figure 2, without assigning any detail of

furniture and building services such as lighting system

or existing HVAC system. ApacheSim was then

employed to perform the preliminary thermal analysis.

The analysis will provide information such as indoor

thermal comfort conditions by verifying the contributed

heat load in general [4]. It is being complimented by

using other IES module namely RadianceIES module

and Suncast module. The solar analysis is based on

Melaka weather data throughout the year was

conducted. It provides the accurate sunpaths and output

of solar radiation transmitted into the building that

eventually affects the building total heat load. On top of

that, change in radiation depends upon the temperature

and surface characteristic of the object [7].

Figure 2 Library model.


For convenient data gathering and analysis, the

library space is divided into eight sections as labelled in

Figure 2. Among the data gathered was the mean value

parameter as listed in Table 3. From Table 3 it is shown

that the maximum mean value for solar gain in a year is

located at section 4 having value of 2.6536 kW.

Table 3 Mean value

Section

Mean parameter

Solar

gain

(kW)

Air

temperatur

e (̊c)

Moisture

content

(kg/kg)

Relative

humidity

(%)

S1 1.8950 23.00 0.01389 78.00

S2 0.0000 23.00 0.01387 77.92

S3 0.0001 23.00 0.01387 77.92

S4 2.6536 23.00 0.01389 78.00

S5 0.1212 23.00 0.01387 77.93

S6 0.1139 23.00 0.01387 77.92

S7 0.1169 23.00 0.01387 77.92

S8 0.1101 23.00 0.01387 77.92

Figure 3 also shows the predicted building chiller

load though the exact occupant comfort while other heat

load contributor does not take into consideration. Given

the parameter showns by the 3D graph between the

time, month and the chiller load in Figure 3, it is known

that the peak building chiller load is 81.7139 kW on

March 2nd at 15.30 p.m.

Figure 3 Predicted building chiller load.

The thermal environment does not only being

influence by HVAC system operated within the facility.

In addition, how the building envelope was designed to

withstand the external weather condition by avoiding

excess heat gain turns out as a crucial issue that

correspond to thermal comfort for an indoor

environment. Thermal simulation is being applied with

the concern of reflecting the analysis with important

aspects such as air heating load, and cooling load. The

simulation using IES<VE> is expected to be reliable

considering that they are having 14 high degree of

certainty

4. CONCLUSION

Based on this study, performance simulation is the

constructive instruments that can demonstrate the

wellbeing of building services by identification of while

ensuring the indoor environment thermal comfort are

achieved.

ACKNOWLEDGMENT

The authors gratefully acknowledge the financial

support from UniversitiTeknikal Malaysia Melaka

(UTeM), Malaysia and Tokushima University, Japan.

REFERENCES.

[1] Hemsath, T. L. (2013). Conceptual energy

modeling for architecture planning and design:

impact of using building performance simulation

in early design stages. Proceedings of BS2013: 13th

Conference of International Building Performance

Simulation Association, 376-384.

[2] Bahar, Y. N., Pere, C., Landrieu, J., & Nicolle, C.

(2013). A thermal simulation tool for building and

its interoperability through the building

information modeling (BIM)

platform. Buildings, 3(2), 380-398.

[3] Akadiri, P. O., Chinyio, E. A., & Olomolaiye, P. O.

(2012). Design of a sustainable building: A

conceptual framework for implementing

sustainability in the building

sector. Buildings, 2(2), 126-152.

[4] Zhu, D., Hong, T., Yan, D., & Wang, C. (2013). A

detailed loads comparison of three building energy

modeling programs: EnergyPlus, DeST and DOE-

2.1 E. Building Simulation, 6(3), 323-335.

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3

4

2

4

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4

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Ngah Nasaruddin et al., 2018

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[5] Cho, Y. K., Alaskar, S., & Bode, T. A. (2010).

BIM-integrated sustainable material and renewable

energy simulation. In Construction Research

Congress 2010: Innovation for Reshaping

Construction Practice, 288-297.

[6] Clarke, J.A., and Hensen, J.L.M. (2015). Clarke, J.

A., & Hensen, J. L. M. (2015). Integrated building

performance simulation: Progress, prospects and

requirements. Building and Environment, 91, 294-

306.

[7] Patra, P.K., and Tripathi, D.P. (2014). Monitoring

the room and controlling its temperature and light

industry from a remote location over a network.

International Journal of Advanced Computing

Technique and Applications, 2(2), 96-101.

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