Optimization of Process Parameter for CNC Turning using Response Surface Methodology

INTERNATIONAL JOURNAL OF R&D IN ENGINEERING, SCIENCE AND MANAGEMENT

Vol.3, Issue 1, Oct 2015, p.p. 26-38, ISSN 2393-865X

Available at :www.rndpublications.com/journal Page 26 © R&D Publications

Optimization of Process Parameter for CNC Turning using

Response Surface Methodology (RSM)

Nitish Kumar1*

, Ashwani Kumar2, Pankaj Kumar

3

1 M.Tech Scholar, Department of Mechanical Engineering, University Institute of Engineering and

Technology, Maharishi Dayanand University Rohtak-124001, Haryana ,INDIA 2

Associate Professor, Department of Mechanical Engineering, University Institute of Engineering and

Technology, Maharishi Dayanand University Rohtak-124001, Haryana ,INDIA 3Senior Manager in R&D Department, Lakshmi Precision Screws Limited, Rohtak-124001, Haryana,

INDIA ABSTRACT

Material removal by turning is the basic and the most important process in metal cutting. Product quality is mainly depends on

the process used and the parameters which affect them. The important parameter which affect the quality of product in turning

are cutting speed, feed rate, depth of cut, cutting tool used, cutting fluid and the material of the workpiece. The present study

involves the identification of the optimized process parameters in CNC turning of AISI 15B25 alloy steel. Three important

parameters like cutting speed, feed and depth of cut have been considered as a machining parameter and the output parameter

which are to be optimized are material removal rate (MRR) and surface roughness (Ra). Response surface methodology of the

Design Expert has been considered for the optimization of process parameters. The optimal values of the Surface finish and

MRR found to be 2.29µa and 1327.93mm3/min respectively.

Keywords – Optimization, CNC turning, Response Surface Methodology(RSM), surface roughness and material removal rate

(MRR).

____________________________________________________________________________

1. INTRODUCTION

Machining is the most important part of a manufacturing process. In metal cutting process

turning is the most commonly employed machining operation. In turning, work material is held

in between the chunk and the tailstock and rotated. The tool post in which tool is mounted

moved at a constant rate along the cutting axis removing a layer of material form the workpiece.

Surface roughness and MRR are the most important response parameters in the manufacturing

process and there optimization depends on different input parameters. During turning operation

the factors which affect the MRR and the surface roughness are spindle speed, feed rate, depth of

Optimization of Process Parameter for CNC Turning using Response Surface Methodology

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cut, tool nose radius, etc. The change in the factors will produce a significant effect on the

output. According to Prasad et al. (1997) reported the development of an optimization module

for determining process parameters for turning operations as part of a pc-based generative CAPP

system.. The objective is taken as to minimize the production time. The constraints considered in

this study include power, surface finish, tolerance, workpiece rigidity, range of cutting speed,

maximum and minimum depth of cut and total depth of cut. The conveyed models are solved by

the combination of geometric and linear programming techniques. J. Paulodavim and

Francisco Mata (2004) presented an optimization study of surface roughness in turning fiber

reinforced plastic (FRP) using polycrystalline diamond cutting tool. The tool geometry used is as

follow rake angle 5ᵒ clearance angle 5

ᵒ edge major tool cutting 55

ᵒ, cutting edge inclination angle

-7ᵒ, and corner radius 0.8mm. surface roughness Ra increase with the feed rate and decrease with

the cutting velocity. Saparudin et al. (2006) focused on the analysis of optimum cutting

conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method.

The results were analyzed using analysis of variance (ANOVA) method. Taguchi method had

shown that the depth of cut has significant role to play in producing lower surface roughness

followed by feed. E. Daniel Kirby (2006) discussed an investigation into the use of Taguchi

Parameter Design for optimizing surface roughness generated by a CNC turning operation. The

study produced a verified combination of controlled factors and a predictive equation for

determining surface roughness with a given set of parameters. Dilbag Singh and P.

Venkateswara Rao(2007) investigated the effect of cutting condition and tool geometry on the

surface roughness in the finished hard turning of bearing steel (AISI 52100). Mixed ceramic

inserts made up of aluminum oxide and titanium carbonitride having different nose radius and

different rake angles were used as cutting tool. The study shows that feed is the dominant factor

followed by nose radius and cutting velocity. M.Kaladhar et al (2010) dealt with optimization

of AISI 202 unaustentic stainless Steel using CVD coated cemented carbide tools. During the

experiment the process parameter such as speed, feed, depth of cut and nose radius to explode

their effect on the surface roughness (Ra) of the workpiece. The experiments have been

conducted using full factorial design in design of experiment (DOE) on CNC lathe. From the

analysis it is observed that feed is the feed is the most significant factor that influences the

surface roughness. S.Ranganathan and T. Senthilvelan(2011) envisaged the multi-response

optimization of machining parameter in hot turning of stainless steel type 316 based on taguchi


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method. The workpiece is heated with liquid petroleum gas flame burned with oxygen and

machined under different parameter i.e cutting speed, feed, depth of cut and workpiece

temperature and effect on surface roughness, tool life and material removal rate (MRR) have

been optimized by conducting response analysis. Experimental results reveal that feed rate and

cutting speed are the dominant variables on multiple performance analysis and further can be

improved by hot turning. Poornima et al (2012) involved in identifying the optimized parameter

in CNC turning of martensitic stainless steel. The optimization technique used in this study was

response surface methodology and Genetic algorithm. The input parameter are basically speed,

feed and depth of cut and their effect is studies on the surface roughness of material. The best

range is 119.93m/min, feed 0.15 and depth of cut 0.5mm. Vipindas M.P. and Dr. Govindan P.

(2013) optimized the quality of machined surface in turning operation through taguchi method.

The comprehensive experimentation and analysis was performed on Al 6061 material .the

commonly used parameter speed, feed, and depth of cut were used for assessment. The

roughness value vary from between 0.3 and 4.4. it is observed that feed has strongest influence

on the quality of machined surface in CNC turning. Harish Kumar et al.( 2013) In metal cutting

turning is the one of the most fundamental cutting process used . Feed rate, speed and the depth

of cut were taken as input parameter and dimensional tolerance as the output parameter. L9 array

has been used in design of experiment for optimization of input parameter. The work material

used was MS 1010 with the tool material of High Speed Steel (HSS). The most affecting

parameter having the impact on dimension tolerance is speed as 59.9%. Sayak Mukherjee et al

(2014) studied the optimization of the process parameter viz. cutting speed, feed and depth of

cut with respect to material removal rate (MRR) in cnc lathe. The material selected was SAE

1020 with carbide cutting tool. The experiment was performed on EMCO Concept turn 105

CNC lathe. Taguchi method L 25 array was used to conduct the experiment. The analysis

showed that depth of cut had the most significant effect on the MRR followed by feed.

From the literature review, it shows the number of studies related to optimization of turning

process using different materials but not much work is reported for optimizing the process

parameters for 15B25 steel using carbide insert. In current study, an optimization model has been

proposed using response surface methodology (RSM) to study the effect of process parameters

on surface roughness (SR) and material removal rate (MRR). The input parameter selected for


Page 29

optimizing are rotational speed, feed rate and depth of cut which are the dominant factor in

turning operation.

2. MATERIAL AND METHOD

Low carbon alloy steel AISI 15B25 has been used for manufacturing the 8.8 grade Bolts, Rivets,

Screws and other fasteners or alloy chains. The material composition and its property are shown

in table 1 and table 2 respectively.

Table 1: Material composition of AISI 15B25

Material Percentage Composition (%)

C 0.22-0.30

Si 0.15-0.30

Mn 0.75-1.25

P 0.04

S 0.04

B 0.0005

Table 2: Properties of AISI 15B25

Density(kg/m3) 7.7-8.03x10

3)

Possion ratio 0.27-0.30

Elastic Modulus(GPa) 190-210

Brinell Hardness(HB) 163

Yield Strength(psi) 69000

Tensile strength 82000

Elongation(%) 12

Reduction in area (%) 35


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2.1 Response Surface Methodology

Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques

useful for the modeling and analysis of problems in which a response of interest or output is

influenced by several variables and the objective is to optimize this response. It has many

application in the design and improvement of product and processes.

y=f(x1,x2)+e (1)

x1,x2 are the independent variable where y is the response ,depends on them. The dependent

variable is a function of x1,x2 and the experimental error term denoted by e. a first and second

order polynomial can be fitted to develop a model.

The purpose of considering a model is as:

1. To establish a relationship, between y and x1, x2, . . . , xk that can be used to predict response

values for given settings of the control variables.

2. To determine, through hypothesis testing, significance of the factors whose levels are

represented by x1, x2, . . . , xk.

3. To determine the optimum settings of x1, x2, . . . , xk that result in the maximum (or minimum)

response over a certain region of ii1interest

A central composite design(CCD) approach is used to analysis the design. The no. of design

point in CCD are 2k

+ 2.k+ no

2.2 Cutting Tool Used

Tool material- tungsten carbide tool

Tool insert used:

TCMT090208-HM


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Table 3 Cutting Tool Dimension

Basic dimension of the tool insert used

L Φ I.C S Φd R (radius)

9.6 5.56 2.38 2.5 0.8

Machine used: The maximum turning diameter is 200mm and the length which can be admitted

is 262mm. The capacity of the spindle motor is 15KW.

Figure 1: CNC Turning Machine

2.3 Process Parameter and Range

From the study of literature survey, three important process parameter i.e speed, feed rate and

depth of cut has been selected. These are the factor which effect or contribute towards the

machining quality of the finished product.

The ranges of the parameter are shown in table 4.


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Table 4 Process Parameter Range

Parameter Range

Speed(rpm) 2500-2900

Feed(mm/rev) 0.15-0.22

Depth of cut(mm) 0.4-0.5

2.4 Data Collection

Experiments are planned using full factorial central composite design of RSM using three

cutting parameters: rotational speed, feed rate and depth of cut with 20 experimental run. The

data corresponding to these run is collected and shown in table 5.

Table 5 Data Collection

Run rotational

speed

(rpm)

Feed

rate(mm)

Depth of

cut(mm)

Material

removal

rate(MRR)

(mm3/min)

Surface

roughness Ra

(µm)

1 2700 0.19 0.45 795 2.56

2 2700 0.24 0.45 1300 3.78

3 2700 0.19 0.53 1980 2.66

4 3036 0.19 0.45 697 2.70

5 2700 0.19 0.37 788 2.70

6 2700 0.19 0.45 890 2.58

7 2700 0.19 0.45 1050 2.50

8 2900 0.22 0.40 1250 3.54

9 2364 0.19 0.45 1400 2.56

10 2900 0.15 0.50 1320 3.80

11 2700 0.19 0.45 1110 2.54

12 2500 0.22 0.50 1410 3.24

13 2900 0.22 0.50 1680 3.40

14 2500 0.22 0.40 1580 3.28

15 2500 0.15 0.40 865 1.98

16 2700 0.13 0.45 1000 1.46

17 2700 0.19 0.45 670 2.60

18 2500 0.15 0.50 1200 2.40

19 2900 0.15 0.40 940 2.02

20 2700 0.19 0.45 835 2.60


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The surface roughness is measured using stylus type profilometer talysurf (Taylor Hobson,

Surtronic 3+, UK). MRR is calculated using the formula given below.

MRR=

(2)

Where Wi and Wf are weight of workpiece measured before and after turning operation. is the

density of material and t is the time for machining.

3. RESULTS AND DISCUSSIONS

3.1 ANOVA

There are a large number of variables controlling the process, so some mathematical models are

required to represent the process. However, using only the significant parameters which

influencing the process are taken into consideration rather than including all the parameters. In

order to achieve this, statistical analysis of the experimental results will have to be processed

using the analysis of variance. ANOVA is a computational technique that estimates the relative

contributions of each of the control factors to the overall measured response. In the present work,

only the important parameters will be used to develop mathematical models using response

surface methodology (RSM). These models would be of great use during the optimization of the

process variables. The value of “p” for a model should be less than 0.05 which indicates that the

terms the terms included in the model are significant, which is desirable as it indicates that the

term in the model have significant effect on the response.

From the table 6 it is clear that the value of p for model is less than 0.05 which indicates that

model is significant. And the value of p for lack of Fit is greater than 0.05. from the table 6 it can

be clearly seen that the factor A, B and C are the significant factor as their value for p is very

small hence they are most concluding factor during machining of AISI 15B25 which gives better

surface finish.


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Table 6 ANOVA for Surface Roughness

Source Sum of

Squares

DF Mean

Square

F

Value

Prob > F

Model 5.34 9 0.59 56.21 < 0.0001 Significant

A 0.36 1 0.36 33.87 0.0002

B 4.08 1 4.08 387.16 < 0.0001

C 0.37 1 0.37 35.42 0.0001

A2 5.840E-003 1 5.840E-003 0.55 0.4740

B2 0.056 1 0.056 5.35 0.0434

C2 0.36 1 0.36 34.11 0.0002

AB 0.024 1 0.024 2.29 0.1609

AC 0.034 1 0.034 3.20 0.1038

BC 0.072 1 0.072 6.84 0.0258

Residual 0.11 10 0.011

Lack of Fit 0.087 5 0.017 4.57 0.0604 not

significant

Pure Error 0.019 5 3.787E-003

Cor. Total 5.44 19

3.2 ANOVA for MRR

Table 7 ANOVA for Material Removal Rate

Source Sum of

Squares

DF Mean

Square

F

Value

Prob > F

Model 1.813E+006 9 2.015E+005 3.42 0.0342 Significant

A 86566.21 1 86566.21 1.47 0.2530

B 3.352E+005 1 3.352E+005 5.70 0.0382

C 6.328E+005 1 6.328E+005 10.76 0.0083

A2 72395.08 1 72395.08 1.23 0.2933

B2 1.643E+005 1 1.643E+005 2.79 0.1257

C2 5.175E+005 1 5.175E+005 8.80 0.0141

AB 10878.13 1 10878.13 0.18 0.6763

AC 58653.13 1 58653.13 1.00 0.3416

BC 30628.13 1 30628.13 0.52 0.4871

Residual 5.883E+005 10 58831.51

Lack of Fit 4.539E+005 5 90776.35 3.38 0.1039 not

significant

Pure Error 1.344E+005 5 26886.67 F

Cor. Total 2.402E+006 19 Mean Value Prob > F


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From the table 7 shown above it may be concluded that depth of cut is the most significant factor

which affect the material removal rate (MRR).

Regression equation obtained as follows:

(a)Regression equation for surface roughness:

(b) Regression equation for material removal rate (MRR):

Figure 2 Surface Plot of MRR Vs Feed Rate and Depth Of Cut

As from the figure 2 it is clear that depth of cut has a significant effect on the MRR. As the depth

of cut increases the MRR also increases. Rotational speed and feed rate also effect the material

removal rate but less as compared to depth of cut.

DESIGN-EXPERT Plot

MRRX = B: feed rateY = C: depth of cut

Actual FactorA: rotational speed = 2700.00

737.979

927.385

1116.79

1306.2

1495.61

MRR

0.15

0.17

0.19

0.20

0.22

0.40

0.43

0.45

0.47

0.50

B: feed rate

C: depth of cut


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Figure 3: Surface Plot of Surface Roughness Vs Feed Rate and Rotational Speed

From the graph it can be clearly defined that as the feed rate and rotational speed increases the

surface roughness also increases. i.e they are the significant factor for surface roughness. Feed

rate is more dominant factor as compared to rotational speed.

SOLUTION

Number Rotational Speed Feed Rate Depth of Cut Surface Finish MRR Desirability

1 2512.00 0.15 0.50 2.29679 1327.93 0.623

4. CONCLUSIONS

Response surface methodology coupled with ANOVA has been employed to estimate the

optimum combination of spindle speed, feed rate and depth of cut for simultaneous

minimization of surface roughness and maximization of MRR. The following are conclusions:

Developed an analytical model for surface roughness and material removal rate (MRR) for

machining based on rotational speed, feed, and depth of cut.

The effect of each parameter on each response and the interactions between the parameters are

studied. It is found that the surface roughness and material removal rate could be controlled in

the design stage which is the most effective and inexpensive way.

DESIGN-EXPERT Plot

surface finishX = A: rotational speedY = B: feed rate

Actual FactorC: depth of cut = 0.45

1.92766

2.28199

2.63632

2.99066

3.34499

surf

ace f

inish

2500.00

2600.00

2700.00

2800.00

2900.00

0.15

0.17

0.19

0.20

0.22

A: rotational speed

B: feed rate


Page 37

feed rate, depth of cut and rotational speed are the significant factors which effect the surface

roughness. Feed rate is the dominant factor and it is directly proportional to the response.

Depth of cut has significant effect on the material removal rate(MRR) followed by rotational

speed. As the depth of cut and rotational speed increases MRR also increases.

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Documents

Optimization of Process Parameter for CNC Turning using Response Surface Methodology