Optimize Machining Parameters in Turning Operation Using ......intelligent algorithm called a Genetic Algorithm (GA) is used to help in solving these problems. This algorithm is more

International Journal of Advancements in Research & Technology, Volume 7, Issue 9, September-2018 26 ISSN 2278-7763

Copyright © 2018 SciResPub.

Optimize Machining Parameters in Turning Operation Using Genetic Algorithm and Network Fitting Tool Modeling

By

1Adnan Jameel Abbas

2Mohamad Bin Minhat

1Middle Technical University, Baghdad, Iraq 2Universiti Teknikal Malaysia Melaka, Malaysia

ABSTRACT

Turning process is one of complicated operations to control its cutting parameters because it depends upon several conflicting cutting parameters that must be adjusted at the same time accurately. In this research, minimization of cutting temperature, work piece surface roughness and cutting time are achieved. A mild steel material type JIS S45C and a tungsten carbide insert type SPG-422 Grade E30 are used as workpiece and cutting tool materials. The temperature of primary plastic deformation zone which called shearing zone, and secondary deformation zone which called chip slides on the rake face zone are measured. This paper adopts the utilization Genetic Algorithm (GA) to achieve the minimization operation. Three objective functions are used as input to GA for minimization purpose, one objective function for temperature minimization and one for surface roughness minimization and one for cutting time minimization. The outputs of huerestics algorithms are; minimum temperature, minimum surface finish, minimum cutting time. The simulation results showed that the GA algorithm gives the best temperature value (30. 306 оC) and surface roughness (0.26 µm) and cutting time (3.33 min). The artificial neural network type Network Fitting Tool (NFTOOL) is used as a modeling technique for manipulating the ideal algorithm parameters. The results of NFTOOL indicates that (9-6-3) network is the ideal type because it gives lower testing mean square error (MSE) equal to (3.97214 *10-12). Keywords: Turning process; temperature; surface roughness; cutting time; Genetic Algorithm; Network Fitting

Tool.

1 INTRODUCTION Minimization of undesired parameters in production operations is very necessary to increase the productivity and reduce the costs. The determination of the ideal parameters and performance are among the most crucial and complex factors in the process planning and economics of metal cutting operations. The lower costs, higher productivity and shorter machining time represent the primary objectives of recent industrial revival [1] , [2] The best combination of performance parameters, such as minimum cutting force, good surface integrity, lowest cutting temperature and minimum power consumption indicate that an operation has relatively good machinability. However, obtaining better performance involves many difficult aspects, including machining, mathematical knowledge, highly experienced requirement, as well as specifying the most

suitable machine operation [3]. Turning process is a complicated operation where the performance depends upon many cutting conditions, mainly because there are many parameters and conflicting objectives that are adjusted simultaneously to minimize the cost and product quality improvement [4], [5], [6] and [7]. Parameters improvement in the turning process is highly constrained and nonlinear, and thus, the cutting parameters such as feed rate, cutting speed and depth of cut must be carefully selected to obtain the economics of machining operations by increasing productivity and decreasing total costs [8]. The temperature in machining plays a key role in tool wear, cutting forces, work piece surface quality and machining precision [9]. It can also result in changing the properties of the work piece and tool materials as well as reducing the tool life. In addition, in

IJOART



metal cutting operations, the spending energy is converted into heat, which translates to the work piece and cutting tool materials.The shear zone temperature affects the work piece-chip material’s mechanical properties, while the tool-chip and tool-work piece temperatures influence the cutting tool wear [10]. Three main heating zones can be recognized in turning using a single point cutting tool. These zones are: the primary plastic deformation zone (shearing plane zone); secondary deformation zone (chip slides on the rake face zone); and tertiary deformation zone (tool relief face slides zone). The tertiary deformation zone is not considered in most cases because it is small, especially when using sharp cutting edges[11]. [12], [13], [14] and [15] Surface finish is one of the major quality attributes of a turning process, since it plays a significant role in the production cost and the indication of the manufacturing processes, as it directly affects the performance of the mechanical parts, as well as the consumer satisfaction.There are many machining variables that influence the surface integrity, such as cutting speed, feed rate, depth of cut and tool geometry. The geometric factors that directly affect the surface finish are the cutting tool nose radius, rake angle, cutting edge angle, work piece, tool material. In this paper a mild steel material type JIS S45C work piece with tungsten carbide insert cutting tool type SPG-422 Grade E30 on CNC turning machines are adopted via dry machining. Cutting time is one of the most important parameters that a company considers in order to deliver the product to customers in the minimum amount of time, and in turn enhance their satisfaction. Cutting parameters such as depth of cut, cutting speed and feed rate have a great impact on time and cost, mainly because they affect the production quality. There has been increasing interest in using smart and heuristic algorithms for a production improvement process in order to achieve higher accuracy. Intelligent algorithms are procedures that aim to compare previous solutions until the ideal solution is reached by selecting the best cutting parameters. Using the cutting parameters from machining databases or specialized handbooks are not associated with better results because they represent starting values. The selection of the ideal conditions of machining parameters has greatly contributed in increasing the productivity and reducing costs. Choosing the machining parameters to achieve good quality properties is a crucial process to obtain high quality and productivity requirements, especially in a turning operation. Heuristics intelligent algorithm called a Genetic Algorithm (GA) is used to help in solving these problems. This algorithm is more reliable, and has effective

methodology for finding better solutions of real problems when compared with conventional methods. Genetic Algorithm is a computation search and minimization method owing its power to the mechanics of natural selection and genetics, such as survival of the fittest (competition) and recombination, which make enable it to find the global solution for a given problem. A GA is good at taking large and potentially huge search spaces, and translating them, in search for best combinations of things and solutions. The Artificial Neural Network type Network Fitting Tool (NFTOOL) is a computational model that uses real values, and is used to estimate or approximate functions that depend on a large number of inputs. The NFTOOL is generally presented as several systems of interconnected "neurons" that can compute values from inputs, and are capable of machine learning and pattern recognition, mainly because of their inherent adaptive nature. It is a numerical and complicate physics based modeling technique used for modeling and developing the performance of the manufacturing technologies. This research adopts the utilization of GA and NFTOOL to find the best cutting parameters that help in finding the ideal cutting temperature, work piece surface roughness and cutting time and then selecting the ideal parameters group which achieves equilibrium among performance parameters in CNC turning operation. A GA is another method to get hold of best possible machine tool’s values (Yusup et al., 2012). It consist of; reproduction, crossover and mutation which are the main competencies of GA. The binary symbols used to encode the parameters and the process is defined in detail in the following sections:- Sahoo [16] work theme was concerning the high carbon steel type (carbon steel has high carbon content carbon steel has high carbon content AISI 1040) mild steel which had chemical vapor deposition diamond carbide coating cutting tool in CNC turning operation environment where random procedures were input to the GA algorithm adequate to find the best values (depth of cut, spindle speed, feed rate) of the cutting procedure. Root mean square, center line and average roughness and line peak spacing were calculated to find the quantitative data of how much the surface smoothness has been affected. The analyses disclosed that feed rate, speed and depth of cut should be controlled and have minor authority if one wants an even coarse. Ahmad and Anwarul [17] contemplated on the methodology arranging parameters streamlining of rotational segment utilizing a GA matlab l toolbox. Minimizing the working time Tm was the fundamental purpose of this function. It was

IJOART

http://en.wikipedia.org/wiki/Computational_model

http://en.wikipedia.org/wiki/Universal_approximation_theorem

http://en.wikipedia.org/wiki/Function_(mathematics)

http://en.wikipedia.org/wiki/Argument_of_a_function

http://en.wikipedia.org/wiki/Artificial_neuron

http://en.wikipedia.org/wiki/Machine_learning

http://en.wikipedia.org/wiki/Pattern_recognition

http://en.wikipedia.org/wiki/Pattern_recognition



to minimize the working time Tm as shown in Equation below:- Tm = Lf * npass / f * Nw Where Tm represents the calculated machining time, Lf represents the measured length of surface of the cutting tool, npass represents the number of passes recorded in the experiment, f represents the value of feed rate and Nw represents the measured rotational speed of work piece the cutting tool conducts. Selecting the ideal cutting parameters for every operating process is among the modern technological challenges in enhancing machining product quality, reducing operation costs, and increasing the effectiveness and productivity of cutting operations.

2 SIMULATION OPERATION USING GA

HEURISTIC ALGORITHM

In order to minimize the present problem using GA, the following parameters have been selected to obtain ideal solutions with the least possible computational effort:- Population size = 30 Maximum number of generations = 450 Crossover probability (Pc) = 0.9 Mutation probability (Pm) = 0.05. To select two strings of population for either mutation or crossover, the roulette wheel technique is used. The technique specified that, for selection, a random number between 0 and 1 is multiplied with the sum of fitness of all the old population strings. When this value is less than or equal to the cumulative fitness of the string, this string is selected from the old population. In this manner, two strings (mate-1 and mate-2) are selected to the mating pool. Using the GA operators, two new strings (child-1 and child-2) are created out of these mates. This process is continued until 30 new strings of population are generated. These strings are replaced into the old population to represent the second generation of the old population. In this manner, 450 generations are continued before the algorithm converges into the fit unique solution. 2.1 GA Minimization by Temperature, Surface Roughness and Cutting Time Objective Functions 2.1.1 GA Minimization by the Temperature Objective Function

The main temperature objective fuction (Tmain) is used as input for the GA, which calculates the ideal parameters that lead to minimize the cutting temperature. The temperature is calculated using the following equation:- T= [B *C *Vc*( Fz * a2 – F * f * sin (ф) / a2 / J *θ *Vc * a *f + Ta] + [C1*( Fz / f *a) * �(Vc ∗ f ∗ sin (ф) / (θ ∗ λ)) 2 ] Where B is the shear energy that is converted into heat = (0.95 ~ 1) in the turning operation (Chattopadhyay, 2009), C is the heat that goes to the chip from the shear zone = (0.7 ~ 0.9) in the turning operation (Chattopadhyay, 2009), Vc is the main cutting velocity (m/min), Fz is the main cutting force (N), a2 is the chip thickness (mm), F is the friction force at the rake surface (N), f is the feed rate (mm/rev), ф is the cutting tool edge angle (in degrees), J is the mechanical equivalent of heat of the chip / work piece material (J/Cal), θ is the volume specific heat (Cal/mm3/оC) of work piece, a is the depth of cut (mm), and Ta is the ambient temperature and equal to 25о, C1 is a constant equal to 121 in the turning operation (Chattopadhyay, 2009), and λ is the thermal conductivity (W/m. оC). 2.1. GA Minimization by Surface Roughness

Objective Function The ideal transverse roughness (Rmax) in the turning operation can be calculated using the Equation below. Ra = 0.032 * f 2 / r Where Ra is the transverse roughness (µm), f is the feed between successive cuts (mm), and r is the radius of the cutting tool (mm). 2.1.3 GA Minimization by Cutting Time Objective Function The cutting time can be calculated using Equations below. t = Lf / f * Nw Nw = 1000 * Vc / π * D Where t is the machining cutting time (min), Lf is the length of the cutting surface (mm), f is the feed rate (mm/rev), Nw is the

IJOART



rotational speed of the work piece (rev/min), and D is the work piece diameter (mm). 2.1.4 GA Variable Boundaries The variable parameters are subject to boundaries and constraints that specifiy the upper and lower limitation values according to standard tables. The simulation results can be collected from the GA using one trail iteration (450 iterations) of this algorithm. The best epoch from each iteration which gives the optimum results is selected as the ideal criteria. Cutting Parameters Standard Recommendations of JIS S45C Mild Steel and SPG-422 E30 Tungsten Carbide Tool is used for selecting the boundary of cutting speed, feed rate and depth of cut as shown in Table 1 below:- Table 1: Cutting Parameters Standard Recommendations of JIS S45C Mild Steel and SPG-422 E30 Tungsten Carbide Tool (Fox Valley Technical College, 2000)

and (Christover et al., 2004).

3 Results The ideal epochs selection operations by GA1 are shown in Appendix G and, and in Table 2.

Table 2: The Ideal Epoch’s Selection by GA

Temperature Objective Function

In Table 2, the minimum cutting temperature and surface roughness can be obtained at epochs [42-450], and are equal to 30.3 0C and 0.26 µm, respectively, as shown in Figure 1and Figure 2.

Fig 1: Minimum Cutting Temperature of

GA

Cutting Speed m/min

Feed rate mm/rev

Depth of cut mm

40-140 0.05-0.3 0.3-1

IJOART



Fig 2: Minimum Surface Roughness of

GA

The minimum cutting time can be

obtained at epochs [4-12], and is equal

to 3.33 min. Figure 3 represents the

relationship between cutting time and

epochs using GA

Fig 3: Minimum Cutting Time of

GA

Epochs [13-21] in Table 2 consist of the

ideal parameters that give equilibrium

between temperature, surface roughness

and cutting time, as shown in Figure 4

Fig 4 Ideal Parameters of

Temperature, Surface Roughness and

Cutting Time of GA

4. NFTOOL Modeling Manipulation

After trying many types of hidden layers it is found that the network (9-6-3) which means nine input nodes, 6 hidden nodes and 3 output nodes represents the ideal network because it gives lower testing (MSE) equal to (5.70113 e-12) which

occures at epochs 535, and better

regression model which equal to (R = 1)

as shown in test number 5 in Table 3.

Table 3: Results of NFTOOL Network

The best performance (lower mean

square error value 5.70113e-12) and

(ideal regression model value 1, 1, 1 for

training, testing and validation data

IJOART



respectively) can be shown in Figures 5

and 6 respectively:-

Fig 5: Best Mean Square Error Value

Fig 6: Ideal Regression Model Value

The best validation performance of

8.2253 e-12 is obtained after 535

iterations as shown in Figure 7.

:-

Fig 7: The best Validation Performance

The ideal NFTOOL network architecture

can be represented as shown in Figure

8.

:-

Figure 5.27: The Ideal NFTOOL Network

Architecture

IJOART



Finally, the ideal NFTOOL which used

the first main GA1 objective function

parameters can be used for future work

for cutting parameters prediction and

shown as in Figure 9.

Fig 9 NFTOO Method

Fig 8 NFTOOL network

Conclusions 4

The main issue that the production

operations face is getting the best

parameters that helping in reducing the

costs and increasing the productivity. In

addition, in minimization operations, the

minimum values of output parameters are

the main request for reducing the loss

and getting high profits.

This research calculates the

cutting parameters that lead to minimize

the cutting temperature, work piece

surface roughness and cutting time

output performance parameters to the

lowest values.The heuristics algorithm

and NFTOOL are adopted to execute the

minimization and modelling in turning

operation respectively. A medium

strength mild steel type JIS S45C with a

tungsten carbide cutting tool type SPG

422 Grade E30 are used as a work

piece and cutting tool materials

respectively. The simulation operations

are executed by GA so as to find the

ideal values of cutting parameters

(speed, feed rate and depth of cut) and

minimize the output performance

IJOART



parameters instead of based on

databases or handbooks

recommendations data. From GA

simulation results, it is noted that the

cutting temperature and thermal

conductivity increase with speed, fade

rate, depth of cut. On the other hand, the

main cutting force and feed force

decrease when the cutting speed rises,

while friction force no more affect and still

constant approximately. In addition, the

cutting time decreases with the feed rate

and speed increasing, while not more

affect with the depth of cut changing. As

well as, the surface roughness decrease

with the cutting speeds increases, while

increase with the feed rate and depth of

cut increasing. The inputs parameters

are; cutting velocity (Vc), feed rate (f),

depth of cut (a), main cutting force (Fz),

friction cutting force (F), chip thickness

(a2), thermal conductivity (ƛ ), chip heat

(C) and shear energy heat (B). The

network output is cutting temperature (T),

work piece surface roughness (Ra) and

cutting time (t). The results of NFTOOL

network indicates that (9-6-3) network

which means nine input nodes, six

hidden nodes and three output nodes is

the ideal network because it gives lower

testing (MSE) equal to (3.97214 e -12)

at epochs 535, and better regression

model equal to (R = 1)

REFERENCES

1- Xie, S., and Guo, Y., 2011. Intelligent Selection of Machining Parameters in Multi-Pass Turnings Using a GA-based Approach. Journal of Computational Information Systems, 7 (5), pp. 1714-1721

2- Savadamuthu,V. , and Muthu, S.,

2012. Optimization of Cutting Parameters

for Turning Process using Genetic

Algorithm. European Journal of Scientific

Research., 69 (1) , pp. 64-71

3- Yusup, N., Mohd, A., and Zaton, S.,

2012. Overview of PSO for optimizing

process parameters of machining.

International workshop on information

and electronics engineering (IWIEE), 29

, pp. 914-923.

4-Sardin, Q., 2006. Genetic Algorithm-

Based Multi-Objective Optimization of

Cutting Parameters in Turning Processes.

IJOART



Eng. Appl. Artif. Intell.,19 (2), pp. 127-

133.

5- Yildiz, A., and Ozturk, F., 2006. Hybrid Enhanced Genetic Algorithm To Select Optimal Machining Parameters In Turning Operation. Proceeding Inst. Mech. Eng. Manuf., 220(12), pp. 2041-2053, 2006.

6- Khalilpourazary, S., Kashtiban, P.,

and Payam, N., 2014. Optimizing Turning

Operation of St37 Steel Using Grey

Relational Analysis. J. Comput. Appl.

Res. Mech. Eng., 3 (2), pp. 135-144.

7- Miroslav, R., 2012. Optimizing Cutting

Parameters Based On Cutting Force In

Tube Turning Using Taguchi Method.

Nonconventional Technologies Review, Romania, pp. 29-34.

8- Ganesan, H., 2011. Optimization of

Machining Parameters in Turning

Process Using Genetic Algorithm and

Particle Swarm Optimization With

Experimental Verification. Int. J. Eng. Sci.

Technol., 3 (2), pp. 1091-1102.

9- Aydın, M., Karakuzu, C., Uçar,C.,

Cengiz, A., and Çavuşlu, M.,2012.

Prediction of surface roughness and

cutting zone temperature in dry turning

processes of AISI304 stainless steel

using ANFIS with PSO learning. Int. J.

Adv. Manuf. Technol., 67 (1–4), pp.

957-967.

10- Balic, F., and Cus, J., 2007.

Intelligent modelling in manufacturing.

Journal of

11- Sullivan, D., and Cotterell, M., 2001.

Temperature measurement in single point

turning. J. Mater. Process. Technol., 118

(1-3), pp. 301-308.

12 - Aneiro, F., Carlos, S., and Brandao,

L., 2008. Turning Hardened Steel Using

Coated Carbide at High Cutting Speeds.

J. Brazilian Soc. Mech. Sci., 30 (2), pp.

104-109.

13- Luke, H., Joseph, C., and Tao, C.,

1999. Effect of Tool/Chip Contact

Length on Orthogonal Turning

Performance Effect of Tool/Chip Contact

Length on Orthogonal Turning

Performance, 15 (2), pp. 1-5.

14- Qehaja, N., Zeqir, H., Kyqyku, A.,

Bunjaku, A., 2011. The Temperature

Research in Increased Speed Processing

by Turning. In:15th International

Research/Expert Conference ”Trends in

the Development of Machinery and

IJOART



Associated Technology”, Prague, Prague,

Czech Republic, 12-18 September 2011.

15- Sahu, M., and Sahu, K., 2014.

Optimization of Cutting Parameters on

Tool Wear,Workpiece Surface

Temperature and Material Removal Rate

in Turning of AISI S teel. International

Journal of Advanced Mechanical

Engineering, 4 (3), pp. 291-298.

16- Sahoo, P., 2011. Optimization Of

Turning Parameters For Surface

Roughness Using RSM and GA. Adv.

Prod. Eng. Manag., 6 (3), pp. 197-208.

17- Ahmad, N., and Anwarul, A., 2001.

Optimization Of Process Planning

Parameters For Rotational Components

By Genetic Algorithms. In: 4th

International Conference on Mechanical

Engineering, Bangladesh, Dhaka, 26-28

December 2001.

.

IJOART

Documents

Optimize Machining Parameters in Turning Operation Using ......intelligent algorithm called a Genetic Algorithm (GA) is used to help in solving these problems. This algorithm is more