Document Type : Article

**Authors**

Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad, P.O. Box 91775-1111, Iran

**Abstract**

In this study Electrical discharge machining (EDM) process, widely used in mold manufacturing, is modeled and optimized using artificial neural network and an optimization heuristic algorithm. Material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) are considered as performance characteristics of the EDM process. Optimization of process parameters in order to find a combination of process parameters to simultaneously minimize TWR and SR and maximize MRR is the objective of this study. In order to establish the relations between the input and the output process parameters, back propagation neural network (BPNN) used. In the last section of this research, particle swarm optimization (PSO) algorithm has been employed for optimization of the multiple response characteristics. A set of verification tests is also performed to verify the accuracy of optimization procedure in determination of the optimal levels of process parameters. Results demonstrate that propose modeling technique (BPNN) can precisely simulate actual EDM process with less than 1% error. Furthermore less than 4% error for PSO algorithm results is quite efficient in optimization procedure.

**Keywords**

- Electrical discharge machining (EDM)
- Design of experiments (DOE)
- optimization
- Back propagation neural network (BPNN)
- Particle swarm optimization (PSO) algorithm

**Main Subjects**

References:

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2. Zohoor, M., and Hosseinali Beigi, A.R. "Application of electrical discharge machining for machining of semiconductor materials", Scientia Iranica, Transactions B: Mechanical Engineering, 21(4), pp. 1341-1346 (2014).

3. Antar, M., Hayward, P.M., Dunleavey, J., and Smith, P.B. "Surface integrity evaluation of modified EDM surface structure", Procedia CIRP, 68(1-3), pp. 308- 312 (2018).

4. Singh, N., Routara, B.C., and Das, D. "Study of machining characteristics of Inconel 601 in EDM using RSM", Materialstoday, 5(2), pp. 3438-3449 (2018).

5. Buschaiah, K., Jagadeeswara Rao, M., and Krishnaiah, A. "Investigation on the influence of EDM parameters on machining characteristics for AISI 304", Materialstoday, 5(2), pp. 3648-3656 (2018).

6. Begum, A. and Reddy, D. "Effect of polarity on the machining characteristics and surface generation in EDM", Materialstoday, 4(8), pp. 7674-7679 (2017).

7. Selvarajan, L., Rajavel, J., Prabakaran, V., Sivakumar, B., and Jeeva, G. "A review paper on EDM parameter of composite material and industrial demand material machining", Materialstoday, 5(2), pp. 5506- 5513 (2018).

8. Surya, V.R., VinayKumar, K.M., Keshavamurthy, R., Ugrasen, G., and Ravindra, H.V. "Prediction of machining characteristics using artificial neural network in wire EDM of Al7075 based in-situ composite", Materialstoday, 4(2), pp. 203-2012 (2017).

9. Pushpendra, S., Bharti, S., Maheshwari, C., and Sharma, C. "Multi-objective optimization of electricdischarge machining process using controlled elitist NSGA-II", Journal of Mechanical Science and Technology, 26(6), pp. 1875-1883 (2012).

10. Mohana, G.K., Hanumantha, D., and Rajurkar, K. "Hybrid modeling and optimization of surface produced by electric discharge machining using artificial neural network and genetic algorithm", Journal of Engineering and Applied Sciences, 5(3), pp. 72-81 (2009).

11. Mahdavi Nejad, R.A. "Modeling and optimization of electrical discharge machining of Sic parameters, using neural network and non-dominating sorting genetic algorithm (NSGA II)", Materials Sciences and Applications, 2(1), pp. 669-675 (2011).

12. Radhika, N., Shivaram, P., and Karthik, K.T. "Multiobjective optimization in electric discharge machining of aluminum composite", Tribology in Industry, 36(2), pp. 428-436 (2014).

13. Panda, S., Mishra, D., Biswal, B.B., and Nanda, P. "Optimization of multiple response characteristics of EDM process using Taguchi-based grey relational analysis and modified PSO", Journal of Advanced Manufacturing Systems, 14(3), pp. 123-148 (2015).

14. Majumder, A. "Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO", Journal of Mechanical Science and Technology, 27(7), pp. 2143-2151 (2013).

15. McCulloch, W.S. and Pitts, W. "A logical calculus of the ideas immanent in nervous activity", The Bulletin of Mathematical Biophysics, 5, pp. 115-133 (1943).

16. Sayyad, H., Khaksar Manshad, A., and Rostami, H. "Application of hybrid neural particle swarm optimization algorithm for prediction of MMP", Fuel, 116(4), pp. 625-633 (2014).

17. Parsana, S., Radadia, N., Sheth, M., Sheth, N., Savsani, V., Eswara Prasad, N., and Ramprabhu, T. "Machining parameter optimization for EDM machining of Mg-RE-Zn-Zr alloy using multi-objective passing vehicle search algorithm", Archives of Civil and Mechanical Engineering, 22(5), pp. 799-817 (2018).

18. Kennedy, J. and Eberhart, R. "Particle swarm optimization", Proceedings of ICNN 95, International Conference on Neural Networks, Perth, WA, Australia, 12(4), pp. 1942-1948 (1995).

19. Agarwal, N., Pradhan, M.K., and Shrivastava, N. "A new multi-response Jaya algorithm for optimisation of EDM process parameters", Materialstoday, 5(11), pp. 23759-23768 (2018).

20. Chauhan, N., Das, A., and Rajesha, S. "Optimization of process parameters using grey relational analysis and Taguchi method during micro-EDMing", Materialstoday, 5(13), pp. 27178-27184 (2018).

21. Assarzadeh, S. and Ghoreishi, M. "Mathematical modeling and optimization of the electro-discharge machining (EDM) parameters on tungsten carbide composite: Combining response surface methodology and desirability function technique", Scientia Iranica, Transactions B: Mechanical Engineering, 22(2), pp. 539-560 (2015).

22. Roy, R.K. "A primer on the Taguchi method", Society of Manufacturing Engineers, 5(2), pp. 25-41 (2010).

23. Sanghani, C.R. and Acharya, G.D. "A review of research on improvement and optimization of performance measures for electrical discharge machining",Journal of Engineering Research and Applications, 4(1), pp. 433-450 (2014).

24. Debabrata, M., Surjya, K., and Partha, S. "Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non dominating sorting genetic algorithm-II", Journal of Materials Processing Technology, 186(1), pp. 54-162 (2007).

25. Markopoulos, A.P., Manolakos, D.E., and Vaxevanidis, N.M. "Artificial neural network models for the prediction of surface roughness in electrical discharge machining", Journal of Intelligence Manufacturing, 12(2), pp. 283-292 (2008).

26. Zolfaghari, A., Goharimanesh, M., and Akbari, A.A. "Optimum design of straight bevel gears pair using evolutionary algorithms", Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(6), pp. 2121-2129 (2017).

27. Lee, K.H. and Kim, K.W. "Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem", International Journal Heat and Mass Transfer, 88(5), pp. 330-337 (2015).

28. Zhi, K., Jia, W., Zhang, G., and Wang, L. "Normal parameter reduction in soft set based particle swarm optimization algorithm", Applied Mathematical Modeling, 39(3), pp. 4808-4820 (2015).

29. Shojaeefard, M.H., Behnagh, R.A., Akbari, M., Besharati, M.K., and Farhani, F. "Modeling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm", Journal of Materials and Design, 44(2), pp. 190-198 (2013).

2. Zohoor, M., and Hosseinali Beigi, A.R. "Application of electrical discharge machining for machining of semiconductor materials", Scientia Iranica, Transactions B: Mechanical Engineering, 21(4), pp. 1341-1346 (2014).

3. Antar, M., Hayward, P.M., Dunleavey, J., and Smith, P.B. "Surface integrity evaluation of modified EDM surface structure", Procedia CIRP, 68(1-3), pp. 308- 312 (2018).

4. Singh, N., Routara, B.C., and Das, D. "Study of machining characteristics of Inconel 601 in EDM using RSM", Materialstoday, 5(2), pp. 3438-3449 (2018).

5. Buschaiah, K., Jagadeeswara Rao, M., and Krishnaiah, A. "Investigation on the influence of EDM parameters on machining characteristics for AISI 304", Materialstoday, 5(2), pp. 3648-3656 (2018).

6. Begum, A. and Reddy, D. "Effect of polarity on the machining characteristics and surface generation in EDM", Materialstoday, 4(8), pp. 7674-7679 (2017).

7. Selvarajan, L., Rajavel, J., Prabakaran, V., Sivakumar, B., and Jeeva, G. "A review paper on EDM parameter of composite material and industrial demand material machining", Materialstoday, 5(2), pp. 5506- 5513 (2018).

8. Surya, V.R., VinayKumar, K.M., Keshavamurthy, R., Ugrasen, G., and Ravindra, H.V. "Prediction of machining characteristics using artificial neural network in wire EDM of Al7075 based in-situ composite", Materialstoday, 4(2), pp. 203-2012 (2017).

9. Pushpendra, S., Bharti, S., Maheshwari, C., and Sharma, C. "Multi-objective optimization of electricdischarge machining process using controlled elitist NSGA-II", Journal of Mechanical Science and Technology, 26(6), pp. 1875-1883 (2012).

10. Mohana, G.K., Hanumantha, D., and Rajurkar, K. "Hybrid modeling and optimization of surface produced by electric discharge machining using artificial neural network and genetic algorithm", Journal of Engineering and Applied Sciences, 5(3), pp. 72-81 (2009).

11. Mahdavi Nejad, R.A. "Modeling and optimization of electrical discharge machining of Sic parameters, using neural network and non-dominating sorting genetic algorithm (NSGA II)", Materials Sciences and Applications, 2(1), pp. 669-675 (2011).

12. Radhika, N., Shivaram, P., and Karthik, K.T. "Multiobjective optimization in electric discharge machining of aluminum composite", Tribology in Industry, 36(2), pp. 428-436 (2014).

13. Panda, S., Mishra, D., Biswal, B.B., and Nanda, P. "Optimization of multiple response characteristics of EDM process using Taguchi-based grey relational analysis and modified PSO", Journal of Advanced Manufacturing Systems, 14(3), pp. 123-148 (2015).

14. Majumder, A. "Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO", Journal of Mechanical Science and Technology, 27(7), pp. 2143-2151 (2013).

15. McCulloch, W.S. and Pitts, W. "A logical calculus of the ideas immanent in nervous activity", The Bulletin of Mathematical Biophysics, 5, pp. 115-133 (1943).

16. Sayyad, H., Khaksar Manshad, A., and Rostami, H. "Application of hybrid neural particle swarm optimization algorithm for prediction of MMP", Fuel, 116(4), pp. 625-633 (2014).

17. Parsana, S., Radadia, N., Sheth, M., Sheth, N., Savsani, V., Eswara Prasad, N., and Ramprabhu, T. "Machining parameter optimization for EDM machining of Mg-RE-Zn-Zr alloy using multi-objective passing vehicle search algorithm", Archives of Civil and Mechanical Engineering, 22(5), pp. 799-817 (2018).

18. Kennedy, J. and Eberhart, R. "Particle swarm optimization", Proceedings of ICNN 95, International Conference on Neural Networks, Perth, WA, Australia, 12(4), pp. 1942-1948 (1995).

19. Agarwal, N., Pradhan, M.K., and Shrivastava, N. "A new multi-response Jaya algorithm for optimisation of EDM process parameters", Materialstoday, 5(11), pp. 23759-23768 (2018).

20. Chauhan, N., Das, A., and Rajesha, S. "Optimization of process parameters using grey relational analysis and Taguchi method during micro-EDMing", Materialstoday, 5(13), pp. 27178-27184 (2018).

21. Assarzadeh, S. and Ghoreishi, M. "Mathematical modeling and optimization of the electro-discharge machining (EDM) parameters on tungsten carbide composite: Combining response surface methodology and desirability function technique", Scientia Iranica, Transactions B: Mechanical Engineering, 22(2), pp. 539-560 (2015).

22. Roy, R.K. "A primer on the Taguchi method", Society of Manufacturing Engineers, 5(2), pp. 25-41 (2010).

23. Sanghani, C.R. and Acharya, G.D. "A review of research on improvement and optimization of performance measures for electrical discharge machining",Journal of Engineering Research and Applications, 4(1), pp. 433-450 (2014).

24. Debabrata, M., Surjya, K., and Partha, S. "Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non dominating sorting genetic algorithm-II", Journal of Materials Processing Technology, 186(1), pp. 54-162 (2007).

25. Markopoulos, A.P., Manolakos, D.E., and Vaxevanidis, N.M. "Artificial neural network models for the prediction of surface roughness in electrical discharge machining", Journal of Intelligence Manufacturing, 12(2), pp. 283-292 (2008).

26. Zolfaghari, A., Goharimanesh, M., and Akbari, A.A. "Optimum design of straight bevel gears pair using evolutionary algorithms", Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39(6), pp. 2121-2129 (2017).

27. Lee, K.H. and Kim, K.W. "Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem", International Journal Heat and Mass Transfer, 88(5), pp. 330-337 (2015).

28. Zhi, K., Jia, W., Zhang, G., and Wang, L. "Normal parameter reduction in soft set based particle swarm optimization algorithm", Applied Mathematical Modeling, 39(3), pp. 4808-4820 (2015).

29. Shojaeefard, M.H., Behnagh, R.A., Akbari, M., Besharati, M.K., and Farhani, F. "Modeling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm", Journal of Materials and Design, 44(2), pp. 190-198 (2013).

Transactions on Mechanical Engineering (B)

May and June 2020Pages 1206-1217