JMMCE  Vol.11 No.10 , October 2012
Optimization of ECM Process Parameters Using NSGA-II
ABSTRACT
Electrochemical machining (ECM) could be used as one of the best non-traditional machining technique for machining electrically conducting, tough and difficult to machine material with appropriate machining parameters combination. This paper attempts to establish a comprehensive mathematical model for correlating the interactive and higher-order influences of various machining parameters on the predominant machining criteria, i.e. metal removal rate and surface roughness through response surface methodology (RSM). The adequacy of the developed mathematical models has also been tested by the analysis of variance (ANOVA) test. The process parameters are optimized through Nondominated Sorting Genetic Algorithm-II (NSGA-II) approach to maximize metal removal rate and minimize surface roughness. A non-dominated solution set has been obtained and reported.

Cite this paper
C. Senthilkumar, G. Ganesan and R. Karthikeyan, "Optimization of ECM Process Parameters Using NSGA-II," Journal of Minerals and Materials Characterization and Engineering, Vol. 11 No. 10, 2012, pp. 931-937. doi: 10.4236/jmmce.2012.1110091.
References
[1]   E. S. Lee, J. W. Park and V. Moon, “A Study on Electro- chemical Micromachining for Fabrication of Micro- grooves in an Air-Lubricated Hydrodynamic Bearing,” International Journal of Advanced Manufacturing Tech- nology, Vol. 20, No. 10, 2002, pp. 720-726. doi:10.1007/s001700200229

[2]   H. Hocheng, P. S. Kao and S. C. Lin,“Development of the Eroded Opening during Electrochemical Boring of Hole,” International Journal of Advanced Manufacturing Tech- nology, Vol. 25, No. 11-12, 2005, pp. 1105-1112. doi:10.1007/s00170-003-1954-x

[3]   J. Paulo Davim, “Study of Drilling of Metal-Matrix Com- posites Based on Taguchi Experiments,” Journal of Ma- terial Processing Technology, Vol. 132, No. 1-3, 2003, pp. 250-254.

[4]   N. D. Chakladar, R. Das and S. Chakraborty, “A Digraph- Based Expert System for Non-Traditional Machining Processes Selection,” International Journal of Advanced Manufacturing Technology, Vol. 43, No. 3-4, 2009, pp. 226-237. doi:10.1007/s00170-008-1713-0

[5]   W. G. Cochran and G. M. Cox, “Experimental Designs,” 2nd Edition, Asia Publishing House, New Delhi, 1997.

[6]   K. Dev, A. Pratap, S. Agarwal and T. Meyarivan, “A Fast and Elitist Multi Objective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Compu- tation, Vol. 6. No. 2, 2002, pp. 182-197. doi:10.1109/4235.996017

[7]   B. Fu, “Piezo Electric Actuator Design via Multiobjective Optimization Methods,” Ph.D. Thesis, University of Pad- erborn, Paderborn, 2005.

[8]   M. M. Raghuwanshi and O. G. Kakde, “Survey on Multi- Objective Evolutionary and Real Coded Genetic Algo- rithm,” Proceedings of the 8th Asia pacific symposium on intelligent and evolutionary systems, Cairns, 6-7 Decem- ber 2004, pp.150-161

[9]   Y. Chen abd S. M. Mahdavain, “Parametric Study into Erosion Wear in a Computer Numerical Controlled Elec- tro-Discharge Machining Process,” Journal of wear, Vol. 236, No. 1-2, 1999, pp. 350-354.

[10]   J. A. Joins and D. Gupta, “Supply Chain Multi Objective Simulation Optimization,” proceedings of the 2002 Win- ter Simulation Conference, Raleigh, 8-12 December 2002, pp. 1306-1314.

 
 
Top