How to cite this paper
Nayak, I., Rana, J & Parida, A. (2017). Performance optimization in electro- discharge machining using a suitable multiresponse optimization technique.Decision Science Letters , 6(3), 283-294.
Refrences
Balasubramanian, P., & Senthilvelan, T. (2014). optimization of machining parameters in EDM process using cast and sintered copper electrodes.Procedia Materials Science, 6, 1292-1302.
Chiang, K. T., Chang, F. P., & Tsai, D. C. (2007). Modeling and analysis of the rapidly resolidified layer of SG cast iron in the EDM process through the response surface methodology. Journal of Materials Processing Technology,182(1), 525-533.
Das, M. K., Kumar, K., Barman, T. K., & Sahoo, P. (2014). Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel. Procedia Materials Science, 6, 741-751.
Dewangan, S., Biswas, C. K., & Gangopadhyay, S. (2014). Optimization of the Surface Integrity Characteristics of EDM Process using PCA based Grey Relation Investigation. Procedia Materials Science, 6, 1091-1096.
Dhar, S., Purohit, R., Saini, N., Sharma, A., & Kumar, G. H. (2007). Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10wt.% SiC P composites. Journal of Materials Processing Technology, 194(1), 24-29.
Kackar, R. N. (1989). Off-line quality control, parameter design, and the Taguchi method. In Quality Control, Robust Design, and the Taguchi Method(pp. 51-76). Springer US.
Montgomery DC. (2001). Design and analysis of experiments, 5th edn, Wiley, Singapore.
Mandal, D., Pal, S. K., & Saha, P. (2007). 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), 154-162.
Lee, H. T., & Liu, C. (2009). Optimizing the EDM hole-drilling strain gage method for the measurement of residual stress. Journal of Materials Processing Technology, 209(15), 5626-5635.
Joshi, S. N., & Pande, S. S. (2010). Thermo-physical modeling of die-sinking EDM process. Journal of manufacturing processes, 12(1), 45-56.
Majumder, A. (2013). 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), 2143-2151.
Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2016). An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Engineering Science and Technology, an International Journal.
Nayak, I., & Rana, J. (2016). Selection of a suitable multiresponse optimization technique for turning operation. Decision Science Letters, 5(1), 129-142.
Puertas, I., & Luis, C. J. (2012). Optimization of EDM conditions in the manufacturing process of B4C and WC-Co conductive ceramics. The International Journal of Advanced Manufacturing Technology, 59(5-8), 575-582.
Raja, S. B., Pramod, C. S., Krishna, K. V., Ragunathan, A., & Vinesh, S. (2015). Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm. Engineering with Computers,31(1), 1-9.
Rangajanardhaa, G., & Rao, S. (2009). Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. Journal of materials processing technology, 209(3), 1512-1520.
Sengottuvel, P., Satishkumar, S., & Dinakaran, D. (2013). Optimization of multiple characteristics of EDM parameters based on desirability approach and fuzzy modeling. Procedia Engineering, 64, 1069-1078.
Singh, P. N., Raghukandan, K., & Pai, B. C. (2004). Optimization by grey relational analysis of EDM parameters on machining Al–10% SiC P composites. Journal of Materials Processing Technology, 155, 1658-1661.
Teimouri, R., & Baseri, H. (2014). Optimization of magnetic field assisted EDM using the continuous ACO algorithm. Applied Soft Computing, 14, 381-389.
Tzeng, Y. F., & Chiu, N. H. (2003). Two-phase parameter design for the optimisation of the electrical-discharge machining process using a Taguchi dynamic experiment. The International Journal of Advanced Manufacturing Technology, 21(12), 1005-1014.
Tzeng, Y. F., & Chen, F. C. (2007). Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Materials & Design, 28(4), 1159-1168.
Wang, Y., Zhou, X. J., & Hu, D. J. (2006). An experimental investigation of dry-electrical discharge assisted truing and dressing of metal bonded diamond wheel. International Journal of Machine Tools and Manufacture,46(3), 333-342.
Yang, S. H., Srinivas, J., Mohan, S., Lee, D. M., & Balaji, S. (2009). Optimization of electric discharge machining using simulated annealing.Journal of Materials Processing Technology, 209(9), 4471-4475.
Chiang, K. T., Chang, F. P., & Tsai, D. C. (2007). Modeling and analysis of the rapidly resolidified layer of SG cast iron in the EDM process through the response surface methodology. Journal of Materials Processing Technology,182(1), 525-533.
Das, M. K., Kumar, K., Barman, T. K., & Sahoo, P. (2014). Application of artificial bee colony algorithm for optimization of MRR and surface roughness in EDM of EN31 tool steel. Procedia Materials Science, 6, 741-751.
Dewangan, S., Biswas, C. K., & Gangopadhyay, S. (2014). Optimization of the Surface Integrity Characteristics of EDM Process using PCA based Grey Relation Investigation. Procedia Materials Science, 6, 1091-1096.
Dhar, S., Purohit, R., Saini, N., Sharma, A., & Kumar, G. H. (2007). Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10wt.% SiC P composites. Journal of Materials Processing Technology, 194(1), 24-29.
Kackar, R. N. (1989). Off-line quality control, parameter design, and the Taguchi method. In Quality Control, Robust Design, and the Taguchi Method(pp. 51-76). Springer US.
Montgomery DC. (2001). Design and analysis of experiments, 5th edn, Wiley, Singapore.
Mandal, D., Pal, S. K., & Saha, P. (2007). 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), 154-162.
Lee, H. T., & Liu, C. (2009). Optimizing the EDM hole-drilling strain gage method for the measurement of residual stress. Journal of Materials Processing Technology, 209(15), 5626-5635.
Joshi, S. N., & Pande, S. S. (2010). Thermo-physical modeling of die-sinking EDM process. Journal of manufacturing processes, 12(1), 45-56.
Majumder, A. (2013). 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), 2143-2151.
Mohanty, C. P., Mahapatra, S. S., & Singh, M. R. (2016). An intelligent approach to optimize the EDM process parameters using utility concept and QPSO algorithm. Engineering Science and Technology, an International Journal.
Nayak, I., & Rana, J. (2016). Selection of a suitable multiresponse optimization technique for turning operation. Decision Science Letters, 5(1), 129-142.
Puertas, I., & Luis, C. J. (2012). Optimization of EDM conditions in the manufacturing process of B4C and WC-Co conductive ceramics. The International Journal of Advanced Manufacturing Technology, 59(5-8), 575-582.
Raja, S. B., Pramod, C. S., Krishna, K. V., Ragunathan, A., & Vinesh, S. (2015). Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm. Engineering with Computers,31(1), 1-9.
Rangajanardhaa, G., & Rao, S. (2009). Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. Journal of materials processing technology, 209(3), 1512-1520.
Sengottuvel, P., Satishkumar, S., & Dinakaran, D. (2013). Optimization of multiple characteristics of EDM parameters based on desirability approach and fuzzy modeling. Procedia Engineering, 64, 1069-1078.
Singh, P. N., Raghukandan, K., & Pai, B. C. (2004). Optimization by grey relational analysis of EDM parameters on machining Al–10% SiC P composites. Journal of Materials Processing Technology, 155, 1658-1661.
Teimouri, R., & Baseri, H. (2014). Optimization of magnetic field assisted EDM using the continuous ACO algorithm. Applied Soft Computing, 14, 381-389.
Tzeng, Y. F., & Chiu, N. H. (2003). Two-phase parameter design for the optimisation of the electrical-discharge machining process using a Taguchi dynamic experiment. The International Journal of Advanced Manufacturing Technology, 21(12), 1005-1014.
Tzeng, Y. F., & Chen, F. C. (2007). Multi-objective optimisation of high-speed electrical discharge machining process using a Taguchi fuzzy-based approach. Materials & Design, 28(4), 1159-1168.
Wang, Y., Zhou, X. J., & Hu, D. J. (2006). An experimental investigation of dry-electrical discharge assisted truing and dressing of metal bonded diamond wheel. International Journal of Machine Tools and Manufacture,46(3), 333-342.
Yang, S. H., Srinivas, J., Mohan, S., Lee, D. M., & Balaji, S. (2009). Optimization of electric discharge machining using simulated annealing.Journal of Materials Processing Technology, 209(9), 4471-4475.