Searching for a Pareto Optimal Solution Set of EDM Responses Applying Multi-Objective Simulated Annealing on RSM Model

Abstract:

Simultaneous optimization of conflicting type responses like material removal rate (MRR) and average surface roughness (Ra) in stochastic type electrical discharge machining (EDM) process is a matter of concern to the process engineers. In this paper, EDM is first modeled by response surface methodology (RSM). Current setting, pulse on time and pulse off time were taken as the input parameters while material removal rate and average surface roughness as the responses. Multi-objective simulated annealing (MOSA) is then applied on these models. Pareto optimal solution set is thus developed. It would assist a process engineer to take decision regarding the optimal setting of the process parameters for a specific need-based requirement.

• Periodical: Advanced Materials Research (Volumes 622-623)
• Pages: 51-55
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.622-623.51
• Online since: December 2012
• Authors: Ushasta Aich, Amit Kumar Pal, Dipak Laha, Simul Banerjee
• Keywords: Electrical Discharge Machining (EDM), Multi-Objective Simulated Annealing (MOSA), Pareto-Optimal Solution, Response Surface Methodology (RSM)

References

[1] K. H. Ho, S. T. Newman, State of the art electrical discharge machining (EDM), International Journal of machine tools and Manufacture 43 (2003) 1287-1300.

DOI: 10.1016/s0890-6955(03)00162-7

[2] M. Kunieda, B. Lauwers, K. P. Rajurkar, B. M. Schumacher, Advancing EDM through fundamental insight of the process, Annals of the CIRP 54(2) (2005) 599-622.

DOI: 10.1016/s0007-8506(07)60020-1

[3] N. M. Abbas, D. G. Solomon, M. F. Bahari, A review on current research trends in electrical discharge machining, International Journal of machine tools and Manufacture 47(7-8) (2007) 1214-1228.

DOI: 10.1016/j.ijmachtools.2006.08.026

[4] D. K. Panda, R. K. Bhoi, Artificial neural network prediction of material removal rate in electro-discharge machining, Materials and Manufacturing Processes 20 (2005) 645-672.

DOI: 10.1081/amp-200055033

[5] P. Saha, A. Singha, S. K. Pal, P. Saha, Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite, International Journal of Advanced Manufacturing Technology 39(1-2) (2008).

DOI: 10.1007/s00170-007-1200-z

[6] S. S. Habib, Study of the parameters in electrical discharge machining through response surface methodology approach, Applied Mathematical Modelling 33 (2009) 4397-4407.

DOI: 10.1016/j.apm.2009.03.021

[7] S. Banerjee, D. Mahapatro, S. Dubey, Some study on electrical discharge machining of ({WC+TiC+TaC/NbC}–Co) cemented carbide, International Journal of Advanced Manufacturing Technology 43 (2009) 1177-1188.

DOI: 10.1007/s00170-008-1796-7

[8] S. Kuriakose, M. S. Shunmugam, Multi-objective optimization of wire-electro discharge machining process by non-dominated sorting genetic algorithm, Journal of Materials Process Technology 170 (2005) 133-141.

DOI: 10.1016/j.jmatprotec.2005.04.105

[9] D. Mandal, S. K. Pal, P. Saha, 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 Process Technology 186(1-3) (2007).

DOI: 10.1016/j.jmatprotec.2006.12.030

[10] S. W. Lin, T. Y. Tseng, S. Y. Chou, S. C. Chen, A simulated annealing based approach for simultaneous parameter optimization and feature selection of back-propagation networks, Expert Systems with Applications, 34 (2008) 1491-1499.

DOI: 10.1016/j.eswa.2007.01.014