A particle swarm approach for multi-objective optimization of electrical discharge machining process

Chinmaya P. Mohanty (), Siba Sankar Mahapatra () and Manas Ranjan Singh ()
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Chinmaya P. Mohanty: National Institute of Technology Rourkela
Siba Sankar Mahapatra: National Institute of Technology Rourkela
Manas Ranjan Singh: National Institute of Technology Rourkela

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 6, No 3, 1190 pages

Abstract: Abstract This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.

Keywords: Electrical discharge machining; Maximum deviation theory; Multi-objective particle swarm optimization; Radial overcut; Surface roughness (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-014-0942-3

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