Studies on Some Aspects of Multi-objective Optimization: A Case Study of Electrical Discharge Machining Process

Abstract

Electrical Discharge Machining (EDM) finds extensive application in manufacturing of dies, molds and critical parts used in the automobile and other industries. The present study investigates the effects of different electrodes, deep cryogenic treatment of tools subjected to different soaking duration and a hybrid approach of powder mixed EDM of cryogenically treated electrodes on machinability of Inconel 718 super alloy. Inconel 718 has been used as the work material owing to its extensive application in aerospace industries. A Box– Behnken design of response surface methodology (RSM) has been adopted to estimate the effect of machining parameters on the performance measures. The machining efficiency of the process is evaluated in terms of material removal rate (MRR), electrode wear ratio (EWR), surface roughness, radial overcut and white layer thickness which are function of process variables viz. open circuit voltage, discharge current, pulse-on-time, duty factor and flushing pressure. In this work, a novel multi-objective particle swarm optimization algorithm
(MOPSO) has been proposed to get the Pareto-optimal solution. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence and to improve the solution quality. To avoid subjectiveness and impreciseness in the decision making, the Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory (MDT). Finally, a thermal model based on finite element method has been proposed to predict the MRR and tool wear rate (TWR) when work piece is machined with variety of electrode materials. A coupled thermo-structural model has been also proposed to estimate the residual stresses. The numerical models were validated through experimentations. Parametric study is carried out on the proposed model to understand the influence of important process parameters on the performance measures. The study offers useful insight into controlling the machining parameters to improve the machining efficiency of the EDMed components.