Multi-response optimization of machining process parameters for powder mixed electro-discharge machining of H-11 die steel using grey relational analysis and topsis

Abstract

ABSTRACTElectro-discharge machining (EDM) is an enormously used nonconventional process for removing material in die making, aerospace, and automobile industries. It consists of limitations like poor volumetric material removal rate (MRR) and reduced surface quality. Powder mixed EDM (PMEDM) is a new development in EDM to enhance its machining capabilities. The present work investigates the effect of powder concentration (Cp), peak current (Ip), pulse on time (Ton), duty cycle (DC) and gap voltage (Vg) on MRR, tool wear rate (TWR), electrode wear ratio (EWR), and surface roughness (SR) simultaneously for H-11 die steel using SiC powder. Taguchi's L27 orthogonal array has been used to conduct the experiments. Multiobjective optimization using grey relational analysis (GRA) and technique for order of preference by similarity to ideal solution (TOPSIS) has been used to maximize the MRR and minimize the TWR, EWR, and SR and determine the optimal set of process parameters. Analysis of variance (ANOVA) has been performed to understand the significance of each process parameter. Results were verified by conducting confirmatory tests. GRA and TOPSIS exhibit an improvement of 0.1843 and 0.14308 in the preference values, respectively. Microstructure analysis has been done using scanning electron microscope (SEM) for the optimum set of parameters.