Optimization of Electric Discharge Machining of Al/Al2O3 Metal Matrix Composites using MOPSO

Abstract

In this article, Response Surface Methodology (RSM) and Multi-objective Particle Swarm Optimization (MOPSO) were used to optimize the output response of Material Removal Rate (MRR) and Surface Roughness(SR) of die-sinking Electrical discharge machining (EDM). An aluminum based metal matrix composites, reinforced with alumina, prepared by stir casting, was used for machining on EDM by Copper (Cu) and Titanium (Ti) tool. Box- Behnken Design (BBD) approach of RSM was used to design the experiment by considering four input factors at three levels. This developed model for multi-objective optimization by MOPSO and an RSM-based multi-objective optimization was also designed for input parameters. And it was found that the MOPSO technique was easy and valuable for parametric optimization of EDM. From MOPSO, optimized input parameters for machining of AMMC using Cu tool are current 4A, Voltage 60V, pulse on-time 100 µs, and duty factor 6. From MOPSO, optimized input parameters for machining of AMMC using Ti tool are current 4.241658A, Voltage 60V, pulse on-time 100 µs, and duty factor 4. The confirmatory test found that MRR and SR decreased by 63.86 % and 53.083% for the Cu tool, respectively, for MOPSO compared to RSM optimize value.