Intelligent modeling and multi-objective optimization of powder mixed electrical discharge diamond grinding of MMC

Abstract

Metal matrix composites (MMCs) poses machining challenges by conventional methods due to its superior mechanical properties. Advanced machining processes (AMPs) are considered to be efficient to machine these MMCs. Electrical discharge machining (EDM) is one of such AMP which is most popular in the current industrial paradigm to machine these advanced materials. But, EDM also inherit the limitations such as low material removal rate (MRR) and high tool wear rate (TWR). Powder mixed EDM (PMEDM) process may help to enhance the productivity of EDM in terms of MRR and TWR. In the present research, the machining performances of copper-iron-graphite MMC using PMEDM have been investigated. Response surface models (RSMs) for MRR and TWR have been developed. Further, a hybrid approach of grey relational analysis, RSM and genetic algorithm has been used for multi-objective optimization of MRR and TWR.