Taguchi and multi-objective genetic algorithm-based optimization during ECDM of SiCp/glass fibers reinforced PMCs

Abstract

The acceptance of electrically nonconductive fibrous materials has been increased over the past decade in industrial applications due to their better strength to weight ratio and electrically nonconductive nature. But precise machining of these types of materials has always been a challenging task for the research fraternity. The precise machining of these materials refers to reduced overcut along with significant material removal rate (MRR). In such perspective, multi-objective genetic algorithm (MOGA) evident to be suitable optimization technique for prediction and process selection in manufacturing industries. The present paper deals with multi-objective optimization of electrochemical discharge machining (ECDM) process parameters during machining of SiC_pand glass fibers reinforced polymer matrix composites (PMCs) using MOGA. The experiments have been designed as per Taguchi’s design of experiments using L₁₆ orthogonal array. Electrolyte concentration, inter-electrode gap, duty factor, and voltage have been used as process parameters whereas MRR and overcut have been observed as output quality characteristics (OQCs). The obtained experimental results have been optimized by multi-response optimization technique MOGA to attain high MRR with minimum possible overcut. The quality of machined holes has been analyzed using scanning electron microscope (SEM). The analysis reveals that result optimized through MOGA produces enhanced output quality characteristics.