A Comprehensive Review on Optimization Techniques for Electrical Discharge Machining (EDM)
Keywords:
Genetic algorithm (GA), Material removal rate (MRR), Surface roughness (SR), Tool wear rate (TWR), Wire electrical discharge machining (WEDM)Abstract
This study presents a comprehensive review of electrical discharge machining (EDM), a widely used non-conventional machining process for electrically conductive materials. The review covers working principles, types of EDM, process parameters, performance measures, optimization techniques, modelling approaches, industrial applications, hybrid developments, and sustainability aspects. Recent research trends, including powder mixed EDM, dry EDM, nano-EDM, AI-based optimization, and Industry 4.0 integration, are discussed. The study includes detailed tables summarizing the effects of parameters and EDM variants. Future research directions are also highlighted. Various EDM variants, such as die-sinking EDM, wire EDM, micro-EDM, powder mixed EDM, Dry EDM, and Hybrid EDM, are also examined. Optimization techniques, including Taguchi method, response surface methodology, artificial neural networks, genetic algorithm, and grey relational analysis, are reviewed and compared based on their effectiveness. The industrial applications of EDM in aerospace, die-mold, medical, and automotive sectors are also highlighted. Future research should focus on smart EDM systems, nano-scale machining, and full integration with Industry 4.0 technologies.
