Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability

Abstract

Electrical Discharge Machining (EDM) has experienced significant advancements in modeling, optimization, and sustainability, reflecting the growing demand for intelligent and environmentally friendly manufacturing practices. Advanced modeling techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven simulations, have improved the accuracy of process predictions, enabling real-time adjustments and precise control of machining parameters. Optimization approaches, including machine learning-based algorithms, multi-objective optimization, and hybrid methods, have enhanced key performance indicators, such as material removal rate (MRR), surface quality, and tool wear, thereby increasing process efficiency and reducing machining time. The incorporation of AI and machine learning is crucial for addressing EDM challenges and driving future development. Moreover, sustainability has become a key area of emphasis in EDM research, with recent advancements focusing on energy-saving discharge techniques, eco-friendly dielectric fluids, and sustainable waste management practices. The progress made is in line with the Sustainable Development Goals (SDGs), ensuring that EDM contributes to advanced manufacturing while minimizing environmental impact. Future studies should focus on the effects of AI-driven approaches on environmentally friendly EDM practices by prioritizing green dielectrics, energy-efficient machining, and waste reduction strategies. This review highlights the interconnected roles of modeling, optimization, and sustainability in advancing EDM and outlines key research directions to address the remaining challenges.