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<p>The electrical discharge machining (EDM) process is used for electrical spark of conducting material, which is produced by heating and rapid cooling cycle process. The efficacy of copper, brass, tungsten–copper, tungsten carbide, and graphite electrode materials was evaluated. The parameters pulse intensity (A), pulse width (µs), pause gap (µs), servo speed (SS), and idle voltage (IV) of the EDM process were checked for their effect on the material removal rate (MRR), electrode wear rate (EWR), electrode undersize (EU), surface roughness (SR), and geometrical tolerances (roundness, cylindricity, and perpendicularity). The Taguchi L25 orthogonal array was used to design experiments. Multi-objective optimization by gorilla troops optimization (GTO), whale optimization algorithm (WOA), and particle swarm optimization (PSO). Recast layer and electrode wear mechanisms were checked through post-machining analysis, which was done using SEM. Choosing the right electrode material clearly affects the performance of machining, as per the results. The best overall performance was observed in the copper electrodes, showing a minimum electrode undersize of 0.0015&#xa0;mm with a surface roughness of 1.248&#xa0;µm. The tungsten-based electrodes were better for dimensional accuracy and wear resistance. Among all the optimization techniques used in the research, WOA has better solution quality and diversity than PSO and GTO. The results deliver a machine setup for optimizing high-precision EDM of Si<sub>3</sub>N<sub>4</sub>-TiN for industry applications.</p>

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Comparative Performance Evaluation of Cu, Brass, Graphite, W-Cu, and W-C Electrodes in EDM of Si3N4-TiN Composites Using Hybrid AI-Based Optimization Algorithms

  • 期刊论文

  • 发表日期：

    2026年3月4日

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  • L. Selvarajan,
  • B. Prakash,
  • C. Arun,
  • K. P. Srinivasa Perumal

摘要

The electrical discharge machining (EDM) process is used for electrical spark of conducting material, which is produced by heating and rapid cooling cycle process. The efficacy of copper, brass, tungsten–copper, tungsten carbide, and graphite electrode materials was evaluated. The parameters pulse intensity (A), pulse width (µs), pause gap (µs), servo speed (SS), and idle voltage (IV) of the EDM process were checked for their effect on the material removal rate (MRR), electrode wear rate (EWR), electrode undersize (EU), surface roughness (SR), and geometrical tolerances (roundness, cylindricity, and perpendicularity). The Taguchi L25 orthogonal array was used to design experiments. Multi-objective optimization by gorilla troops optimization (GTO), whale optimization algorithm (WOA), and particle swarm optimization (PSO). Recast layer and electrode wear mechanisms were checked through post-machining analysis, which was done using SEM. Choosing the right electrode material clearly affects the performance of machining, as per the results. The best overall performance was observed in the copper electrodes, showing a minimum electrode undersize of 0.0015 mm with a surface roughness of 1.248 µm. The tungsten-based electrodes were better for dimensional accuracy and wear resistance. Among all the optimization techniques used in the research, WOA has better solution quality and diversity than PSO and GTO. The results deliver a machine setup for optimizing high-precision EDM of Si3N4-TiN for industry applications.