<p>The study investigates the effects of process parameters for Electrical Discharge Machining (EDM) on the machining performance of hard materials, such as ceramic matrix composites (CMC). For difficult-to-machine materials (Silicon Nitride-Titanium Nitride), EDM provides benefits including low machining force and affordable equipment. To improve control parameters, like discharge current, pulse-on (Pon) and pulse-off times, and dielectric pressure, experiments were carried out utilizing a complete factorial method and Taguchi L<sub>25</sub> Orthogonal Array. Significant results were examined, including geometric tolerances, surface roughness, electrode wear rate (EWR), and material removal rate (MRR). The most important components were found to be current and pulse-on time after correlations between input parameters and output features were established using Response Surface Methodology (RSM). Optimal Pareto solutions were found using Genetic Algorithm-based Particle Swarm Optimization (GAPSO), which was validated by confirmation studies. The findings showed notable gains in machining efficiency, such as shorter machining times, higher rates of MRR (0.0118&#xa0;g/min), decreased rates of EWR (0.001&#xa0;g/min), and better geometric tolerances to roughness (3.108&#xa0;μm). The outcome of a global optimization that factored in all seven replies is also shown According to these results, EDM may be used for intricate ceramic parts with ideal process parameters, advancing contemporary manufacturing sectors.</p>

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Genetic algorithm-Based particle swarm optimization of RSM surrogate models for multi-responses of EDM machining parameters of silicon nitride-titanium nitride composite

  • Mohammed Yunus,
  • Rami Alfattani,
  • Turki Alamro

摘要

The study investigates the effects of process parameters for Electrical Discharge Machining (EDM) on the machining performance of hard materials, such as ceramic matrix composites (CMC). For difficult-to-machine materials (Silicon Nitride-Titanium Nitride), EDM provides benefits including low machining force and affordable equipment. To improve control parameters, like discharge current, pulse-on (Pon) and pulse-off times, and dielectric pressure, experiments were carried out utilizing a complete factorial method and Taguchi L25 Orthogonal Array. Significant results were examined, including geometric tolerances, surface roughness, electrode wear rate (EWR), and material removal rate (MRR). The most important components were found to be current and pulse-on time after correlations between input parameters and output features were established using Response Surface Methodology (RSM). Optimal Pareto solutions were found using Genetic Algorithm-based Particle Swarm Optimization (GAPSO), which was validated by confirmation studies. The findings showed notable gains in machining efficiency, such as shorter machining times, higher rates of MRR (0.0118 g/min), decreased rates of EWR (0.001 g/min), and better geometric tolerances to roughness (3.108 μm). The outcome of a global optimization that factored in all seven replies is also shown According to these results, EDM may be used for intricate ceramic parts with ideal process parameters, advancing contemporary manufacturing sectors.