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<p>This study investigates the optimization of Electric Discharge Machining (EDM) parameters for Inconel alloy and the effect of various tool geometry viz., circular, square, and hexagonal copper tools on the output responses. The research aims to determine the optimal input settings (peak current, pulse-on time, pulse-off time) and tool geometry (circular, square, and hexagonal) for maximizing Material Removal Rate (MRR) and minimizing Tool Wear Rate (TWR) and Surface Roughness (SR). Utilizing Taguchi and ANOVA methods with an L9 Orthogonal Array, the study identified optimal parameter combinations for each response. Furthermore, Multi-Attribute Decision Making (MADM) techniques namely CoCoSo, MABAC, and CODAS were employed to predict the optimal tool geometry, revealing the square profile as superior for enhancing machining characteristics. The results indicated that the square shaped tool profile was identified as the optimal tool response with input settings (35A, 20&#xa0;µs, 9&#xa0;µs)for machining Inconel alloy. Also, the pulse on time contributes 38.19% for CoCoSo-ANOVA, 51.43%for MABAC-ANOVA, and 22.65% for CODAS-ANOVA. The findings provide valuable insights for improving EDM precision machining of hard-to-machine materials.</p>

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Enhancing EDM machining of Inconel: integrating Taguchi, ANOVA, and CoCoSo, MABAC, CODAS for parameter and tool optimization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Jana Sai Ram,
  • Jeavudeen Shiek,
  • M. Abdur Rahman

摘要

This study investigates the optimization of Electric Discharge Machining (EDM) parameters for Inconel alloy and the effect of various tool geometry viz., circular, square, and hexagonal copper tools on the output responses. The research aims to determine the optimal input settings (peak current, pulse-on time, pulse-off time) and tool geometry (circular, square, and hexagonal) for maximizing Material Removal Rate (MRR) and minimizing Tool Wear Rate (TWR) and Surface Roughness (SR). Utilizing Taguchi and ANOVA methods with an L9 Orthogonal Array, the study identified optimal parameter combinations for each response. Furthermore, Multi-Attribute Decision Making (MADM) techniques namely CoCoSo, MABAC, and CODAS were employed to predict the optimal tool geometry, revealing the square profile as superior for enhancing machining characteristics. The results indicated that the square shaped tool profile was identified as the optimal tool response with input settings (35A, 20 µs, 9 µs)for machining Inconel alloy. Also, the pulse on time contributes 38.19% for CoCoSo-ANOVA, 51.43%for MABAC-ANOVA, and 22.65% for CODAS-ANOVA. The findings provide valuable insights for improving EDM precision machining of hard-to-machine materials.