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This study models and optimizes the material removal rate (MRR) in UV-EDM of Hardox 500 using response surface methodology (RSM). Full-quadratic models with interaction terms and AIC-based stepwise variants are fitted to experimental data (factors: discharge amplitude A, Ton, Toff, IP, SV). Model adequacy is evaluated via ANOVA, partial ηₚ2, multicollinearity diagnostics (VIF), residual checks, and predictive metrics (PRESS and cross-validated Pred-R2). Main-effect trends and pairwise interactions are interpreted to quantify parameter influence on MRR. A single-objective optimization framework (maximize MRR within observed factor bounds) is implemented on the selected RSM surface and benchmarked against the best observed condition. The workflow yields a robust and interpretable predictive model, highlights the dominant contributors to MRR, and provides a practically actionable setting for maximizing throughput in UV-EDM of Hardox 500. The proposed pipeline—full/stepwise RSM, diagnostics, and CV-verified optimization—can be reused for other UV-EDM materials and multi-criteria extensions.

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Parameter Influence and RSM-Based Optimization of Material Removal Rate for Hardox 500 under UV-EDM

  • 会议论文

  • 发表日期：

    2026年

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  • Tran Huu Danh,
  • Nguyen Cong Danh,
  • Muthuramalingam Thangaraj,
  • Mai Tat Loi,
  • Dinh Van Thanh,
  • Vu Ngoc Pi,
  • Nguyen Manh Cuong

摘要

This study models and optimizes the material removal rate (MRR) in UV-EDM of Hardox 500 using response surface methodology (RSM). Full-quadratic models with interaction terms and AIC-based stepwise variants are fitted to experimental data (factors: discharge amplitude A, Ton, Toff, IP, SV). Model adequacy is evaluated via ANOVA, partial ηₚ2, multicollinearity diagnostics (VIF), residual checks, and predictive metrics (PRESS and cross-validated Pred-R2). Main-effect trends and pairwise interactions are interpreted to quantify parameter influence on MRR. A single-objective optimization framework (maximize MRR within observed factor bounds) is implemented on the selected RSM surface and benchmarked against the best observed condition. The workflow yields a robust and interpretable predictive model, highlights the dominant contributors to MRR, and provides a practically actionable setting for maximizing throughput in UV-EDM of Hardox 500. The proposed pipeline—full/stepwise RSM, diagnostics, and CV-verified optimization—can be reused for other UV-EDM materials and multi-criteria extensions.