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<p>This study presents an integrated optimization framework to improve the sustainability of AZ31 magnesium alloy milling by minimizing machining vibration and surface roughness while maximizing hardness. A Taguchi L25 orthogonal array was used to design experiments across five levels of three input parameters. Empirical models were developed using stepwise regression with a 15% confidence threshold, and NSGA-II was applied to generate 18 Pareto-optimal solutions. Grey Relational Analysis (GRA) ranked the solutions, identifying the most effective trade-off: vibration = 10.3&#xa0;mm/s², roughness = 1.6&#xa0;μm, and hardness = 233.23 HV at optimized settings (X<sub>1</sub> = 1004.3&#xa0;rpm, X<sub>2</sub> = 1231.8&#xa0;mm/min, X<sub>3</sub> = 1.566&#xa0;mm). Experimental validation showed close alignment with theoretical predictions, with deviations of 8% for vibration, 2% for roughness, and 0.7% for hardness. The results confirm the robustness of the proposed method and its effectiveness in achieving sustainable surface integrity in magnesium alloy machining.</p>

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Surface integrity optimization in AZ31 milling using integrated NSGA-II and GRA methodology

  • 期刊论文

  • 发表日期：

    2025年11月6日

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  • Muhammad Atif Saeed

摘要

This study presents an integrated optimization framework to improve the sustainability of AZ31 magnesium alloy milling by minimizing machining vibration and surface roughness while maximizing hardness. A Taguchi L25 orthogonal array was used to design experiments across five levels of three input parameters. Empirical models were developed using stepwise regression with a 15% confidence threshold, and NSGA-II was applied to generate 18 Pareto-optimal solutions. Grey Relational Analysis (GRA) ranked the solutions, identifying the most effective trade-off: vibration = 10.3 mm/s², roughness = 1.6 μm, and hardness = 233.23 HV at optimized settings (X1 = 1004.3 rpm, X2 = 1231.8 mm/min, X3 = 1.566 mm). Experimental validation showed close alignment with theoretical predictions, with deviations of 8% for vibration, 2% for roughness, and 0.7% for hardness. The results confirm the robustness of the proposed method and its effectiveness in achieving sustainable surface integrity in magnesium alloy machining.