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<p>This study presents a comprehensive experimental and decision-analytic framework for optimizing the drilling performance of Aluminum Alloy 5083, a material extensively used in marine and aerospace structures. Precision drilling of AA5083 remains challenging due to burr formation, built-up edge (BUE), and geometric deviations. To systematically address these issues, a comprehensive full-factorial Design of Experiments (3 × 4 × 4) structure was employed, necessitating 48 unique experimental runs to evaluate the influence of varying the primary input factors—drill diameter (6, 8, and 10&#xa0;mm), spindle speed (3000, 3500, 4000, and 4500&#xa0;rpm), and feed rate (150, 200, 250, and 300&#xa0;mm/min)—on drilling performance. The captured output responses comprise top burr thickness, bottom burr thickness, circularity error, cylindricity error, and chip thickness. Chip morphology was characterized via Scanning Electron Microscopy (SEM) to elucidate the deformation mechanisms influencing hole quality. Analysis of Variance (ANOVA) revealed that drill diameter exerted the most significant influence on top burr formation (26.06%) and chip thickness (25.38%), whereas feed rate predominantly governed cylindricity error (34.95%). To identify the optimal parameter combination, an integrated Multi-Criteria Decision-Making (MCDM) framework—incorporating CRITIC for objective weighting with TOPSIS and WASPAS for ranking—was implemented. The optimal condition—specifically an 8&#xa0;mm drill diameter, 4500&#xa0;rpm spindle speed, and 150&#xa0;mm/min feed rate—yielded superior overall performance, achieving minimal top and bottom burr thicknesses of 0.01&#xa0;mm and 0.02&#xa0;mm, respectively, while maximizing geometric precision. This work bridges statistical modeling, chip-level micromechanics, and MCDM-based optimization, offering a robust pathway for high-precision machining of AA5083 components which has not been collectively addressed in extant literature.</p>

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Integrated statistical–micromechanical framework for optimizing drilling of aluminum alloy 5083 using CRITIC-TOPSIS-WASPAS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Rakesh Sarker,
  • Mst. Nazma Sultana,
  • Abdullah Al Siam

摘要

This study presents a comprehensive experimental and decision-analytic framework for optimizing the drilling performance of Aluminum Alloy 5083, a material extensively used in marine and aerospace structures. Precision drilling of AA5083 remains challenging due to burr formation, built-up edge (BUE), and geometric deviations. To systematically address these issues, a comprehensive full-factorial Design of Experiments (3 × 4 × 4) structure was employed, necessitating 48 unique experimental runs to evaluate the influence of varying the primary input factors—drill diameter (6, 8, and 10 mm), spindle speed (3000, 3500, 4000, and 4500 rpm), and feed rate (150, 200, 250, and 300 mm/min)—on drilling performance. The captured output responses comprise top burr thickness, bottom burr thickness, circularity error, cylindricity error, and chip thickness. Chip morphology was characterized via Scanning Electron Microscopy (SEM) to elucidate the deformation mechanisms influencing hole quality. Analysis of Variance (ANOVA) revealed that drill diameter exerted the most significant influence on top burr formation (26.06%) and chip thickness (25.38%), whereas feed rate predominantly governed cylindricity error (34.95%). To identify the optimal parameter combination, an integrated Multi-Criteria Decision-Making (MCDM) framework—incorporating CRITIC for objective weighting with TOPSIS and WASPAS for ranking—was implemented. The optimal condition—specifically an 8 mm drill diameter, 4500 rpm spindle speed, and 150 mm/min feed rate—yielded superior overall performance, achieving minimal top and bottom burr thicknesses of 0.01 mm and 0.02 mm, respectively, while maximizing geometric precision. This work bridges statistical modeling, chip-level micromechanics, and MCDM-based optimization, offering a robust pathway for high-precision machining of AA5083 components which has not been collectively addressed in extant literature.