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<p>This study develops a hybrid multi-objective optimization framework to enhance the drilling performance of Wire Arc Additive Manufacturing (WAAM)-fabricated aluminum components, addressing the need for precision post-processing of additively manufactured parts. Response Surface Methodology (RSM) is employed to model the influence of drilling parameters—cutting speed, feed rate, tool point angle, and coolant composition—on surface roughness (Ra), material removal rate (MRR), hole diameter accuracy (HDA), and energy consumption (EC). Intuitionistic Fuzzy MARCOS (IF-MARCOS) is integrated to enable uncertainty-aware multi-criteria decision-making and global ranking of alternatives. The optimal conditions were identified as a cutting speed of 50&#xa0;m/min, a feed rate of 0.3&#xa0;mm/rev, a tool point angle of 135 °, and an oil-based coolant. To clarify the improvement basis, the worst-ranked condition, consisting of 150&#xa0;m/min cutting speed, 0.1&#xa0;mm/rev feed rate, 90° tool point angle, and water-based coolant, was used as the baseline/reference condition. Compared with this baseline, the optimal condition reduced Ra from 1.6667&#xa0;μm to 1.2334&#xa0;μm (26.0% reduction) and EC from 280&#xa0;W to 120&#xa0;W (57.14% reduction). In addition, MRR increased from 70 to 72&#xa0;cm<sup>3</sup>/min, giving a 2.86% improvement, while HDA improved from 90% to 96%, corresponding to a 6.67% improvement. The results demonstrate that a moderate feed rate, stable tool geometry, and lubrication-dominant cooling improve machining stability, surface integrity, dimensional accuracy, and energy efficiency. The proposed RSM–IF-MARCOS framework provides a robust, uncertainty-aware optimization approach, offering both methodological advancement and practical guidance for the sustainable machining of WAAM-fabricated aluminum components.</p>

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An experimental study of multi-response optimization for drilling performance in WAAM-fabricated aluminum using RSM and intuitionistic fuzzy MARCOS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • S. P. Sundar Singh Sivam,
  • Stalin Kesavan,
  • A. Johnson Santhosh

摘要

This study develops a hybrid multi-objective optimization framework to enhance the drilling performance of Wire Arc Additive Manufacturing (WAAM)-fabricated aluminum components, addressing the need for precision post-processing of additively manufactured parts. Response Surface Methodology (RSM) is employed to model the influence of drilling parameters—cutting speed, feed rate, tool point angle, and coolant composition—on surface roughness (Ra), material removal rate (MRR), hole diameter accuracy (HDA), and energy consumption (EC). Intuitionistic Fuzzy MARCOS (IF-MARCOS) is integrated to enable uncertainty-aware multi-criteria decision-making and global ranking of alternatives. The optimal conditions were identified as a cutting speed of 50 m/min, a feed rate of 0.3 mm/rev, a tool point angle of 135 °, and an oil-based coolant. To clarify the improvement basis, the worst-ranked condition, consisting of 150 m/min cutting speed, 0.1 mm/rev feed rate, 90° tool point angle, and water-based coolant, was used as the baseline/reference condition. Compared with this baseline, the optimal condition reduced Ra from 1.6667 μm to 1.2334 μm (26.0% reduction) and EC from 280 W to 120 W (57.14% reduction). In addition, MRR increased from 70 to 72 cm3/min, giving a 2.86% improvement, while HDA improved from 90% to 96%, corresponding to a 6.67% improvement. The results demonstrate that a moderate feed rate, stable tool geometry, and lubrication-dominant cooling improve machining stability, surface integrity, dimensional accuracy, and energy efficiency. The proposed RSM–IF-MARCOS framework provides a robust, uncertainty-aware optimization approach, offering both methodological advancement and practical guidance for the sustainable machining of WAAM-fabricated aluminum components.