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<p>This study aims to optimize the high-speed machining process of WAAM-fabricated aluminum, focusing on enhancing machinability indicators while aligning with sustainable manufacturing practices. A novel hybrid optimization framework combining Response Surface Methodology (RSM), Fuzzy Logarithmic Methodology of Additive Weights (LMAW), and Fuzzy WASPAS was developed to optimize parameters such as surface finish, material removal rate, cutting force, and power consumption. The optimization model identified optimal machining parameters (8000 RPM cutting speed, 0.1&#xa0;mm/tooth feed rate, 1&#xa0;mm depth of cut, and a four-flute tool), achieving a surface roughness (Ra) of 0.35 microns, material removal rate (MRR) of 38.4&#xa0;mm³/min, cutting force (Fc) of 114&#xa0;N, and power consumption (Pc) of 312&#xa0;W. Validation tests confirmed the accuracy of the predicted results. The study focuses on specific machining conditions and tool configurations. Future research could explore different tool materials and machining environments to further enhance the model’s applicability. The findings provide insights that can help industry practitioners optimize machining processes, improve efficiency, and contribute to sustainable manufacturing aligned with SDGs. This research introduces a novel hybrid optimization methodology combining RSM, Fuzzy LMAW, and Fuzzy WASPAS, contributing to both scientific understanding and industrial practices in sustainable machining.</p>

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Development of a novel integrated RSM-Fuzzy MCDA framework for high-speed machinability assessment of WAAM-Fabricated aluminium

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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

摘要

This study aims to optimize the high-speed machining process of WAAM-fabricated aluminum, focusing on enhancing machinability indicators while aligning with sustainable manufacturing practices. A novel hybrid optimization framework combining Response Surface Methodology (RSM), Fuzzy Logarithmic Methodology of Additive Weights (LMAW), and Fuzzy WASPAS was developed to optimize parameters such as surface finish, material removal rate, cutting force, and power consumption. The optimization model identified optimal machining parameters (8000 RPM cutting speed, 0.1 mm/tooth feed rate, 1 mm depth of cut, and a four-flute tool), achieving a surface roughness (Ra) of 0.35 microns, material removal rate (MRR) of 38.4 mm³/min, cutting force (Fc) of 114 N, and power consumption (Pc) of 312 W. Validation tests confirmed the accuracy of the predicted results. The study focuses on specific machining conditions and tool configurations. Future research could explore different tool materials and machining environments to further enhance the model’s applicability. The findings provide insights that can help industry practitioners optimize machining processes, improve efficiency, and contribute to sustainable manufacturing aligned with SDGs. This research introduces a novel hybrid optimization methodology combining RSM, Fuzzy LMAW, and Fuzzy WASPAS, contributing to both scientific understanding and industrial practices in sustainable machining.