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<p>This study investigates the effect of soybean-oil-based biodegradable cutting fluids on the drilling performance of AM60 magnesium alloy under dry, wet, and Minimum Quantity Lubrication (MQL) conditions to enhance machining efficiency and support sustainable manufacturing. A hybrid decision-making framework combining Response Surface Methodology (RSM) for modeling, Spherical Fuzzy AHP for criterion weighting, and Fuzzy VIKOR for ranking was implemented. Experiments were conducted using synthetic, semi-synthetic, and biodegradable MQL, with statistical validation via ANOVA, independent T-tests, and Tukey’s HSD. Biodegradable MQL achieved the highest material removal rate (13.1&#xa0;cm³/min), lowest surface roughness (2.3&#xa0;μm), and minimum tool wear (6.8&#xa0;μm) at 3000 RPM, 0.1&#xa0;mm/min feed, and 0.75&#xa0;mm depth of cut. Significant improvement in MRR was observed compared to synthetic lubrication (<i>p</i> = 0.045), while other geometric and dimensional responses showed comparable performance. The integrated hybrid framework effectively predicted machining responses and identified optimal drilling conditions. These results highlight biodegradable MQL as a sustainable alternative to conventional lubricants, offering practical benefits for high-precision applications in aerospace and automotive sectors, while demonstrating the efficacy of the RSM–Spherical Fuzzy AHP–Fuzzy VIKOR approach for multi-criteria optimization in magnesium alloy drilling.</p>

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A hybrid RSM–Spherical Fuzzy AHP–Fuzzy VIKOR approach for optimizing drilling of AM60 magnesium alloy using biodegradable MQL

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月19日

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  • S. P. Sundar Singh Sivam,
  • Stalin Kesavan,
  • N. Manikandan,
  • P. Thejasree,
  • Bamidele Charles Olaiya

摘要

This study investigates the effect of soybean-oil-based biodegradable cutting fluids on the drilling performance of AM60 magnesium alloy under dry, wet, and Minimum Quantity Lubrication (MQL) conditions to enhance machining efficiency and support sustainable manufacturing. A hybrid decision-making framework combining Response Surface Methodology (RSM) for modeling, Spherical Fuzzy AHP for criterion weighting, and Fuzzy VIKOR for ranking was implemented. Experiments were conducted using synthetic, semi-synthetic, and biodegradable MQL, with statistical validation via ANOVA, independent T-tests, and Tukey’s HSD. Biodegradable MQL achieved the highest material removal rate (13.1 cm³/min), lowest surface roughness (2.3 μm), and minimum tool wear (6.8 μm) at 3000 RPM, 0.1 mm/min feed, and 0.75 mm depth of cut. Significant improvement in MRR was observed compared to synthetic lubrication (p = 0.045), while other geometric and dimensional responses showed comparable performance. The integrated hybrid framework effectively predicted machining responses and identified optimal drilling conditions. These results highlight biodegradable MQL as a sustainable alternative to conventional lubricants, offering practical benefits for high-precision applications in aerospace and automotive sectors, while demonstrating the efficacy of the RSM–Spherical Fuzzy AHP–Fuzzy VIKOR approach for multi-criteria optimization in magnesium alloy drilling.