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

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.