Intuitionistic Fuzzy TOPSIS‐Based Ranking Alternatives of Machining Performance of Ultrasonic‐Assisted Turning in Fabricated Magnesium Alloys for Sustainability

This study is aimed at optimizing the machining performance of magnesium alloys in ultrasonic‐assisted turning (UAT) by enhancing surface quality, tool life, and overall efficiency while minimizing tool wear and energy consumption. Magnesium alloys were fabricated using a bottom‐pouring stir casting technique with commercial pure magnesium (99.9% purity) and alloyed with silicon (1.22%), calcium (0.54%), and zinc (0.54%). The casting process was performed at 850°C–950°C, followed by mechanical and microstructural analysis using optical microscopy and SEM. A Box–Behnken design with 17 runs and a quadratic model explores the effects of cutting speed (11–42 m/min), feed rate (0.0500–0.1500 mm/rev), and percentage intensity of ultrasonic power (PIUP, 0%–100%). Response surface methodology (RSM) was used for experimental analysis, and the intuitionistic fuzzy technique for order of preference by similarity to the ideal solution (IF‐TOPSIS) guided multicriteria decision‐making to identify optimal parameters. Run 10, with a cutting speed of 26.5 m/min, a feed rate of 0.05 mm/rev, and 100% ultrasonic power intensity, demonstrated the best results, including surface roughness of 0.5 μ m, MRR of 40 cm 3 /min, EC of 350 W, TW of 22 μ m, and dimensional accuracy of 98%. In contrast, Run 13 showed poorer performance. The study′s results, applicable to aerospace, automotive, and medical sectors, demonstrate the influence of ultrasonic parameters on machining performance and provide a novel hybrid optimization framework for improving UAT in complex manufacturing environments.