Sustainable and cost-efficient machining of ZE41 magnesium alloy through optimization with Intuitionistic Fuzzy TOPSIS.

This study addresses the pressing need to reduce production costs and energy consumption in machining lightweight magnesium alloys, a challenge that has gained significance with the growing emphasis on sustainable manufacturing. Although ZE41 magnesium alloy is widely used in aerospace and automotive applications due to its excellent strength-to-weight ratio, limited research has explored integrated cost-energy optimization in its machining. To bridge this gap, a novel Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS) was employed to evaluate and optimize four critical machining parameters-cutting speed, feed per tooth, depth of cut, and tool diameter-each varied at three levels. Experimental trials were conducted using industrially relevant cost and energy data. The optimization framework was further validated using ANOVA, sensitivity analysis, confirmation experiments, and product quality checks, including dimensional accuracy, surface finish, and mechanical integrity. The optimized parameter set (cutting speed 550 m/min, feed 0.6125 mm/tooth, depth of cut 2.5 mm, and tool diameter 25 mm) yielded a production cost of 50.04 INR/part and energy consumption of 0.0000045 kWh/part. Compared with the least efficient condition, these values represent improvements of 30.7% in cost and 25% in energy efficiency. Tool diameter and feed per tooth were identified as the most influential factors, with prediction-experiment deviations remaining within ± 5%. This work demonstrates, for the first time, the effectiveness of IF-TOPSIS in simultaneously addressing economic and environmental objectives in machining. The proposed framework offers a validated pathway for sustainable and cost-effective ZE41 machining, with potential for broader industrial adoption.