A hybrid two stage Taguchi-regression-NSGA II-AHP-GRA, multi-objective optimization framework for sustainable straight slot milling of AZ31 magnesium alloy

This study proposes a novel two-stage optimization framework to enhance the economic and environmental sustainability of AZ31 magnesium alloy milling. By integrating Taguchi's method, stepwise regression, NSGA-II, AHP, and Grey Relational Analysis (GRA), the framework systematically optimizes machining parameters. The first stage independently optimizes economic and environmental objectives using NSGA-II, while the second stage consolidates them through AHP. GRA ranks Pareto-optimal solutions, ensuring practical implementation. Sensitivity analysis highlights key influencing factors, and experimental validation demonstrates a 61.3 % improvement in economic sustainability and a 33 % enhancement in environmental sustainability. A case study further confirms the framework's effectiveness, achieving a 6.4 % increase in economic feasibility and a 52.3 % reduction in environmental impact. The framework's scalability makes it a valuable tool for sustainable manufacturing across diverse engineering applications..