A Hybrid Optimization Approach for Material Configuration in Rigid Flange Couplings Using Taguchi Design of Experiments, Multi-Criteria Decision-Making, and Genetic Algorithm

This study proposes a hybrid optimization framework for rigid flange couplings, integrating the Taguchi Design of Experiments, Multi-Criteria Decision-Making, and Genetic Algorithm (GA) to enhance both mechanical performance and environmental sustainability. Nine material configurations, involving carbon steels and gray cast irons, were evaluated using finite element analysis under ambient air and high-pressure oil conditions. Mechanical responses-total deformation, equivalent stress, shear stress, and normal stress-were assessed alongside sustainability indicators such as carbon footprint, energy usage, recyclability, and end-of-life impact. Configurations were ranked using a Composite Sustainability-Performance Index (CSPI) via TOPSIS under variable weight scenarios (α = 0.3, 0.5, 0.7). GA refinement identified an optimal material set, improving CSPI by 6.5% over the best Taguchi solution. Results validate robust performance across environmental conditions. The methodology aligns with Industry 5.0 principles, offering a scalable and eco-conscious approach to mechanical component design.