Optimizing economic and environmental objectives in sustainable machining processes

This work presents a two-stage optimization approach designed to improve the sustainability of machining processes by integrating multi-objective optimization, multi-criteria decision-making, and experimental design approaches. The proposed work uses the non-dominated sorting genetic algorithm-II (NSGA-II) to balance economic and environmental objectives. A case study on the machining of EN8 steel demonstrated significant improvements after applying the proposed framework, achieving a 65.9 % increase in the environmental sustainability assessment (EnSA) and a 28.8 % improvement in the economic sustainability assessment (ESA). Key performance indicators, including total energy consumption of machine (TECM), temperature (T), and surface roughness (Ra), improved by 24.5 %, 25 %, and 48.8 %, respectively, though trade-offs between energy efficiency (EE) and process flexibility (PF). Sensitivity analysis highlighted that axial depth ('X₄') was the most influential factor, accounting for 50 % of ESA variation and 86 % of EnSA variation. This framework offers a practical approach to optimizing machining parameters, contributing to sustainable manufacturing practices. Future research could extend its application to other manufacturing processes and incorporate additional sustainability dimensions, such as social impacts, to further promote overall sustainability in the industry.