Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

This study presents an integrated framework that combines multi-objective optimization (MOO) with multiple-criteria decision-making (MCDM) to enhance sustainable machining processes. Developed through extensive literature review and experimental validation, the framework incorporates sustainability indicators and employs MOO to identify optimal machining conditions. MCDM techniques are utilized to select the best solution, considering economic feasibility, environmental impact, and social well-being. This study focused on optimizing machining parameters-spindle speed (SS), feed rate (FR), and depth of cut (DOC)-for face milling of AISI 1045 steel by analyzing the cutting force, surface roughness, material removal rate, power consumption, specific cutting energy, tool vibrations, and acoustics. Using the Taguchi design of experiments, 27 experimental trials were conducted, followed by Minitab analysis including Pareto charting, ANOVA, and MOO. The Taguchi-Grey relational analysis (GRA) identified the best solution from MOO's results, normalizing responses for quality and decision-making. Sustainability metrics were factored into the selection of the best solution based on GRG values and an Analytical Hierarchy Process (AHP) survey. Pareto analysis revealed that the FR significantly affects the surface roughness, power consumption, material removal rate, and cutting force, while DOC influences the material removal rate, cutting force, and specific cutting energy. SS mainly impacts tool vibrations and acoustic emissions. ANOVA results indicated high R-squared values for all responses, showing strong predictive accuracy. The recommended optimal parameters are a DOC of 0.2 mm, an FR of 0.2 mm/rev, and an SS of 2000 rpm, ensuring sustainable machining practices. This framework offers valuable guidance for sustainable manufacturing, providing significant economic and environmental benefits.