Balancing Surface Quality and Energy Demand in Machining of Medium-Carbon Steel: A Pareto-Based Optimization Study

Machining performance is strongly influenced by the selection of cutting parameters, as cutting speed, feed rate, and depth of cut directly affect both surface quality and energy consumption during material removal. In practical manufacturing environments, improving surface finish often requires operating conditions that increase power demand, while efforts to reduce energy usage may compromise surface integrity. This inherent conflict transforms machining parameter selection into a multi-objective decision problem rather than a single-criterion optimization task. To systematically investigate this tradeoff, the present study develops a simulation-based multi-objective optimization framework that simultaneously minimizes surface roughness and cutting power. Literature-validated empirical models are employed to represent the influence of cutting parameters on process responses, enabling structured performance evaluation without the need for additional experimental trials. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to generate Pareto-optimal machining conditions that capture the compromise between surface quality and energy efficiency. The obtained Pareto front clearly illustrates the opposing behavior of the two objectives: improved surface finish is generally associated with higher power consumption, whereas reduced energy demand corresponds to moderately increased surface roughness. By explicitly visualizing and analysing these trade-offs, the proposed framework functions as a decision-support tool, allowing manufacturing engineers to select machining parameters based on specific production priorities. The methodology provides a transparent and adaptable approach for balancing quality and energy considerations in machining operations.