Multi-objective optimization of machining parameters for sustainable manufacturing: Balancing energy efficiency and noise reduction

Abstract

Sustainability has become a vital objective in modern manufacturing, encompassing a careful balance between environmental conservation, economic efficiency, and the well-being of workers. While many studies in machining focus on reducing energy consumption or production cost reduction, few integrate the social dimension, particularly the reduction of harmful noise emissions that directly affect human health. A framework for optimization is presented in this paper for all three pillars of sustainability through the proper optimal selection of machining parameters. Experimental studies were conducted on milling, both surfacing and contouring, by minimizing energy consumption and noise levels by adjusting three key parameters: ‘depth of cut’, ‘cutting speed’, and ‘feed rate’. Experimental investigations were conducted for this purpose according to a structured Design of Experiments (DOE) supported by Analysis of Variance (ANOVA). Predictive models were developed, and a multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the best compromise solution was identified using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). These results demonstrate that significant reductions in energy use and noise emissions can be achieved. For AU4G, the measured responses reached up to 90 dBA and 303 W, yet the optimization reduced them to 77.57 dBA and 218.03 W. For IRON-500, the maximum recorded values were 100 dBA and 322 W for both operations, whereas the optimized settings lowered these responses to 84.8 dBA with 250.88 W in contouring, and 84.51 dBA with 276.62 W in surfacing. This approach provides a practical pathway toward truly sustainable manufacturing, where environmental, economic and human-centered objectives are achieved in an integrated way.