Achieving sustainability by identifying the influences of cutting parameters on the carbon emissions of a milling process

Computer numerical control (CNC) machining tools emit considerable amounts of carbon owing to their energy consumption,material waste and coolant usage which can harm the environment, society, and public health. This study explores the environmental impact of CNC machining tools which focus on carbon emissions. This study investigated the influence of four machining parameters on carbon emissions for a Mytech 850VS (Vertical Spindle) CNC machine tool. Analysis of variance identified spindle speed as the most influential parameter, explaining 43.44% of the carbon emissions variance. Of the five machine learning techniques evaluated the XGBoost algorithm was found to have the highest performance in predicting carbon emissions. The Shapley plots confirmed the key role of spindle speed. Furthermore, novel metaheuristic algorithms were employed to identify the optimal combinations of cutting parameters to minimize carbon emissions. This integrated approach is a robust framework for mitigating the environmental impacts of machining processes aligned with sustainability objectives. This study’s insights into the influence of specific cutting parameters on carbon emissions can contribute to sustainability goals by reducing the environmental footprint of machining operations. By optimizing the machining parameters, manufacturers can successfully decrease the environmental impacts while enhancing the process efficiency. This study innovatively focuses on carbon emissions from CNC machine tools, highlighting spindle speed's significant role in explaining 43.44% of emissions variance. Advanced XGBoost machine learning is employed for precise prediction, complemented by Shapley plots to visually confirm spindle speed's pivotal role. Innovative metaheuristic algorithms contribute by identifying optimal cutting parameter combinations to minimize carbon emissions, forming an integrated framework that assesses and actively mitigates environmental impacts. By offering actionable insights for the manufacturers to optimize the machining processes, this study pushes forward sustainable manufacturing practices, emphasizing both efficiency gains and environmental stewardship in the CNC machining operations.