Optimization of Sustainable Machining of Ti6Al4V Alloy Using Genetic Algorithm for Minimized Carbon Emissions and Machining Costs, and Maximized Energy Efficiency and Human Health Benefits

Sustainable machining operations have in recent times become meaningful and viable options due to the need for significantly reducing the abundant and indiscriminate use of cutting fluids, especially petroleum-based. In this study, sustainable machining operations were performed on Ti6Al4V alloy under dry, minimum quantity lubrication (MQL), cryogenic (Liquid Nitrogen - LN2) machining and at two different hybrid cooling/lubricating conditions. In the orthogonal turning tests performed, three different cutting speeds and a constant undeformed chip thickness were used as cutting parameters. During orthogonal machining experiment, cutting force components were measured and used to calculate the consumed cutting power which was subsequently utilized to determine the carbon emissions, energy efficiency and machining costs. A multi-objective optimization algorithm was developed and used for sustainable machining of Ti6Al4V alloy to achieve minimized carbon emissions, improved energy efficiency and human health conditions, and reduced machining costs through Non-dominated Sorting Genetic Algorithm II (NSGA-II). Cutting parameters such as cutting speed, feed, depth of cut, and the cooling/lubricating methods which significantly affect the machining process were considered in the optimization of machining operations for maximizing the machining performance with minimized carbon emissions and machining costs, and maximized energy efficiency and human health benefits.