Modeling and Optimization of Power Consumption for Economic Analysis, Energy-Saving Carbon Footprint Analysis, and Sustainability Assessment in Finish Hard Turning Under Graphene Nanoparticle–Assisted Minimum Quantity Lubrication

The present work addresses the issue on power consumption in finish hard turning of die steel under nanofluid-assisted minimum quantity lubrication condition. This study also aims to assess the propitious role of minimum quantity lubrication using graphene nanoparticle-enriched radiator green coolant-based nano-cutting fluid for machinability improvement of hardened steel. The hard turning trials are performed based on design of experiments by considering the geometrical parameters (insert’s nose radius) and machining parameters (cutting speed, axial feed, depth of cut). Combined approach of central composite design—analysis of variance, desirability function analysis, and response surface methodology—have been subsequently employed for analysis, predictive modeling, and optimization of machining power consumption. With a motivational philosophy of “Go Green-Think Green-Act Green”, the work also deals with energy-saving carbon footprint analysis, economic analysis, and sustainability assessment under environmental-friendly nanofluid-assisted minimum quantity lubrication condition. Results showed that machining with nanofluid-minimum quantity lubrication provided an effective cooling-lubrication strategy, safer and cleaner production, environmental friendliness, and assisted to improve sustainability.