Abstract
This research investigates the machinability of AISI 02 tool steel under various lubrication conditions, focusing on the application of Minimum Quantity Lubrication (MQL) and an innovative Cupric oxide (CuO)-based nanofluid. A temperature and tool wear investigation were undertaken for machining environment. A comprehensive experimental setup, utilizing L36 Taguchi-based orthogonal arrays to conduct trials under dry, MQL, and NMQL (Nanofluid MQL) conditions. The study meticulously examines the impact of four principal machining parameters: cutting speed, feed rate, environment, and cutting depth on critical outcomes such as surface roughness, cutting force, and power consumption. Employing Response Surface Methodology (RSM), the research delineates the optimal machining conditions that enhance these parameters. Notably, the feed rate was found to significantly affect surface roughness, while both cutting depth and feed rate were instrumental in determining cutting force and power consumption. The use of Cu nanofluid with MQL substantially enhanced machining performance. The paper culminates with an exploration of cutting condition optimization through the Desirability Function (DF) and the multi objectives Manta Ray Foraging Optimizer (MOMRFO), aiming to minimize surface roughness (Ra), cutting force (Ft), and power consumption (Pc). The results indicate that both DF and MOMRFO yield highly effective optimal settings, offering substantial contributions to the domain of hard machining.