Predictive Modeling of Surface Roughness, Tool Wear, and Cutting Temperature in High-Speed Turning under Sustainable Machining Environments

<div>The utilization of Inconel 718 is increasing daily in stringent operating conditions such as aircraft engine parts, space vehicles, chemical tanks, and the like due to its physical properties such as maintaining strength and corrosion resistance at higher temperature conditions. Besides, Inconel 718 is one of the difficult materials for machining because of maintaining its strength at elevated temperature, which generates higher cutting force leading to observed multiple tool wear mechanisms that affect the surface quality; lower thermal conductivity of materials produces high temperature generation that impacts the tool performance by reducing tool life. In addition, the presence of carbides and high hardness of IN 718 affects the machining performance. Therefore, in this view, this article describes the effect of cutting environments and machining parameters on the machining of Inconel 718 and optimizes the cutting conditions for sustainable machining. Three input parameters namely cutting speed, feed rate, and depth of cut as well as three cutting environments such as flood cooling, MQL (minimum quantity lubrication), and NMQL (nano minimum quantity lubrication) were considered for the experimentation. Experimental runs were designed based on the Taguchi method, which had a total of 27 runs performed on the CNC turning. TiAlN-coated triangular-shaped cutting inserts were used for all experimental runs. This research study addresses three output parameters namely surface roughness, tool wear, and cutting temperature. Finally, the cutting condition was optimized by using the Taguchi method and predicting the relationship between the input parameters and the output parameter using the RSM method. Experimental results observed that the NMQL cutting environment shows better machining performance than the MQL and flood cooling due to the presence of nanoparticles in the base fluid, which act as heat carriers. Whereas minimal surface roughness 0.4 μm and lower cutting temperature (85°C) were observed at low cutting speed, feed rate, and depth of cut (78.54 mm/min, 0.1 mm/rev, 0.1 mm) combination and minimum tool wear was found in moderate cutting speed conditions (117.81 mm/min, 0.1 mm/rev, 0.1 mm). Whereas highest cutting temperature and tool wear such as 130°C and 0.3 mm, respectively, observed in flood cooling environment at the cutting speed (157.08 mm/min, 0.3 mm/rev, 0.3 mm). Using the Taguchi method optimum condition was found in the NMQL cutting environment, at the combination of cutting speed 78.54 m/min, feed 0.1 mm/rev, and depth of cut 0.1 mm. From the ANOVA results, develop the predictive model whose results match with the experimental result. Finally, regression model was developed between the response variable and input parameters.</div>