Optimizing Milling Parameters and Halloysite Nanotube Concentration to Enhance Surface Quality and Reduce Energy Consumption

Numerous studies have focused on determining the optimal machining parameters for various steels, aiming to reduce both energy consumption and the average surface roughness (R a) of manufactured parts. In this study, a Computer Numerical Control (CNC) milling machine was used to machine AISI 4340 steel bars with varying input parameters, including spindle speed (rpm), depth of cut (in), feed rate (in/min), and HNT concentration (wt.%). A design of experiments based on a three-level Box-Behnken approach was employed to identify the optimal values for these milling parameters. Spindle load (SL) and surface roughness (Ra) of the milled steel bars were measured after each test. A Response Surface Methodology (RSM) model was developed to optimize the input variables, which indicated that the optimal HNT concentration ranged from 0.11 to 0.17 wt.%. The regression models for Ra and SL demonstrated determination coefficients (R²) of 61.65% and 81.64%, respectively. The optimal values were a spindle speed of 920 rpm, a depth of cut of 0.02 in, a feed rate of 10.5 in/min, and an HNT concentration of 0.12 wt.%. The predicted values were 682 nm for Ra and 1.5 kW for SL, while confirmatory experiments resulted in R a and SL values of 764 nm and 1.6 kW, respectively. These findings show that optimizing machining parameters, combined with the addition of HNTs to cutting fluids, can enhance the surface roughness of milled parts while reducing energy consumption. This optimization provides significant benefits, including reduced production costs, improved part quality, and a lower carbon footprint in the manufacturing process.