▎ 摘　　要

The removal of materials occurs due to interface temperature generation and plastic deformation during the machining process. The temperature of the cutting zone during the machining of carbon fiber-reinforced polymer (CFRP) is a critical factor for cutting quality and productivity indices. As the temperature rises, degradation of the resin will begin inside the workpiece or surface layer of the resin. A Silicon (Si) substrate-based reduced Graphene Oxide nanoflakes (rGO) reinforced Carbon Fabric (CF) based polymer (rGO/CF) nanocomposites are machined using a Solid Carbide end-milling cutter (with Si-based coating) under a variety of process constraints. This research paper examines the influence of cutting zone temperature on tool-workpiece while milling of developed nanocomposite at different loadings of rGO nanoflakes. An infrared thermal imager captures the cutting zone temperature (Tc) during the Milling process. According to Taguchi Orthogonal Array (OA), machining laminated polymer samples was conducted. The correlation between cutting temperature and other machining constraints was investigated. The Milling parameters such as nanofiller wt%, feed, cutting speed, and depth of cut were optimized using Genetic Algorithms (GA). The findings reveal that the optimal parameters were obtained as rGO wt% - 1.5, spindle speed - 500 rpm, feed rate - 80 mm/min, and depth of cut - 1 mm, while the optimum cutting temperature is found to be 35.93 degrees C. In addition, these results demonstrated that the depth of cut and rGO weight% of rGO/CF polymer nanocomposites are the most important parameters influencing the machining characteristics. The confirmatory test signifies the improved predictive efficacy of the GA module with a 9.09% error. This states the optimum sets for cutting temperature with a desirable machining response. The proposed results could be endorsed to the manufacturing sector for quality and productivity control under varying conditions.