Abstract
Carbon fiber-reinforced polymers (CFRPs) are vulnerable to damage locally through machining operations due to an applied cutting force and high-temperature generation. Though traditional lubri-cooling medium will reduce the heat-generated damage of CFRPs, the use of synthetic fluids, however, significantly affects the environment and public health equally. Therefore, this paper aims to explore the milling performance of CFRPs in sustainable lubri-cooling mediums, i.e., dry, minimal lubrication (MQL), cryogenic-liquid nitrogen (N2(liquid)), and carbon dioxide (CO2(ice)). Furthermore, the correct choice of process parameters and lubri-cooling environments influences the cutting mechanism in any metal cutting operations. Accordingly, response surface methodology (RSM) is used to create a relationship between responses to machining inputs. In addition, two evolutionary techniques named Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Particle Swarm Optimization (PSO) were used to execute parametric optimization. Eventually, the performances of the adopted approaches are compared in this manuscript, which reveals the prediction made by NSGA-II and PSO is quite consistent with the experimental results. However, in view of convergence characteristics and computational time, the PSO is shown to surpass the NSGA-II approach. Moreover, empirical cumulative distribution-based data analysis indicates that all responses are good compatible with the function of the normal distribution. At the 95% confidence level, the established ANOVA models were found to be substantial. Furthermore, lubri-cooling medium was the most critical factor affecting the response parameters with a 49% contribution in minimizing F-r, 46% contribution in decreasing V-B, 38.89% contribution in R-a, and 50.21% contribution in reducing the T value.