Machinability Assessment and Multi-Objective Optimization of Graphene Nanoplatelets-Reinforced Aluminum Matrix Composite in Dry CNC Turning

This study examined machinability aspects in terms of the main cutting force and surface roughness in dry CNC turning of graphene-reinforced composite aluminum with 0.5 wt%. The cutting speed, feed rate and depth of cut influence were investigated in regard to the responses of main cutting force Fz and surface roughness Ra when turning high-purity aluminum (Al 96.83%) and graphene-reinforced aluminum with 0.5% graphene nanoplatelets for comparative analysis. A customized central composite design of the experiments with nine runs was established, and the results were assessed through analysis of variance and response surface regression. Full quadratic prediction models were generated based on the experimental results and they were examined for their validity and efficiency in predicting the response of the main cutting force and surface roughness of the machined graphene-reinforced composite aluminum. The NSGA-II algorithm was finally applied for simultaneously minimizing the main cutting force and surface roughness by providing a well-spread Pareto front of non-dominated solutions. The results indicated that the feed rate was the dominant parameter affecting both objectives, namely the main cutting force and surface roughness, while the NSGA-II algorithm was capable of delivering advantageous solutions for enhancing machinability with less than 10% error predictions when comparing simulated and actual machining results.