Multiple Regression Analysis and Non-Dominated Sorting Genetic Algorithm II Optimization of Machining Carbon-Fiber-Reinforced Polyethylene Terephthalate Glycol Parts Fabricated via Additive Manufacturing Under Dry and Lubricated Conditions

Abstract

The present research deals with the processing of the additively manufactured Carbon-Fiber-Reinforced Polymer (CFRP) under dry and lubricated cutting conditions, focusing on the generated surface roughness. The cutting speed, feed, and depth of cut were selected as the continuous variables. A comparison between the generated surface roughness of the dry and the lubricated cuts revealed that the presence of coolant contributed towards reducing surface roughness by more than 20% in most cases. Next, a regression analysis was performed with the obtained measurements, yielding a robust prediction model, with the determination coefficient R2 being equal to 94.65%. It was determined that feed and the corresponding interactions contributed more than 45% to the model’s R2, followed by the depth of cut and the machining condition. In addition, the cutting speed was the variable with the least effect on the response. The Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) was employed to identify the front of optimal solutions that consider both minimizing surface roughness and maximizing Material Removal Rate (MRR). Finally, a set of extra experiments proved the validity of the model by exhibiting relative error values, between the measured and predicted roughness, below 10%.