Modeling and multi-constrained optimization in drilling process of carbon fiber reinforced epoxy composite

Abstract

The goal of this study is to present a methodology for the determination of the optimal cutting parameters (spindle speed, feed rate and tool point angle) during the drilling process of carbon fiber reinforced polymer composites (CFRP) to maximize the material removal rate by considering surface roughness, delamination and thrust force as the constraints through coupling Response Surface Method (RSM) and Genetic Algorithm (GA). In this regard, the advantages of statistical experimental design technique, experimental measurements, Response Surface Method (RSM) and the genetic optimization method are exploited in an integrated manner. To this end, the experiments on CFRP were conducted to obtain surface roughness, delamination factor and thrust force values based on the full factorial design of experiments, and then analysis of variance (ANOVA) is performed. The predictive models for outputs were created using Response Surface Method (RSM) taking advantage of the experimental data. Material removal rate constituted the main function for the genetic algorithm, and thrust force, delamination, and surface roughness were applied as the constraints of the GA function. The function was optimized by the GA code, and finally, the optimum variables were obtained, and the results of the GA were tested experimentally. It can be clearly observed that good agreement exists between the predicted values and the experimental measurements.