Multi optimization in slot milling of CFRP composites through grey relational- based Taguchi analysis

Abstract

Carbon fiber-reinforced plastic (CFRP) composite materials are challenging to machine due to their anisotropy and heterogeneity. Thus, the experimental study of milling CFRP composite material is very crucial. In the current study, based on Taguchi’s L9 orthogonal array, slot milling experiments were performed on CFRP composite samples to make a decision on a parametric optimization of multiple responses such as material removal rate (MRR), delamination factor (Fd) and surface roughness (Ra) using grey relational-based Taguchi analysis. The selected milling parameters are cutting speed (A), feed (B), and depth of cut (C). Based on Grey Relational Grade (GRG), Analysis of Variance (ANOVA) was used to determine the parameters' significant contributions and the parameters' optimal levels. The results showed, with a 95% confidence level, that all of the chosen cutting parameters have a substantial impact on all of the measured responses. Based on a confirmatory test performed under ideal milling conditions, MRR has been increased with an improvement of 31.25 %, Fd has been decreased with an improvement of 1.66% and Ra has been decreased with an improvement of 28.3%. These improvements in all measured responses are equivalent to an improvement of GRG by 3%.