Multi-objective optimization (MOO) helps to achieve simultaneous improvement of several output characteristics in machining processes where complex interaction between the input parameter exists. Grey Relational analysis (GRA), multi objective optimization based on ratio analysis (MOORA) and Composite desirability (CD) are the three most widely used optimization techniques in material processing. To assign weights according to the relative importance of the input responses, principal component analysis (PCA) has been used. This study compares the hybrid approach of GRA-PCA-RSM-CD and MOORA-PCA-RSM-CD for MOO of laser drilling process. Experiments were conducted with millisecond duration Nd:YAG laser using Box–Behnken design (BBD) approach of RSM. The output parameters, i.e., top hole circularity (THC), bottom hole circularity (BHC) and hole taper (HT) were determined for various input parameters like pulse current (I),\documentclass[12pt]{minimal}
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\begin{document}$$\theta )$$\end{document} during laser percussion inclined hole drilling (LPIHD) in glass fiber reinforced plastics (GFRP) of three different thickness at various angle of incidence angle. Regression model to correlate the input parameters with the multiobjective index obtained by GRA-PCA and MOORA-PCA has been developed to determine the most significant input factors. The regression models were further optimized through composite desirability approach. The analysis reveals that pulse current, and thickness of workpiece are the two most significant factor for controlling the overall hole quality in LPIHD. The combined approach of GRA-PCA-RSM-CD yields an average improvement of 1%, 15%, and 29% in THC, BHC and HT, whereas the corresponding improvement using the MOORA-PCA-RSM-CD approach is 0.5%, 10%, and 17%, respectively.