Optimization of Cut Quality Characteristics during Nd:YAG Laser Straight Cutting of Ni-Based Superalloy Thin Sheet Using Grey Relational Analysis with Entropy Measurement

Abstract

This article presents application of a hybrid approach for the optimization of Nd:YAG laser straight cutting of Ni-based superalloy thin sheet with multiple performance characteristics. The approach first finds the experimental results using Taguchi-based experimental design. The designed experimental results are used in the grey relational analysis (GRA) for optimization of multiperformance characteristics simultaneously. The quality characteristics considered are average kerf taper (T a ) and average surface roughness (R a ) measured through Optical Measuring Microscope and Surface Roughness Tester, respectively. The essential process input parameters were identified as oxygen pressure, pulse width, pulse frequency, and cutting speed. The entropy measurement method is specially employed to evaluate the values of weights corresponding to each performance characteristics so that their relative significance can be properly described. The results of confirmation experiments verify that the proposed grey-based Taguchi method has the ability to find out the optimal laser cutting parameters with multiple quality characteristics. The application of grey relational analysis has reduced T a and R a by 56% and 18%, respectively.