Multi-response design of Nd:YAG laser drilling of Ni-based superalloy sheets using Taguchi's quality loss function, multivariate statistical methods and artificial intelligence

Abstract

This paper presents a hybrid design strategy for the determination of the optimum laser drilling parameters which simultaneously meets the requirements for seven quality characteristics (responses) of the holes produced during pulsed Nd:YAG laser drilling of a thin sheet of nickel-based superalloy Nimonic 263. The process was designed using two approaches based on the experimental data. In the first approach, the quality losses of seven correlated responses were uncorrelated into a set of components using the principal component analysis; then the grey relational analysis was applied to synthesise components into a synthetic performance measure. Since this approach considered only parameter values used in the experiment, the second approach was developed to find the global optimal parameters solution using an artificial neural network to model the relation between parameters and a synthetic performance measure, and a genetic algorithm to perform a search for the global optimum in a continual multidimensional space. The analysis of the application indicated that the proposed approaches gave a better result, in terms of the optimal parameter settings that yield the maximal synthetic performance measure, than several commonly used methods for multi-response process parameters design. The results demonstrated that the robust Nd:YAG laser drilling of Ni-based superalloy sheets was designed with respect to the requirements for seven quality characteristics of the drilled holes, by using the proposed strategy.