Modeling and optimization of laser beam percussion drilling of nickel-based superalloy sheet using Nd: YAG laser

Abstract

The creation of small diameter holes in thin sheets (<3mm) of superalloys using a laser beam is a challenging task. Knowledge of the effect of laser related process variables on hole related responses with respect to variation of sheet thickness is essential to obtain a hole of requisite quality. Therefore, in this paper a coupled methodology comprising of Finite Element Method (FEM) and Artificial Neural Network (ANN) has been used to develop a prediction model for the Laser Beam Percussion Drilling (LBPD) process. First, a 2D axisymmetric FEM-based thermal model for LBPD has been developed incorporating temperature-dependent thermal properties, optical properties and phase change phenomena of the sheet material. The developed FEM-based thermal model is validated with self-conducted experimental results in terms of hole taper which is further used to generate adequate input and output data for training and testing of the ANN model. Gray Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) has been effectively used for the multi-objective optimization of the LBPD process utilizing the data predicted by the trained ANN model. The developed ANN model has been used to predict the performance characteristics of the LBPD process. The results predicted by the ANN model show that with the increase in pulse width and peak power the hole taper, material removal rate (MRR) and heat-affected zone (HAZ) increases. The acquired combination of optimal process variables produce a hole with good integral quality, i.e., a reduction of hole taper by 32.1%, increase of material removal rate by 28.9% and reduction of extent of HAZ by 4.5%.