Modeling and optimization of laser beam percussion drilling of thin aluminum sheet

Abstract

Modeling and optimization of machining processes using coupled methodology has been an area of interest for manufacturing engineers in recent times. The present paper deals with the development of a prediction model for Laser Beam Percussion Drilling (LBPD) using the coupled methodology of Finite Element Method (FEM) and Artificial Neural Network (ANN). First, 2D axisymmetric FEM based thermal models for LBPD have been developed, incorporating the temperature-dependent thermal properties, optical properties, and phase change phenomena of aluminum. The model is validated after comparing the results obtained using the FEM model with self-conducted experimental results in terms of hole taper. Secondly, sufficient input and output data generated using the FEM model is used for the training and testing of the ANN model. Further, Grey Relational Analysis (GRA) coupled with Principal Component Analysis (PCA) has been effectively used for the multi-objective optimization of the LBPD process using data predicted by the trained ANN model. The developed ANN model predicts that hole taper and material removal rates are highly affected by pulse width, whereas the pulse frequency plays the most significant role in determining the extent of HAZ. The optimal process parameter setting shows a reduction of hole taper by 67.5%, increase of material removal rate by 605%, and reduction of extent of HAZ by 3.24%.