Hybrid Approach for Modeling and Optimization of Hole Taper During Laser Trepan Drilling of Ti-6Al-4V Alloy Sheet

Abstract

The study represents a hybrid approach of artificial neural network (ANN) and genetic algorithm (GA) for modeling and optimization of hole taper during laser trepan drilling (LTD) of 1.4 mm thick titanium alloy (Grade-5) sheet. A feed forward ANN model to predict the hole taper more precisely then a second order regression model, has been developed by utilizing the experimental data obtained during a well designed L27 orthogonal array (OA) based matrix experimentation. Further this ANN model has been formulated as an objective function to be minimized using GA tool. The results of GA optimization suggest a considerable reduction in hole taper value.