Artificial neural network modelling of Nd:YAG laser microdrilling on titanium nitride—alumina composite

Abstract

Selection of machining parameter combinations for obtaining optimum circularity at entry and exit and hole taper is a challenging task in laser microdrilling owing to the presence of a large number of process variables. There is no perfect combination of parameters that can simultaneously result in higher circularity at entry and exit and lower hole taper. The current paper attempts to develop a strategy for predicting machining parameter settings for the generation of the maximum circularity at entry and exit and minimum hole taper. An artificial neural network (ANN) is used for process modelling of laser microdrilling of titanium nitride—alumina composite and a feed-forward back-propagation network is developed to model the machining process. The model, after proper training, is capable of predicting the response parameters as a function of five different control parameters. Experimental results demonstrate that the machining model is suitable and the optimization strategy satisfies practical requirements. The developed model is found to be unique, powerful, and flexible.