Development of artificial neural network models to study the effect of process parameters on burr size in drilling

Burr size at the exit of the holes in drilling is a quality index and hence it becomes essential to predict the size of the burr formed in order to cater to the demand of product quality and functionability. In this paper, artificial neural network (ANN)-based models have been developed to study the effect of process parameters such as cutting speed, feed, drill diameter, point angle, and lip clearance angle on burr height and burr thickness during drilling of AISI 316L stainless steel. A multilayer feed-forward ANN; trained using error back-propagation training algorithm (EBPTA) has been employed for this purpose. The input-output patterns required for training are obtained from drilling experimentation planned through Box-Behnken design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of drilling process parameters on burr size.