A comparative study of the ANN and RSM modeling approaches for predicting burr size in drilling

This paper describes the comparison of the burr size predictive models based on artificial neural networks (ANN) and response surface methodology (RSM). The models were developed based on three-level full factorial design of experiments conducted on AISI 316L stainless steel work material with cutting speed, feed, and point angle as the process parameters. The ANN predictive models of burr height and burr thickness were developed using a multilayer feed forward neural network, trained using an error back propagation learning algorithm (EBPA), which is based on the generalized delta rule. The performance of the developed ANN models were compared with the second-order RSM mathematical models of burr height and thickness. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. The details of experimentation, model development, testing, and performance comparison are presented in the paper.