Prediction of Torque in Milling by Response Surface Method and Neural Network - K., Kadirgama (2008)
The present paper discusses the development of the ﬁrst-order model for predicting the cutting torque in the milling operation of ASSAB 618 stainless steel using coated carbide cutting tools. The ﬁrst-order equation was developed using response surface method (RSM). The input cutting parameters were the cutting speed, feed rate, radial depth and axial depth of cut. The study found that the predictive model was able to predict torque values close to those readings recorded experimentally with a 95% conﬁdent interval. The results obtained from the predictive model were also compared by using multilayer perceptron with back-propagation learning rule artiﬁcial neural network. The ﬁrst-order equation revealed that the feed rate was the most dominant factor which was followed by axial depth, radial depth and cutting speed. The cutting torque value predicted by using Neural Network was in good agreement with that obtained by RSM. This observation indicates the potential use of RSM in predicting cutting parameters thus eliminating the need for exhaustive cutting experiments to obtain the optimum cutting conditions in terms of torque.