Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network

In this study, artificial neural networks (ANNs) was used for modeling the effects of machinability on chip removal cutting parameters for face milling of stellite 6 in asymmetric milling processes. Cutting forces with three axes (Fx, Fy and Fz) were predicted by changing cutting speed (Vc), feed rate (f) and depth of cut (ap) under dry conditions. Experimental studies were carried out to obtain training and test data and scaled conjugate gradient (SCG) feed-forward back-propagation algorithm was used in the networks. Main parameters for the experiments are the cutting speed (Vc, m/min), feed rate (f, mm/min), depth of cut (ap, mm) and cutting forces (Fx, Fy and Fz, N). Vc, f and ap were used as the input dataset while Fx, Fy and Fz were used as the output dataset. Average percentage error (APEs) values for Fx, Fy and Fz using the proposed model were obtained around 2 and 10% for training and testing, respectively. These results show that the ANNs can be used for predicting the effects of machinability on chip removal cutting parameters for face milling of stellite 6 in asymmetric milling processes.