COMPARISON OF NEURAL NETWORK MODELS ON MATERIAL REMOVAL RATE OF C-SIC

In this work , two artificial neural networks (ANNs) models : Back propagation neural network (BPN) and Radial basis function neural network (RBFN) are presented for the prediction of material removal rate (MRR) of C-SiC using vertical milling machine. The spindle speed, feed and diameter of the drill are chosen as input variables and material removal rate is the output parameter for the artificial intelligence models. Using the factorial design methodology, drilling experiment is conducted on C-SiC to acquire data for training and testing the neural network. It is found that neural models could predict the process performance with reasonable accuracy .However RBFN model is faster than BPN for this experimental data set. Moreover, the neural network models are considered as valuable tools as they can give reliable predictions and provide a way to avoid time and money consuming experiments