The application of a radial basis function neural network for predicting the surface roughness in a turning process

This study considers the performance of a radial basis function neural network for predicting the surface roughness in a turning process. A simple algorithm is proposed for finding the upper and lower estimates of the surface roughness. A code is developed that automatically fits the best network architecture for a given training and testing dataset. The validation of the methodology is carried out for dry and wet turning of mild steel using HSS and carbide tools, and is compared to the performance of the studied network with the reported performance of a multi-layer perception neural network. It is observed that the performance of the radial basis function network is slightly inferior compared to multi-layer perceptron neural network. However, the training procedure is simpler and requires less computational time.