On-line cutting quality recognition in milling using a radical basis function neural network

Tool wear, chatter vibration, chip breaking and built-up edge are main phenomena to be monitored in modern manufacturing processes, which are considered as important factors to the quality of products. They are closely related to the cutting parameters, which are to be selected in manufacturing process. However, it is very difficult to measure directly the cutting quality based on on-line monitoring. In this study, the relationship between the cutting parameters and cutting quality is analyzed. A Radical Basis Function (RBF) neural network based on-line quality recognition scheme is also presented, which monitors the level of surface roughness. The experimental results reveal that the RBF neural network has a high prediction success rate.