Artificial Neural Networks for Wear Estimation

Abstract Results of our investigations in feature selection and wear estimation for cutting tools by turning machines using artificial neural networks are presented. The features describing the cutting process are calculated from solid borne sound (vibration) signals. Furthermore, the performance of a Multilayer Perception Network with back propagation learning rule, a Learning Vector Quantization (LVQ) and a Condensed Nearest Neighbour Network (CNNN) used for wear estimation are analysed and the results are compared.