On-line monitoring of tool breakage in face milling using a self-organized neural network

Abstract

This study introduces a new tool breakage monitoring methodology consisting of an unsupervised neural network combined with an adaptive time-series modeling algorithm. Cutting force signals are modeled by a discrete autoregressive model in which parameters are estimated recursively at each sampling instant using a parameter-adaptation algorithm based on a recursive least square. The experiment shows that monitoring the evolution of autoregressive parameters during milling is effective for detecting tool breakage. An adaptive resonance network based on Grossberg's adaptive resonance theory (ART 2) is employed for clustering tool states using model parameters, and this network has unsupervised learning capability. This system subsequently operates successfully with a fast monitoring time in a wide range of cutting conditions without a priori knowledge of the cutting process.