The increasing extent of automation in manufacturing processes requires flexible and reliable tool monitoring systems. One of the most important and most difficult tasks in this context is the on-line supervision of a tool's wear. Considering the state of wear and the actual working process (e.g. rough or finish turning) it is possible to exchange a tool just in time, which offers significant economic advantages. This paper presents a new method to classify a characteristic wear parameter by means of neural networks. In order to find an appropriate network paradigm, multilayer perceptrons, Fuzzy ARTMAPS, self-organizing maps and NEFCLASS networks are investigated. The input parameters of the networks are process-specific parameters (like the feed rate or the cutting speed) and specific coefficients extracted from three measured force signals.
['Bernhard Sick']
Classifying the Wear of Turning Tools with Neural Networks