Classifying the wear of turning tools with neural networks

Abstract

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, FuzzyARTMAPS, 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.