On-line tool wear monitoring in turning using neural networks

The on-line supervision of a tool's wear is the most difficult task in the context of tool monitoring. Based on an in-process acquisition of signals with multi-sensor systems, it is possible to estimate or classify wear parameters by means of neural networks. This article demonstrates that solutions can be improved significantly by using available secondary information about physical models of the cutting process and about the temporal development of wear. Process models describing the influence of process parameters are used for a dedicated pre-processing of the sensor signals. The essential signal behaviour in a certain time window is described by means of polynomial coefficients. These coefficients are used as inputs for feedforward networks considering the temporal development of wear (multilayer perceptrons with a sliding window technique and time-delay neural networks). With a combination of the proposed measures it is possible to obtain remarkable improvements of both tool wear estimation and classification.