On-Line and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: a Review of More than a Decade of Research

The supervision of tool wear is the most difficult task in the context of tool condition monitoring for metal-cutting processes. Based on a continuous acquisition of signals with multi-sensor systems it is possible to estimate or to classify certain wear parameters by means of neural networks. However, despite of more than a decade of intensive scientific research, the development of tool wear monitoring systems is an on-going attempt. This article aims to investigate, why it has not been possible to develop appropriate monitoring systems up to now. In order to describe the 'state of the art', 138 publications dealing with on-line and indirect tool wear monitoring in turning by means of artificial neural networks are evaluated. The article compares the methods applied in these publications as well as the methodologies used to select certain methods, to carry out simulation experiments, to evaluate and to present results, etc. As a conclusion, possible directions for future research in this area are pointed out. Many of the recommendations are valid for other machining processes using tools with or without defined cutting edges, too.