Author Description

This work relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. It is used a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associated with the configuration of the proposed system. With the combination of sensor-based information and inference rules, the result is an on-line system that can learn from experience and can update the knowledge base pertaining to information associated with different cutting conditions. Two neural networks resolve the problem of interpreting the complex sensor inputs while the Expert System, keeping track of previous success, estimates which of the two neural networks is more reliable. In this study an on-line tool wear monitoring system for turning processes has been developed which can reliably estimate the tool wear under common workshop conditions.