Modern advanced machining systems in the 'unmanned' factory must possess the ability automatically to change tools that have been subjected to wear or damage. This can ensure machining accuracy and reduce the production costs. A practical on-line tool wear monitoring and classification system is needed by industrial users and this paper presents an intelligent system for intermittent machining processes, such as milling. The system is fitted with multi-sensors to collect different signals from the machining process and the data are processed by the use of intelligent techniques. Different types of transducers were initially investigated during a large number of experiments and, as a result, four sensors, ie load, force, vibration and acoustic emission, were chosen. Fuzzy pattern recognition techniques have been used to accomplish sensor fusion and tool wear state classification.
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