An advanced fuzzy pattern recognition architecture for condition monitoring

An important element of the automatic machining process control function is the on-line monitoring of cutting tool wear and fracture mechanisms. This can ensure machining accuracy and reduce the production costs. This paper presents a knowledge-based intelligent pattern recognition algorithm for tool condition monitoring. Redundant signal features are removed by using a fuzzy clustering feature filter. The fuzzy-driven neural network can carry out the integration and fusion of multi-sensor information effectively. The algorithm has strong learning and noise suppression ability which leads to successful tool wear classification under a range of machining conditions.