A neurofuzzy pattern recognition algorithm and its application in tool condition monitoring process

An important element of the automatic machining process control function is the on-line monitoring of cutting tool wear and fracture mechanisms. This paper presents an intelligent tool condition monitoring system. The multisensor signals reflect the tool condition comprehensively. Redundant signal features are removed by using a fuzzy clustering feature filter. A unique fuzzy driven neural network has been developed to carry out the fusion of multi-sensor information and tool wear classification. It combines the transparent representation of fuzzy systems with the learning ability of neural networks hence the algorithm has strong modelling and noise suppression ability. Successful tool wear classification can be realized under a range of machining conditions.