An advanced fuzzy pattern recognition architecture for condition monitoring

Authors: Fu, Pan1; Hope, A D2

Source: Insight - Non-Destructive Testing and Condition Monitoring, Volume 46, Number 7, 1 July 2004 , pp. 409-413(5)

Publisher: The British Institute of Non-Destructive Testing

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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.

Document Type: Research Article


Affiliations: 1: Department of Measurement Technology and Instrumentation, Faculty of Mechanical Engineering, Southwest Jiao Tong University, Chengdu City, PR China 2: Faculty of Technology, Southampton Institute, East Park Terrace, Southampton SO14 0RD, UK

Publication date: July 1, 2004

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