Skip to main content

An advanced fuzzy pattern recognition architecture for condition monitoring

Buy Article:

$25.00 plus tax (Refund Policy)


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

More about this publication?

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more