Intelligent classification of cutting tool wear states

  • Authors:

  • Pan Fu;Anthony D. Hope

  • Affiliations:

  • Mechanical Engineering Faculty, Southwest Jiao Tong University, Chengdu, China;Systems Engineering Faculty, Southampton Institute, Southampton, U.K.

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

In manufacturing processes, it is very important that the condition of the cutting tool, particularly the indications when it should be changed, can be monitored. Cutting tool condition monitoring is a very complex process and thus sensor fusion techniques and artificial intelligence signal processing algorithms are employed in this study. A unique fuzzy neural hybrid pattern recognition algorithm has been developed which combines the transparent representation of fuzzy system with the learning ability of neural networks. The algorithm has strong modeling and noise suppression ability. These leads to successful tool wear classification under a range of machining conditions.