Intelligent Classification of Cutting Tool Wear States

  • Pan Fu
  • Anthony D. Hope
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


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.


Acoustic Emission Tool Wear Fuzzy Neural Network Fuzzy Membership Function Tool Condition Monitoring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pan Fu
    • 1
  • Anthony D. Hope
    • 2
  1. 1.Mechanical Engineering FacultySouthwest Jiao Tong UniversityChengduChina
  2. 2.Systems Engineering FacultySouthampton InstituteSouthamptonU.K.

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