Condition Monitoring of Cutting Tools Using Artificial Neural Networks

  • N. Gindy
  • A. Al-Habaibeh
Chapter

Summary

The paper presents a methodology for using neural network techniques and simple data processing algorithms for monitoring the condition of milling cutters during peripheral milling . The learning algorithms considered in this research utilise artificial neural networks to map some machining parameters to sensory signals. Cutting force and acceleration signals recorded during machining are first simplified and then fed into the input layer of the neural network. Using the back-propagation method, the output of the neural network is used to recognise “normal” as well as “faulty” milling cutters and the depth of cut used. The experimental results show that the proposed approach of using simple data processing algorithms with neural networks is capable of successfully identifying common fault conditions in milling cutters in peripheral operations.

Keywords

condition monitoring artificial neural networks milling tool breakage pattern recognition 

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References

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    Al-Habaibeh Amin (1996), “Machine Condition Monitoring Using Artificial Neural Networks”, MSc Thesis, Department of Manufacturing Engineering and Operations Management, University of Nottingham.Google Scholar
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Copyright information

© Department of Mechanical Engineering University of Manchester Institute of Science and Technology 1997

Authors and Affiliations

  • N. Gindy
    • 1
  • A. Al-Habaibeh
    • 1
  1. 1.University of NottinghamUK

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