An Approach to the Tool Wear Model Construction Using Acoustic Signals of Cutting Process


A way of tool wear model definition is presented, which is the combination of wavelet packet transform for acoustic signal processing and artificial neural network for tool wear estimation.

An acoustic signal is chosen as an essential informational source of the process or the object under consideration. Measurement acoustic signals are processed using wavelet transform, which decomposes the acoustic signal into approximation and detail components at different levels. Wavelet transform decomposition is used to gather time-frequency-based information from acoustic signals. The artificial neural network is proposed as a tool of relation establishment between the results of wavelet decomposition and parameters of interest.

The results indicate that wavelet transform is an effective tool in the analysis of acoustic signals by providing information relative to the process under question. The performance of the method is demonstrated by means of numerical and experimental investigations.

The presented method implementation is prospective in on-line tool wear diagnostic system.

  • Abstract
  • Introduction
  • Fast and Packet Wavelet Transform
  • Acoustic Signal Processing Using Packet Wavelet Transform
  • Tool Wear Estimation Using Artificial Neural Network
  • Conclusion
  • References

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