Paper Title:
Tool Wear Intelligence Measure in Cutting Process Based on HMM
  Abstract

A method of tool wear intelligence measure based on Discrete Hidden Markov Models (DHMM) is proposed to monitor tool wear and to predict tool failure. FFT features are first extracted from the vibration signal and cutting force in cutting process, and then FFT vectors are presorted and converted into integers by SOM. Finally, these codes are introduced to DHMM for machine learning and 3 models for different tool wear stage are built up. Pattern of HMM is recognised by calculating probability. The results of tool wear intelligence measure and pattern recognition of tool wear experiments show that the method is effective.

  Info
Periodical
Edited by
Zhou Mark
Pages
482-487
DOI
10.4028/www.scientific.net/AMM.52-54.482
Citation
J. Kang, L. N. Guan, "Tool Wear Intelligence Measure in Cutting Process Based on HMM", Applied Mechanics and Materials, Vols. 52-54, pp. 482-487, 2011
Online since
March 2011
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