Tool Wear Intelligence Measure in Cutting Process Based on HMM

Abstract:

Article Preview

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 and 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

Authors:

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.