RETRACTED: Tool Wear Intelligence Measure in Cutting Process Based on HMM

Author:

Kang Jing1,Guan Li Na1

Affiliation:

1. Dalian Nationalities University

Abstract

RETRACTED PAPER: 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.

Publisher

Trans Tech Publications, Ltd.

Reference10 articles.

1. L. Atlas, M. Ostendod, G. D. Bernard: HIDDEN MARKOV MODELS FOR MONITORING MACHINING TOOL-WEAR, proceedings of IEEE ICASSP'00 3887~3890.

2. H. M. Ertunc, K. A. Loparo, E. Ozdernir etc: Real Time Monitoring of Tool Wear Using Multiple Modeling Method, proceedings of IEEE IEMDC20 687~ 691 Oct. (2001).

3. Gao Hong-li: The Investigation of Intelligent Tool Wear Monitoring Techniques for Metal Cutting Process,Ph.D. Southwest Jiaotong University,China, Sep. (2005).

4. Wang Wei:Research on Too1 Condition Monitoring and on-line Compensation Technology in Milling Special Spiral Rod,Ph.D. Northeast University,Feb. (2006).

5. Gao Hong-li, Xu Ming-heng, Fu Pan etc, Tool Wear Monitoring Based on Dynamic Tree, J. CHINESE JOURNAL OF MECHANICAL ENGINEERING. Vol. 42, No. 7, Jul. (2006).

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