Hidden Markov Model-based Tool Wear Monitoring in Turning-Reference-Cited by-同舟云学术

Hidden Markov Model-based Tool Wear Monitoring in Turning

Published:2002-07-11 Issue:3 Volume:124 Page:651-658
ISSN:1087-1357
Container-title:Journal of Manufacturing Science and Engineering
language:en
Short-container-title:

Author:

Wang Litao¹,Mehrabi Mostafa G.¹,Kannatey-Asibu, Elijah¹

Affiliation:

1. Department of Mechanical Engineering, Engineering Research Center for Reconfigurable Machining Systems, University of Michigan, Ann Arbor, MI 48109-2125

Abstract

This paper presents a new modeling framework for tool wear monitoring in machining processes using hidden Markov models (HMMs). Feature vectors are extracted from vibration signals measured during turning. A codebook is designed and used for vector quantization to convert the feature vectors into a symbol sequence for the hidden Markov model. A series of experiments are conducted to evaluate the effectiveness of the approach for different lengths of training data and observation sequence. Experimental results show that successful tool state detection rates as high as 97% can be achieved by using this approach.

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/manufacturingscience/article-pdf/124/3/651/5598525/651_1.pdf

Reference32 articles.

1. Kuo, R. J., and Cohen, P. H., 1999, “Multi-sensor Integration for On-line Tool Wear Estimation Through Radial Basis Function Networks and Fuzzy Neural Network,” Neural Networks, 12, pp. 355–370.

2. Kurada, S., and Bradley, C., 1997, “A Review of Machine Vision Sensors for Tool Condition Monitoring,” Computer in Industry, 34, pp. 55–72.

3. Taraman, K., Swando, R., and Yamauchi, W., 1974, “Relationship Between Tool Forces and Flank Wear,” SME Tech Pap, March, 15p.

4. Fromson, R., and Shum, L. Y., 1984, “Tool Wear and Tool Failure Monitoring System,” Westinghouse Electric Corp., USA, USP 04442494.

5. Liang, S. Y., and Dornfeld, D. A., 1989, “Tool Wear Detection Using Time Series Analysis of Acoustic Emission,” ASME J. Eng. Ind., 111, pp. 199199.

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