Hidden Markov Model-based Tool Wear Monitoring in Turning

[+] Author and Article Information
Litao Wang, Mostafa G. Mehrabi, Elijah Kannatey-Asibu

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

J. Manuf. Sci. Eng 124(3), 651-658 (Jul 11, 2002) (8 pages) doi:10.1115/1.1475320 History: Received May 01, 2001; Revised October 01, 2001; Online July 11, 2002
Copyright © 2002 by ASME
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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.
Kurada,  S., and Bradley,  C., 1997, “A Review of Machine Vision Sensors for Tool Condition Monitoring,” Computer in Industry, 34, pp. 55–72.
Taraman, K., Swando, R., and Yamauchi, W., 1974, “Relationship Between Tool Forces and Flank Wear,” SME Tech Pap, March, 15p.
Fromson, R., and Shum, L. Y., 1984, “Tool Wear and Tool Failure Monitoring System,” Westinghouse Electric Corp., USA, USP 04442494.
Liang,  S. Y., and Dornfeld,  D. A., 1989, “Tool Wear Detection Using Time Series Analysis of Acoustic Emission,” ASME J. Eng. Ind., 111, pp. 199.
El-wardany,  T. I., Gao,  D., and Elbestawi,  M. A., 1996, “Tool Condition Monitoring in Drilling Using Vibration Signature Analysis,” Int. J. Mach. Tools Manuf., 36, pp. 687–711.
Emel,  E., and Kannatey-Asibu,  E., 1988, “Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals,” ASME J. Eng. Ind., 110, pp. 137–145.
Emel,  E., and Kannatey-Asibu,  E., 1989, “Acoustic Emission and Force Sensor Fusion for Monitoring the Cutting Process,” Int. J. Mech. Sci., 31, pp. 795–809.
Ko,  T. J., Cho,  D. W., and Lee,  J. M., 1992, “Fuzzy Pattern Recognition for Tool Wear Monitoring in Diamond Turning,” CIRP Ann., 41, pp. 125–128.
Lee,  W. B., Cheung,  C. F., Chiu,  W. M., and Chan,  L. K., 1997, “Automatic Supervision of Blanking Tool Wear using Pattern Recognition Analysis,” Int. J. Mach. Tools Manuf., 37, pp. 1079–1095.
Lim,  G. H., 1995, “Tool-wear Monitoring in Machine Turning,” J. Mater. Process. Technol., 51, pp. 25–36.
Miyoshi,  Y., 1996, “Abnormal Cutting State Detection Using Model Parameters,” Int. J. Jpn. Soc. Precis. Eng., 30(1), pp. 41–46.
Ravindra,  H. V., Srinivasa,  Y. G., and Krishnamurthy,  R., 1997, “Acoustic Emission for Tool Condition Monitoring in Metal Cutting,” Wear, 212, pp. 78–84.
Kumar,  S. A., Ravindra,  H. V., and Srinivasa,  Y. G., 1997, “In-process Tool Wear Monitoring Through Time Series Modeling and Pattern Recognition,” Int. J. Prod. Res., 35, pp. 739–751.
Dornfeld,  D. A., 1990, “Neural Network Sensor Fusion for Tool Condition Monitoring,” CIRP Ann., 39(1), pp. 101–105.
Wang, Z., and Dornfeld, D. A., 1992, “In-process Tool Wear Monitoring Using Neural Networks,” Japan/USA Symposium on Flexible Automation, 1 , pp. 263–269.
Lin,  C. T., and Lee,  C. S. G., 1994, “Reinforcement Structure/Parameter Learning for Neural Network Based Fuzzy Logic Control Systems,” IEEE Trans. Fuzzy Syst., 2, pp. 46–63.
Das,  S., Chattopadhyay,  A. B., and Murthy,  A. S. R., 1996, “Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach,” Neural Networks, 9, pp. 1639–1645.
Lin,  S. C., and Ting,  C. J., 1996, “Drill Wear Monitoring Using Neural Networks,” Int. J. Mach. Tools Manuf., 36, pp. 465–475.
Kuo,  R. J., and Cohen,  P. H., 1998, “Intelligent Tool Wear Estimation System Through Artificial Neural Networks and Fuzzy Modeling,” Artif. Intell. Eng., 12(3), pp. 229–242.
Cho,  Wongyu, Lee,  S. W., and Kim,  Jin H., 1995, “Modeling and Recognition of Cursive Words with Hidden Markov Models,” Pattern Recogn., 28(12), pp. 1941–1953.
Heck, L. P., and McClellan, J. H., 1991, “Mechanical System Monitoring using Hidden Markov Models,” IEEE International Conference on Acoustic, Speech and Signal Processing Proceedings 91, 3 , pp. 1697–1700.
Owsley,  L. M. D., Atlas,  L. E., and Bernard,  G. D., 1997, “Self-organizing Feature Maps and Hidden Markov Models for Machine-tool Monitoring,” IEEE Trans. Signal Process., 45(11), pp. 2787–2798.
Ertunc,  H. M., Loparo,  K. A., and Ocak,  H., 2001, “Tool Wear Condition Monitoring in Drilling Operations using Hidden Markov Models (HMMs),” Int. J. Mach. Tools Manuf., 41, pp. 1363–1384.
Lee,  M. Y., Thomas,  C. E., and Wildes,  G., 1987, “Review—Prospects for Inprocess Diagnosis of Metal Cutting by Monitoring Vibration Signals,” J. Mater. Sci., 22, pp. 3821–3890.
Rabiner,  L. R., 1989, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc.-IEEE Ultrason. Symp., 77(2), pp. 257–286.
Lee, Kai-Fu, 1989, Automatic Speech Recognition, Kluwer Academic Publishers.
ISO 3685, 1993, “Tool-Life Testing with Single-point Turning Tools,” ISO 3685:1993(E), International Standard, Second Edition, 1993-11-15.
Wang, L., Mehrabi, M. G., and Kannatey-Asibu, E. Jr., 2001, “Tool Wear Monitoring in Machining Processes Through Wavelet Analysis,” to be published in Transactions of NAMRI/SME, Vol. XXIX.
Linde,  Y., Buzo,  A., and Gray,  R. M., 1980, “An Algorithm for Vector Quantizer Design,” IEEE Trans. Commun., 28, pp. 84–95.
Lu,  M. C., and Kannatey-Asibu,  E., 2000, “Analysis of Sound Signal Characteristics Associated with Adhesive Wear in Machining,” Transaction of NAMRI, XXVIII, pp. 257–262.
Rabiner,  J. C., 1985, “Some Properties of Continuous Hidden Markov Model Representations,” AT&T Tech. J., 64(6), pp. 1251–1269.


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A first-order, three state hidden Markov model
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Decision-making process of an earthworm
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Flow chart of the HMM training procedure
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Block diagram of the procedure for tool condition detection
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(a) Time domain vibration signals (b) variation of wavelet coefficients for different tool states
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Feature elements in a codebook
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An observed vibration signal from a sharp tool
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Feature vectors for sharp and worn tools (cutting speed: 300 sfpm; feed rate: 0.005 ipr)
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Feature vectors for sharp and worn tools (cutting speed: 250 sfpm; feed rate: 0.006 ipr)
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Feature vectors for sharp and worn tools (cutting speed: 350 sfpm; feed rate: 0.004 ipr)
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Detection rate versus length of training data (3-state HMM with observation sequence length of 5 and codebook size of 10)
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Detection rate versus observation sequence length (3-state HMM with codebook size of 10)
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Decision space for sharp and worn tools




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