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TECHNICAL PAPERS

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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References

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Figures

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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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