The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Hidden Markov Model-based Tool Wear Monitoring in Turning
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.
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Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
An Algorithm for Vector Quantizer Design
Linde, Y., Buzo, A., & Gray, R. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094577 · Cited by 7,201
Cited by
Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results
Zhu, K., Wong, Y. S., & Hong, G. S. (2009) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2009.02.003 · Cited by 406
Metadata
- Authors
- Wang, L., Mehrabi, M. G., & Kannatey-Asibu
- Venue
- Journal of Manufacturing Science and Engineering
- Publisher
- ASME International
- Published
- 2002-07-11
- Volume
- 124
- Issue
- 3
- Pages
- 651-658
- ISSN
- 1087-1357 (ISSN-L)
- 1528-8935
- OpenAlex
- W2016589303
- Cited by
- 165
- References
- 2
- FWCI
- 10.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Hidden Markov Model-based Tool Wear Monitoring in Turning. https://app.cochat.ai/papers/10.1115/1.1475320
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- Wang, L., Mehrabi, M. G., & Kannatey-Asibu. (2002). Hidden Markov Model-based Tool Wear Monitoring in Turning. Journal of Manufacturing Science and Engineering. https://doi.org/10.1115/1.1475320
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
