Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.ymssp.2008.04.010
DC Field | Value | |
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dc.title | Multi-category micro-milling tool wear monitoring with continuous hidden Markov models | |
dc.contributor.author | Zhu, K. | |
dc.contributor.author | Wong, Y.S. | |
dc.contributor.author | Hong, G.S. | |
dc.date.accessioned | 2014-06-17T06:27:57Z | |
dc.date.available | 2014-06-17T06:27:57Z | |
dc.date.issued | 2009-02 | |
dc.identifier.citation | Zhu, K., Wong, Y.S., Hong, G.S. (2009-02). Multi-category micro-milling tool wear monitoring with continuous hidden Markov models. Mechanical Systems and Signal Processing 23 (2) : 547-560. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ymssp.2008.04.010 | |
dc.identifier.issn | 08883270 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/60840 | |
dc.description.abstract | In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods. © 2008 Elsevier Ltd. All rights reserved. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.ymssp.2008.04.010 | |
dc.source | Scopus | |
dc.subject | Feature selection | |
dc.subject | Hidden Markov models | |
dc.subject | Micro-milling | |
dc.subject | Tool wear monitoring | |
dc.type | Article | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.description.doi | 10.1016/j.ymssp.2008.04.010 | |
dc.description.sourcetitle | Mechanical Systems and Signal Processing | |
dc.description.volume | 23 | |
dc.description.issue | 2 | |
dc.description.page | 547-560 | |
dc.description.coden | MSSPE | |
dc.identifier.isiut | 000261852500022 | |
Appears in Collections: | Staff Publications |
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