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|Title:||Multi-category micro-milling tool wear monitoring with continuous hidden Markov models||Authors:||Zhu, K.
Hidden Markov models
Tool wear monitoring
|Issue Date:||Feb-2009||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||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.||Source Title:||Mechanical Systems and Signal Processing||URI:||http://scholarbank.nus.edu.sg/handle/10635/60840||ISSN:||08883270||DOI:||10.1016/j.ymssp.2008.04.010|
|Appears in Collections:||Staff Publications|
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