Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ymssp.2008.04.010
Title: Multi-category micro-milling tool wear monitoring with continuous hidden Markov models
Authors: Zhu, K. 
Wong, Y.S. 
Hong, G.S. 
Keywords: Feature selection
Hidden Markov models
Micro-milling
Tool wear monitoring
Issue Date: Feb-2009
Source: 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
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