Please use this identifier to cite or link to this item: https://doi.org/10.1109/AIM.2009.5229978
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dc.titleTool wear forecast using singular value decomposition for dominant feature identification
dc.contributor.authorPang, C.K.
dc.contributor.authorZhou, J.-H.
dc.contributor.authorLewis, F.L.
dc.contributor.authorZhong, Z.-W.
dc.date.accessioned2014-06-19T03:30:44Z
dc.date.available2014-06-19T03:30:44Z
dc.date.issued2009
dc.identifier.citationPang, C.K., Zhou, J.-H., Lewis, F.L., Zhong, Z.-W. (2009). Tool wear forecast using singular value decomposition for dominant feature identification. IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM : 421-426. ScholarBank@NUS Repository. https://doi.org/10.1109/AIM.2009.5229978
dc.identifier.isbn9781424428533
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72038
dc.description.abstractIdentification and prediction of lifetime of industrial cutting tools using minimal sensors is crucial to reduce production costs and down-time in engineering systems. In this paper, we provide a formal decision software tool to extract the dominant features enabling tool wear prediction. This decision tool is based on a formal mathematical approach that selects dominant features using the Singular Value Decomposition (SVD) of real-time measurements from the sensors of an industrial cutting tool. It is shown that the proposed method of dominant feature selection is optimal in the sense that it minimizes the least-squares estimation error. The identified dominant features are used with the Recursive Least Squares (RLS) algorithm to identify parameters in forecasting the time series of cutting tool wear on an industrial high speed milling machine. ©2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/AIM.2009.5229978
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/AIM.2009.5229978
dc.description.sourcetitleIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
dc.description.page421-426
dc.identifier.isiut000277062800072
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