Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/54551
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dc.titleA new tool life criterion for tool condition monitoring using a neural network
dc.contributor.authorZhou, Q.
dc.contributor.authorHong, G.S.
dc.contributor.authorRahman, M.
dc.date.accessioned2014-06-16T09:32:19Z
dc.date.available2014-06-16T09:32:19Z
dc.date.issued1995-10
dc.identifier.citationZhou, Q.,Hong, G.S.,Rahman, M. (1995-10). A new tool life criterion for tool condition monitoring using a neural network. Engineering Applications of Artificial Intelligence 8 (5) : 579-588. ScholarBank@NUS Repository.
dc.identifier.issn09521976
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54551
dc.description.abstractOn-line tool condition monitoring is important to prevent workpieces and tools from damage, and to increase the effective machining time of a machine tool. It is necessary to define tool-life criteria clearly, for indirect methods of on-line tool condition monitoring. There are many tool life criteria that depend on wear manner, economic considerations, workpiece dimensional tolerance and surface roughness. However, the signal measured by a sensor (e.g. cutting force) usually represents the tool wear condition contributed from a different wear zone. This implies that it is difficult to extract a single wear criterion from a convoluted sensor signal. When multiple signal features are used, the response of the features to the tool life cannot be clearly seen, and the tool life prediction may not be reliable. This paper presents an investigation into tool life criteria in raw turning. A new tool-life criterion depending on a pattern-recognition technique is proposed. The neural network and wavelet techniques are used to realize the new criterion. The experimental results show that this criterion is applicable to tool condition monitoring in a wide range of cutting conditions. © 1995.
dc.sourceScopus
dc.subjectneural networks
dc.subjectTool life criteria
dc.subjectwavelet decomposition
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.description.sourcetitleEngineering Applications of Artificial Intelligence
dc.description.volume8
dc.description.issue5
dc.description.page579-588
dc.description.codenEAAIE
dc.identifier.isiutNOT_IN_WOS
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