Please use this identifier to cite or link to this item: https://doi.org/10.1007/s001700170161
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dc.titleSurface texture indicators of tool wear - a machine vision approach
dc.contributor.authorBradley, C.
dc.contributor.authorWong, Y.S.
dc.date.accessioned2014-06-17T05:18:04Z
dc.date.available2014-06-17T05:18:04Z
dc.date.issued2001
dc.identifier.citationBradley, C., Wong, Y.S. (2001). Surface texture indicators of tool wear - a machine vision approach. International Journal of Advanced Manufacturing Technology 17 (6) : 435-443. ScholarBank@NUS Repository. https://doi.org/10.1007/s001700170161
dc.identifier.issn02683768
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/58754
dc.description.abstractThere has been much research on the automated monitoring of cutting tool wear. This research has tended to focus on three main areas that attempt to quantify the cutting tool condition: monitoring of specific machine tool parameters in order to infer tool condition, direct observations made on the cutting tool; and measurements taken from the chips produced by the tool. However, considerably less work has been performed on the development of surface texture sensors that provide information on the condition of the tool employed in machining the surface. A preliminary experimental study is presented for accomplishing this texture analysis using a machine vision-based sensor system. In particular, an investigation of the condition of a two-flute end mill used in a standard face milling operation is presented. The degree of tool wear is estimated by extracting three parameters from video camera images of the machined surface. The performance of three image-processing algorithms, in estimating the tool condition, is presented: analysis of the intensity histogram; image frequency domain content; and spatial domain surface texture.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s001700170161
dc.sourceScopus
dc.subjectImage processing
dc.subjectMachine vision
dc.subjectSurface texture
dc.subjectTool wear monitoring
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1007/s001700170161
dc.description.sourcetitleInternational Journal of Advanced Manufacturing Technology
dc.description.volume17
dc.description.issue6
dc.description.page435-443
dc.description.codenIJATE
dc.identifier.isiut000168931900005
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