Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/547
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dc.contributor.authorPanda, S S-
dc.contributor.authorCharkraborty, D-
dc.contributor.authorPal, S K-
dc.date.accessioned2007-11-07T06:39:03Z-
dc.date.available2007-11-07T06:39:03Z-
dc.date.issued2005-
dc.identifier.citationProceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austriaen
dc.identifier.urihttp://hdl.handle.net/2080/547-
dc.descriptionCopyright for this article belongs to IASTEDen
dc.description.abstractThe present work deals with drill wear monitoring using artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high speed steel (HSS) drill bit for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameters like feed-rate, spindle speed, drill diameter on thrust force and torque in the time domain has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction.en
dc.format.extent162820 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIASTEDen
dc.subjectFlant Wearen
dc.subjectArtificial Neural Networken
dc.subjectDrillingen
dc.subjectChip Thiknessen
dc.titleMonitoring of Drill Flank Wear in the Time Domainen
dc.typeArticleen
Appears in Collections:Conference Papers

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