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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/547

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contributor.authorPanda, S S-
contributor.authorCharkraborty, D-
contributor.authorPal, S K-
date.accessioned2007-11-07T06:39:03Z-
date.available2007-11-07T06:39:03Z-
date.issued2005-
identifier.citationProceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austriaen
identifier.urihttp://hdl.handle.net/2080/547-
descriptionCopyright for this article belongs to IASTEDen
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
format.extent162820 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIASTEDen
subjectFlant Wearen
subjectArtificial Neural Networken
subjectDrillingen
subjectChip Thiknessen
titleMonitoring of Drill Flank Wear in the Time Domainen
typeArticleen
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