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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/545

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contributor.authorPanda, S S-
contributor.authorSingh, A K-
contributor.authorChakraborty, D-
contributor.authorPal, S K-
date.accessioned2007-11-07T06:12:50Z-
date.available2007-11-07T06:12:50Z-
date.issued2006-
identifier.citationJournal of Materials Processing Technology, Vol 172, Iss 2, P 283-290en
identifier.urihttp://dx.doi.org/10.1016/j.jmatprotec.2005.10.021-
identifier.urihttp://hdl.handle.net/2080/545-
descriptionCopyright for the article belongs to Elsevieren
description.abstractPresent work deals with prediction of flank wear of drill bit using back propagation neural network (BPNN). Drilling operations have been performed in mild steel work-piece by high-speed steel (HSS) drill bits over a wide range of cutting conditions. Important process parameters have been used as input for BPNN and drill wear has been used as output of the network. Inclusion of chip thickness as an input in addition to conventional parameters leads to better training of the network. Performance of the neural network has been found to be satisfactory while validated with experimental result.en
format.extent926069 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherElsevieren
subjectFlank Wearen
subjectArtificial Neural Networken
subjectDrillingen
subjectChip Thiknessen
titleDrill wear monitoring using back propagation neural networken
typeArticleen
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