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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/544

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contributor.authorSingh, A K-
contributor.authorPanda, S S-
contributor.authorChakraborty, D-
contributor.authorPal, S K-
date.accessioned2007-11-07T05:58:47Z-
date.available2007-11-07T05:58:47Z-
date.issued2006-
identifier.citationThe International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462en
identifier.urihttp://dx.doi.org/10.1007/s00170-004-2376-0-
identifier.urihttp://hdl.handle.net/2080/544-
descriptionCopyright for the published version belongs to Springeren
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 parameter like feeed-rate, spindle speed, drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data and found to be satisfactory.en
format.extent1245184 bytes-
format.mimetypeapplication/msword-
language.isoen-
publisherSpringeren
subjectFlant Wearen
subjectArtificial Neural Networken
subjectDrillingen
titleDrill wear prediction using artificial neural networken
typeArticleen
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