DSpace@nitr: Item 2080/544

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

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DC Field	Value	Language
contributor.author	Singh, A K	-
contributor.author	Panda, S S	-
contributor.author	Chakraborty, D	-
contributor.author	Pal, S K	-
date.accessioned	2007-11-07T05:58:47Z	-
date.available	2007-11-07T05:58:47Z	-
date.issued	2006	-
identifier.citation	The International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462	en
identifier.uri	http://dx.doi.org/10.1007/s00170-004-2376-0	-
identifier.uri	http://hdl.handle.net/2080/544	-
description	Copyright for the published version belongs to Springer	en
description.abstract	The 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.extent	1245184 bytes	-
format.mimetype	application/msword	-
language.iso	en	-
publisher	Springer	en
subject	Flant Wear	en
subject	Artificial Neural Network	en
subject	Drilling	en
title	Drill wear prediction using artificial neural network	en
type	Article	en
Appears in Collections:	Journal Articles

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