DSpace@nitr: Item 2080/547

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DC Field	Value	Language
contributor.author	Panda, S S	-
contributor.author	Charkraborty, D	-
contributor.author	Pal, S K	-
date.accessioned	2007-11-07T06:39:03Z	-
date.available	2007-11-07T06:39:03Z	-
date.issued	2005	-
identifier.citation	Proceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austria	en
identifier.uri	http://hdl.handle.net/2080/547	-
description	Copyright for this article belongs to IASTED	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 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.extent	162820 bytes	-
format.mimetype	application/pdf	-
language.iso	en	-
publisher	IASTED	en
subject	Flant Wear	en
subject	Artificial Neural Network	en
subject	Drilling	en
subject	Chip Thikness	en
title	Monitoring of Drill Flank Wear in the Time Domain	en
type	Article	en
Appears in Collections:	Conference Papers

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