Prediction of Drill Flank Wear Using Radial Basis Function Neural Network

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/548

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DC Field	Value	Language
dc.contributor.author	Panda, S S	-
dc.contributor.author	Charkraborty, D	-
dc.contributor.author	Pal, S K	-
dc.date.accessioned	2007-11-07T06:50:41Z	-
dc.date.available	2007-11-07T06:50:41Z	-
dc.date.issued	2006	-
dc.identifier.citation	Proceedings of the National Conference on Soft computing Techniques for Engineering Applications, SCT-2006, 24-26 March 2006, NIT, Rourkela	en
dc.identifier.uri	http://hdl.handle.net/2080/548	-
dc.description	Copyright for the published Version belongs to NITR	en
dc.description.abstract	In the present work, different type of artificial neural network (ANN) architectures have been used in an attempt to predict flank wear in drill bits. Flank wear in drill bit depends upon speed, federate, drill diameter and hence these parameters along with other derived parameters such as thrust force and torque have been used to predict flank wear using ANN. The results obtained from different ANN architectures have been compared and some useful conclusions have been made.	en
dc.format.extent	1216290 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	NITR, Rourkela	en
dc.subject	Neuron	en
dc.subject	Cluster	en
dc.subject	Centre Vector	en
dc.subject	sensor signal	en
dc.subject	Flank Wear	en
dc.title	Prediction of Drill Flank Wear Using Radial Basis Function Neural Network	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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