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

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contributor.authorPanda, S S-
contributor.authorCharkraborty, D-
contributor.authorPal, S K-
date.accessioned2007-11-07T06:50:41Z-
date.available2007-11-07T06:50:41Z-
date.issued2006-
identifier.citationProceedings of the National Conference on Soft computing Techniques for Engineering Applications, SCT-2006, 24-26 March 2006, NIT, Rourkelaen
identifier.urihttp://hdl.handle.net/2080/548-
descriptionCopyright for the published Version belongs to NITRen
description.abstractIn 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
format.extent1216290 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherNITR, Rourkelaen
subjectNeuronen
subjectClusteren
subjectCentre Vectoren
subjectsensor signalen
subjectFlank Wearen
titlePrediction of Drill Flank Wear Using Radial Basis Function Neural Networken
typeArticleen
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