DSpace@nitr >
National Institue of Technology- Rourkela >
Journal Articles >

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

Full metadata record

DC FieldValueLanguage
contributor.authorSingh, A K-
contributor.authorPanda, S S-
contributor.authorChakraborty, D-
contributor.authorPal, S K-
identifier.citationThe International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462en
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-
subjectFlant Wearen
subjectArtificial Neural Networken
titleDrill wear prediction using artificial neural networken
Appears in Collections:Journal Articles

Files in This Item:

File Description SizeFormat
sspanda-IJAMT-1.doc1216KbMicrosoft WordView/Open

Show simple item record

All items in DSpace are protected by copyright, with all rights reserved.


Powered by DSpace Feedback