Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSingh, A K-
dc.contributor.authorPanda, S S-
dc.contributor.authorChakraborty, D-
dc.contributor.authorPal, S K-
dc.identifier.citationThe International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462en
dc.descriptionCopyright for the published version belongs to Springeren
dc.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
dc.format.extent1245184 bytes-
dc.subjectFlant Wearen
dc.subjectArtificial Neural Networken
dc.titleDrill wear prediction using artificial neural networken
Appears in Collections:Journal Articles

Files in This Item:
File Description SizeFormat 
sspanda-IJAMT-1.doc1.22 MBMicrosoft WordView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.