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dc.contributor.authorPanda, S S-
dc.contributor.authorCharkraborty, D-
dc.contributor.authorPal, S K-
dc.identifier.citationProceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austriaen
dc.descriptionCopyright for this article belongs to IASTEDen
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 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
dc.format.extent162820 bytes-
dc.subjectFlant Wearen
dc.subjectArtificial Neural Networken
dc.subjectChip Thiknessen
dc.titleMonitoring of Drill Flank Wear in the Time Domainen
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