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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/547

Title: Monitoring of Drill Flank Wear in the Time Domain
Authors: Panda, S S
Charkraborty, D
Pal, S K
Keywords: Flant Wear
Artificial Neural Network
Drilling
Chip Thikness
Issue Date: 2005
Publisher: IASTED
Citation: Proceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austria
Abstract: The 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.
Description: Copyright for this article belongs to IASTED
URI: http://hdl.handle.net/2080/547
Appears in Collections:Conference Papers

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