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

Title: Drill wear prediction using artificial neural network
Authors: Singh, A K
Panda, S S
Chakraborty, D
Pal, S K
Keywords: Flant Wear
Artificial Neural Network
Drilling
Issue Date: 2006
Publisher: Springer
Citation: The International Journal of Advanced Manufacturing Technology, Volume 28, Iss 5-6, P 456-462
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 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.
Description: Copyright for the published version belongs to Springer
URI: http://dx.doi.org/10.1007/s00170-004-2376-0
http://hdl.handle.net/2080/544
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