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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/545

Title: Drill wear monitoring using back propagation neural network
Authors: Panda, S S
Singh, A K
Chakraborty, D
Pal, S K
Keywords: Flank Wear
Artificial Neural Network
Drilling
Chip Thikness
Issue Date: 2006
Publisher: Elsevier
Citation: Journal of Materials Processing Technology, Vol 172, Iss 2, P 283-290
Abstract: Present work deals with prediction of flank wear of drill bit using back propagation neural network (BPNN). Drilling operations have been performed in mild steel work-piece by high-speed steel (HSS) drill bits over a wide range of cutting conditions. Important process parameters have been used as input for BPNN and drill wear has been used as output of the network. Inclusion of chip thickness as an input in addition to conventional parameters leads to better training of the network. Performance of the neural network has been found to be satisfactory while validated with experimental result.
Description: Copyright for the article belongs to Elsevier
URI: http://dx.doi.org/10.1016/j.jmatprotec.2005.10.021
http://hdl.handle.net/2080/545
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