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 |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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sspanda-IJAMT-1.doc | 1.22 MB | Microsoft Word | View/Open |
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