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| 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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| sspanda-AIA-1.pdf | | 159Kb | Adobe PDF | View/Open |
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