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DC Field | Value | Language |
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dc.contributor.author | Panda, S S | - |
dc.contributor.author | Charkraborty, D | - |
dc.contributor.author | Pal, S K | - |
dc.date.accessioned | 2007-11-07T06:39:03Z | - |
dc.date.available | 2007-11-07T06:39:03Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Proceedings of the International Conference on Artificial Intelligence and Applications (AIA 2005),Innsbruck, Austria | en |
dc.identifier.uri | http://hdl.handle.net/2080/547 | - |
dc.description | Copyright for this article belongs to IASTED | en |
dc.description.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. | en |
dc.format.extent | 162820 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | IASTED | en |
dc.subject | Flant Wear | en |
dc.subject | Artificial Neural Network | en |
dc.subject | Drilling | en |
dc.subject | Chip Thikness | en |
dc.title | Monitoring of Drill Flank Wear in the Time Domain | en |
dc.type | Article | en |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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sspanda-AIA-1.pdf | 159 kB | Adobe PDF | View/Open |
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