Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/548
Title: | Prediction of Drill Flank Wear Using Radial Basis Function Neural Network |
Authors: | Panda, S S Charkraborty, D Pal, S K |
Keywords: | Neuron Cluster Centre Vector sensor signal Flank Wear |
Issue Date: | 2006 |
Publisher: | NITR, Rourkela |
Citation: | Proceedings of the National Conference on Soft computing Techniques for Engineering Applications, SCT-2006, 24-26 March 2006, NIT, Rourkela |
Abstract: | In the present work, different type of artificial neural network (ANN) architectures have been used in an attempt to predict flank wear in drill bits. Flank wear in drill bit depends upon speed, federate, drill diameter and hence these parameters along with other derived parameters such as thrust force and torque have been used to predict flank wear using ANN. The results obtained from different ANN architectures have been compared and some useful conclusions have been made. |
Description: | Copyright for the published Version belongs to NITR |
URI: | http://hdl.handle.net/2080/548 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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sspanda-NITR-1.pdf | 1.19 MB | Adobe PDF | View/Open |
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