Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network

Abstract:

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Steel ball, as a rolling body of all kinds of bearings, it direct affects the bearings precision, dynamic performance and service life. This paper introduces the digital image technology Radial Basis Function (RBF)-Neural network, based on extracting the Steel Ball surface defect image features, used the strategy which is combined with static- dynamic clustering to union the two-stage study and design the hidden layer structure. Simulation and experiment show that the RBF-neural network runs stably, has fast convergence and overall accuracy rate of 96%. These can meet the needs of practical application.

Info:

Periodical:

Edited by:

Kai Cheng, Yongxian Liu, Xipeng Xu and Hualong Xie

Pages:

1000-1004

DOI:

10.4028/www.scientific.net/AMM.16-19.1000

Citation:

Y. L. Zhao et al., "Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network", Applied Mechanics and Materials, Vols. 16-19, pp. 1000-1004, 2009

Online since:

October 2009

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Price:

$35.00

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