Paper Title:
Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network
  Abstract

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, F. L. Wu, P. Wang, J. Y. Zhang, "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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