Identify Steel Ball Surface Defect Based on Combination of Dynamic and Static RBF Neural Network
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.
Kai Cheng, Yongxian Liu, Xipeng Xu and Hualong Xie
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