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Research on the recognition of surface defects in copper strip based on fuzzy neural network

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4 Author(s)
Xue-Wu Zhang ; Comput. & Inf. Inst., Hohai Univ., Changzhou ; Yan-yun Lv ; Yan-qiong Ding ; Zhen-tao Zhou

The quality of copper strips directly affects the performance and quality of copper and its products. So there is great significance to detect and recognize the surface defects in copper strips. The testing results from traditional manual inspection methods are unsatisfactory. So, this paper presents a novel recognition method of surface defects in copper strip based on fuzzy neural network. In this paper, the feature vectors of typical defects picked by the moment invariants form the neural network training samples and fuzzy wavelet neural network based on learning rate dynamically regulated BP algorithm identifies defects. Experiments show that this method can effectively detect surface defects in copper strips in the production line. Besides, it has a high recognition accuracy and speed.

Published in:

Cybernetics and Intelligent Systems, 2008 IEEE Conference on

Date of Conference:

21-24 Sept. 2008