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

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 present...

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Main Authors:	Zhang, Xue-wu, Lv, Yan-yun, Ding, Yan-qiong, Zhou, Zhen-tao
Format:	Conference Proceeding
Language:	English
Subjects:	Communication industry Construction industry Copper Copper strips Electronics industry Feature extraction Fuzzy neural network Fuzzy neural networks Identification Inspection Moment invariant Neural networks Shipbuilding industry Strips
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creator	Zhang, Xue-wu Lv, Yan-yun Ding, Yan-qiong Zhou, Zhen-tao
description	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.
doi_str_mv	10.1109/ICCIS.2008.4670927
format	conference_proceeding
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recordid	cdi_ieee_primary_4670927
source	IEEE Xplore All Conference Series
subjects	Communication industry Construction industry Copper Copper strips Electronics industry Feature extraction Fuzzy neural network Fuzzy neural networks Identification Inspection Moment invariant Neural networks Shipbuilding industry Strips
title	Research on the recognition of surface defects in copper strip based on fuzzy neural network
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