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...

Full description

Saved in:
Bibliographic Details
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
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2326-8123
DOI:10.1109/ICCIS.2008.4670927