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Development of a Neural Network Classifier for the Classification of Surface Defects of Cold Rolled Strips  

Moon, Chang-In (서울산업대학교 산업대학원 메카트로닉스공학과)
Choi, Se-Ho (POSCO 기술연구소 계측연구그룹, 서울산업대학교 기계설계 자동화공학부)
Kim, Gi-Bum (POSCO 기술연구소 계측연구그룹, 서울산업대학교 기계설계.자동화공학부)
Kim, Cheol-Ho (서울산업대학교 산업대학원 메카트로닉스공학과)
Joo, Won-Jong (서울산업대학교 기계설계.자동화공학부)
Publication Information
Journal of the Korean Society for Precision Engineering / v.24, no.4, 2007 , pp. 76-83 More about this Journal
Abstract
A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.
Keywords
Neural Networks; Surface Inspection; Steel Strip Defects; Feature Extraction; Data Normalization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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