Application of artificial neural networks to strip steel surface defect diagnosis

Author

Qinghe, Hu ; Jiazhuo, Xu ; Weidong, Chen ; Dalei, Yang

Author_Institution

Coll. of Inf. & Sci. Eng., Northeastern Univ., Shenyang, China

fYear

2009

fDate

17-19 June 2009

Firstpage

2476

Lastpage

2479

Abstract

Based on the analysis of strip steel surface quality examination carried at home and abroad, the paper analyzes flaws and corresponding factors beginning with the design of examination system. It studies deeply the related theories and key techniques of strip steel surface quality examination system, applied neural networks for strip steel surface defect recognizing successfully. It is applied successfully to whole flow quality control technique and equipment composite diagnosis system (TQC-DS) in a steel company.

Keywords

artificial intelligence; fault diagnosis; neural nets; production engineering computing; quality control; steel industry; artificial neural networks; equipment composite diagnosis system; steel company; strip steel surface defect diagnosis; strip steel surface quality examination; surface defect recognition; surface quality examination system; whole flow quality control technique; Artificial neural networks; Companies; Flow production systems; Inspection; Manufacturing processes; Neural networks; Production systems; Quality control; Steel; Strips; Defect recognition; Neural network; Strip steel surface defect diagnosis; Whole flow quality control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192460

Filename

5192460