DocumentCode
481697
Title
Research on Intelligent Control Model of Leveler Based on Multi and Structured Light Detection Method
Author
Kai, Liu ; Xu, Hongzhe ; Peng, Xiaohui ; Yue, Li ; Ming, Chen
Author_Institution
Sch. of Mech. & Precision Instrum. Eng., Xian Univ. of Technol., Xian
Volume
1
fYear
2008
fDate
19-20 Dec. 2008
Firstpage
163
Lastpage
169
Abstract
Leveler is the main equipment to improve steel flatness, which is one of the keys of Industrial materials quality control but was traditionally artificially controlled, in this way the precision of control is restricted, for the dependency on workspsila technical experience and the limitation of naked eye observation. Furthermore, it canpsilat achieve automated control system like PLC. This paper presents an intelligent control model based on multi and structured detection method. On one hand, it captures the flatness information by multi and structured light. On the other hand, it study historic sample and combine it with the flatness information to confirm best technological parameters, using genetic algorithm and SVM regression algorithm. By apply this model in the product line of Tangshan iron and steel plant, we have prove its practicability in industrial production.
Keywords
genetic algorithms; industrial control; intelligent control; iron; object detection; quality control; regression analysis; shapes (structures); steel industry; support vector machines; SVM regression algorithm; Tangshan iron-steel plant; genetic algorithm; industrial material quality control; intelligent leveler control model; product line; steel flatness; steel shape detection system; structured multi light detection method; Automatic control; Building materials; Control systems; Electrical equipment industry; Industrial control; Intelligent control; Metals industry; Programmable control; Quality control; Steel;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Industrial Application, 2008. PACIIA '08. Pacific-Asia Workshop on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3490-9
Type
conf
DOI
10.1109/PACIIA.2008.352
Filename
4756545
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