FlatnessControlB_省略_orColdStripMills_ed6.pdf

FlatnessControlB_省略_orColdStripMills_ed6.pdf

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FlatnessControlB_省略_orColdStripMills_ed6

CHINESE JOURNAL OF MECHANICAL ENGINEERING Vol. 22,aNo. 2,a2009 ·287· DOI: 10.3901/CJME.2009.02.287, available online at ; Flatness Control Based on Dynamic Effective Matrix for Cold Strip Mills LIU Hongmin1, 2, *, HE Haitao3, SHAN Xiuying1, 2, and JIANG Guangbiao4 1 Engineering Research Center of Rolling Equipment and Complete Technology of Ministry of Education, Yanshan University, Qinhuangdao 066004, China 2 State Key Laboratory of Metastable Materials Science Technology,Yanshan University, Qinhuangdao 066004, China 3 College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China 4 Baogang Branch, Baoshan Iron and Steel Co., Ltd., Shanghai 200941, China Received August 26, 2008; revised December 8, 2008; accepted January 21, 2009; published electronically January 22, 2009 Abstract: Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in rolling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced: the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy

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