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Open AccessArticle

Investigation and Optimization of Load Distribution for Tandem Cold Steel Strip Rolling Process

1
State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China
2
HBIS Group Tangsteel Company, Tangshan 063012, China
*
Author to whom correspondence should be addressed.
Metals 2020, 10(5), 677; https://doi.org/10.3390/met10050677
Received: 26 April 2020 / Revised: 14 May 2020 / Accepted: 18 May 2020 / Published: 21 May 2020
In order to improve the cold rolled steel strip flatness, the load distribution of the tandem cold rolling process is subject to investigation and optimization. The strip deformation resistance model is corrected by an artificial neural network that is trained with the actual measured data of 4500 strip coils. Based on the model, a flatness prediction model of strip steel is established in a five-stand tandem cold rolling mill, and the precision of the flatness prediction model is verified by rolling experiment data. To analyze the effect of load distribution on flatness, the flatness of stand 4 is calculated to be 7.4 IU, 10.6 IU, and 16.8 IU under three typical load distribution modes. A genetic algorithm based on the excellent flatness is proposed to optimize the load distribution further. In the genetic algorithm, the classification of flatness of stand 4 calculated by the developed flatness prediction model is taken as the fitness function, with the optimal reduction of 28.6%, 34.6%, 27.3%, and 18.6% proposed for stands 1, 2, 3, and 4, respectively. The optimal solution is applied to a 1740 mm tandem cold rolling mill, which reduce the flatness classification from 10.8 IU to 3.2 IU for a 1-mm thick steel strip. View Full-Text
Keywords: cold rolling; flatness; artificial neural network; load distribution; steel strip cold rolling; flatness; artificial neural network; load distribution; steel strip
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Jin, X.; Li, C.; Wang, Y.; Li, X.; Xiang, Y.; Gu, T. Investigation and Optimization of Load Distribution for Tandem Cold Steel Strip Rolling Process. Metals 2020, 10, 677.

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