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Application of Adaptable Neural Networks for Rolling Force Set-Up in Optimization of Rolling Schedules

  • Jingming Yang
  • Haijun Che
  • Yajie Xu
  • Fuping Dou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

Abstract

This paper presents two optimization procedures–single and multi objective optimization for 1370mm tandem cold rolling schedules, in which back propagation (BP) neural network is adopted to predict the rolling force instead of traditional models. Analysis and comparison with existing schedules are offered. The results show that the proposed schedules are more promising.

Keywords

Back Propagation Back Propagation Neural Network Roll Speed Rolling Force Adaptable Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Wang, D.D., Tieu, A.K., de Boer, F.G.: Toward a Heuristic Optimum Design of Rolling Schedule for Tandem Cold Rolling Mills. Engineering Appl. of Artificial Intelligence 13(4), 397–406 (2000)CrossRefGoogle Scholar
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    Lee, D.M., Choi, S.G.: Application of On-line Adaptable Neural Network for Rolling Force Set-up of A Plate Mill. Engineering Appl. of Artificial Intelligence 17(5), 557–565 (2004)CrossRefMathSciNetGoogle Scholar
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    Zhao, H.J., Zhang, Y.H., Hu, H.T.: The Optimized Design of Copper Strips Rolling Rules by Dynamic Programming Method. Journal of Southern Institute of Metallurgy 22(4), 243–246 (2001)Google Scholar
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    Di, H.S., Xu, J.Z., Gong, D.Y.: Effect of Load Distribution on Strip Crown in Hot Strip Rolling. J. Maser. Sci. Technol. 20(3), 330–334 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jingming Yang
    • 1
  • Haijun Che
    • 1
  • Yajie Xu
    • 1
  • Fuping Dou
    • 1
  1. 1.Institute of Electrical EngineeringYanshan UniversityQinhuangdaoP.R. China

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