Paper Title:
Optimum Design of Rolling Schedule for Tandem Cold Mill Using SLPSO
  Abstract

This paper proposes a new method to optimize cold strip rolling schedule by means of self-adaptive learning based particle swarm optimization (SLPSO). Multiple strategies may be adopted based on their previous behaviors in the searching. This particle swarm optimization version is robust and effective in solving complex problems. Function of power cost was constructed to heuristically direct the SLPSO searching, based on the consideration of power distribution, speed and rolling constraints. The results of simulation demonstrate that SLPSO is more efficient in calculating than others, and provides a new valid method for the intelligent optimum design of scheduling tandem cold strip mill.

  Info
Periodical
Chapter
Chapter 2: Simulation and Engineering Optimization
Edited by
Di Zheng, Yiqiang Wang, Yi-Min Deng, Aibing Yu and Weihua Li
Pages
443-446
DOI
10.4028/www.scientific.net/AMM.101-102.443
Citation
S. J. Liu, B. C. Wu, "Optimum Design of Rolling Schedule for Tandem Cold Mill Using SLPSO", Applied Mechanics and Materials, Vols. 101-102, pp. 443-446, 2012
Online since
September 2011
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Price
$32.00
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