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The cold rolling load distribution of the nuclear power zirconium alloy based on the self-adaptive particle swarm optimization algorithm

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Abstract

Aiming at the problem of load distribution during multi-pass cold rolling of nuclear zirconium alloy strip, the load distribution model with good plate shape is established by the self-adaptive particle swarm optimization (SAPSO) algorithm, considering the main constraint conditions including rolling force, reduction, and torque in cold rolling process. Based on the penalty function method transforming the constraint problem into the unconstrained problem, the particle swarm optimization algorithm (PSO) combined with self-adaptive inertia weight factor optimized the load distribution model is developed to improve the local search ability of the particle swarm optimization algorithm. Compared with the original nuclear zirconium alloy cold rolling schedule, the simulation results of load distribution based on the SAPSO algorithm can keep good plate shape in multi-pass cold rolling process with the high prediction accuracy. The industrial experiments demonstrate that the proportional crown difference value is consistent, and the plate shape flatness is good.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Science and Technology Major Project of China (2019ZX06002001-004), the Scientific and Technological Innovation Foundation of Shunde Graduate School of University of Science and Technology Beijing (BK19A006), and the Innovation Method Fund of China (2016IM010300).

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Cao Yuan, Cao Jian-guo, Wang Tao & Wang Lei-lei

  2. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China

    Cao Yuan, Cao Jian-guo, Wang Tao & Wang Lei-lei

  3. National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing, 100083, China

    Cao Yuan, Cao Jian-guo, Wang Tao & Wang Lei-lei

  4. China Academy of Machinery Science and Technology Group, Beijing, 100083, China

    Cao Yuan & Li Fang

  5. State Nuclear Bao Ti Zirconium Industry Company, Baoji, 721013, Shanxi, China

    Luo Qian-qian & Zhang Peng-fei

  6. University of Science and Technology Beijing, Beijing, 100083, China

    Cao Jian-guo

Authors
  1. Cao Yuan
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  2. Cao Jian-guo
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  3. Wang Tao
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  4. Wang Lei-lei
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  5. Li Fang
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  6. Luo Qian-qian
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  7. Zhang Peng-fei
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Contributions

Cao Jian-guo: conceptualization, supervision, project administration.

Cao Yuan: investigation, validation, writing (review and editing).

Wang Tao: investigation, theoretical analysis, validation, writing (original draft).

Wang Lei-lei: investigation, validation, writing (review and editing).

Li Fang: supervision, validation.

Luo Qian-qian: supervision, resources, validation.

Zhang Peng-fei: supervision, resources, validation.

Corresponding author

Correspondence to Cao Jian-guo.

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Yuan, C., Jian-guo, C., Tao, W. et al. The cold rolling load distribution of the nuclear power zirconium alloy based on the self-adaptive particle swarm optimization algorithm. Int J Adv Manuf Technol 119, 6007–6016 (2022). https://doi.org/10.1007/s00170-021-08272-z

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  • DOI: https://doi.org/10.1007/s00170-021-08272-z

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