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An online intelligent method for roller path design in conventional spinning

Author

Listed:
  • Pengfei Gao

    (Northwestern Polytechnical University)

  • Xinggang Yan

    (Northwestern Polytechnical University)

  • Yao Wang

    (Northwestern Polytechnical University)

  • Hongwei Li

    (Northwestern Polytechnical University)

  • Mei Zhan

    (Northwestern Polytechnical University)

  • Fei Ma

    (Sichuan Aerospace Changzheng Equipment Manufacturing Corporation)

  • Mingwang Fu

    (The Hong Kong Polytechnic University)

Abstract

The optimization design of roller path is critical in conventional spinning as the roller path greatly influences the spinning status and forming quality. In this research, an innovative online intelligent method for roller path design was developed, which can capture the dynamic change of the spinning status under flexible roller path and greedily optimize the roller movement track progressively to achieve the design of whole roller path. In tandem with these, an online intelligent design system for roller path was developed with the aid of intelligent sensing, learning, optimization and execution. It enables the multi-functional of spinning condition monitoring, real-time prediction of spinning status, online dynamic processing optimization, and autonomous execution of the optimal processing. Through system implementation and verification by case studies, the results show that the intelligent processing optimization and self-adaptive control of the spinning process can be efficiently realized. The optimal roller path and matching spinning parameters (mandrel speed, feed ratio) can be efficiently obtained by only one simulation of the spinning process and no traditional trial-and-error is needed. Moreover, the optimized process can compromise the multi-objectives, including forming qualities (wall thickness reduction and flange fluctuation) and forming efficiency. The developed methodology can be generalized to handle other incremental forming processes.

Suggested Citation

  • Pengfei Gao & Xinggang Yan & Yao Wang & Hongwei Li & Mei Zhan & Fei Ma & Mingwang Fu, 2023. "An online intelligent method for roller path design in conventional spinning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3429-3444, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02006-y
    DOI: 10.1007/s10845-022-02006-y
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    References listed on IDEAS

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    1. Miguel Cuartas & Estela Ruiz & Diego Ferreño & Jesús Setién & Valentín Arroyo & Federico Gutiérrez-Solana, 2021. "Machine learning algorithms for the prediction of non-metallic inclusions in steel wires for tire reinforcement," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1739-1751, August.
    2. Ali Jallal, Mohammed & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A novel deep neural network based on randomly occurring distributed delayed PSO algorithm for monitoring the energy produced by four dual-axis solar trackers," Renewable Energy, Elsevier, vol. 149(C), pages 1182-1196.
    3. Grosso, A. & Jamali, A.R.M.J.U. & Locatelli, M., 2009. "Finding maximin latin hypercube designs by Iterated Local Search heuristics," European Journal of Operational Research, Elsevier, vol. 197(2), pages 541-547, September.
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