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Digital-Twin-Based Monitoring System for Slab Production Process

Author

Listed:
  • Tianjie Fu

    (School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China)

  • Peiyu Li

    (School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China)

  • Chenke Shi

    (School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China)

  • Youzhu Liu

    (School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China)

Abstract

The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry’s processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification.

Suggested Citation

  • Tianjie Fu & Peiyu Li & Chenke Shi & Youzhu Liu, 2024. "Digital-Twin-Based Monitoring System for Slab Production Process," Future Internet, MDPI, vol. 16(2), pages 1-16, February.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:59-:d:1338045
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    References listed on IDEAS

    as
    1. Meng Lu & He Qing & Xie Zhi & Yang Weimin & Ci Ying & Zhang Chuanyi & Gao Hongliang, 2013. "Tundish Cover Flux Thickness Measurement Method and Instrumentation Based on Computer Vision in Continuous Casting Tundish," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-15, August.
    2. Tiejun, Dai, 2011. "The influence of iron flow on iron resource efficiency in the steel manufacturing process," Resources, Conservation & Recycling, Elsevier, vol. 55(8), pages 760-771.
    3. Lopez, Gabriel & Galimova, Tansu & Fasihi, Mahdi & Bogdanov, Dmitrii & Breyer, Christian, 2023. "Towards defossilised steel: Supply chain options for a green European steel industry," Energy, Elsevier, vol. 273(C).
    4. Kangning Zheng & Zuopeng Zhang & Jeffrey Gauthier, 2022. "RETRACTED ARTICLE: Blockchain-based intelligent contract for factoring business in supply chains," Annals of Operations Research, Springer, vol. 308(1), pages 777-797, January.
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