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Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system

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  • Qiang Liu
  • Hao Zhang
  • Jiewu Leng
  • Xin Chen

Abstract

Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.

Suggested Citation

  • Qiang Liu & Hao Zhang & Jiewu Leng & Xin Chen, 2019. "Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3903-3919, June.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:12:p:3903-3919
    DOI: 10.1080/00207543.2018.1471243
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    Cited by:

    1. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    2. Jun-Qiang Wang & Yun-Lei Song & Peng-Hao Cui & Yang Li, 2023. "A data-driven method for performance analysis and improvement in production systems with quality inspection," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 455-469, February.
    3. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    4. Jiewu Leng & Zisheng Lin & Zhiqiang Huang & Ruijun Ye & Qiang Liu & Xin Chen, 2024. "Rapid simplification of 3D geometry model of mechanisms in the digital twins-driven manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2765-2786, August.
    5. Shimin Liu & Pai Zheng & Jinsong Bao, 2024. "Digital Twin-based manufacturing system: a survey based on a novel reference model," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2517-2546, August.
    6. Dinara Dikhanbayeva & Sabit Shaikholla & Zhanybek Suleiman & Ali Turkyilmaz, 2020. "Assessment of Industry 4.0 Maturity Models by Design Principles," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    7. Leng, Jiewu & Ruan, Guolei & Jiang, Pingyu & Xu, Kailin & Liu, Qiang & Zhou, Xueliang & Liu, Chao, 2020. "Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    8. Wang, Jiancheng & He, Shuping & Luan, Xiaoli & Liu, Fei, 2020. "Fuzzy fault detection of conic-type nonlinear systems within the finite frequency domain," Applied Mathematics and Computation, Elsevier, vol. 378(C).
    9. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    10. Fan Zhang & Wenyu Zhang & Xiwen Luo & Zhigang Zhang & Yueteng Lu & Ben Wang, 2022. "Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems," Agriculture, MDPI, vol. 12(2), pages 1-19, February.
    11. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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