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

By Qiang Liu, Hao Zhang, Jiewu Leng, Xin Chen. International Journal of Production Research, 2019

314 citations recorded by the source corpus.

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Characterising the Digital Twin: A systematic literature review (2020, background): “Reported examples of these are largely manufacturing related [7,20,49,16,33,108,64,99, 105,52,106,30,71,54,14,109,27,35,17,91,48,55,101,15,63,104,21,112,- 95,9,80,36], although more speci ﬁ c examples include: smart factories [57,5], iron and steel manufacturing process (coking, sintering, blast…”
Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2019, background): “…key issue in smart manufacturing is the convergence of physical systems and cyber systems, where digital twin, a reference model to realise the interoperation and fusion between physical space and virtual space of manufacturing (Liu et al. 2018), is nowadays regarded as the most effective solution.”
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, methodology): “Deep learning with DT: Implementing deep learning solutions requires computational resources, expertise and research [42].”
A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics (2021, unknown): “Multi-objective optimization Design of automatic flow-shop manufacturing system [91]”
Digital twin paradigm: A systematic literature review (2021, methodology): “The digital twin in design stage is designed to generate the digital product design before the real execution (Q. Liu et al., 2018), (H. Zhang et al., 2017).”