Cited by 370Digital Transformation in IndustryFlexible and Reconfigurable Manufacturing SystemsManufacturing Process and Optimization

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

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

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.

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Registry

Crossref · 10.1080/00207543.2018.1471243

Cites

52 of 52 references verified0 citation errors · 0 not checkable

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  • Proposed improvements are reached by reducing energy consumption.

    “The proposed improvements are reached by reducing the energy consumption [37,40]”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [40]

  • Some reviewed papers mainly belong to the first service group but also offer services of the second group, and vice versa.

    “Also, there are papers that mainly belong to the first group but also offer services of the second group ([39,40,50,70]), and vice versa ([56,61,62]).”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [40]

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Authors
Liu, Q., Zhang, H., Leng, J., & Chen, X.
Affiliations
Guangdong University of Technology (CN)
Corresponding author
Leng, Jiewu
Venue
International Journal of Production Research
Published
2018-05-08
Volume
57
Issue
12
Pages
3903-3919
ISSN
  • 0020-7543 (ISSN-L)
  • 1366-588X
OpenAlex
W2799347500
Cited by
370
References
52
Citations by year
2026: 21 · 2025: 41 · 2024: 37 · 2023: 65 · 2022: 67 · 2021: 60 · 2020: 57 · 2019: 21 · 2018: 1
FWCI
28.72
Type
journal article
Language
en
Open access
closed
Locations
  • International Journal of Production Research (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • National Natural Science Foundation of China (51675108, 51705091)
  • Science and Technology Plan Project of Guangzhou (201804020092)
  • Science and Technology Planning Project of Guangdong Province of China (2015B010128007, 2016A010106006)
  • Fundamental Research Funds for the Central Universities (2015ZZ079)
  • Science and Technology Plan Project of Guangdong Province of China (2015B010128007, 2016A010106006)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
Keywords
digital twin, part families, semi-physical simulation, multi-objective optimization, decision support

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