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

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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

DOI 10.1080/00207543.2018.1471243

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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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Authors: Liu, Q., Zhang, H., Leng, J., & Chen, X.
Affiliations: Guangdong University of Technology (CN)
Corresponding author: Leng, Jiewu
ORCID: Liu, Qiang
Zhang, Hao
Leng, Jiewu
Chen, Xin
Venue: International Journal of Production Research
Publisher: Informa UK Limited
Published: 2018-05-08
Volume: 57
Issue: 12
Pages: 3903-3919
ISSN: 0020-7543 (ISSN-L)
1366-588X
DOI: 10.1080/00207543.2018.1471243
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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Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system

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