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Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system
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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“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]
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“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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Metadata
- 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
- Publisher
- Informa UK Limited
- 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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