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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]
Citations
Cites
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · cited 1×
“…the DT (Digital Twin), which was fi rstly adopted in spacecraft design by NASA (Boschert and Rosen 2016; Brenner and Hummel 2017; Ferguson, Bennett, and Ivashchenko 2017; Grieves 2014; Grieves and Vickers 2017), can be viewed as a combination of mod-elling-based method and optimisation-based method.”
MethodDigital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920
Self-citationAtashpaz-Gargari, E., & Lucas, C. (2007) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2007.4425083 · Cited by 2,741
An overview of bilevel optimization
Colson, B., Marcotte, P., & Savard, G. (2007) · Annals of Operations Research · doi:10.1007/s10479-007-0176-2 · Cited by 1,452
The industrial management of SMEs in the era of Industry 4.0
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1372647 · Cited by 1,188
Digital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158
Reengineering aircraft structural life prediction using a digital twin
Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011) · International Journal of Aerospace Engineering · doi:10.1155/2011/154798 · Cited by 1,119
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Institute of Electrical and Electronics Engineers (2023) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.6221021 · Cited by 1,099
Design of reconfigurable manufacturing systems
Koren, Y., & Shpitalni, M. (2010) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2011.01.001 · Cited by 781
C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems
Alam, K. M., & El Saddik, A. (2017) · IEEE Access · doi:10.1109/access.2017.2657006 · Cited by 741
Research works printed but not linked: 1
- Cochran D. S., 2016, Transactions of the ASME: Journal of Manufacturing Science and Engineering, 138, 1
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Rocca, R. L., Santacruz, R. F. B., Sassanelli, C., Rosa, P., Fumagalli, L., & Negri, E. (2023) · Springer handbooks · doi:10.1007/978-3-030-67822-7_34 · Cited by 2
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Jiang, H., He, Z., Liu, S., Hai, Y., Liu, C., & Miao, S. J. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-1252-0_63 · Cited by 2
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Wang, L., Tao, F., Liu, A., & Nee, A. Y. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818918-4.00005-1 · Cited by 2
Park, K. T., Park, Y. H., Choi, Y.-H., Park, M.-W., & Noh, S. D. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-16411-8_62 · Cited by 2
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Durão, L. F. C. S., Zancul, E. D. S., & Schützer, K. (2021) · Research Square · doi:10.21203/rs.3.rs-186777/v1 · Cited by 2
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Raza, M., Bilberg, A., Malik, A. A., & da Silva, E. R. D. R. (2026) · Smart and Sustainable Manufacturing Systems · doi:10.1520/ssms20250001
Guo, H., Peng, W., Zhang, M., Li, C., & Jiao, F. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-0554-2_46 · Cited by 1
Digital twin-enhanced laser cutting production system design and operation
Fu, Z., Zhao, N., & Luo, L. (2024) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2024.2328041 · Cited by 1
Co-Evolution DTs: Achieving Value-Added Cognitive Digital Twins Across the Entire Lifecycle
Tong, X., Bao, J., & Liu, T. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711333 · Cited by 1
Volkov, A. A., Shilova, L., & Качанов, С. А. (2018) · Global Smart Industry Conference (GloSIC) · doi:10.1109/glosic.2018.8570103 · Cited by 1
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Zhang, Z., Feng, S., Xiao, M., Yang, Y., & Song, G. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-3300-6_8 · Cited by 1
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Savolainen, J., & Collan, M. (2020) · FUZZY ECONOMIC REVIEW · doi:10.25102/fer.2020.01.04
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Michelsen, K.-E., Collan, M., Savolainen, J., & Ritala, P. (2021) · Handbook of Smart Materials, Technologies, and Devices · doi:10.1007/978-3-030-58675-1_2-1 · Cited by 2
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Hao, R., & Ren, Q. (2022) · Research Square · doi:10.21203/rs.3.rs-1637590/v1
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Shao, Z., Li, W., Tan, Y., & Otto, K. (2023) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/au02.20230109
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Zheng, Z., Liu, B., Wang, Y., & Wang, L. (2025) · Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · doi:10.1177/09544062251324276
Lan, X., & Yang, S. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0585
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Semeraro, C. (2020) · theses.fr (ABES) · doi:10.70675/444f6160z43f3z4541zbc9fz2686f477f659
Bóna, K., & Molnár-Major, P. (2026) · Logistics · doi:10.3390/logistics10040091 · Cited by 2
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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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