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

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1080/00207543.2018.1471243

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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.”
Method
Digital 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-citation
Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition
Atashpaz-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

Show all 52 references

Research works printed but not linked: 1

Cochran D. S., 2016, Transactions of the ASME: Journal of Manufacturing Science and Engineering, 138, 1

Sorted by citations

Items 101–200 of 370

Digital Twin and Reinforcement Learning-Based Resilient Production Control for Micro Smart Factory
Park, K. T., Son, Y. H., Ko, S. W., & Noh, S. D. (2021) · Applied Sciences · doi:10.3390/app11072977 · Cited by 60
Digital Twins-Based Smart Design and Control of Ultra-Precision Machining: A Review
Wu, L., Leng, J., & Ju, B. (2021) · Symmetry · doi:10.3390/sym13091717 · Cited by 59
Self-citation
Digital twins in manufacturing: systematic literature review for physical–digital layer categorization and future research directions
Atalay, M., Murat, U., Oksuz, B., Parlaktuna, A. M., Pisirir, E., & Testik, M. C. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.2022762 · Cited by 60
Can the digital economy improve green total factor productivity? An empirical study based on Chinese urban data
Liu, Y., Ma, C., & Huang, Z. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023296 · Cited by 59
Digital twin-driven clamping force control for thin-walled parts
Wang, G., Cao, Y., & Zhang, Y. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101468 · Cited by 59
Knowledge map and forecast of digital twin in the construction industry: State-of-the-art review using scientometric analysis
Xie, H. S., Xin, M., Lu, C., & Xu, J. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.135231 · Cited by 60
Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris
Gros, A., Guillem, A., Luca, L. D., Baillieul, É., Duvocelle, B., Malavergne, O., Leroux, L., & Zimmer, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-32504-9 · Cited by 58
Service-oriented platform for smart operation of dyeing and finishing industry
Park, K. T., Im, S. J., Kang, Y.-S., Noh, S. D., Kang, Y. T., & Yang, S. G. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1572225 · Cited by 58
Digital twins’ implications for innovation
Holopainen, M., Saunila, M., Rantala, T., & Ukko, J. (2022) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2022.2115881 · Cited by 58
Concept and engineering development of cyber physical production systems: a systematic literature review
Wu, X., Goepp, V., & Siadat, A. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06110-2 · Cited by 55
Digital Twin and Smart Manufacturing in Industries: A Bibliometric Analysis with a Focus on Industry 4.0
Moiceanu, G., & Paraschiv, G. (2022) · Sensors · doi:10.3390/s22041388 · Cited by 60
A digital twin framework development for apparel manufacturing industry
Alam, M. D., Kabir, G., & Mirmohammadsadeghi, S. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100252 · Cited by 57
A review of digital twin technology for electromechanical products: Evolution focus throughout key lifecycle phases
Cui, Z., Yang, X., Yue, J., Liu, X., Tao, W., Xia, Q., & Wu, C. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.07.016 · Cited by 54
A Bibliometric Analysis of Digital Twin in the Supply Chain
Siew, L. W., Hoe, L. W., & Lee, P. F. (2023) · Mathematics · doi:10.3390/math11153350 · Cited by 57
VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell
Park, K. T., Yang, J., & Noh, S. D. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01586-x · Cited by 51
Building blocks for digital twin of reconfigurable machine tools from design perspective
Huang, S., Wang, G., & Yan, Y. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1847340 · Cited by 50
Dynamic design method of digital twin process model driven by knowledge-evolution machining features
Liu, J., Zhao, P., Jing, X., Cao, X., Sheng, S., Zhou, H., Liu, X., & Feng, F. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1887531 · Cited by 50
Mass-Individualisation – the twenty first century manufacturing paradigm
Gu, X., & Koren, Y. (2022) · International Journal of Production Research · doi:10.1080/00207543.2021.2013565 · Cited by 50
A standardized approach for measuring the performance and flexibility of digital twins
Psarommatis, F., & May, G. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2139005 · Cited by 51
Multi-objective coupling optimization of electrical cable intelligent production line driven by digital twin
Yuan, G., Liu, X., Zhu, C., Wang, C., Zhu, M., & Sun, Y. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102682 · Cited by 48
Revolution on digital twin technology—a patent research approach
Wang, K., Lee, T., & Hsu, Y. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05314-w · Cited by 48
An integrated framework for active discovery and optimal allocation of smart manufacturing services
Zhang, G., Chen, C., Zheng, P., & Zhong, R. Y. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123144 · Cited by 46
Implementation strategy of physical entity for manufacturing system digital twin
Wei, Y., Hu, T., Wang, Y., Wei, S., & Luo, W. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102259 · Cited by 45
Digital-twin driven energy-efficient multi-crane scheduling and crane number selection in workshops
Zhao, N., Fu, Z., Sun, Y. J., Pu, X., & Luo, L. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.130175 · Cited by 45
A digital twin framework for large comprehensive ports and a case study of Qingdao Port
Yang, W., Bao, X., Zheng, Y., Zhang, L., Zhang, Z., Zhang, Z., & Li, L. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10625-1 · Cited by 46
DTUMOS, digital twin for large-scale urban mobility operating system
Yeon, H., Eom, T., Jang, K., & Yeo, J. (2023) · Scientific Reports · doi:10.1038/s41598-023-32326-9 · Cited by 44
Digital Twins along the product lifecycle: A systematic literature review of applications in manufacturing
Pronost, G., Mayer, F., Camargo, M., & Dupont, L. (2024) · Digital Twin · doi:10.12688/digitaltwin.17807.2 · Cited by 44
Digital Twin Smart Water Conservancy: Status, Challenges, and Prospects
Li, W., Ma, Z., Li, J., Li, Q., Li, Y., & Yang, J. (2024) · Water · doi:10.3390/w16142038 · Cited by 49
An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops
Zhang, C., Wang, Z., Ding, K., Chan, F. T. S., & Ji, W. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1748904 · Cited by 44
Study on the construction theory of digital twin mechanism model for mechatronics equipment
Wei, Y., Hu, T., Yue, P., Luo, W., & Ma, S. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09144-w · Cited by 43
Digital twin application with horizontal coordination for reinforcement-learning-based production control in a re-entrant job shop
Park, K. T., Jeon, S., & Noh, S. D. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1884309 · Cited by 43
An IoT-enabled simulation approach for process planning and analysis: a case from engine re-manufacturing industry
Lu, Y., Min, Q., Liu, Z., & Wang, Y. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1571237 · Cited by 42
Resilience dynamics modeling and control for a reconfigurable electronic assembly line under spatio-temporal disruptions
Zhang, D., Xie, M., Yan, H., & Liu, Q. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.01.011 · Cited by 41
Self-citation
Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines
Lee, D., Kim, C.-K., Yang, J., Cho, K.-Y., Choi, J.-H., Noh, S. D., & Nam, S. (2022) · Machines · doi:10.3390/machines10121147 · Cited by 41
A review: Insight into smart and sustainable ultra-precision machining augmented by intelligent IoT
Xu, Z., Zhu, T., Luo, F. L., Zhang, B., Poon, H., Yip, W. S., & To, S. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.03.008 · Cited by 44
Resource constrained project scheduling and material ordering problem with discounted cash flows
Asadujjaman, M., Rahman, H. F., Chakrabortty, R. K., & Ryan, M. J. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107427 · Cited by 40
Comparison of lot streaming division methodologies for multi-objective hybrid flowshop scheduling problem by considering limited waiting time
Yılmaz, B. G., Yılmaz, Ö. F., & Yeni, F. B. (2024) · Journal of Industrial and Management Optimization · doi:10.3934/jimo.2024058 · Cited by 39
Implementation of Digital Twin for Engine Block Manufacturing Processes
Bambura, R., Šolc, M., Dado, M., & Kotek, L. (2020) · Applied Sciences · doi:10.3390/app10186578 · Cited by 42
A data-driven smart management and control framework for a digital twin shop floor with multi-variety multi-batch production
Zhang, J., Liu, J., Zhuang, C., Guo, H., & Ma, H. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-10815-5 · Cited by 38
Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application
Hu, S., Li, C., Li, B., Yang, M., Wang, X., Gao, T., Xu, W., Dambatta, Y. S., Zhou, Z., & Xu, P. (2024) · Intelligent and sustainable manufacturing · doi:10.35534/ism.2024.10007 · Cited by 42
Multi‐dimensional digital twin of energy storage system for electric vehicles: A brief review
Vandana, V. P., Garg, A., & Panigrahi, B. K. (2021) · Energy Storage · doi:10.1002/est2.242 · Cited by 40
How the technologies underlying cyber-physical systems support the reconfigurability capability in manufacturing: a literature review
Napoleone, A., Negri, E., Macchi, M., & Pozzetti, A. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2074323 · Cited by 37
Digital twins’ impact on organizational control: perspectives on formal vs social control
Ukko, J., Saunila, M., Nasiri, M., Rantala, T., & Holopainen, M. (2022) · Information Technology and People · doi:10.1108/itp-09-2020-0608 · Cited by 41
Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering
Tong, X., Bao, J., & Tao, F. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102554 · Cited by 37
HMM-Based Asynchronous Controller Design of Markovian Jumping Lur’e Systems Within a Finite-Time Interval
Nie, R., He, S., Liu, F., Luan, X., & Shen, H. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.2964643 · Cited by 37
Design of intelligent manufacturing system based on digital twin for smart shop floors
Sun, M., Cai, Z., & Zhao, N. (2022) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2022.2128212 · Cited by 37
Digital twin-driven safety management and decision support approach for port operations and logistics
Wang, K., Xu, H., Wang, H., Qiu, R., Hu, Q., & Liu, X. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2024.1455522 · Cited by 35
Development and analysis of digital twins of production systems
Overbeck, L., Graves, S., & Lanza, G. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2242525 · Cited by 32
Digital Twin-Driven Rapid Customized Design of Board-Type Furniture Production Line
Yan, D., Liu, Q., Leng, J., Zhang, D., Zhao, R., Zhang, H., & Wei, L. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4050617 · Cited by 34
Self-citation
Digital Twin-Based Integrated Assessment of Flexible and Reconfigurable Automotive Part Production Lines
Yang, J., Son, Y. H., Lee, D., & Noh, S. D. (2022) · Machines · doi:10.3390/machines10020075 · Cited by 37
Digital twin enabled optimal reconfiguration of the semi-automatic electronic assembly line with frequent changeovers
Zhang, D., Leng, J., Xie, M., Yan, H., & Liu, Q. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102343 · Cited by 35
Self-citation
Toward digital twins for sawmill production planning and control: benefits, opportunities, and challenges
Chabanet, S., El-Haouzi, H. B., Morin, M., Gaudreault, J., & Thomas, P. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2068086 · Cited by 33
Digital Twin based Smart Manufacturing; From Design to Simulation and Optimization Schema
Ebni, M., Bamakan, S. M. H., & Qu, Q. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.08.109 · Cited by 36
A Maturity Model for Digital Twin Implementations in the Commercial Aerospace OEM Industry
Medina, F. G., Umpierrez, A. W., Martinez, V., & Fromm, H. (2021) · International Conference on Industrial Technology and Management (ICITM) · doi:10.1109/icitm52822.2021.00034 · Cited by 32
Digital Twin Technology — A bibliometric study of top research articles based on Local Citation Score
Ji, T., Huang, H., & Xu, X. W. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.016 · Cited by 33
Enhancing the Optimization of the Selection of a Product Service System Scheme: A Digital Twin-Driven Framework
Li, Y., & Li, L. (2020) · Strojniški vestnik – Journal of Mechanical Engineering · doi:10.5545/sv-jme.2020.6621 · Cited by 31
Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode
Zhang, Y., Qu, T., Li, M., Zhang, Z., Zhang, K., Hong, Z., Huang, G. Q., & Chen, Z. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2408671 · Cited by 30
Multidimensional Data Modeling and Model Validation for Digital Twin Workshop
Qian, W., Guo, Y. H., Cui, K., Wu, P., Fang, W., & Liu, D. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4049634 · Cited by 30
A Novel Framework for Intelligent Automation
Coito, T., Viegas, J. L., Martins, M. S. E., Mesquita-Cunha, M., Figueiredo, J., Vieira, S. M., & Sousa, J. M. C. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.11.501 · Cited by 30
Automatic generation of structural geometric digital twins from point clouds
Mirzaei, K., Arashpour, M., Asadi, E., Masoumi, H., & Li, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-26307-7 · Cited by 31
Industrial applications of digital twins: A systematic investigation based on bibliometric analysis
Ren, J., Ahmad, R., Li, D., Ma, Y., & Hui, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103264 · Cited by 32
Augmented reality assisted calibration of digital twins of mobile robots
Williams, R., Erkoyuncu, J. A., Masood, T., & Vrabič, R. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.11.033 · Cited by 29
Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems
Zhang, F., Zhang, W., Luo, X., Zhang, Z., Lu, Y., & Wang, B. (2022) · Agriculture · doi:10.3390/agriculture12020310 · Cited by 29
A simulation-based approach to design an automated high-mix low-volume manufacturing system
Herps, K., Dang, Q.-V., Martagan, T., & Adan, I. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.05.013 · Cited by 29
Digital twin design and analytics for scaling up electric vehicle battery production using robots
Sharma, A., & Tiwari, M. K. (2022) · International Journal of Production Research · doi:10.1080/00207543.2022.2152896 · Cited by 31
Digital twin-driven manufacturing equipment development
Wei, Y., Hu, T., Dong, L., & Ma, S. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102557 · Cited by 29
Opportunities and Threats of Adopting Digital Twin in Construction Projects: A Review
Wang, M., Ashour, M., Mahdiyar, A., & Sabri, S. (2024) · Buildings · doi:10.3390/buildings14082349 · Cited by 29
A Review of Digital Twinning for Rotating Machinery
Inturi, V., Ghosh, B., Sabareesh, G. R., & Pakrashi, V. (2024) · Sensors · doi:10.3390/s24155002 · Cited by 33
Digital twin-based smart shop-floor management and control: A review
Zhuang, C., Zhang, L., Liu, S., Leng, J., Liu, J., & Pei, F. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103102 · Cited by 28
Self-citation
Digital twin-driven variant design of a 3C electronic product assembly line
Yan, D., Sha, W., Wang, D., Yang, J., & Zhang, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-07894-x · Cited by 31
Social Cyber-Physical Systems and Digital Twins Networks: A perspective about the future digital twin ecosystems
Hamzaoui, M. A., & Julien, N. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.08.006 · Cited by 37
Digital Twin-Driven Intelligent Construction: Features and Trends
Zhang, H., Zhou, Y., Zhu, H., Šumarac, D. M., & Cao, M. (2021) · Structural durability & health monitoring · doi:10.32604/sdhm.2021.018247 · Cited by 28
Self-citation
Augmented Reality Authoring of Digital Twins: Design, Implementation and Evaluation in an Industry 4.0 Context
Bégout, P., Kubicki, S., Bricard, E., & Duval, T. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.918685 · Cited by 27
Making electric vehicle batteries safer through better inspection using artificial intelligence and cobots
Sharma, A. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2180308 · Cited by 28
A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms
Lee, J., Chua, P. C., Liu, B., Moon, S. K., & Lopez, M. (2024) · International Journal of Production Economics · doi:10.1016/j.ijpe.2024.109447 · Cited by 28
[Retracted] Digital Twin‐Based Ecogreen Building Design
Liu, Y., Sun, Y., Yang, A., & Gao, J. P. (2021) · Complexity · doi:10.1155/2021/1391184 · Cited by 29
Retracted
Personalized PSS Design Optimization based on Digital Twin and Extended Reality
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.267 · Cited by 26
Combined Anomaly Detection Framework for Digital Twins of Water Treatment Facilities
Wei, Y., Law, A. W., Yang, C., & Tang, D. (2022) · Water · doi:10.3390/w14071001 · Cited by 26
A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events
Wang, Y., Wang, S., Yang, W., Chunfeng, S., & Li, J. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110274 · Cited by 26
Event-Triggered Optimal Control for Macro–Micro Composite Stage System via Single-Network ADP Method
Chen, X., Chen, X., Bai, W., & Guo, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2984462 · Cited by 25
Self-citation
Fitness Monitoring System Based on Internet of Things and Big Data Analysis
Qiu, Y., Zhu, X., & Lu, J. (2021) · IEEE Access · doi:10.1109/access.2021.3049522 · Cited by 27
A Digital Twin Approach for Enforcing Diagnosability in Petri Nets
Hu, S., & Li, Z. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3321781 · Cited by 25
Tangible digital twin with shared visualization for collaborative air traffic management operations
Chen, K., Nadirsha, T. N. M., Lilith, N., Alam, S., & Svensson, Å. (2024) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2024.104546 · Cited by 24
Simulated-based methodology for the interface configuration of cyber-physical production systems
Ait-Alla, A., Kreutz, M., Rippel, D., Lütjen, M., & Freitag, M. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1778209 · Cited by 20
Digital Twin Driven Requirement Conversion in Smart Customized Design
Gu, Y., Zhang, S., & Qiu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3075069 · Cited by 23
Contrasting digital twin vision of manufacturing with the industrial reality
Savolainen, J., & Knudsen, M. S. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1972471 · Cited by 26
Digital-Twins-Driven Semi-Physical Simulation for Testing and Evaluation of Industrial Software in a Smart Manufacturing System
Cheng, K., Wang, Q., Yang, D., Dai, Q., & Wang, M. (2022) · Machines · doi:10.3390/machines10050388 · Cited by 22
A digital twin modeling method for production resources of shop floor
Liu, X., Qiu, C., Shi, J., Huang, J., Zhu, C., Ni, Z., Zhu, M., & Liu, T. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11903-2 · Cited by 22
A Case Study of Digital Twin for a Manufacturing Process Involving Human Interactions
Latif, H., Shao, G.-D., & Starly, B. (2020) · Online World Conference on Soft Computing in Industrial Applications · doi:10.1109/wsc48552.2020.9383897 · Cited by 15
A data-driven method for performance analysis and improvement in production systems with quality inspection
Wang, J., Song, Y.-L., Cui, P.-H., & Li, Y. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01780-5 · Cited by 21
Rapid simplification of 3D geometry model of mechanisms in the digital twins-driven manufacturing system design
Leng, J., Lin, Z., Huang, Z., Ye, R., Liu, Q., & Chen, X. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02178-1 · Cited by 21
Self-citation
Digital twin-based shop-floor reconfiguration design for uncertainty management
Zhang, C., Tao, F., Qi, Q., Cheng, Y., Cheng, J., Wang, B., & Nee, A. Y. C. (2025) · International Journal of Production Research · doi:10.1080/00207543.2024.2448605 · Cited by 21
Modelling retail inventory pricing policies under service level and promotional efforts during COVID-19
Priyamvada, & Kumar, A. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.134784 · Cited by 20
Six-Sigma Reference Model for Industry 4.0 Implementations in Textile SMEs
Caballero‐Morales, S., Cuautle-Gutiérrez, L., Guridi, J. D. J. C., & Álvarez-Tamayo, R. I. (2023) · Sustainability · doi:10.3390/su151612589 · Cited by 22
Unlocking factors of digital twins for smart manufacturing: a case of emerging economy
Gardas, B. B., Gunasekaran, A., & Narwane, V. S. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2023.2257655 · Cited by 22
Manufacturing crisis and twin-oriented manufacturing
Xiao, W., Qiu, T., Liu, Q., Zhao, G. L., Xing, H., & Li, R. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.02.002 · Cited by 20
Self-citation
Applications of Digital Twin
Tao, F., Zhang, M., & Nee, A. Y. C. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817630-6.00002-3 · Cited by 21
Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives
Maharjan, R., Kim, N. A., Kim, K. H., & Jeong, S. H. (2025) · International Journal of Pharmaceutics X · doi:10.1016/j.ijpx.2025.100409 · Cited by 30
Leadership in the Digital Realm: What Are the Main Challenges?
Corte, V. D., Gaudio, G. D., & Sepe, F. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.89856 · Cited by 21
Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor
Ding, K., Lei, J., Zhang, F., Wang, Y., & Wang, C. (2019) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405419875340 · Cited by 19

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks