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

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×

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

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

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The industrial management of SMEs in the era of Industry 4.0

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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

Characterising the Digital Twin: A systematic literature review

Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422

Review of digital twin about concepts, technologies, and industrial applications

Liu, M., Fang, S., Dong, H., & Xu, C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 2,075

Digital twin: origin to future

Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038

Digital twin paradigm: A systematic literature review

Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103469 · Cited by 1,129

Review of digital twin applications in manufacturing

Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874

Digital twins-based smart manufacturing system design in Industry 4.0: A review

Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.011 · Cited by 801

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01512-w · Cited by 700

Digital twin-based sustainable intelligent manufacturing: a review

He, B., & Bai, K.-J. (2020) · Advances in Manufacturing · doi:10.1007/s40436-020-00302-5 · Cited by 642

Digital Twins: State of the art theory and practice, challenges, and open research questions

Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587

Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey

Leng, J., Ruan, G., Jiang, P., Xu, K., Liu, Q., Zhou, X., & Liu, C. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110112 · Cited by 520

Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry

Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.020 · Cited by 436

Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing

Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1607978 · Cited by 433

A survey on AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics

Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418

Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework

Kamble, S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121448 · Cited by 414

Digital twin in smart manufacturing

Li, L., Lei, B., & Mao, C. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2021.100289 · Cited by 374

Applications of Digital Twin across Industries: A Review

Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376

Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model

Leng, J., Liu, Q., Ye, S., Jing, J., Wang, Y., Zhang, C., Zhang, D., & Chen, X. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101895 · Cited by 343

Digital twin for smart manufacturing, A review

Soori, M., Behrooz Arezoo, B., & Dastres, R. (2023) · Sustainable Manufacturing and Service Economics · doi:10.1016/j.smse.2023.100017 · Cited by 411

Simulation in industry 4.0: A state-of-the-art review

Ferreira, W. D. P., Armellini, F., & de Santa-Eulália, L. A. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106868 · Cited by 351

Industry 4.0 smart reconfigurable manufacturing machines

Morgan, J., Halton, M., Qiao, Y., & Breslin, J. G. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.001 · Cited by 336

Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system

Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., Zhao, R., Zhang, H., & Chen, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.012 · Cited by 315

Digital twin modeling method based on biomimicry for machining aerospace components

Liu, S., Bao, J., Lu, Y., Li, J., Lu, S., & Sun, X. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.04.014 · Cited by 310

Recent advances on industrial data-driven energy savings: Digital twins and infrastructures

Teng, S. Y., Touš, M., Leong, W. D., How, B. S., Lam, H. L., & Máša, V. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110208 · Cited by 325

A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools

Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304

How to model and implement connections between physical and virtual models for digital twin application

Jiang, H., Qin, S., Fu, J., Zhang, J., & Ding, G. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.012 · Cited by 286

Blockchain-Secured Smart Manufacturing in Industry 4.0: A Survey

Leng, J., Ye, S., Zhou, M., Zhao, J. L., Liu, Q., Guo, W., Cao, W., & Fu, L. (2021) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.3040789 · Cited by 291

Blockchain Security: A Survey of Techniques and Research Directions

Leng, J., Zhou, M., Zhao, J. L., Huang, Y., & Bian, Y. (2020) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2020.3038641 · Cited by 295

Human Digital Twin for Fitness Management

Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971576 · Cited by 259

The adoption of urban digital twins

Ferré-Bigorra, J., Casals, M., & Gangolells, M. (2022) · Cities · doi:10.1016/j.cities.2022.103905 · Cited by 254

ManuChain: Combining Permissioned Blockchain With a Holistic Optimization Model as Bi-Level Intelligence for Smart Manufacturing

Leng, J., Yan, D., Liu, Q., Xu, K., Zhao, J. L., Shi, R., Wei, L., Zhang, D., & Chen, X. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2019.2930418 · Cited by 251

Makerchain: A blockchain with chemical signature for self-organizing process in social manufacturing

Leng, J., Jiang, P., Xu, K., Liu, Q., Zhao, J. L., Bian, Y., & Shi, R. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.06.265 · Cited by 249

Digital twins-based remote semi-physical commissioning of flow-type smart manufacturing systems

Leng, J., Zhou, M., Xiao, Y., Zhang, H., Liu, Q., Shen, W., Su, Q., & Li, L. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127278 · Cited by 227

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

Li, L., Qu, T., Liu, Y., Zhong, R. Y., Xu, G., Sun, H., Gao, Y., Lei, B., Mao, C., Pan, Y., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3026541 · Cited by 215

Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges

Hu, W., Zhang, T., Deng, X., Liu, Z., & Tan, J. (2021) · Journal of Intelligent Manufacturing and Special Equipment · doi:10.1108/jimse-12-2020-010 · Cited by 228

Implementing Industry 4.0 principles

Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021) · Computers & industrial engineering · doi:10.1016/j.cie.2021.107379 · Cited by 173

A digital-twin visualized architecture for Flexible Manufacturing System

Fan, Y., Yang, J.-Z., Chen, J.-H., Hu, P., Wang, X., Xu, J.-C., & Zhou, B. (2021) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2021.05.010 · Cited by 181

Reconfigurable manufacturing systems from an optimisation perspective: a focused review of literature

Yelles-Chaouche, A. R., Гуревский, Е. Е., Brahimi, N., & Dolgui, A. B. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1813913 · Cited by 204

The connotation of digital twin, and the construction and application method of shop-floor digital twin

Zhuang, C., Miao, T., Liu, J., & Xiong, H. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102075 · Cited by 202

Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing

Qin, Y., Wu, X., & Luo, J. T. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3089340 · Cited by 204

A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing

Wang, P., & Luo, M. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.11.012 · Cited by 184

Digital Twin for Machining Tool Condition Prediction

Qiao, Q., Wang, J., Ye, L.-K., & Gao, R. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.04.049 · Cited by 157

Digital twin-based cyber physical production system architectural framework for personalized production

Park, K. T., Lee, J.-H., Kim, H.-J., & Noh, S. D. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04653-7 · Cited by 178

Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review

Nguyen, T. D., Duong, Q. H., Nguyen, T. V., Zhu, Y., & Zhou, L. (2021) · International Journal of Production Economics · doi:10.1016/j.ijpe.2021.108381 · Cited by 179

The framework design of smart factory in discrete manufacturing industry based on cyber-physical system

Chen, G., Wang, P., Feng, B., Li, Y., & Liu, D. (2019) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2019.1699254 · Cited by 164

Digital Twins in Product Lifecycle for Sustainability in Manufacturing and Maintenance

Rojek, I., Mikołajewski, D., & Dostatni, E. (2020) · Applied Sciences · doi:10.3390/app11010031 · Cited by 166

Digital Twin-driven machining process for thin-walled part manufacturing

Zhu, Z., Xi, X., Xu, X. W., & Cai, Y. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.03.015 · Cited by 155

Digital twin for the structural health management of reusable spacecraft: A case study

Ye, Y., Yang, Q., Yang, F., Huo, Y., & Meng, S. (2020) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2020.107076 · Cited by 144

Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters

Wang, J., Yang, C., Shen, H., Cao, J., & Rutkowski, L. (2020) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2020.2979860 · Cited by 138

Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy

Soni, D., & Kumar, N. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103419 · Cited by 144

Consistency retention method for CNC machine tool digital twin model

Wei, Y., Hu, T., Zhou, T., Ye, Y., & Luo, W. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.002 · Cited by 134

How does digital economy affect green total factor productivity at the industry level in China: from a perspective of global value chain

Meng, F., & Zhao, Y. L. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-21434-0 · Cited by 134

Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits

Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.119299 · Cited by 131

Digital twin for ship life-cycle: A critical systematic review

Mauro, F., & Kana, A. A. (2022) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113479 · Cited by 141

Digital twin of composite assembly manufacturing process

Polini, W., & Corrado, A. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1714091 · Cited by 125

State-of-the-art survey on digital twin implementations

Liu, Y. K., Ong, S. K., & Nee, A. Y. C. (2022) · Advances in Manufacturing · doi:10.1007/s40436-021-00375-w · Cited by 126

A Cross-Domain Systematic Mapping Study on Software Engineering for Digital Twins

Dalibor, M., Jansen, N., Rumpe⋆, B., Schmalzing, D., Wachtmeister, L., Wimmer, M., & Wortmann, A. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111361 · Cited by 147

The architectural design and implementation of a digital platform for Industry 4.0 SME collaboration

Liu, Z., Sampaio, P., Pishchulov, G., Mehandjiev, N., Cisneros-Cabrera, S., Schirrmann, A., Jiru, F., & Bnouhanna, N. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103623 · Cited by 119

Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review

dos Santos, C. H., Montevechi, J. A. B., de Queiroz, J. A., Miranda, R. D. C., & Leal, F. (2021) · International Journal of Production Research · doi:10.1080/00207543.2021.1898691 · Cited by 122

A digital twin-driven production management system for production workshop

Ma, J., Chen, H., Zhang, Y., Guo, H., Ren, Y., Mo, R., & Liu, L. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05977-5 · Cited by 119

Digital twin and its potential applications in construction industry: State-of-art review and a conceptual framework

Su, S., Zhong, R. Y., Jiang, Y., Song, J., Yang, F., & Cao, H. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102030 · Cited by 130

Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods

Leng, J., Guo, J. F., Xie, J., Zhou, X., Liu, A., Gu, X., Mourtzis, D., Qi, Q., Liu, Q., Shen, W., et al. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.07.012 · Cited by 115

Integrating large language model and digital twins in the context of industry 5.0: Framework, challenges and opportunities

Chen, C., Zhao, K., Leng, J., Liu, C., Fan, J., & Zheng, P. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.102982 · Cited by 118

A Reconfigurable Modeling Approach for Digital Twin-based Manufacturing System

Zhang, C., Xu, W., Liu, J., Liu, Z., Zhou, Z., & Pham, D. T. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.141 · Cited by 107

Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition

Deebak, B. D., & Al-Turjman, F. M. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22493 · Cited by 110

Digital twin for integration of design-manufacturing-maintenance: an overview

Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98

Digital Twin-Driven Cyber-Physical System for Autonomously Controlling of Micro Punching System

Zhao, R., Yan, D., Liu, Q., Leng, J., Wan, J., Chen, X., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2891060 · Cited by 95

Structural fatigue life prediction considering model uncertainties through a novel digital twin-driven approach

Wang, M., Feng, S., İncecik, A., Królczyk, G., & Li, Z. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114512 · Cited by 96

Assessment of Industry 4.0 Maturity Models by Design Principles

Dikhanbayeva, D., Shaikholla, S., Suleiman, Z., & Türkyılmaz, A. (2020) · Sustainability · doi:10.3390/su12239927 · Cited by 91

Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor

Zhang, Z., Guan, Z., Gong, Y. Y., Luo, D., & Yue, L. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1849846 · Cited by 89

A digital twin-based layout optimization method for discrete manufacturing workshop

Guo, H., Zhu, Y., Zhang, Y., Ren, Y., Chen, M., & Zhang, R. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-06568-0 · Cited by 90

Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model

Liu, S., Lu, Y., Li, J., Song, D., Sun, X., & Bao, J. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102123 · Cited by 89

Toward digital validation for rapid product development based on digital twin: a framework

Huang, S., Wang, G., Lei, D., & Yan, Y. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-08475-4 · Cited by 93

Application framework of digital twin-driven product smart manufacturing system: A case study of aeroengine blade manufacturing

Zhang, X., & Zhu, W. (2019) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419880663 · Cited by 90

Integration of real-time locating systems into digital twins

Ruppert, T., & Abonyi, J. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100174 · Cited by 83

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand, F., Ramaji, I. J., & Sadeghi, N. (2023) · Buildings · doi:10.3390/buildings13061426 · Cited by 98

A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems

Qamsane, Y., Moyne, J. R., Toothman, M., Kovalenko, I., Balta, E. C., Faris, J., Tilbury, D. M., & Barton, K. L. (2021) · IEEE Access · doi:10.1109/access.2021.3065971 · Cited by 94

From simple digital twin to complex digital twin part II: Multi-scenario applications of digital twin shop floor

Jia, W., Wang, W., & Zhang, Z. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.101915 · Cited by 89

Federated learning-empowered smart manufacturing and product lifecycle management: A review

Leng, J., Li, R. C., Xie, J., Zhou, X., Li, X., Liu, Q., Chen, X., Shen, W., & Wang, L. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103179 · Cited by 88

Bridge management through digital twin-based anomaly detection systems: A systematic review

Ríos, A. J., Plevris, V., & Nogal, M. (2023) · Frontiers in Built Environment · doi:10.3389/fbuil.2023.1176621 · Cited by 83

Digital Twin Applications in the Water Sector: A Review

Bam, P. G., Rezaei, N., Roubanis, A., Austin, D. M., Austin, E. S., Tarroja, B., Takacs, I., Villez, K., & Rosso, D. (2025) · Water · doi:10.3390/w17202957 · Cited by 98

Machining process-oriented monitoring method based on digital twin via augmented reality

Liu, S., Lu, S., Li, J., Sun, X., Lu, Y., & Bao, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06838-5 · Cited by 80

Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing

Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., & D’Amato, E. (2019) · Sensors · doi:10.3390/s20010097 · Cited by 79

Digital twin technology for smart manufacturing and industry 4.0: A bibliometric analysis of the intellectual structure of the research discourse

Ante, L. (2021) · Manufacturing Letters · doi:10.1016/j.mfglet.2021.01.003 · Cited by 42

Past, present, and future research of digital twin for smart manufacturing

Son, Y. H., Kim, G.-Y., Kim, H. C., Jun, C., & Noh, S. D. (2021) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwab067 · Cited by 79

Digital Twin-based manufacturing system: a survey based on a novel reference model

Liu, S., Zheng, P., & Bao, J. (2023) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02172-7 · Cited by 84

Edge intelligence-driven digital twin of CNC system: Architecture and deployment

Yu, H., Yu, D. I., Wang, C., Hu, Y., & Li, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102418 · Cited by 72

Secure Blockchain Middleware for Decentralized IIoT towards Industry 5.0: A Review of Architecture, Enablers, Challenges, and Directions

Leng, J., Chen, Z., Huang, Z., Zhu, X., Su, H., Lin, Z., & Zhang, D. (2022) · Machines · doi:10.3390/machines10100858 · Cited by 82

Leveraging digital twin into dynamic production scheduling: A review

Ouahabi, N., Chebak, A., Kamach, O., Laayati, O., & Zegrari, M. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102778 · Cited by 84

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Zhang, J., Deng, T., Jiang, H., Chen, H., Qin, S., & Ding, G. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.05.007 · Cited by 71

Bibliometric analysis of digital twin literature: a review of influencing factors and conceptual structure

Wang, J., Li, X., Wang, P., & Liu, Q. (2022) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2022.2026320 · Cited by 77

Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing

Patel, D., Sahu, C. K., & Rai, R. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2262050 · Cited by 77

Next generation DES simulation: A research agenda for human centric manufacturing systems

Turner, C. J., & Garn, W. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100354 · Cited by 70

Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions

Badakhshan, E., & Ball, P. D. (2023) · International Journal of Production Research · doi:10.1080/00207543.2023.2244604 · Cited by 66

Digital Twin Models: Functions, Challenges, and Industry Applications

Rayhana, R., Bai, L., Xiao, G. G., Liao, M., & Liu, Z. (2024) · IEEE Journal of Radio Frequency Identification · doi:10.1109/jrfid.2024.3387996 · Cited by 68

Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin

Zhao, L., Fang, Y., Lou, P., Yan, J., & Xiao, A. (2021) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-021-00486-1 · Cited by 65

A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation

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Digital twin monitoring and simulation integrated platform for reconfigurable manufacturing systems

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