Digital twins in Industry 5.0
Open accessAbstract
This work aims to explore the impact of Digital Twins Technology on industrial manufacturing in the context of Industry 5.0. A computer is used to search the Web of Science database to summarize the Digital Twins in Industry 5.0. First, the background and system architecture of Industry 5.0 are introduced. Then, the potential applications and key modeling technologies in Industry 5.0 are discussd. It is found that equipment is the infrastructure of industrial scenarios, and the embedded intelligent upgrade for equipment is a Digital Twins primary condition. At the same time, Digital Twins can provide automated real-time process analysis between connected machines and data sources, speeding up error detection and correction. In addition, Digital Twins can bring obvious efficiency improvements and cost reductions to industrial manufacturing. Digital Twins reflects its potential application value and subsequent potential value in Industry 5.0 through the prospect. It is hoped that this relatively systematic overview can provide technical reference for the intelligent development of industrial manufacturing and the improvement of the efficiency of the entire business process in the Industrial X.0 era.
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DT proves invaluable in optimizing production schedules, evaluating logistical strategies, and facilitating key aspects such as product development, product inception, and efficient distribution within the broader supply chain ecosystem.
“It proves invaluable in optimizing production schedules, evaluating logistical strategies, and facilitating key aspects such as product development, product inception, and efficient distribution within the broader supply chain ecosystem [102].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [102]
The Introduction describes DT-assisted product research and development, while “Enabling technology of DT applied in 5IR” describes testing hypothetical logistics situations and improving supply-chain decisions and efficiency (page numbers unavailable). The copy does not specifically establish optimization of production schedules, product inception, or efficient product distribution. (LLM, conf=0.83)
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Zhao, H., Zhao, Q., & Ślusarczyk, B. (2019) · Sustainability · doi:10.3390/su11174717 · Cited by 75 · see citing works
Padovano, A., Longo, F., Nicoletti, L., & Mirabelli, G. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.389 · Cited by 71 · see citing works
The Evolution of Manufacturing Systems
Mourtzis, D., & Doukas, M. (2014) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-4666-5039-8.ch001 · Cited by 69 · cited 1× · see citing works
“Mourtzis and Doukas [30] illustrated the evolution of manufacturing paradigms, their underlying principles, and the linkages between them, taking into account each era's remarkable technological advances and other sociopolitical reasons.”
Cao, X. Z., Zhao, G. L., & Xiao, W. (2020) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/0954405420937869 · Cited by 66 · see citing works
Ślusarczyk, B., Tvaronavičienė, M., Haque, A. U., & Oláh, J. (2020) · Technological and Economic Development of Economy · doi:10.3846/tede.2020.13376 · Cited by 66 · see citing works
Li, F., Liu, W., & Bi, K. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101483 · Cited by 66 · cited 1× · see citing works
“Emerging technologies are applied in modern industrial production [9].”
Rapid construction method of equipment model for discrete manufacturing digital twin workshop system
Zhang, Y., Zhang, C., Yan, J., Yang, C., & Liu, Z. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102309 · Cited by 64 · cited 1× · see citing works
“This process must express physical objects as digital models recognized by computers and networks [90,91].”
Beier, G., Matthess, M., Guan, T., Pereira, D. I. D. O., Xue, B., de Lima, E. P., & Chen, L. (2022) · Sustainable Production and Consumption · doi:10.1016/j.spc.2022.02.017 · Cited by 60 · see citing works
A Novel Method for Tunnel Digital Twin Construction and Virtual-Real Fusion Application
Wu, Z., Ying, C., Li, Q., & Cai, R. (2022) · Electronics · doi:10.3390/electronics11091413 · Cited by 60 · see citing works
An Innovative Methodology for Big Data Visualization for Telemedicine
Galletta, A., Carnevale, L., Bramanti, A., & Fazio, M. C. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2842234 · Cited by 58 · see citing works
Digital twins in human understanding: a deep learning-based method to recognize personality traits
Sun, J., Tian, Z., Fu, Y., Geng, J., & Liu, C. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1757155 · Cited by 58 · cited 1× · see citing works
“[99] believed that there are 2 differences between DT and digital factories in the process industry.”
Application Status and Prospect of Digital Twin for On-Orbit Spacecraft
Yang, W., Zheng, Y., & Li, S. (2021) · IEEE Access · doi:10.1109/access.2021.3100683 · Cited by 58 · see citing works
Digital Twin-Based Integrated Monitoring System: Korean Application Cases
Choi, S., Woo, J., Kim, J., & Lee, J. Y. (2022) · Sensors · doi:10.3390/s22145450 · Cited by 57 · see citing works
Intelligent manufacturing in industry 4.0: A case study of Sany heavy industry
Shan, S., Wen, X., Wei, Y., Wang, Z., & Chen, Y. (2020) · Systems Research and Behavioral Science · doi:10.1002/sres.2709 · Cited by 55 · cited 1× · see citing works
“It considers people orientation, sustainability, and flexibility [4,5].”
Liu, Y., Tong, K., Mao, F., & Yang, J. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04284-y · Cited by 54 · cited 1× · see citing works
“[69] showed that the traditional monitoring system and equipment could not meet the needs of enterprises.”
Trybała, P., Blachowski, J., Błażej, R., & Zimroz, R. (2020) · Remote Sensing · doi:10.3390/rs13010055 · Cited by 54 · see citing works
Wang, J., Rui, C., Liu, M., & Liao, P. (2021) · Sensors · doi:10.3390/s21186172 · Cited by 54 · cited 1× · see citing works
“Intelligent machinery, equipment, and new management concepts have been promoted [61].”
An ontology for interoperability assessment: A systemic approach
Leal, G. D. S. S., Guédria, W., & Panetto, H. (2019) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2019.07.001 · Cited by 52 · see citing works
Efficient container virtualization-based digital twin simulation of smart industrial systems
Lin, T. Y., Shi, G., Yang, C., Zhang, Y., Wang, J., Jia, Z., Guo, L., Xiao, Y., Wei, Z., & Lan, S. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124443 · Cited by 52 · cited 1× · see citing works
“It greatly improves the efficiency of analysis and decision-making [86,87].”
End-to-End Transmission Control for Cross-Regional Industrial Internet of Things in Industry 5.0
Zong, L., Memon, F. H., Li, X., Wang, H., & Dev, K. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3133885 · Cited by 52 · cited 1× · see citing works
“[55] noted that the physical-virtual crossing had realized the horizontal and vertical interconnection of the entire value chain.”
A Survey of Hybrid Human-Artificial Intelligence for Social Computing
Wang, W., Ning, H., Shi, F., Dhelim, S., Zhang, W., & Chen, L. (2021) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3131683 · Cited by 51 · cited 1× · see citing works
“[60] proposed that the 3 key technologies behind human–machine integration were AI, sensors, and HCI.”
Mehdiabadi, A., Shahabi, V., Shamsinejad, S., Amiri, M., Spulbar, C. M., & Birău, R. (2022) · Applied Sciences · doi:10.3390/app12105126 · Cited by 47 · see citing works
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 · see citing works
(2021) · Journal of Self-Governance and Management Economics · doi:10.22381/jsme9220211 · Cited by 43 · cited 1× · see citing works
“In the past technological revolution, robots and automation have incurred paradigm changes in global manufacturing [70,71].”
Construction and application of smart factory digital twin system based on DTME
Xia, L., Lu, J., Zhang, H., Xu, M., & Li, Z. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-08971-1 · Cited by 40 · see citing works
Braun, M., Broy, N., Pfleging, B., & Alt, F. (2019) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-019-00301-2 · Cited by 39 · cited 1× · see citing works
“Intelligent devices and robots can realize seeing, listening, smelling, or feeling by combining relevant software to understand their environment and make corresponding responses intuitively [63,64].”
Digital Twin: Industrial Robot Kinematic Model Integration to the Virtual Reality Environment
Kuts, V., Cherezova, N., Sarkans, M., & Otto, T. (2020) · Journal of Machine Engineering · doi:10.36897/jme/120182 · Cited by 36 · cited 1× · see citing works
“This process must express physical objects as digital models recognized by computers and networks [90,91].”
Digitalization of the tourism industry: What are the impacts of the new wave of technologies
Zeqiri, A., Dahmani, M., & Youssef, A. B. (2020) · HAL · Cited by 36 · see citing works
Schimanski, C. P., Monizza, G. P., Marcher, C., & Matt, D. T. (2020) · Frontiers of Engineering Management · doi:10.1007/s42524-020-0105-5 · Cited by 35 · cited 1× · see citing works
“[84] found that integrating DT and lean construction was the key to project success.”
Bryndin, E. (2020) · American Journal of Mechanical and Industrial Engineering · doi:10.11648/j.ajmie.20200502.12 · Cited by 35 · cited 1× · see citing works
“Bryndin [45] observed that 5IR offered a different perspective and highlighted deep learning and innovative capability in supporting long-term services to humanity globally.”
Bonaccorsi, A. J., Chiarello, F., Fantoni, G., & Kammering, H. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113645 · Cited by 34 · see citing works
(2021) · Journal of Self-Governance and Management Economics · doi:10.22381/jsme9220212 · Cited by 34 · cited 1× · see citing works
“Brown [72] observed that 3D digital visualization technology could collect, monitor, and analyze the real-time information of industrial equipment.”
Yun, S., Kwon, J.-W., & Kim, W.-T. (2022) · Sensors · doi:10.3390/s22134774 · Cited by 34 · see citing works
Kaiser, B., Reichle, A., & Verl, A. W. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.04.063 · Cited by 34 · see citing works
Planetary-Scale Geospatial Open Platform Based on the Unity3D Environment
Lee, A., Chang, Y., & Jang, I. (2020) · Sensors · doi:10.3390/s20205967 · Cited by 31 · see citing works
Complex product manufacturing and operation and maintenance integration based on digital twin
Wang, Y., Ren, W., Li, Y., & Zhang, C. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-07350-6 · Cited by 31 · see citing works
Aceta, C., Fernández, I., & Soroa, A. (2022) · Applied Sciences · doi:10.3390/app12031192 · Cited by 29 · see citing works
Catalano, M., Chiurco, A., Fusto, C., Gazzaneo, L., Longo, F., Mirabelli, G., Nicoletti, L., Solina, V., & Talarico, S. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.389 · Cited by 28 · see citing works
Mattila, J., Ala-Laurinaho, R., Autiosalo, J., Salminen, P., & Tammi, K. (2022) · Machines · doi:10.3390/machines10040225 · Cited by 27 · see citing works
Machine Tool: From the Digital Twin to the Cyber-Physical Systems
Armendia, M., Alzaga, A., Peysson, F., Fuertjes, T., Cugnon, F., Öztürk, E., & Flum, D. (2019) · Springer eBooks · doi:10.1007/978-3-030-02203-7_1 · Cited by 25 · cited 1× · see citing works
“[80] explored the application status of DT in CPSs.”
Implications of Industry 4.0 to companies’ performance: A comparison between Brazil and Germany
Brunheroto, P. H., Tomanek, D. P., & Deschamps, F. (2021) · Brazilian Journal of Operations & Production Management · doi:10.14488/bjopm.2021.009 · Cited by 21 · see citing works
Sun, X., Liu, S., Bao, J., Li, J., & Liu, Z. (2021) · Machines · doi:10.3390/machines10010011 · Cited by 21 · cited 1× · see citing works
“It greatly improves the efficiency of analysis and decision-making [86,87].”
Automatic creation of a 3D cartoon from natural language story
El-Mashad, S. Y., & Hamed, E.-H. S. (2021) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2021.11.010 · Cited by 18 · cited 1× · see citing works
“Intelligent sensors enable robots to imitate human sensory systems to sense the environment and promote the development of human–machine integration [62].”
Introducing VR technology for increasing the digitalization of SMEs
Liagkou, V., & Stylios, C. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.11.101 · Cited by 14 · see citing works
Defense-in-depth against insider attacks in cyber-physical systems
Ning, X., & Jiang, J. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.12.001 · Cited by 14 · cited 2× · see citing works
“Ning and Jiang [88] used the laboratory scale network physical system platform based on industrial communication networks and physical sensors for verification and found that both data-driven and model-based technologies were considered to be able to capture stealth attacks and prevent them.”
Story Analysis Using Natural Language Processing and Interactive Dashboards
Mitri, M. (2020) · Journal of Computer Information Systems · doi:10.1080/08874417.2020.1774442 · Cited by 10 · cited 1× · see citing works
“Intelligent devices and robots can realize seeing, listening, smelling, or feeling by combining relevant software to understand their environment and make corresponding responses intuitively [63,64].”
Барна, М., Melnyk, I., & Baran, R. (2021) · Baltic Journal of Economic Studies · doi:10.30525/2256-0742/2021-7-3-29-36 · Cited by 9 · see citing works
Sharma, R., & Villányi, B. J. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.09.001 · Cited by 7 · see citing works
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Alfaro-Viquez, D., Zamora-Hernández, M.-A., Fernandez-Vega, M., García‐Rodríguez, J., & Azorín-López, J. (2025) · Electronics · doi:10.3390/electronics14040646 · Cited by 110 · see citing works
How will the digital twin shape the future of industry 5.0?
Barata, J., & Kayser, I. (2024) · Technovation · doi:10.1016/j.technovation.2024.103025 · Cited by 94 · see citing works
“Virtual reality is an important enabler of user-friendly DT adoption in Industry 5.0 (Lv, 2023).”
InfluentialSupriya, Y., Bhulakshmi, D., Bhattacharya, S., Gadekallu, T. R., Vyas, P., Kaluri, R., Sumathy, S., Koppu, S., Brown, D. J., & Mahmud, M. (2024) · IEEE Access · doi:10.1109/access.2024.3401473 · Cited by 111 · see citing works
“Educators can use DT to demonstrate real-world applications of theoretical concepts, making learning more tangible and relevant [113].”
BackgroundDigital Twin Approach for Operation and Maintenance of Transportation System—Systematic Review
Werbińska-Wojciechowska, S., Giel, R., & Winiarska, K. (2024) · Sensors · doi:10.3390/s24186069 · Cited by 109 · see citing works
IoT-Based Framework for Digital Twins in the Industry 5.0 Era
Awouda, A. M. M., Traini, E., Bruno, G., & Chiabert, P. (2024) · Sensors · doi:10.3390/s24020594 · Cited by 85 · see citing works
“Another review carried out by [41] analyzed the application of digital twins in the context of Industry 5.0.”
InfluentialUnlocking the potential of digital twins in supply chains: A systematic review
Zaidi, S. A. H., Khan, S. A., & Chaabane, A. (2024) · Supply Chain Analytics · doi:10.1016/j.sca.2024.100075 · Cited by 89 · see citing works
Interoperability levels and challenges of digital twins in cyber–physical systems
Acharya, S., Khan, A. A., & Päivärinta, T. (2024) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2024.100714 · Cited by 79 · see citing works
Krupáš, M., Kajáti, E., Liu, C., & Zolotová, I. (2024) · Sensors · doi:10.3390/s24072232 · Cited by 78 · see citing works
Piccarozzi, M., Silvestri, L., Silvestri, C., & Ruggieri, A. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123467 · Cited by 79 · see citing works
Zang, T., Wang, S., Wang, Z., Li, C., Liu, Y., Xiao, Y., & Zhou, B. (2024) · Energies · doi:10.3390/en17123013 · Cited by 62 · see citing works
“Integrating advanced sensing devices with IoT and digital twin technologies [162] enables the establishment of a wide-area monitoring system.”
Digital twin systems for musculoskeletal applications: A current concepts review
Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R. (2025) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1002/ksa.12627 · Cited by 60 · see citing works
Singh, K., Patra, F., Ghosh, T., Mahnot, N. K., Dutta, H., & Duary, R. K. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100694 · Cited by 56 · see citing works
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 · see citing works
Hao, Y., Zhu, L., Wang, J., Shu, X., Yong, J., Xie, Z., Qin, S., Pei, X., Yan, T., Qin, Q., et al. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.08.002 · Cited by 48 · see citing works
Ma, Y., Zheng, S., Yang, Z., Zheng, P., Leng, J., & Hong, J. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.019 · Cited by 41 · see citing works
On-machine measurement and compensation of thin-walled surface
Zhu, L., Hao, Y., Qin, S., Pei, X., Yan, T., Qin, Q., Lü, H., Yan, B., Shu, X., & Yong, J. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.109308 · Cited by 37 · see citing works
The impact of industry 5.0 on supply chain performance
Nazarian, H., & Khan, S. A. (2024) · International Journal of Engineering Business Management · doi:10.1177/18479790241297022 · Cited by 36 · see citing works
Lakhan, A. R., Lateef, A. A. A., Ghani, M. K. A., Abdulkareem, K. H., Mohammed, M. A., Nedoma, J., Martínek, R., & García-Zapirain, B. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101747 · Cited by 35 · see citing works
“The industrial Internet of Things (IIoT) is a revolutionary paradigm to improve digital productivity in different businesses (e.g., healthcare, manufacturing, and transport) (Lv, 2023).”
InfluentialParallel Driving with Big Models and Foundation Intelligence in Cyber–Physical–Social Spaces
Wang, X., Huang, J., Tian, Y., Sun, C., Yang, L., Lou, S., Lv, C., Sun, C., & Wang, F. (2024) · Research · doi:10.34133/research.0349 · Cited by 29 · see citing works
Liu, S., Li, P., Wang, J., & Liu, P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35162 · Cited by 29 · see citing works
“…Information Technology (IT), and Industrial Internet of Things (IIoT) [13,14], from personalized customer demands to digital twin simulation design [15], and then to the flexibility and sustainability of supply chains [9], intelligent manufacturing improves resource utilization efficiency and…”
BackgroundThe Importance of Digital Transformation (5.0) in Supply Chain Optimization: An Empirical Study
Chidozie, B. C., Ramos, A. L., Ferreira, J. V., & Ferreira, L. P. (2024) · Production Engineering Archives · doi:10.30657/pea.2024.30.12 · Cited by 27 · see citing works
Digital twins in sport: Concepts, taxonomies, challenges and practical potentials
Hliš, T., Fister, I., & Fister, I. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125104 · Cited by 27 · see citing works
Tao, M., Liao, L., Zhang⋆, Y., Liu, L., Min, G., Niyato, D. T., & Dustdar, S. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3595117 · Cited by 24 · see citing works
“Therefore, with the ability of necessitating the model training based on physical data, the data-driven method is more applicable for the DT construction/update in the DITEN for IIoT [17].”
The Future of Digital Twin Research and Development
Bossuyt, D. L. V., Allaire, D., Bickford, J. F., Bozada, T. A., Chen, W., Cutitta, R. P., Cuzner, R. M., Fletcher, K. M., Giachetti, R. E., Hale, B., et al. (2025) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4068082 · Cited by 24 · see citing works
Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
“It proves invaluable in optimizing production schedules, evaluating logistical strategies, and facilitating key aspects such as product development, product inception, and efficient distribution within the broader supply chain ecosystem [102].”
Hao, Y., Zhu, L., Qin, S., Pei, X., Yan, T., Qin, Q., Hao, L., & Yan, B. (2024) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ad6de3 · Cited by 20 · see citing works
Sun, L., Huang, X., Zhao, J., Jiang, Z., & Jiang, F. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104266 · Cited by 19 · see citing works
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 29 · see citing works
“Consequently, these developments not onlOy support decentralized smart manufacturing under Industry 4.0 but also align with the resilience concept of Industry 5.0 (Leng et al., 2023; Lv, 2023; Mazumder et al., 2023).”
InfluentialDigital Twins for Consumer Electronics
Sai, S., Rastogi, A., & Chamola, V. (2023) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2023.3322013 · Cited by 18 · see citing works
Asmat, H., Din, I. U., Almogren, A., & Khan, M. Y. (2025) · IEEE Access · doi:10.1109/access.2025.3546085 · Cited by 17 · see citing works
“Using dynamic learning and real-time data, it will ensure efficiency, quality, and safety of workers, which are required for Industry 5.0 [15].”
InfluentialChallenges and opportunities of the transition from Industry 4.0 to Industry 5.0
Mourtzis, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00004-1 · Cited by 15 · see citing works
A CatBoost‐Based Modeling Approach for Predicting End‐Point Carbon Content of Electric Arc Furnace
Lu, H., Zhu, H., Jiang, Z., Li, H., Yang, C., Feng, H., & Zhang, S. (2024) · steel research international · doi:10.1002/srin.202400053 · Cited by 14 · see citing works
Artificial Intelligence-Aided Digital Twin Design: A Systematic ReviewPreprint
Hao, N., Li, Y., Liu, K., Liu, S., Lu, Y., Xu, B., Li, C., Chen, J., Yue, L., Fu, T., et al. (2024) · Preprints.org · doi:10.20944/preprints202408.2063.v1 · Cited by 14 · see citing works
Rahman, A., Ali, M. H., Mahmood, M. A., & Liou, F. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16782-3 · Cited by 12 · see citing works
“Therefore, HMIs act as a mediator to mitigate unnecessary manual labor and facilitate purposeful and efficient human engagement amid the delicate equilibrium where machine reliability complements human creativity in the spirit of Industry 5.0 paradigms [97].”
InfluentialCold Laser Sintering of Medicines: Toward Carbon Neutral Pharmaceutical Printing
Elbadawi, M., Li, H., Ghosh, P., Alkahtani, M. E., Lu, B., Basit, A. W., & Gaisford, S. (2024) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.4c01439 · Cited by 12 · see citing works
Industry 5.0 and Supply Chain Management: Coevolution and Future Research Directions
Bandara, A. M. A. S. M., Thibbotuwawa, A., Perera, H. N., & Nielsen, P. (2024) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2024.09.152 · Cited by 12 · see citing works
Jaouhari, A. E., Arif, J., Samadhiya, A., Dhayal, K. S., Luthra, S., & Singh, S. (2024) · Engineering Management Journal · doi:10.1080/10429247.2024.2407254 · Cited by 15 · see citing works
“0 overcome past technical obstacles by spotting them more quickly, recognizing things that can be upgraded or reconfigured based on their productivity, making accurate predictions, foreseeing mistakes, and preventing sizable financial losses (Lv, 2023).”
Ruan, W., Duan, H., Sun, Y., Yuan, W., & Xia, J. (2023) · Research · doi:10.34133/research.0246 · Cited by 12 · see citing works
“This problem is derived from the actual civil, industrial, and military backgrounds [2,3], which is motivated by the applications in the field of autonomous robots and aerospace [4–6], such as border defense, mis-sile interception, region security, and unmanned aerial vehicle (UAV) target guarding,…”
BackgroundFederated multimodal AI for precision-equitable diabetes care
Bai, B., Liu, X., & Li, H. Q. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1678047 · Cited by 10 · see citing works
“Scalability is still a challenge, requiring federated learning and cloud-based architectures to deploy DTs across healthcare systems without sacrificing data security (279, 280).”
Uncertainty Quantification for Digital Twins in Smart Manufacturing and Robotics: A Review
Battula, S. S. K., Alla, S. N., Ramana, E. V., Namala, K. K., & Murthy, S. B. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2837/1/012059 · Cited by 12 · see citing works
Rojek, I., Mikołajewski, D., Dostatni, E., Cybulski, J., & Kozielski, M. (2025) · Applied Sciences · doi:10.3390/app15158525 · Cited by 13 · see citing works
Marketing 5.0 como eje de posicionamiento en las empresas del Ecuador
Morales-López, D. A., & Zaldumbide-Peralvo, D. A. (2023) · 593 Digital Publisher CEIT · doi:10.33386/593dp.2023.3-1.1868 · Cited by 10 · see citing works
A survey of joint security-safety for function, information and human in industry 5.0
Hong, Y., Wu, J., & Guan, X. (2024) · Security and Safety · doi:10.1051/sands/2024014 · Cited by 11 · see citing works
Alguirat, I., Zouari, A., & Lehyani, F. (2024) · International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA) · doi:10.1109/logistiqua61063.2024.10571453 · Cited by 7 · see citing works
“As a result, digital twins facilitate integrated intelligent upgrading of equipment, improving maintenance planning and reducing latency in industrial environments [1].”
BackgroundMethodInfluentialDigital Twin: State-of-the-Art and Future Perspectives
Xu, Q., Wang, J., Gao, W., Ren, S., & Li, Z. (2024) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3708036.3708159 · Cited by 7 · see citing works
“Digital twin technology, as discussed in reference [22], facilitates automated, real-time process analysis between interconnected machinery and data sources, accelerating the identification and rectification of errors.”
Combination of Digital Twin Technology and FinOps in Management Accounting Modeling
Zadorozhnyi, Z.-M., Desyatnyuk, O., Muravskyi, V., & Shevchuk, O. (2023) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acit58437.2023.10275621 · Cited by 7 · see citing works
“For example, Lv Zhihan identifies digital twin technology as a key path to 5.0 Industry by providing automated real-time analysis of business processes in real time for rapid identification and correction of potential errors [1].”
MethodInfluentialIndustry 5.0 and Overall Supply Chain Performance: A Proposed Conceptual Framework
Nazarian, H., & Khan, S. A. (2024) · International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP) · doi:10.3390/engproc2024076077 · Cited by 6 · see citing works
“Lv [31] investigated the influence of DT Technology on industrial manufacturing within the framework of I5.”
Wu, X., Huang, X., Liang, Y., Lu, L., Yang, S., Liao, J., Chen, H., Liu, F., Zhong, Y., Liang, Q., et al. (2025) · Advanced Science · doi:10.1002/advs.202513680 · Cited by 6 · see citing works
Imandi, R., Chethana, B., Prasad, B. M. P., Sethi, K., & N, P. K. B. (2025) · Springer eBooks · doi:10.1007/978-3-031-87837-4_9 · Cited by 6 · see citing works
Interactive Digital Twins: Integrating XR and Motion Capture for Timber Assembly
Moisi, A., Kenny, J. C., Bucklin, O., Aejmelaeus‐Lindström, P., Kango, A., Casas, G., Mitterberger, D., Gramazio, F., & Köhler, M. (2025) · ACM Symposium on Computational Fabrication (SCF) · doi:10.1145/3745778.3766661 · Cited by 5 · see citing works
“Using wearable interactive devices, sensors, markers, computer vision, and machine learning, enables data to be immediately and effectively captured, transmitted, and recorded [Akanmu et al. 2021; Lv 2023].”
Impact of Digital Twins on Real Practices in Manufacturing Industries
Khan, M. Q., Alvi, M. A. H., Nawaz, H., & Umar, M. (2025) · Inventions · doi:10.3390/inventions10060106 · Cited by 6 · see citing works
“The report foresees additional DT applications in Industry 5.0, directing the advancement of intelligence in industrial manufacturing going forward [101].”
InfluentialAn AR-assisted Human-Robot Interaction System for Improving LLM-based Robot Control
Zhao, Z., Lou, S., Tan, R., & Lv, C. (2024) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram61939.2024.10673005 · Cited by 7 · see citing works
“Compared to traditional production monitoring and debugging methods, the digital twin offers significant advantages in 3D visualization, synchronized information transmission, and monitoring interaction technology [8].”
MethodEmpirical insights on interoperability in digital twins: Challenges & LCIM perspectives
Acharya, S., Xu, Y., Tripathi, N., Päivärinta, T., & Khan, A. A. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108048 · Cited by 5 · see citing works
Massaro, A. S. (2025) · Machines · doi:10.3390/machines13090755 · Cited by 5 · see citing works
Gupta, K. K., Saha, S., Sahoo, S. K., & Goswami, S. S. (2023) · Journal of Technology Innovations and Energy · doi:10.56556/jtie.v2i3.610 · Cited by 4 · see citing works
Implementation of the 5IR for efficient service delivery in the public sector, South Africa
Shibambu, A. (2025) · Africa’s Public Service Delivery and Performance Review · doi:10.4102/apsdpr.v13i1.887 · Cited by 3 · see citing works
Digital twin modeling: A comparison of current approaches
Sommer, L. (2024) · Open Research Europe · doi:10.12688/openreseurope.16728.1 · Cited by 3 · see citing works
Digital village analytics using digital twins
Fobiri, G., Abaitey, B. A., Nyenku, E. A., Agyemang, E. A., & Chikukwa, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28884-5.00007-5 · Cited by 4 · see citing works
Digital Twins of Manufacturing Processes Under Industry 5.0
Papacharalampopoulos, A., Sabatakakis, K., Karagianni, O. M., & Stavropoulos, P. G. (2025) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-86489-6_1 · Cited by 5 · see citing works
DriveRP: RAG and Prompt Engineering Embodied Parallel Driving in Cyber-Physical-Social Spaces
Huang, J., Ma, H., Zhang, T., Lin, F., Ma, S., Wang, X., & Wang, F. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778684 · Cited by 2 · see citing works
“DTs have garnered significant attention for their ability to enhance the modeling precision of traffic environments through decoupling and mapping approaches [10], [11].”
Webb, L., Tokhi, M. O., & Alkan, B. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101133 · Cited by 2 · see citing works
Kulkarni, M. S., & Patil, K. P. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50204-0_6 · Cited by 2 · see citing works
Exploring the Technologies of Industry 5.0, Benefits and Applications: A Systematic Review
Ghoujdam, M. E. K., Chaabita, R., Elkhalfi, O., Zehraoui, K., & Alaoui, H. E. (2024) · Studies in systems, decision and control · doi:10.1007/978-3-031-70996-8_2 · Cited by 2 · see citing works
Personalized digital human modeling in the customized clothing industry under Industry 5.0
Yu, J., Shi, H., & Duan, W. (2025) · Textile Research Journal · doi:10.1177/00405175251318243 · Cited by 1 · see citing works
“Three-dimensional (3D) human scanning systems have provided a solution for the rapid development of DHMs. 3 Numerous studies have been conducted on schemes based on principal component analysis (PCA) for the classification of female torso shapes, 14 lower body shapes in obese male populations, 15 and upper lateral bod…”
Enhancing Industrial Processes Through Digital Twin Technology in the Era of Industry 5.0
Prathiba, S. B., Dhanalakshmi, R., Kumar, S., Kumar, S., & Kumar, A. (2025) · International Conference on Technology Enabled Economic Changes (InTech) · doi:10.1109/intech64186.2025.11198355 · Cited by 1 · see citing works
“[3] Stresses the importance of real-time data analytics for effective implementation.”
A Bibliometric and Network Analysis of Digital Twins and BIM in Water Distribution Systems
Canivete, C. R. C., Chitauro, M., Flörke, M., & Okorie, M. E. (2025) · Technologies · doi:10.3390/technologies13120575 · Cited by 1 · see citing works
“…such as Cognitive DT is crucial for directing industrial processes towards sustainability and inclusion, particularly in developing nations aiming to align with Sustainable Develop-Technologies ment Goals [53], and is a motivator for accelerating the realization of Industry 5.0 in the future [60].”
InfluentialFilho, M. F., Szejka, A. L., & Loures, E. D. F. R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-15576-4_6 · Cited by 3 · see citing works
Towards intelligent production: The contribution of digital twins to production process optimization
Sibousse, M., & Ouatmane, M. (2025) · IEEE International Conference on Advanced Technologies in Supply Chain Management (ATSCM) · doi:10.1109/atscm67505.2025.11473487 · Cited by 1 · see citing works
“The digital twin even promotes the consideration of external data, such as geographical constraints, growing demand trends, or unexpected events, to proactively adjust logistics planning and increase network resilience [38].”
Niu, Y., Xuan, Z., Wang, Y., Zhang, X., Guo, Y., Li, X., Jin, S., Zhao, J., Xiao, Q., & Chang, H. (2026) · JMIR Aging · doi:10.2196/81075 · Cited by 1 · see citing works
“…Things, machine learning, and deep learning, the intelligence of DT can be further advanced through the integration of multisensor fusion, edge-cloud computing synergy, virtual reality or augmented reality interactive technologies, and generative adversarial networks for data augmentation [38,39].”
Wang, Z., Zhang, M., Bhandari, B., & Rui, L. (2026) · Food Research International · doi:10.1016/j.foodres.2026.120334 · Cited by 1 · see citing works
Emphasis Interaction Based Industry 5.0 Smart city Digital Twin’s Technology
Ramal, P. J., & Anbalagan, E. (2024) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct61902.2024.10673201 · Cited by 1 · see citing works
“Lyu et al. [11] examines the progressive nature of industrial revolutions, with a specific emphasis on the period spanning from Industry 1.0 to the conceptualized 5IR (Fifth Industrial Revolution).”
BackgroundInfluentialSingh, B. (2025) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-9740-4.ch011 · Cited by 1 · see citing works
(2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322135-4.00019-5
Ángulo, C., Chacón, A., & Ponsa, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-322135-4.00010-9
Future of Smart Manufacturing With IoT in Industry 5.0
Singh, B., & Jermsittiparsert, K. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-5498-8.ch011
Future of Smart Manufacturing as an Engine of Prosperity
Singh, B., Kaunert, C., Jermsittiparsert, K., Chandra, S., & Jain, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-5350-9.ch017
Utilizing Digital Twins for Predictive Modelling in Manufacturing Cyber-Physical Systems
Maheswari, B. U., Sujatha, R., Deekshitha, V., & Ananth, V. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-86302-8_20
Revolutionizing Smart Manufacturing for Industry 5.0
Singh, B., & Chandra, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0923-1.ch006
Cloud Computing as an Industry 5.0 Enabling Technology
López, C. E. B. (2025) · Ingeniería e Investigación · doi:10.15446/ing.investig.112793
“[13] explored the impact of digital twins on industrial manufacturing in the context of Industry 5.0, discussing their potential applications and key modeling technologies.”
InfluentialA FIWARE-Based Digital Twin for the Textile & Clothing Sector: The Adalberto Use Case
Quesado, G., da Cruz, A. M. R., Pedroso, J., & Lopes, S. I. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03722-0_14
Kashangura, C. (2025) · World Futures · doi:10.1080/02604027.2025.2569013
“…can be achieved through implementing digital twin technologies (Botín-Sanabria et al., 2022; Fukawa & Rindfleisch, 2023; Kaur et al., 2017; Lv, 2023; Yao et al., 2023, Zayed et al., 2023) in the 6 G and beyond edge intelligence AI networks that create a virtual duplicate of our real…”
FEDTWIN – trustworthy digital twin as a service for visually impaired
Priya, N. H., & Annadurai, P. (2025) · Automatika · doi:10.1080/00051144.2025.2572148
Mbara, J. M. Y.-B., Jaafar, F., & Tardif, P.-M. (2025) · International Conference on Blockchain Computing and Applications (BCCA) · doi:10.1109/bcca66705.2025.11229546
“Digital twins (DTs) are among the most transformative technologies driving Industry 5.0, enabling real-time synchronization between physical systems and their virtual counterparts [1].”
InfluentialBayesian generation of synthetic datasets for machine-learning tasks: a performance study
Fosci, P., Nieves, J., Psaila, G., Boffelli, J., & Bringas, P. G. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132508
Industrial Engineering in the Age of Industry 5.0: Bridging Technology, Humanity, and Sustainability
Patel, A., Parikh, P., & Shah, P. (2025) · American Journal of Mechanical and Industrial Engineering · doi:10.11648/j.ajmie.20251006.14
Drivers for Adoption of Industry 5.0 in Food Supply Chain
Malik, S., Panghal, A., Sindhu, S., & Rana, T. (2026) · Springer eBooks · doi:10.1007/978-3-031-98141-8_8
Optimization of Operations in Solid Bulk Port Terminals Using Digital Twin
Neyra, J., Silva, L., Netto, J. F., & Medina, A. C. (2025) · Winter Simulation Conference · doi:10.1109/wsc68292.2025.11339094
Kaur, R., & Kaur, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-30192-6.00027-6
Beyond Industry 4.0: Enabling Technologies and the Socio-Technical Foundations of Industry 5.0
Sahin, O., & Çark, Ö. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1231.c4973
Xiao, M., Cui, S. G., & Wang, L. (2026) · Cyber-Physical Energy Systems · doi:10.1016/j.cpes.2026.01.001 · Cited by 1 · see citing works
Development of a Digital Twin Prototype for a Crushing and Screening Plant
Okolnishnikov, V. V., Валерьевич, Р. С., & Zhuravlev, S. S. (2026) · International Russian Smart Industry Conference (SmartIndustryCon) · doi:10.1109/smartindustrycon68821.2026.11492991
Dimitrov, K., Elenkova, L., & Panchukov, D. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1669-5.ch012
Additive Manufacturing and 3D Printing Revolution for Tissue Engineering and Regenerative Medicine
Kumar, S., Panwar, S., Mehra, S., Kumar, S., & Verma, Y. K. (2026) · Springer eBooks · doi:10.1007/978-3-031-91500-0_14
Economic Implications of Digital Twin Adoption in Autonomous Vehicle Systems
Χαλλουμής, C. C. Κ. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-7785-8.ch009
Jana, T. K., Manna, N., Dey, P., Roy, S., & Paul, S. (2026) · International Journal on Interactive Design and Manufacturing (IJIDeM) · doi:10.1007/s12008-026-02649-8
“Such functionalities are extremely crucial for man–machine integration, as articulated in Industry 5.0 [39–41].”
InfluentialMeta-Twins: a system-of-systems digital twin for self-descriptive and autonomous data infrastructure
Lee, Z. T., Shadravan, A., & Parsaei, H. R. (2026) · Human-Intelligent Systems Integration · doi:10.1007/s42454-026-00105-2
Preventing Desynchronization in Digital Twins Through Bidirectional Age of Incorrect Information
Favero, M., Buratto, A., & Badia, L. (2026) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops63917.2026.11586484
“The coexistence of technological innovation with emerging societal needs has laid the foundation for the transition from Industry 4.0 to 5.0 [1].”
InfluentialZhao, Z., Mei, J., Li, H., Teng, X., & Du, J. (2026) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2026.101164
Human-centered Digital Twin in a dynamic manufacturing environment
Garate, E., de Calle-Etxabe, K. L., Calvo, B., Ferreiro, S., Arnáiz, A., & García, Á. (2026) · Data-Centric Engineering · doi:10.1017/dce.2026.10065
“…et al. (2023), which emphasizes interpretability, context awareness, and continuous improvement, and in broader reviews such as Massaro (2025) and Lv (2023), which highlight the growing relevance of human interaction and adaptability in Industry 5.0-oriented Digital Twins while also noting the…”
Influential
- Authors
- Lv, Z.
- Affiliations
- Uppsala University (SE)
- Corresponding author
- Lv, Zhihan
- ORCID
- Lv, Zhihan
- Venue
- Research
- Published
- 2023-01
- Volume
- 6
- Pages
- 0071-0071
- ISSN
- 2639-5274 (ISSN-L)
- PMC
- PMC10014023
- OpenAlex
- W4317543104
- Cited by
- 108
- References
- 132
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 37 · 2023: 9
- FWCI
- 14.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61902203)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- digital twin, Industry 5.0, manufacturing industry, real-time process analysis, error detection and correction, manufacturing efficiency, cost reduction
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