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A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
Open accessAbstract
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human-robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.
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“This helps manufacturers to identify and eliminate bottlenecks, reduce waste, and increase productivity [63] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [63]
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“Recent studies indicate that incorporating AI has the potential to enhance the adaptability of DTs to cope with dynamic alterations in workshops and factories.”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [53]
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“In the same way, real-time AR instruction for technicians can revolutionize remote help by decreasing the requirement of physical presence, increasing productivity, and reducing cost [53].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [53]
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Cites
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
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×
“Later, DT was indicated by Grieves and Vickers as a dynamic model based on massive amounts of information and computing capability that change over the lifecycle, including creation, production, operations and disposal [17].”
MethodDigital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
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
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
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404
Ivanov, D., & Dolgui, A. B. (2020) · Production Planning & Control · doi:10.1080/09537287.2020.1768450 · Cited by 2,254
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
Flagged first, then by citations
Research works printed but not linked: 5
- Zhang, M., Zuo, Y., and Tao, F. (2018, January 27–29). Equipment Energy Consumption Management in Applications. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China.
- Zambal, S., Eitzinger, C., Clarke, M., Klintworth, J., and Mechin, P.y. (2018, January 18–20). Effects of defects analysis based on manufacturing data. Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), Porto, Portugal.
- Malik, A.A., and Brem, A. (2020). Man, machine and work in a digital twin setup: A case study. arXiv.
- Schluse, M., and Rossmann, J. (2016, January 3–5). From Simulation to Experimentable Digital Twins. Proceedings of the IEEE International Symposium on Systems Engineering, Edinburgh, UK.
- Stehling, M., Schmiedinger, T., and Affenzeller, P. (2021, September 21). Why Robots do not matter! Using Digital Twin and Augmented Learning for Continuous Improvement in the context of Manufacturing. Researchgate.Net. Available online: https://www.researchgate.net/publication/329218023_Why_Robots_do_not_matter_Using_Digital_Twin_and_Augmented_Learning_for_Continuous_Improvement_in_the_context_of_Manufacturing.
Cited by
Sorted by citations
Items 101–200 of 418
Saranti, A., Pfeifer, B., Gollob, C., Stampfer, K., & Holzinger, A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1554 · Cited by 24
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Hoebert, T., Seibel, S., Amersdorfer, M., Vincze, M., Lepuschitz, W., & Merdan, M. (2024) · Robotics · doi:10.3390/robotics13070104 · Cited by 20
“Additionally, the presence of human users in a collaborative workspace raises the level of unpredictability, which is not easy to cover with simulation tools [45].”
InfluentialZhang, X., Wang, X., Yao, H., Luo, W., Qiang, Z., & Luo, D. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104273 · Cited by 20
Khatiri, S., Amin, F. M., Panichella, S., & Tonella, P. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10697-z · Cited by 20
“For this reason, researchers proposed to use simulation environments to test general CPSs and UAVs in a diversified set of scenarios [41,23,61,17,60,45], and to support testing automation [6,85], automated test generation [45], regression testing [18,19] and debugging [72,65] activities.”
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Hasan, A., & Nguyen, T. D. (2026) · Array · doi:10.1016/j.array.2026.100721 · Cited by 21
Image-based deep learning for smart digital twins: a review
Islam, M. R., Subramaniam, M., & Huang, P.-C. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11002-y · Cited by 25
“…such as LoRa, and cellular communication systems like 5G and 6G. Additionally, it uses a wide range of protocols, including IoT gateways and Wi-Fi access points, wireless personal area networks (WPAN), and ZigBee to low-power wide-area network (LPWAN) technologies, narrowband IoT (NB-IoT)…”
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Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022) · Green Manufacturing Open · doi:10.20517/gmo.2022.12 · Cited by 19
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Kokkinis, A., Frantzis, T., Skordis, K., Nikolakopoulos, G., & Koustoumpardis, P. Ν. (2024) · Machines · doi:10.3390/machines12120845 · Cited by 18
“• AI-assisted DTs: Advancements that are currently being made in the field of artificial intelligence suggest that AI will be further integrated into FEA simulation software to provide better insights regarding mining data [63,64].”
Drones and Autonomous Robotics Incorporating Computational Intelligence
Thangamani, R., Suguna, R., & Kamalam, G. K. (2024) · doi:10.1002/9781394175437.ch8 · Cited by 19
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Mourtzis, D., & Angelopoulos, J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00012-0 · Cited by 18
Wang, Z., Wang, S., Xie, Y., Xiong, T., & Wang, C. (2025) · Sensors · doi:10.3390/s25072014 · Cited by 17
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Rojek, I., Mikołajewski, D., Piszcz, A., Małolepsza, O., & Kozielski, M. (2025) · Applied Sciences · doi:10.3390/app151810102 · Cited by 16
Digital Twins in Industry 5.0: Challenges in Modeling and Communication
Balogh, M., Földvári, A., & Varga, P. (2023) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms56928.2023.10154424 · Cited by 16
“The emerging use of machine learning (ML) and artificial intelligence (AI) solutions [15] in the behavioral modeling of DTs may cause some concern [16].”
BackgroundWood, D. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13422-7.00017-9 · Cited by 17
Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review
Rocha-Jácome, C., Carvajal, R. G., Muñoz, F., Guevara-Cabezas, E. A., & Fort, E. H. (2021) · Sensors · doi:10.3390/s22010066 · Cited by 15
Yang, M., Moon, J., Jeong, J., Sin, S., & Kim, J. (2022) · Applied Sciences · doi:10.3390/app12020553 · Cited by 16
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Baaden, M. (2022) · Virtual Reality & Intelligent Hardware · doi:10.1016/j.vrih.2022.03.001 · Cited by 18
A digital shadow framework using distributed system concepts
AboElHassan, A., & Yacout, S. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02028-6 · Cited by 18
“German Academic Association for Production Technology (WGP) was the first to define Digital Shadow (DS) in 2016 (Huang et al., 2021; Riesener et al., 2019).”
BackgroundSu, W., Xu, G., He, Z., Machica, I. K. D., Quimno, V., Du, Y. P., & Kong, Y. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126623501682 · Cited by 15
Tailoring MLOps Techniques for Industry 5.0 Needs
Hegedűs, C., & Varga, P. (2023) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm59352.2023.10327814 · Cited by 17
“[9] provided a very comprehensive survey.”
BackgroundSwathi, B., Polyakov, S. V., Kandavalli, S. R., Singh, D., Murthy, M. Y. B., & Gopi, A. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-14155-w · Cited by 15
Liu, D., Li, Z., Wu, Z., & Li, C. (2024) · Journal of Field Robotics · doi:10.1002/rob.22473 · Cited by 15
“…performance optimizing the crop row extraction through combining line extraction algorithms such as HT (Hough transform), LR (linear regression), and HF (horizontal fringes) (Huang et al. 2021; De Silva et al. 2024; Ahmadi, Halstead, and McCool 2021; Winterhalter et al. 2021; Liang et al. 2022).”
AI advances in wheelchair navigation and control: A comprehensive review
Sahoo, S. K., & Choudhury, B. B. (2023) · Journal of Process Management New Technologies · doi:10.5937/jpmnt11-45181 · Cited by 18
Huang, Z., Wiesch, M., Fey, M., & Brecher, C. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.04.059 · Cited by 14
Zhang, W., Zhang, Y., Lan, X., & Song, M. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108713 · Cited by 13
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Mahajan, A., Singh, G., Devgan, S., Singh, G., & Kaur, P. (2026) · Computers & industrial engineering · doi:10.1016/j.cie.2025.111618 · Cited by 10
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Duan, H., Gao, S., Yang, X., & Li, Y. (2023) · Digital Twin · doi:10.12688/digitaltwin.17599.2 · Cited by 13
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Ono, S., Yamazaki, T., Miyoshi, T., Taya, A., Nishiyama, Y., & Sezaki, K. (2023) · IEEE Access · doi:10.1109/access.2023.3304374 · Cited by 12
“Within this context, digital twins play a crucial role [1], [2], [3], [4], [5], as they enable physical simulations that are closer to reality, thereby reducing the costs of experimentation and data analysis [6], [7], [8], [9], [10], [11].”
BackgroundMinz, N. K., & Khattar, Y. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-1151-6.ch010 · Cited by 12
Hussain, M. D., Rahman, M. H., & Ali, N. M. (2024) · GLOBAL MAINSTREAM JOURNAL · doi:10.62304/ijse.v1i3.161 · Cited by 20
Physics-Informed Neural Network: Principles and Applications
Son, S., Jeong, J., Jeong, D., Sun, K. H., & Oh, K. (2024) · Artificial intelligence · doi:10.5772/intechopen.1006130 · Cited by 13
Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects
Yang, J., Chen, B., Li, R., Huang, B.-Q., Zhao, M.-H., Liu, P., Zhang, J., & Ye, Z. (2024) · Current Medical Science · doi:10.1007/s11596-024-2960-5 · Cited by 11
“In addition to harnessing the parallel computing power of the graphics processing units (GPUs) for near-real-time process simulation, AI can be trained as an alternative model to effectively build DTs and increase the accuracy and real-time performance of virtual models [28] .”
Shehata, A. O., Farsangi, E. N., Morrison, G. M., Rashidi, M., Yang, T. Y., & Mirdan, M. M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115880 · Cited by 11
Kalfas, D., Kalogiannidis, S., Spinthiropoulos, K. G., Chatzitheodoridis, F., & Ziouziou, E. (2025) · Urban Science · doi:10.3390/urbansci9070267 · Cited by 11
Dinmohammadi, F. (2023) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac57885.2023.10275230 · Cited by 11
“The digital twin technology was first used by NASA’s Apollo program in 2010, where two identical space vehicles were built to allow mirroring the conditions of the space vehicle during the mission [15].”
MethodInnovative Metaheuristic Optimization Approach with a Bi-Triad for Rehabilitation Exoskeletons
Méndez, D. S., García, C., Bedolla-Martínez, D., & González, A. M. (2024) · Sensors · doi:10.3390/s24072231 · Cited by 10
“…generation and use of a DT make it possible to create sustainable work environments (they establish human–robot interaction in the modeling and simulation of the device), to evaluate the virtual prototype before manufacturing the physical device (reducing costs and manufacturing time) [34,44,45].”
MethodA Survey on AI-Empowered Softwarized Industrial IoT Networks
Rojas, E., Carrascal, D., Lopez‐Pajares, D., Álvarez-Horcajo, J., Carral, J. A., Arco, J. M., & Martinez-Yelmo, I. (2024) · Electronics · doi:10.3390/electronics13101979 · Cited by 10
“Meanwhile, Huang et al. [25] undertake a comprehensive survey of AI-driven implementation in Digital Twins within the context of Industry 4.0.”
BackgroundInfluentialImpact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry
Patel, P. (2024) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.i9949.13090824 · Cited by 10
Weingram, A., Cui, C., Lin, S., Munoz, S., Jacob, T., Viers, J. H., & Lu, X. (2025) · Frontiers in High Performance Computing · doi:10.3389/fhpcp.2025.1536501 · Cited by 14
AI-Powered CNC Digital Twin for Predictive Maintenance
Singh, S., Sethi, S., Sharma, R., Vaibhavi, D., & Tiwari, A. (2025) · International Conference on Power, Control and Computing Technologies (ICPC2T) · doi:10.1109/icpc2t63847.2025.10958573 · Cited by 10
Bhatia, M., & Kumar, V. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3575476 · Cited by 15
“Particularly when facing large data volumes, the system iden-tifies patterns and anomalies that create predictive capabilities for improved decision-making [15].”
The development of a digital twin concept system
Duan, H., Gao, S., Yang, X., & Li, Y. (2025) · Digital Twin · doi:10.12688/digitaltwin.17599.3 · Cited by 10
Prior Knowledge-Embedded Machine Learning-Driven Cutting Force Monitoring in Machinery Industry
Huang, Z., Xi, T., Fey, M., & Brecher, C. (2022) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet55527.2022.9872585 · Cited by 10
“This soft sensing of critical production parameters with sufficient accuracy and low time delay serves as the foundation for a digital shadow or digital twin [4]–[6].”
BackgroundDigitale Zwillinge in der Bioprozesstechnik – Chancen und Möglichkeiten
Richter, J., Lange, F., Scheper, T. H., Solle, D., & Beutel, S. (2022) · Chemie Ingenieur Technik · doi:10.1002/cite.202200166 · Cited by 10
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Yadav, R., Roopa, Y. M., Lavanya, M., Ramesh, J. V. N., Chitra, N. T., & Babu, G. R. (2023) · SN Computer Science · doi:10.1007/s42979-023-01976-x · Cited by 10
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Weichbroth, P., Jandy, K., & Zurada, J. M. (2024) · Telecom · doi:10.3390/telecom5030039 · Cited by 13
“In general, the architecture of a Digital Twin in terms of its modeling can be divided into three main layers [24,25]:”
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Kaur, H., & Bhatia, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3459965 · Cited by 17
“1) The analysis and seminal works, such as [27], [55], [60], [63], [66], and [92] have significantly shaped discussions and advancements in DT applications and methodologies.”
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“The integration of IoT, DTs, and AI enables the fusion of domain knowledge, simulation expertise, and data science capabilities with product understanding [18].”
Lin, K.-Y., Chang, K., Lin, Y., & Wu, M.-J. (2025) · Systems · doi:10.3390/systems13010025 · Cited by 9
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Wang, M., Cai, J., Hu, D., Hu, Y., Han, Z., & Li, S. (2025) · Frontiers of Engineering Management · doi:10.1007/s42524-025-4099-x · Cited by 11
“In addition, the Voronoi diagrams decompose the space into regions closest to a set of predefined points (Huang et al., 2021a).”
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“Recently, DTs have seen initial applications, e.g., data access management and smart manufacturing [156].”
Sun, J., Zhang, Y., Ma, X., Li, B., Yang, M., Xu, L., Xin, H., An, Q., Zhu, L., Bie, Q., et al. (2025) · International Journal of Extreme Manufacturing · doi:10.1088/2631-7990/ae1be8 · Cited by 11
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Alves, J., Arantes, L., & Moreira, A. C. (2025) · Tourism and Hospitality Research · doi:10.1177/14673584251409416 · Cited by 9
Alheet, A. F. (2026) · Logistics · doi:10.3390/logistics10010015 · Cited by 11
“[45] AI analytics, ref.”
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“Several application scenarios that employ DTs often cannot rely on supervised or heuristic approaches due to the nearly intractable multi-objective optimization problems that describe them [6].”
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Rossi, F., Bernardeschi, C., & Cococcioni, M. (2024) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2024.109238 · Cited by 7
Kang, Y. S., Choi, J. G., Yang, S. M., Lim, S., Kim, D. C., & Park, H. W. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02642-0 · Cited by 8
“…digital twin (DT) technology has emerged as a comprehensive solution that not only integrates AI-driven predictive models but also enables real-time synchronization between physical and virtual machining environments (Huang et al., 2021 ; Muth-uswamy & Shunmugesh, 2023 ; Mata et al., 2025).”
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Luis-Gómez, J., Martínez, F., Barberá, A. G., Mascarós, J., Monrós-Andreu, G., Chiva, S., Borrás, E., & Martı́nez-Cuenca, R. (2025) · Fluids · doi:10.3390/fluids10070163 · Cited by 8
“A reader willing to explore previous works in this area may be referred to the following review [27].”
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Rabaia, M. K. H., Semeraro, C., Soudan, B., Salameh, T. S. Z., Abdelkareem, M. A., & Olabi, A. G. (2025) · Energy Nexus · doi:10.1016/j.nexus.2025.100482 · Cited by 10
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Reddy, K. S. S., Rajesh, R., Raj, P. A. C., Arya, N., Bhaskaran, R., & Prasad, J. L. (2023) · SN Computer Science · doi:10.1007/s42979-023-01969-w · Cited by 8
A cognitive digital twin for process chain anomaly detection and bottleneck analysis
Iyer, S. V., Sangwan, K. S., & Dhiraj (2024) · Journal of Industrial and Production Engineering · doi:10.1080/21681015.2024.2381728 · Cited by 7
“Ahmed [9] defined CDT as an augmentation and intelligent companion of physical system with its DT. CDT extend the traditional boundaries of model based approaches [10] by integrating the data driven prediction models.”
Enhancing Digital Twins With Deep Reinforcement Learning: A Use Case in Maintenance Prioritization
Chen, S., Lopes, P. V., Marti, S., Rajashekarappa, M., Bandaru, S., Windmark, C., Bokrantz, J., & Skoogh, A. (2024) · Winter Simulation Conference · doi:10.1109/wsc63780.2024.10838867 · Cited by 9
“This oversight limits the potential for digital twins to effectively support intelligent maintenance (Huang et al. 2021), suggesting a clear gap in the application of AI to optimize maintenance strategies.”
Exploring digital twin in sustainable development: a scientometric analysis
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“Huang et al. [72] review over 300 studies on AI-driven DTs in Industry 4.0, highlighting applications in smart manufacturing, sustainability, and integration challenges.”
InfluentialDigital Twins for Human-Robot Collaboration: A Future Perspective
Shaaban, M., Carfì, A., & Mastrogiovanni, F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-44851-5_33 · Cited by 9
Dostatni, E., Osiński, F., Mikołajewski, D., Sapietová, A., & Rojek, I. (2024) · Sustainability · doi:10.3390/su16198616 · Cited by 7
Hanna, B. S. H., Xu, G., Wang, X., & Hossain, M. J. (2024) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc64747.2024.10780659 · Cited by 7
“Digital twins of factories and industrial parks powered by physics-constrained ML can identify opportunities for waste heat recovery, material recycling, and process electrification [86].”
Chaber, P., Domański, P. D., Dąbrowski, D., Ławryńczuk, M., Nebeluk, R., Plamowski, S., & Zarzycki, K. (2022) · Sensors · doi:10.3390/s22020678 · Cited by 7
The development of a digital twin concept system
Duan, H., Gao, S., Yang, X., & Li, Y. (2022) · Digital Twin · doi:10.12688/digitaltwin.17599.1 · Cited by 20
RMDTs: Process-oriented function-triggered robotic milling digital twin system for service-expansion
Zhao, S., Peng, F., Shan, Y., Su, J., Tang, X., & Yan, R. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.015 · Cited by 6
Karkadakattil, A. (2026) · Journal of Intelligent Manufacturing and Special Equipment · doi:10.1108/jimse-10-2025-0023 · Cited by 6
ChatGPT on campus: how top U.S. universities govern generative AI across higher education
Alba, C., McIlwain, C. D., & An, R. (2026) · Policy Reviews in Higher Education · doi:10.1080/23322969.2026.2622674 · Cited by 10
“…models for enhancing market forecasting and automating risk assessment in business and finance (Cao, Yang, and Yu 2021; Tellez Gaytan et al. 2022), and AI-driven simulations and digital twin technologies for designing complex engineering systems (Ekundayo 2024; Huang et al. 2021; Mozo et al. 2022).”
Energy 4.0: Challenges and Enablers of Digital Twin Application in Power Plant
Shah, B. S. M. I., Ishak, A. J., Hassan, M. K., & Norsahperi, N. M. H. (2024) · ELEKTRIKA- Journal of Electrical Engineering · doi:10.11113/elektrika.v23n1.487 · Cited by 6
AI Explainability Methods in Digital Twins: A Model and a Use Case
Kreuzer, T., Papapetrou, P., & Zdravković, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78338-8_1 · Cited by 8
“In the literature [1, 12], similar terms to AI-DT system have been used to characterize the concept of integrating AI with a digital twin, however, the term has not been defined in the past, so we give a formal definition here: Definition 5 (AI-DT system).”
MethodA Survey on AI-Driven Autonomous Robots for Smart Manufacturing and Industrial Automation
Patel, R. K., & Patel, P. (2022) · Technix International Journal for Engineering Research · doi:10.56975/tijer.v9i2.158819 · Cited by 6
Intelligent Edge-Embedded Technologies for Digitising Industry
Vermesan, O., & Nava, M. D. (2022) · doi:10.13052/rp-9788770226103 · Cited by 7
Special Issue: Artificial Intelligence and Smart Sensor-Based Industrial Advanced Technology
Jia, L., Sun, B., Tan, W., Zhang, S., Zhang, B., & Zhu, J. (2024) · Sensors · doi:10.3390/s24227391 · Cited by 6
Guerra-Zubiaga, D. A., Aksu, M., Richards, G., & Kuts, V. (2025) · Applied Sciences · doi:10.3390/app15137049 · Cited by 7
Diniță, A., Roșca, C.-M., Tănase, M., & Stancu, A. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.066276 · Cited by 7
Li, H., Sahrani, S., Sarker, M. R., & Xiao, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3594091 · Cited by 6
Rojek, I., Mikołajewski, D., & Prokopowicz, P. (2025) · Energies · doi:10.3390/en18195138 · Cited by 6
Akyıldız, S. T. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1783566 · Cited by 7
Emerging Networking Technologies for Industry 4.0
Santhiya, M., Jeyalakshmi, J., & Venu, H. (2023) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-0593-5.ch015 · Cited by 5
State-of-the-Art Digital Twin Applications for Shipping Sector Decarbonization
Karakostas, B., & Katsoulakos, T. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/978-1-6684-9848-4 · Cited by 5
Horvath, A.-S., & Pouliou, P. (2024) · CRC Press eBooks · doi:10.1201/9781003425724-46 · Cited by 7
Palakurti, N. R., & Kolasani, S. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-2909-2.ch005 · Cited by 5
CLRiuS: Contrastive Learning for intrinsically unordered Steel Scrap
Schäfer, M., Faltings, U., & Glaser, B. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100573 · Cited by 5
On‐Board Digital Twin Based on Impedance and Model Predictive Control for Aerial Robot Grasping
Qahmash, A., Al‐Darraji, I., Khadidos, A. O., Tsaramirsis, G., Khadidos, A. O., & Alghamdi, M. H. (2023) · Journal of Sensors · doi:10.1155/2023/9662100 · Cited by 5
Chochliouros, I. P., Vardakas, J. S., Ramantas, K., Pollin, S., Mayrargue, S., Ksentini, A., Nitzold, W. P., Rahman, M. A., O'Meara, J., Chawla, A., et al. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-34171-7_1 · Cited by 5
“Through fast iterative optimization and decision-making, digital entities can also be generated in centralized or distributed mode as required [66].”
Scientometric Insights of Digital Twins in Industry 4.0Preprint
Kaur, H., & Bhatia, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4380510/v1 · Cited by 5
A literature review on AI and the global green value chains
Jing, N., & Yang, Z. (2025) · Digital Economy and Sustainable Development · doi:10.1007/s44265-025-00071-8 · Cited by 6
“AI not only achieves accurate quantification of environmental costs through smart carbon pricing models, but also optimizes the allocation efficiency of sustainable capital with the help of green fintech, thus reshaping the value-added logic of green industries (Huang et al. 2021).”
Machine learning algorithms for predictive maintenance in milling operations
Sutar, P. S., Mishra, S. K., Kolte, G., Kadam, P., & Upadhyay, R. K. (2025) · Next research. · doi:10.1016/j.nexres.2025.100933 · Cited by 4
Zaloznova, Y., Vyshnevskyi, O., Cherevatskyi, D., & Bozhyk, M. (2025) · Economy of Industry · doi:10.15407/econindustry2025.04.003 · Cited by 4
Venkateswarlu, L., Mohan, C. R., Reddy, P. K., Bande, V., Archana, M., & Manellore, P. K. R. (2025) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec67334.2025.11459737 · Cited by 5
“Simultaneously, Digital Twin technology provides a dynamic virtual replica of physical assets and processes, facilitating real-time simulation, monitoring, and predictive diagnostics using live sensor data [4].”
Responsible manufacturing toward Industry 5.0
Tian, Y., & Liu, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00008-9 · Cited by 4
Hybrid Model Based Autonomous System for Controlling Double Side Grinding Process
Park, H., & Tran, N.-H. (2024) · International Journal of Precision Engineering and Manufacturing-Smart Technology · doi:10.57062/ijpem-st.2024.00094 · Cited by 4
“Integration of AI and DT has more efficiency with production planning, production control, and quality control applications [15].”
Background
Metadata
- Authors
- Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C.
- Affiliations
- RWTH Aachen University (DE)
- UBTECH North America Research and Development Center, Pasadena, CA 91101-4858, USA
- University of California, Los Angeles (US)
- Corresponding author
- Huang, Ziqi
- Shen, Yang
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-09-23
- Volume
- 21
- Issue
- 19
- Pages
- 6340
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC8512418
- OpenAlex
- W3199754434
- Cited by
- 418
- References
- 296
- Citations by year
- 2026: 75 · 2025: 129 · 2024: 112 · 2023: 59 · 2022: 41 · 2021: 2
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deutsche Forschungsgemeinschaft (390621612)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, artificial intelligence, Industry 4.0, smart manufacturing, advanced robotics, metal machining, industrial automation, 3D printing, human-robot interaction, human-robot collaboration, sustainable development, artificial intelligence integration
- MeSH terms
- Algorithms, Artificial Intelligence, Automation, Humans, Industry, Robotics
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