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.
“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]
Section 2.1 (Overview; page unavailable) describes digital twins as enhancing manufacturing productivity, and Section 2.4 (Process and Material; page unavailable) discusses resource savings and process optimization. The passages do not establish that manufacturers use them to identify and eliminate bottlenecks, so that part of the attributed claim is unsupported. (LLM, conf=0.82)
Experimental — accuracy not yet measuredRecent studies indicate that incorporating AI has the potential to enhance the adaptability of DTs to cope with dynamic alterations in workshops and factories.
“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]
Real-time AR instruction for technicians can revolutionize remote help by decreasing the requirement of physical presence, increasing productivity, and reducing cost.
“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]
Section 2.2.1 mentions mixed-reality assistance systems, and Section 3.6 says AR can make human–robot interaction more intuitive. Neither passage addresses real-time instructions for technicians or remote help, reduced need for physical presence, productivity gains, or cost savings. Searches for AR remote assistance and those proposed benefits found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.72)
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hưng, Đ. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B. S., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,115 · see citing works
“In [14], Huang et al. put together a detailed survey on the AI-driven DTs in the context of smart manufacturing and advanced robotics.”
BackgroundSerrano, D. R., Luciano, F. C., Anaya, B. J., Öngoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., et al. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16101328 · Cited by 561 · see citing works
“AI-driven digital twins will simulate and optimize manufacturing processes in real-time, while predictive maintenance algorithms will prevent equipment failures and minimize downtime, facilitating more agile and responsive manufacturing operations [171–173].”
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“With the increased interconnection of devices, processes, and systems in Industry 4.0, the potential for AI-driven insights and optimizations is growing dramatically (Huang et al., 2021).”
InfluentialDigital twin of a city: Review of technology serving city needs
Lehtola, V., Koeva, M., Elberink, S. O., Raposo, P., Virtanen, J., Vahdatikhaki, F., & Borsci, S. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.102915 · Cited by 206 · see citing works
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“Networks (CNNs), Long Short-term Memory (LSTMs), Hidden Markov Model (HMM), and Spiking Neural Networks (SNN) have been used in the literature for motion and intent detection and prediction in HRC [51–53].”
MethodSoft Robotic Perception System with Ultrasonic Auto-Positioning and Multimodal Sensory Intelligence
Shi, Q., Sun, Z., Le, X., Xie, J., & Lee, C. (2023) · ACS Nano · doi:10.1021/acsnano.2c12592 · Cited by 157 · see citing works
AI-driven warehouse automation: A comprehensive review of systems
Sodiya, E. O., Umoga, U. J., Amoo, O. O., & Atadoga, A. (2024) · GSC Advanced Research and Reviews · doi:10.30574/gscarr.2024.18.2.0063 · Cited by 158 · see citing works
“Digital twins enable warehouse managers to analyze the behavior of AI-driven systems before implementing them in the physical environment (Huang et al.,2021).”
BackgroundDigital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives
Caprari, G., Castelli, G., Montuori, M., Camardelli, M., & Malvezzi, R. (2022) · Sustainability · doi:10.3390/su14106263 · Cited by 159 · see citing works
“In short, theories and experiments on DT can be found in aerospace prototyping [30], product and manufacturing [31,32], design robotics and automation [33], biomedical, engineering, and transport applications [34], or crisis management [35], to name but a few.”
BackgroundSoudagar, M. E. M., Shelare, S. D., Marghade, D. T., Belkhode, P. N., Nur‐E‐Alam, M., Kiong, T. S., Ramesh, S. S. R., Rajabi, A., Venu, H., Khan, T. M. Y., et al. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.118337 · Cited by 145 · see citing works
Digital twins in safety analysis, risk assessment and emergency management
Zio, E., & Miqueles, L. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110040 · Cited by 148 · see citing works
Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025) · Applied Sciences · doi:10.3390/app15063166 · Cited by 133 · see citing works
“Exploring how GenAI can seamlessly integrate with IoT sensors and edge computing can enhance data collection and error detection capabilities [81].”
Edge and Cloud Computing in Smart Cities
Τρίγκα, Μ., & Dritsas, E. (2025) · Future Internet · doi:10.3390/fi17030118 · Cited by 140 · see citing works
Digital twins and industrial internet of things: uncovering operational intelligence in Industry 4.0
Attaran, S., Attaran, M., & Celik, B. G. (2024) · Decision Analytics Journal · doi:10.1016/j.dajour.2024.100398 · Cited by 122 · see citing works
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“Industrial intelligence can not only complete products efficiently and enhance product quality, but also decrease the demand for low-skilled labor and reduce the labor cost of enterprises (Huang et al., 2021).”
BackgroundBaratta, A., Cimino, A., Longo, F., & Nicoletti, L. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109764 · Cited by 130 · see citing works
Olawade, D. B., Ige, A. O., Olaremu, A. G., Ijiwade, J. O., & Adeola, A. O. (2024) · Nano Trends · doi:10.1016/j.nwnano.2024.100052 · Cited by 121 · see citing works
Hybrid learning-based digital twin for manufacturing process: modeling framework and implementation
Huang, Z., Fey, M., Liu, C., Beysel, E., Xu, X., & Brecher, C. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2023.102545 · Cited by 117 · see citing works
The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0
Attaran, M., Attaran, S., & Çelik, B. G. (2023) · Advances in Computational Intelligence · doi:10.1007/s43674-023-00058-y · Cited by 121 · see citing works
Recent Advances in Artificial Intelligence Sensors
Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107 · see citing works
A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry
Zhang, Z., Wei, Z., Court, S., Yang, L., Wang, S., Thirunavukarasu, A., & Zhao, Y. (2024) · Buildings · doi:10.3390/buildings14041113 · Cited by 102 · see citing works
“Research in AI and machine learning is equally crucial for optimizing and expanding digital twin technologies [137].”
BackgroundFrom Simulation to Autonomy: Reviews of the Integration of Artificial Intelligence and Digital Twins
Sajadieh, S. M. M., & Noh, S. D. (2025) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-025-00750-z · Cited by 114 · see citing works
“Emmert-Streib [137], Salini and Ivy [138], and Huang et al. [139] highlighted the role of AI in Simulation & Scenario Analysis, allowing DTs to simulate various strategies, assess risks, and generate synthetic data for reinforcement learning models.”
Zhang, Z., Liu, X., Zhou, H., Xu, S., & Lee, C. (2023) · Small Structures · doi:10.1002/sstr.202300325 · Cited by 94 · see citing works
Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential
Badidi, E. (2022) · Sustainability · doi:10.3390/su14137609 · Cited by 94 · see citing works
A 3D functional plant modelling framework for agricultural digital twins
Mitsanis, C., Hurst, W., & Teki̇nerdoğan, B. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108733 · Cited by 86 · see citing works
“Digital twins (DT) represent a fundamental technology (Huang et al., 2021), enabling the recreation of physical objects within a virtual environment, faithfully reproducing their characteristics and behaviour over time (Boschert & Rosen, 2016; Grieves & Vickers, 2017).”
BackgroundIntelligent robotic systems in Industry 4.0: A review
Soori, M., Dastres, R., Behrooz Arezoo, B., & Jough, F. K. G. (2024) · Journal of Advanced Manufacturing Science and Technology · doi:10.51393/j.jamst.2024007 · Cited by 80 · see citing works
Shop Floor Digital Twin in Smart Manufacturing: A Systematic Literature Review
Corallo, A., Vecchio, V. D., Lezzi, M., & Morciano, P. (2021) · Sustainability · doi:10.3390/su132312987 · Cited by 78 · see citing works
Digital twin for personalized medicine development
Saratkar, S., Langote, M., Kumar, P., Gote, P. M., Weerarathna, I. N., & Mishra, G. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1583466 · Cited by 82 · see citing works
“Immersion technologies like VR, AR, and mixed reality (MR) are all included in the broad term “ XR ” (40).”
Krupáš, M., Kajáti, E., Liu, C., & Zolotová, I. (2024) · Sensors · doi:10.3390/s24072232 · Cited by 78 · see citing works
“One of the advantages of AI-equipped DTs over conventional ones is the ability to better respond to HMC applications due to the uncertainties in the environment and sensors and the randomness and diversity of human behaviour [71].”
BackgroundKarkaria, V. N., Tsai, Y.-K., Chen, Y.-P., & Chen, W. (2025) · Engineering Optimization · doi:10.1080/0305215x.2024.2434201 · Cited by 78 · see citing works
“Moreover, MPC within digital twins offers significant advantages when dealing with the complex, multivariable systems typically found in advanced manufacturing (Z. Huang et al .”
An effective MBSE approach for constructing industrial robot digital twin system
Zhang, X., Wu, B., Zhang, X., Duan, J., Wan, C., & Hu, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102455 · Cited by 70 · see citing works
Khatiri, S., Panichella, S., & Tonella, P. (2023) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst57152.2023.00034 · Cited by 70 · see citing works
“Researchers proposed the use of digital-twins , i.e., virtual representations of real-time, physical objects or processes, to simulate and test CPS in diversified scenarios [19], [20], [21], [22], [23], [24], and to support test automation [25], [26].”
BackgroundLiberty, J. T., Habanabakize, E., Adamu, P. I., & Bata, S. M. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104705 · Cited by 76 · see citing works
A comprehensive survey of digital twins: Applications, technologies and security challenges
Jeremiah, S. R., Azzaoui, A. E., Xiong, N. N., & Park, J. H. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103120 · Cited by 77 · see citing works
Virtual reality-based digital twins for greenhouses: A focus on human interaction
Slob, N., Hurst, W., van de Zedde, R., & Tekinerdoğan, B. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.107815 · Cited by 57 · see citing works
Digital Twin in Industries: A Comprehensive Survey
Zami, M. B. A., Shaon, S., Khanh, Q. V., & Nguyen, D. C. (2025) · IEEE Access · doi:10.1109/access.2025.3551532 · Cited by 83 · see citing works
“[169].”
Cognitive Soil Digital Twin for Monitoring the Soil Ecosystem: A Conceptual Framework
Tsakiridis, N., Samarinas, N., Kalopesa, E., & Zalidis, G. C. (2023) · Soil Systems · doi:10.3390/soilsystems7040088 · Cited by 66 · see citing works
Chang, X., Zhang, R., Mao, J., & Fu, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3401716 · Cited by 67 · see citing works
“Originally, the digital shadow was used before the concept of DT e.g., real-time simulation and assessment for decision-making processes [7].”
MethodChen, S., Chen, H.-M., Chen, H.-K., & Li, C.-L. (2024) · Processes · doi:10.3390/pr12122723 · Cited by 63 · see citing works
AI-powered blockchain technology in industry 4.0, a review
Soori, M., Dastres, R., & Behrooz Arezoo, B. (2023) · Journal of Economy and Technology · doi:10.1016/j.ject.2024.01.001 · Cited by 73 · see citing works
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Kaur, R., Kumar, R., & Aggarwal, H. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10290-z · Cited by 59 · see citing works
Virtual Human: A Comprehensive Survey on Academic and Applications
Cui, L., & Liu, J. (2023) · IEEE Access · doi:10.1109/access.2023.3329573 · Cited by 56 · see citing works
“It relies on advanced AI technology to read, parse, and recognize external textual or audio information [65].”
BackgroundDigital twins for Industry 4.0 in the 6G era
Han, B., Habibi, M. A., Richerzhagen, B., Schindhelm, K., Zeiger, F., Lamberti, F., Pratticò, F. G., Upadhya, K., Korovesis, C., Belikaidis, I.-P., et al. (2023) · IEEE Open Journal of Vehicular Technology · doi:10.1109/ojvt.2023.3325382 · Cited by 54 · see citing works
“A rich review of how AI is used in the field of industrial IoT context is provided in [44].”
BackgroundRecent Advancements in Artificial Intelligence in Battery Recycling
Jose, S. A., Cook, C. A. D., Palacios, J., Seo, H., Ramirez, C. E. T., Wu, J., & Menezes, P. L. (2024) · Batteries · doi:10.3390/batteries10120440 · Cited by 55 · see citing works
“…digital twins—a digital replica of a physical entity that maintains a strong connection to the original—will play a pivotal role in this evolution, allowing for the testing of maintenance strategies in a risk-free virtual environment and enabling real-time performance optimization [52].”
Moiceanu, G., & Paraschiv, G. (2022) · Sensors · doi:10.3390/s22041388 · Cited by 60 · see citing works
Intelligent Machine Fault Diagnosis Using Convolutional Neural Networks and Transfer Learning
Zhang, W., Zhang, T., Cui, G., & Pan, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3173444 · Cited by 54 · see citing works
“…branch of machine learning, DL has expanded the field of artificial intelligence and been successfully applied in many other research fields, such as object detection [5], image segmentation [6], natural language processing [7], fault diagnosis [8], visual tracking [9], and smart manufacturing [10].”
MethodTop 20 influential AI-based technologies in chemistry
Ananikov, V. P. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100075 · Cited by 62 · see citing works
Challenges and Opportunities in the Implementation of AI in Manufacturing: A Bibliometric Analysis
Espina-Romero, L. C., Hurtado, H. G., Parra, D. E. R., Pirela, R. A. V., Talavera-Aguirre, R., & Ochoa-Díaz, A. (2024) · Sci · doi:10.3390/sci6040060 · Cited by 64 · see citing works
A Digital Twin Driven Human-Centric Ecosystem for Industry 5.0
Villani, V., Picone, M., Mamei, M., & Sabattini, L. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3410703 · Cited by 55 · see citing works
“The design and development MDTs has been massively studied in the literature and recently revamped following both industrial specifications and scientific contributions [40], [41], [42].”
Zhu, Z.-J., Lin, Z.-M., Huang, J., Zheng, L., & He, B. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11827-x · Cited by 44 · see citing works
Mowla, M. N., Asadi, D., Durhasan, T., Jafari, J. R., & Amoozgar, M. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110102 · Cited by 51 · see citing works
Robot digital twin systems in manufacturing: Technologies, applications, trends and challenges
Qin, Q., Liu, Z., Zhong, R., Wang, X. V., Wang, L., Wiktorsson, M., & Wang, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103103 · Cited by 50 · see citing works
Ranawaka, A. S. R. A., Alahakoon, D., Sun, Y., & Hewapathirana, K. (2024) · Energies · doi:10.3390/en17215342 · Cited by 54 · see citing works
Cob-Parro, A. C., Lalangui, Y., & Lazcano, R. (2024) · Agronomy · doi:10.3390/agronomy14020259 · Cited by 41 · see citing works
García, Á., Bregón, A., & Martínez‐Prieto, M. A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101094 · Cited by 48 · see citing works
Digital Twins and Industry 4.0 Technologies for Agricultural Greenhouses
Slob, N., & Hurst, W. (2022) · Smart Cities · doi:10.3390/smartcities5030059 · Cited by 42 · see citing works
Puttegowda, M., & Nagaraju, S. B. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109910 · Cited by 44 · see citing works
Calvo-Bascones, P., Voisin, A., Do, P., & Sanz-Bobi, M. A. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103767 · Cited by 45 · see citing works
Ryalat, M., Almtireen, N., Al-refai, G., ElMoaqet, H., & Rawashdeh, N. A. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14040076 · Cited by 56 · see citing works
Extending factory digital Twins through human characterisation in Asset Administration Shell
Cutrona, V., Bonomi, N., Montini, E., Ruppert, T., Delinavelli, G., & Pedrazzoli, P. (2023) · International journal of computer integrated manufacturing (Print) · doi:10.1080/0951192x.2023.2278108 · Cited by 31 · see citing works
“Many examples are available that demonstrate how digital copies of machines, equipment, products, or entire production systems have been exploited to increase performance, make predictions, and make decisions (Huang et al. 2021; Mylonas et al. 2021).”
Cognitive intelligence in industrial robots and manufacturing
Mukherjee, A., Divya, A., Sivvani, M., & Pal, S. K. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110106 · Cited by 41 · see citing works
Leading-Edge Technologies for Architectural Design: A Comprehensive Review
Rane, N. L., Choudhary, S. P., & Rane, J. (2023) · International Journal of Architecture and Planning · doi:10.51483/ijarp.3.2.2023.12-48 · Cited by 45 · see citing works
Wang, G., Zhang, C., Liu, S., Zhang, Y., Zhang, Y., & Wang, L. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.06.014 · Cited by 38 · see citing works
The philosophical foundations of digital twinning
Wagg, D. J., Burr, C., Shepherd, J., Conti, Z. X., Enzer, M., & Niederer, S. (2025) · Data-Centric Engineering · doi:10.1017/dce.2025.4 · Cited by 36 · see citing works
“…et al. (2020); Jones et al. (2020); Liu et al. (2020); Melesse et al. (2020); Wagg et al. (2020); Wanasinghe et al. (2020); He and Bai (2021); Huang et al. (2021); Jiang et al. (2021); Lo et al. (2021); Semeraro et al. (2021); Shahat et al. (2021); Korenhof et al. (2021); Niederer et al.…”
Lehmhus, D. (2024) · Metals · doi:10.3390/met14030334 · Cited by 36 · see citing works
Ahmmed, M. S., Isanaka, S. P., & Liou, F. (2024) · Machines · doi:10.3390/machines12100681 · Cited by 43 · see citing works
“CPS and DTs can improve manufacturing systems’ resilience, intelligence, and efficiency by creating feedback loops that allow physical processes to influence cyber parts and vice versa [64].”
Quamar, M. M., & Nasir, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4827147 · Cited by 32 · see citing works
Cost-effective simulation-based test selection in self-driving cars software
Birchler, C., Ganz, N., Khatiri, S., Gambi, A., & Panichella, S. (2023) · Science of Computer Programming · doi:10.1016/j.scico.2023.102926 · Cited by 31 · see citing works
“Consequently, researchers proposed the usage of Digital-Twins 1 technologies to simulate and test CPSs in a diversified set of scenarios [9, 10, 11, 12, 13] to support testing automation [14, 15], regression testing [12, 16], and de-bugging [17, 18] activities.”
Hybrid Virtual Commissioning of a Robotic Manipulator with Machine Vision Using a Single Controller
Noga, M., Juhás, M., & Gulan, M. (2022) · Sensors · doi:10.3390/s22041621 · Cited by 31 · see citing works
“, , device [10]. de-46”
Bao, Q., Zheng, P., & Dai, S. L. (2023) · Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · doi:10.1177/09544054231180513 · Cited by 33 · see citing works
“13 In the field of manufacturing, the theory of digital twin is extensively used to guide the overall process with the systematic framework including detailed modeling methodology guidance, 14,15 comprehensive intelligent service interfaces, 16 and diversified cyber-physical interaction means.”
MethodDigital twins: A scientometric investigation into current progress and future directions
Kaur, H., & Bhatia, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125917 · Cited by 33 · see citing works
Construction of a digital twin system for the blended-wing-body underwater glider
Hu, S., Liang, Q., Huang, H., & Yang, C. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113610 · Cited by 30 · see citing works
Lee, M.-F. R. (2023) · Processes · doi:10.3390/pr11113171 · Cited by 26 · see citing works
“…indicating escalating interest among researchers and Smart manufacturing integrates Internet of Things (IoT) devices [47–52], cloud computing [51,53–56], robotics [57–60], and artificial intelligence (AI) algorithms [61–64] to ensure real-time oversight and management of production processes.”
BackgroundIndustrial applications of digital twins: A systematic investigation based on bibliometric analysis
Ren, J., Ahmad, R., Li, D., Ma, Y., & Hui, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103264 · Cited by 32 · see citing works
ARTIFICIAL INTELLIGENCE IN THE ERA OF 4IR: DRIVERS, CHALLENGES AND OPPORTUNITIES
Ibegbulam, C. M., Olowonubi, J. A., Fatounde, S. A., & Oyegunwa, O. A. (2023) · Engineering Science & Technology Journal · doi:10.51594/estj.v4i6.668 · Cited by 29 · see citing works
Smart Manufacturing for High-Performance Materials: Advances, Challenges, and Future Directions
Jose, S. A., Tonner, A., Feliciano, M., Roy, T., Shackleford, A., & Menezes, P. L. (2025) · Materials · doi:10.3390/ma18102255 · Cited by 37 · see citing works
Review of Applications of Digital Twins and Industry 4.0 for Machining
da Silva, L. R. R., Pimenov, D. Y., da Silva, R. B., Erçetin, A., & Giasin, K. (2025) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp9070211 · Cited by 34 · see citing works
AI-enhanced manufacturing robotics: A review of applications and trends
Adebayo, R. A., Obiuto, N. C., Olajiga, O. K., & Festus-Ikhuoria, I. C. (2023) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.3.0924 · Cited by 30 · see citing works
“The adaptability of AI-driven robotic systems allows them to seamlessly adjust to changes in production demands, product variations, and environmental conditions (Huang et al ., 2021).”
The Gompertz model for biohydrogen production kinetics: Origin, application and solving methods
Guo, X., & Wang, J. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.09.200 · Cited by 27 · see citing works
A digital twin framework for enhancing human-agentic AI-machine collaboration
Nicoletti, B., & Appolloni, A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02637-x · Cited by 30 · see citing works
Cucchi, M., Volpi, L., Ferrari, A. M., García‐Muiña, F. E., & Settembre‐Blundo, D. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-20601-7 · Cited by 26 · see citing works
“Embracing this change of perspective makes it possible to revo-lutionize the very concept of making industry, favouring and facilitating those processes that meet the principles of sustainability according to ESG (Environmental, Social and Govern-ance) criteria (Huang et al. 2021).”
BackgroundGranular flow in screw conveyors: A review of experiments and discrete element method (DEM) studies
Pezo, M. L., Pezo, L. L., Lončar, B. L., Kojić, P. S., Ilić, M., & Jovanović, A. P. (2025) · Powder Technology · doi:10.1016/j.powtec.2025.121040 · Cited by 25 · see citing works
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“(Huang et al. 2021).”
Mousavi, V., Rashidi, M., Ghazimoghadam, S., & Samali, B. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103534 · Cited by 25 · see citing works
Relativistic Digital Twin: Bringing the IoT to the Future
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- 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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