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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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Citations
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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
“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
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
Lee, M. R. (2023) · Processes · doi:10.3390/pr11113171 · Cited by 37
“…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
ARTIFICIAL INTELLIGENCE IN THE ERA OF 4IR: DRIVERS, CHALLENGES AND OPPORTUNITIES
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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
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
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 34
“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
A digital twin framework for enhancing human-agentic AI-machine collaboration
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“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
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“(Huang et al. 2021).”
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Relativistic Digital Twin: Bringing the IoT to the Future
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Industry 4.0 and Enabling Technologies: Integration Framework and Challenges
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The Impact of Industry 4.0 on the Medical Device Regulatory Product Life Cycle Compliance
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“Without the proper infrastructure implementing Industry 4.0 can become challenging [67,68].”
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“Thе intеgration of rеal-timе monitoring and control capabilitiеs facilitatеd by DTs in thе contеxt of 3D printing offеrs a rangе of transformativе bеnеfits that еnhancе production prеcision, procеss optimization and product quality assuancе (Huang et al ., 2021).”
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General purpose digital twin framework using digital shadow and distributed system concepts
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
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“This improved adaptability offers useful applications, especially in areas such as control, quality assurance, and production planning [53].”
Unleashing Sustainable Efficiency: The Integration of Computer Vision into Industry 4.0
Gill, R., Srivastava, D., Hooda, S., Singla, C., & Chaudhary, R. (2024) · Engineering Management Journal · doi:10.1080/10429247.2024.2383518 · Cited by 22
“It is more objective, speedier, and constantly operational (Huang et al., 2021).”
InfluentialElbasheer, M., Longo, F., Mirabelli, G., Nicoletti, L., Padovano, A., & Solina, V. (2022) · IET Collaborative Intelligent Manufacturing · doi:10.1049/cim2.12066 · Cited by 18
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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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