Digital Twin for Smart Manufacturing: Synthesis of Results, Methodology, and Research Gaps - Implementation
Keywords:
human–robot, real-world industrial, smart manufacturing, sensor integrationAbstract
This study maps the development and research directions of Digital Twin (DT) in manufacturing by reviewing 25 recent publications that examine frameworks, methodologies, and the implementation of data-driven technologies, artificial intelligence, big data, and machine learning. The findings indicate that DT can enhance productivity, efficiency, and predictive accuracy, while also supporting human–robot collaboration and predictive maintenance through simulation, sensor integration, and synthetic data generation. The dominant methods applied include data-driven approaches, finite element modeling, generative models, knowledge graphs, Markov decision processes, as well as integration with large language models and augmented reality. On the other hand, several limitations remain prominent, such as dependence on data quality, high computational demands, limited validation in real industrial environments, and the lack of methodological standards that can be adopted across sectors. This review highlights that although DT has strong potential to become the backbone of smart manufacturing, the gap between academic research and industrial practice remains wide. Therefore, future research should focus on standardization, interoperability, and validation of DT implementation in real-world industrial settings.
References
Abed, M., Gameros, A., Mohammad, A., & Axinte, D. (2023). Swift feedback and immediate error control using a lightweight simulation approach – A case study of the digital twin-in-the-loop for machining thin-wall structures. Journal of Manufacturing Systems, 71(September), 309–322. https://doi.org/10.1016/j.jmsy.2023.09.016
Aznar Lapuente, G., Morella Avinzano, P., Lamban Castillo, M.P., & Seneviratne, D. (2026). Methodologies in digital twin for manufacturing industry: A systematic literature review. Future Generation Computer Systems, 174(June 2025), 107997. https://doi.org/10.1016/j.future.2025.107997
Baratta, A., Cimino, A., Longo, F., & Nicoletti, L. (2024). Digital twin for human-robot collaboration enhancement in manufacturing systems: Literature review and direction for future developments. Computers and Industrial Engineering, 187(November 2023), 109764. https://doi.org/10.1016/j.cie.2023.109764
Chand, S., Zheng, H., & Lu, Y. (2024). A vision-enabled fatigue-sensitive human digital twin towards human-centric human-robot collaboration. Journal of Manufacturing Systems, 77(October), 432–445. https://doi.org/10.1016/j.jmsy.2024.10.002
Chen, S., Thompson, A., Dodwell, T., Hallett, S., & Belnoue, J. (2025). A comparison between robust design and digital twin approaches for Non-Crimp fabric (NCF) forming. Composites Part A: Applied Science and Manufacturing, 193(December 2024). https://doi.org/10.1016/j.compositesa.2025.108864
Chen, S., Turanoglu Bekar, E., Bokrantz, J., & Skoogh, A. (2025). AI-enhanced digital twins in maintenance: Systematic review, industrial challenges, and bridging research–practice gaps. Journal of Manufacturing Systems, 82(June), 678–699. https://doi.org/10.1016/j.jmsy.2025.07.006
Chen, Y.P., Karkaria, V., Tsai, Y.K., Rolark, F., Quispe, D., Gao, R.X., Cao, J., & Chen, W. (2025). Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks. Journal of Manufacturing Systems, 80(March), 412–424. https://doi.org/10.1016/j.jmsy.2025.03.009
Chia, J. W. Y., Verhagen, W. J. C., Silva, J. M., & Cole, I. S. (2024). A review and outlook of airframe digital twins for structural prognostics and health management in the aviation industry. Journal of Manufacturing Systems, 77(August), 398–417. https://doi.org/10.1016/j.jmsy.2024.09.024
Cimino, A., Longo, F., Nicoletti, L., & Solina, V. (2024). Simulation-based Digital Twin for enhancing human-robot collaboration in assembly systems. Journal of Manufacturing Systems, 77(October), 903–918. https://doi.org/10.1016/j.jmsy.2024.10.024
De Giacomo, G., Favorito, M., Leotta, F., Mecella, M., & Silo, L. (2023). Digital twins composition in smart manufacturing via Markov decision processes. Computers in Industry, 149(April 2022), 103916. https://doi.org/10.1016/j.compind.2023.103916
Friederich, J., Francis, D. P., Lazarova-Molnar, S., & Mohamed, N. (2022). A framework for data-driven digital twins for smart manufacturing. Computers in Industry, 136, 103586. https://doi.org/10.1016/j.compind.2021.103586
Gautam, A., Aryal, M.R., Deshpande, S., Padalkar, S., Nikolaenko, M., Tang, M., & Anand, S. (2025). IIoT-enabled digital twin for legacy and smart factory machines with LLM integration. Journal of Manufacturing Systems, 80(April), 511–523. https://doi.org/10.1016/j.jmsy.2025.03.022
Hinchy, E. P., Carcagno, C., O'Dowd, N. P., & McCarthy, C. T. (2020). Using finite element analysis to develop a digital twin of a manufacturing bending operation. Procedia CIRP, 93(March), 568–574. https://doi.org/10.1016/j.procir.2020.03.031
Jin, L., Zhai, X., Wang, K., Zhang, K., Wu, D., Nazir, A., Jiang, J., & Liao, W. H. (2024). Big data, machine learning, and digital twin assisted additive manufacturing: A review. Materials and Design, 244(May), 113086. https://doi.org/10.1016/j.matdes.2024.113086
Lang, S., Zorzini, M., Scholze, S., Mayr, J., & Bambach, M. (2025). Sensor placement utilizing a digital twin for thermal error compensation of machine tools. Journal of Manufacturing Systems, 80(January), 243–257. https://doi.org/10.1016/j.jmsy.2025.03.003
Liu, Z., Lang, Z. Q., Gui, Y., Zhu, Y. P., & Laalej, H. (2024). Digital twin-based anomaly detection for real-time tool condition monitoring in machining. Journal of Manufacturing Systems, 75(September 2023), 163–173. https://doi.org/10.1016/j.jmsy.2024.06.004
Mo, F., Rehman, H.U., Monetti, F.M., Chaplin, J.C., Sanderson, D., Popov, A., Maffei, A., & Ratchev, S. (2023). A framework for manufacturing system reconfiguration and optimization utilizing digital twins and modular artificial intelligence. Robotics and Computer-Integrated Manufacturing, 82(September 2022), 102524. https://doi.org/10.1016/j.rcim.2022.102524
Moussa, M., Abbas, M., & Elmaraghy, H. (2025). Industry 4.0 in Automotive Manufacturing: A Digital Twin Approach. Procedia CIRP, 134, 825–830. https://doi.org/10.1016/j.procir.2025.02.197
Mu, H., He, F., Yuan, L., Hatamian, H., Commins, P., & Pan, Z. (2024). Online distortion simulation using generative machine learning models: A step toward digital twin of metallic additive manufacturing. Journal of Industrial Information Integration, 38(January), 100563. https://doi.org/10.1016/j.jii.2024.100563
Perno, M., Hvam, L., & Haug, A. (2023). A machine learning digital twin approach for critical process parameter prediction in a catalyst manufacturing line. Computers in Industry, 151(June), 103987. https://doi.org/10.1016/j.compind.2023.103987
Psarommatis, F. (2021). A generic methodology and a digital twin for zero defect manufacturing (ZDM) performance mapping towards design for ZDM. Journal of Manufacturing Systems, 59(March), 507–521. https://doi.org/10.1016/j.jmsy.2021.03.021
Risling, M., Oberle, M., & Bauernhansl, T. (2024). Analyzing The Purpose And Technologies Of Digital Twins In Distributed Manufacturing: A Systematic Literature Review. Procedia Computer Science, 232(2023), 368–376. https://doi.org/10.1016/j.procs.2024.01.036
Su, S., Nassehi, A., Hicks, B., & Ross, J. (2023). Characterization and evaluation of identicality for digital twins for the manufacturing domain. Journal of Manufacturing Systems, 71(August), 224–237. https://doi.org/10.1016/j.jmsy.2023.09.004
Urgo, M., & Terkaj, W. (2025). Integrating digital factory twin and AI for monitoring manufacturing systems through synthetic data generation and vision transformers. CIRP Annals, 74(1), 639–643. https://doi.org/10.1016/j.cirp.2025.04.037
Yang, C., Yu, H., Zheng, Y., Feng, L., Ala-Laurinaho, R., & Tammi, K. (2025). A digital twin-driven industrial context-aware system: A case study of overhead crane operation. Journal of Manufacturing Systems, 78(December 2024), 394–409. https://doi.org/10.1016/j.jmsy.2024.12.006
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Johan Alfian Pradana, Anang Siswanto, Wawan Setyo Budi, Saiful Rowi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This material is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This license permits users to copy, distribute, and adapt the material, provided that appropriate credit is given, a link to the license is provided, and any modifications are clearly indicated. The material may only be utilized for non-commercial purposes and must not be exploited for commercial gain. Any derivative works, adaptations, or modifications must be distributed under the same license as the original. For further details, please refer to the Creative Commons BY-NC-SA 4.0.
Chers Publishers