Authors : Dr. Priti Bharambe; Dr. Vikas Mahandule; Vishakha Shashank Rawte; Manisha More; Manjusha Ganpati Khamkar

Volume/Issue : Volume 10 - 2025, Issue 4 - April

---

Google Scholar : https://tinyurl.com/3cfmzaep

Scribd : https://tinyurl.com/5n6pxyur

DOI : https://doi.org/10.38124/ijisrt/25apr1541

Google Scholar

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 15 to 20 days to display the article.

---

Abstract : With the ability to provide real-time visualization of machines, processes, and systems, Digital Twins (DTs) have rapidly emerged as a vital component of smart manufacturing. This study explores the use of DTs across various industries, highlighting key challenges such as data integration, scalability, and cybersecurity, while outlining future opportunities driven by advances in AI, IoT, and edge computing. DTs have transformative potential to enhance operational efficiency, enable predictive maintenance, and support data-driven decision-making, even as they face technical and ethical obstacles. Through case studies and literature review, this paper presents a comprehensive understanding of the current landscape and the future direction of DTs in smart manufacturing.

Keywords : Industry 4.0, Artificial Intelligence, Cybersecurity, Data Integration, Scalability, Smart Manufacturing, Digital Twins.

References :

1. Batty, M. (2018). Digital Twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820. https://doi.org/10.1177/2399808318796416
2. Chhetri, S. R., Rashid, R. A., & Faezi, S. (2022). Cybersecurity challenges in Digital Twin applications: A systematic review. Computers & Security, 115, 102603. https://doi.org/10.1016/j.cose.2022.102603
3. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technology, Challenges and Open Research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
4. General Electric. (2021). Leveraging Digital Twins for Predictive Maintenance. Retrieved from https://www.ge.com
5. Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
6. Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
7. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in Manufacturing: A Categorical Literature Review and Classification. IFAC-PapersOnLine, 51(11), 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
8. Lu, Y., Liu, C., Wang, K. I.-K., Huang, H., & Xu, X. (2020). Digital Twin-driven Smart Manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837. https://doi.org/10.1016/j.rcim.2019.101837
9. Minerva, R., Lee, G. M., & Crespi, N. (2020). Digital Twin in the IoT context: A survey on technical features, scenarios, and architectural models. Proceedings of the IEEE, 108(10), 1785–1824. https://doi.org/10.1109/JPROC.2020.2998530
10. Negri, E., Fumagalli, L., & Macchi, M. (2017). A Review of the Roles of Digital Twin in CPS-based Production Systems. Procedia Manufacturing, 11, 939–948. https://doi.org/10.1016/j.promfg.2017.07.198
11. Qi, Q., & Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access, 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265
12. Siemens. (2020). Digital Twins in Manufacturing: Case Studies and Best Practices. Retrieved from https://www.siemens.com
13. Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2019). Digital Twin in Industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186