Digital Twin Systems for Next-Generation Product Engineering

  • Authors

    • P. K. Iyengar Scientific Computing Researcher, BARC, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PII7R7D

    Published 10-03-2024

  • Digital Twin, Product Engineering, Industry 4.0, Artificial Intelligence, Internet of Things, Cyber-Physical Systems, Smart Manufacturing, Predictive Maintenance, Cloud Computing

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

  • Abstract

    Digital Twin (DT) technology enables real-time virtual representations of physical systems, transforming modern product engineering. By integrating Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and big data analytics, Digital Twins enhance product design, manufacturing, predictive maintenance, and lifecycle management. This paper reviews recent advancements, industrial applications, and implementation approaches of Digital Twin systems while proposing an AI-enabled framework for real-time monitoring, simulation, and optimization. The findings demonstrate improvements in product quality, operational efficiency, maintenance accuracy, and cost reduction, highlighting Digital Twins as a key enabler of smart manufacturing and Industry 4.0. Future developments in edge computing and generative AI are expected to further expand their capabilities.

  • References

    [1] M. Grieves and J. Vickers, "Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems," in Transdisciplinary Perspectives on Complex Systems, Cham, Switzerland: Springer, 2017, pp. 85–113.

    [2] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, "Digital Twin in Industry: State-of-the-Art," IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405–2415, Apr. 2019.

    [3] F. Tao and M. Zhang, "Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing," IEEE Access, vol. 5, pp. 20418–20427, 2017.

    [4] E. Negri, L. Fumagalli, and M. Macchi, "A Review of the Roles of Digital Twin in CPS-Based Production Systems," Procedia Manufacturing, vol. 11, pp. 939–948, 2017.

    [5] A. Fuller, Z. Fan, C. Day, and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," IEEE Access, vol. 8, pp. 108952–108971, 2020.

    [6] S. Boschert and R. Rosen, "Digital Twin—The Simulation Aspect," in Mechatronic Futures, Cham, Switzerland: Springer, 2016, pp. 59–74.

    [7] K. Thramboulidis, "Digital Twins and the Evolution of Intelligent Manufacturing Systems," Computers in Industry, vol. 126, Art. no. 103417, 2021.

    [8] L. Monostori, "Cyber-Physical Production Systems: Roots, Expectations and R&D Challenges," Procedia CIRP, vol. 17, pp. 9–13, 2014.

    [9] J. Lee, B. Bagheri, and H. A. Kao, "A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems," Manufacturing Letters, vol. 3, pp. 18–23, Jan. 2015.

    [10] H. Kagermann, W. Wahlster, and J. Helbig, Recommendations for Implementing the Strategic Initiative Industrie 4.0. Frankfurt, Germany: German National Academy of Science and Engineering (acatech), 2013.

    [11] L. Da Xu, W. He, and S. Li, "Internet of Things in Industries: A Survey," IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, Nov. 2014.

    [12] M. Javaid and A. Haleem, "Industry 4.0 Applications in Medical Field: Digital Twin Technology and Smart Healthcare," Journal of Industrial Integration and Management, vol. 4, no. 4, pp. 1–18, 2019.

    [13] Y. Lu, "Industry 4.0: A Survey on Technologies, Applications and Open Research Issues," Journal of Industrial Information Integration, vol. 6, pp. 1–10, Jun. 2017.

    [14] S. Wang, J. Wan, D. Li, and C. Zhang, "Implementing Smart Factory of Industrie 4.0: An Outlook," International Journal of Distributed Sensor Networks, vol. 12, no. 1, pp. 1–10, 2016.

    [15] F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, "Digital Twins and Cyber-Physical Systems Toward Smart Manufacturing and Industry 4.0: Correlation and Comparison," Engineering, vol. 5, no. 4, pp. 653–661, Aug. 2019.

    [16] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

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