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

    How to Cite

    [1]
    P. K. Iyengar, “Digital Twin Systems for Next-Generation Product Engineering”, ijmiet, vol. 7, no. 2, pp. 01–16, Oct. 2024, doi: 10.67228/30715628/IJMIET-2024PII7R7D.

  • 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.

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