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Digital Twins and Engineering Education: Current Status

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  • M Khalid Shaikh

    (Assistant Professor, Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Karachi)

Abstract

This paper presents a comprehensive review of the use of Digital twins in engineering education among various disciplines. A total of 83 research papers were analyzed, spanning the last decade from 2012 to 2022. Almost all publications were reported after the year 2018, indicating a recent surge in interest and development in this area. The review reveals that digital twin technology offers students an interactive experience with virtual models of real-world products and systems, significantly enhancing theeffectiveness of engineering education. It also improves industrial competitiveness through predictive maintenance and fault diagnosis. Digital twins can be used in various engineering disciplines and for personalized learning. However, challenges such as model accuracy and data transfer must be considered when implementing them. Overall, this technologycan improve student learning outcomes, increase education accessibility and cost-effectiveness,and improve production systems'safety, visibility,and accessibility. Future requirements of the field are also discussed in this paper.

Suggested Citation

  • M Khalid Shaikh, 2024. "Digital Twins and Engineering Education: Current Status," International Journal of Innovations in Science & Technology, 50sea, vol. 6(2), pages 459-490, May.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:2:p:459-490
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    File URL: https://journal.50sea.com/index.php/IJIST/article/view/743/1346
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    1. Lavinia Chiara Tagliabue & Fulvio Re Cecconi & Sebastiano Maltese & Stefano Rinaldi & Angelo Luigi Camillo Ciribini & Alessandra Flammini, 2021. "Leveraging Digital Twin for Sustainability Assessment of an Educational Building," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    2. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    3. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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