pisco_log
banner

Application of Digital Twin Technology in Aerospace Manufacturing

Yuan Tu

Abstract


Digital twin (DT) technology the creation of a dynamic, data-driven digital replica of physical assets, processes, or systems is
rapidly transforming aerospace manufacturing by enabling virtual testing, predictive maintenance, and optimized production workflows. This
paper reviews the current state of DT applications in aerospace manufacturing, synthesizes enabling technologies and architecture patterns,
and evaluates implementation challenges including data interoperability, certification, and organizational integration. Evidence from recent
systematic reviews, industry case studies, and engineering analyses indicates that DTs can shorten design cycles, improve first-time yield, and
reduce life-cycle sustainment costs when combined with machine learning, high-fidelity simulation, and robust IoT architectures.

Keywords


Digital twin;Aerospace manufacturing; Predictive maintenance; Industry 4.0; Digital engineering

Full Text:

PDF

Included Database


References


[1] Xiong, M., & Wang, H. (2022). Digital twin applications in aviation industry: A review. The International Journal of Advanced

Manufacturing Technology, 121(7), 56775692.

[2] Moenck, K., Rath, J.-E., Koch, J., Wendt,A., Kalscheuer, F., Schppstuhl, T., & Schoepflin, D. (2024).Digital twins in aircraft produc

tion and MRO: Challenges and opportunities. CEAS Aeronautical Journal.

[3] Hananto,A. L., Saeedi, S., et al. (2024).Digital Twins and Their Applications in Modeling Different Levels of Manufacturing Systems:A

Review. Computers, 13(4), 100. doi:10.3390/computers13040100 .

[4] Tao, F., Sun, Q., Sun, H., et al. (2024). Aero-engine digital twin engineering: Concept and key technologies. Acta Aeronautica et As

tronautica Sinica.

[5] Huang, W., Liang, Z., Wang, M., Zhang, W., & Wang, Y. (2024).Application and outlook of digital twin in aerospace structure design,

manufacturing and operation. Journal of Graphics (In Chinese with English abstract).

[6] Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In

Transdisciplinary Perspectives on Complex Systems (pp. 85113). Springer.

[7] Qi, Q., &Tao,F. (2018).Digital twin and big data towards smart manufacturing and industry 4.0: 360view. IEEE Access, 6, 35853593.

[8] 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), 10161022.

[9] Raguseo, E., & Vitari, C. (2020). Digital twin technologies and applications for smart manufacturing:A review. Computers in Indus

try, 123, 103294.

[10] Tao, F., Sui, F., Liu,A., Qi, Q., Zhang, M., & Zhang, H. (2021).Digital twin-driven product design, manufacturing and service with big

data. The International Journal of Advanced Manufacturing Technology, 107(2), 635646.

[11] Lu, Y., & Cecil, J. (2016). An argument for and observations toward predictive manufacturing interpolation models using digital twin

data. Procedia CIRP, 52, 190195.

[12] Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. In 53rd AIAA/ASME/

ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.




DOI: http://dx.doi.org/10.70711/frim.v4i4.9070

Refbacks

  • There are currently no refbacks.