AI-Generated Digital Twins for Autonomous Aerospace Manufacturing Processes
Keywords:
AI-generated digital twins, generative AI, aerospace manufacturing,Abstract
In autonomous aerospace manufacturing AI-generated digital twin is emerging as a revolutionary technology which utilises generative AI to enhance the real-time monitoring, predictive analytics, and process optimization. The objective of this study is to explore the integration of ai driven simulations with automated aerospace production lines by focusing the capacity to refine robotic assembly, enhance welding precision and minimising production defect.
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References
J. Liu, Y. Liu, Y. Xu, and L. Zhang, “Digital twin-driven rapid individualised design of
automated flow shop manufacturing system,” International Journal of Production
Research, vol. 57, no. 12, pp. 3903–3919, 2019.
M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable
emergent behavior in complex systems,” in Transdisciplinary Perspectives on
Complex Systems: New Findings and Approaches, Springer, 2017, pp. 85–113.
George, Jabin Geevarghese. "Advancing Enterprise Architecture for Post-Merger
Financial Systems Integration in Capital Markets laying the Foundation for Machine
Learning Application." Aus. J. ML Res. & App 3.2 (2023): 429.
Dash, S. "Architecting Intelligent Sales and Marketing Platforms: The Role of
Enterprise Data Integration and AI for Enhanced Customer Insights." Journal of
Artificial Intelligence Research 3.2 (2023): 253-291.
Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to
cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12
(2023): 2164-2167.
George, Jabin Geevarghese. "HARNESSING GENERATIVE AI FOR ENTERPRISE
APPLICATION MODERNIZATION: ENHANCING CYBERSECURITY AND DRIVING
INNOVATION." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN
ENGINEERING AND TECHNOLOGY (IJARET) 15.3 (2024): 377-392.
Dash, S. "Designing Modular Enterprise Software Architectures for AI-Driven Sales
Pipeline Optimization." Journal of Artificial Intelligence Research 3.2 (2023): 292-334.
Godbole, Aditi, Jabin Geevarghese George, and Smita Shandilya. "Leveraging Long-
Context Large Language Models for Multi-Document Understanding and
Summarization in Enterprise Applications." arXiv preprint arXiv:2409.18454 (2024).
Akhilandeswari, P., and Jabin G. George. "Secure Text Steganography." Proceedings
of International Conference on Internet Computing and Information Communications:
ICICIC Global 2012. Springer India, 2014.
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and
Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022):
-113.
Santosh Kumar, Singu. "Maximizing financial intelligence-the role of optimized etl in
fintech data warehousing." INTERNATIONAL JOURNAL OF COMPUTER
ENGINEERING AND TECHNOLOGY (IJCET) 15, no. 4 (2024): 464-471.
R. Schmidt, P. Möhring, and C. Heine, “AI-driven predictive quality control using
digital twins in aerospace manufacturing,” Procedia CIRP, vol. 95, pp. 25–30, 2020.
Z. Wang, J. Huang, and Y. Li, “Generative adversarial networks for real-time defect
prediction in aerospace digital twins,” IEEE Transactions on Neural Networks and
Learning Systems, vol. 33, no. 4, pp. 1578–1591, Apr. 2022.
X. Zhang, P. Wang, and J. Lee, “Industrial big data analytics for predictive
maintenance in smart manufacturing,” Journal of Manufacturing Systems, vol. 58, pp.
–361, 2021.
S. Feng and M. Liu, “Quantum-enhanced machine learning for digital twins in
aerospace applications,” Nature Computational Science, vol. 2, no. 7, pp. 415–426,
H. Kaur, B. Kumar, and P. Singh, “Blockchain-based security framework for AI-driven
digital twin ecosystems in aerospace,” IEEE Transactions on Industrial Informatics,
vol. 18, no. 11, pp. 7509–7518, Nov. 2022.
M. Ríos, L. Chen, and C. Gao, “AI-powered anomaly detection in cyber-physical
aerospace manufacturing,” IEEE Sensors Journal, vol. 22, no. 3, pp. 2489–2498,
Mar. 2022.
Y. Chen, J. Zhao, and X. Wang, “A survey of digital twin technology and its
applications in aerospace engineering,” IEEE Access, vol. 9, pp. 124698–124718,
T. Müller and R. Klein, “Simulation-driven generative models for aerospace
component optimization,” Computers & Industrial Engineering, vol. 168, pp. 107989,
D. Park and L. Zhou, “Machine learning-enabled predictive maintenance for
aerospace components,” Journal of Aerospace Engineering, vol. 35, no. 2, pp. 1–15,
G. Xu, W. Sun, and J. Li, “Digital twin-enabled smart manufacturing: A framework
and case study in the aerospace industry,” Manufacturing Letters, vol. 26, pp. 61–65,
