Gadinger M, Deutschmann T, Krause D, Wartzack S (2025)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2025
Publisher: Cambridge University Press
Book Volume: 5
Pages Range: 209 - 218
Conference Proceedings Title: Proceedings of the Design Society: Volume 5: ICED25
Open Access Link: https://doi.org/10.1017/pds.2025.10035
Lightweight design is critical for improving the efficiency and sustainability of engineering applications. Laminated composites, with their high strength-to-weight ratio and tailored material properties, play a key role but introduce interlaminar stresses, particularly near free edges where delamination failures often occur. Addressing these stresses typically requires computationally expensive 3D finite element simulations, limiting their use in early design stages. This study presents a machine learning approach using Gaussian process regression and artificial neural networks to efficiently predict interlaminar stresses based on in-plane stress data from shell FE simulations. Achieving high predictive accuracy, this method enables cost-effective, early-stage composite design optimization under complex loading scenarios.
APA:
Gadinger, M., Deutschmann, T., Krause, D., & Wartzack, S. (2025). A new design method to account for interlaminar stresses in laminated composites using machine learning. In Proceedings of the Design Society: Volume 5: ICED25 (pp. 209 - 218). Cambridge University Press.
MLA:
Gadinger, Marc, et al. "A new design method to account for interlaminar stresses in laminated composites using machine learning." Proceedings of the Proceedings of the Design Society: Volume 5: ICED25 Cambridge University Press, 2025. 209 - 218.
BibTeX: Download