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Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
Publikationstyp
Journal Article
Date Issued
2017-06-15
Sprache
English
Author(s)
TORE-URI
Journal
Volume
183
Start Page
550
End Page
562
Citation
Composite Structures (183): 550-562 (2018)
Publisher DOI
Publisher
Elsevier
The stiffness prediction of textile composites has been studied intensively over the last 20 years. It is the complex yarn architecture that adds exceptional properties but also requires computationally expensive methods for the accurate solution of the homogenization problem. Braided composites are of special interest for the aerospace and automotive industry and have thus drawn the attention of many researchers, studying and developing analytical and numerical methods for the extraction of the effective elastic properties. This paper intends to study the effect of uncertainties caused by the automated manufacturing procedure, to the elastic behavior of braided composites. In this direction, a fast FEM-based multiscale algorithm is proposed, allowing for uncertainty introduction and response variability calculation of the macro-scale properties of 3D braided composites, within a Monte Carlo framework. Artificial neural networks are used to reduce the computational effort even more, since they allow for rapid generation of large samples when trained. With this approach it is feasible to apply a variance-based global sensitivity analysis in order to identify the most crucial uncertain parameters through the costly Sobol indices. The proposed method is straightforward, quite accurate and highlights the importance of realistic uncertainty quantification.
Subjects
Artificial neural networks
Braided composites
Global sensitivity analysis
Homogenization
Multiscale analysis
Probabilistic analysis
DDC Class
004: Informatik
600: Technik
620: Ingenieurwissenschaften