Details
Original language | English |
---|---|
Pages (from-to) | 550-562 |
Number of pages | 13 |
Journal | Composite Structures |
Volume | 183 |
Early online date | 15 Jun 2017 |
Publication status | Published - 1 Jan 2018 |
Abstract
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.
Keywords
- Artificial neural networks, Braided composites, Global sensitivity analysis, Homogenization, Multiscale analysis, Probabilistic analysis
ASJC Scopus subject areas
- Materials Science(all)
- Ceramics and Composites
- Engineering(all)
- Civil and Structural Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Composite Structures, Vol. 183, 01.01.2018, p. 550-562.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
AU - Balokas, Georgios
AU - Czichon, Steffen
AU - Rolfes, Raimund
N1 - Funding information: This work is implemented within the framework of the research project “FULLCOMP: Fully Integrated Analysis, Design, Manufacturing and Health-Monitoring of Composite Structures” under European Union’s Horizon 2020 research and innovation program and is funded by the European Commission under a Marie Sklodowska-Curie Innovative Training Networks Grant (No. 642121 ) for European Training Networks (ETN). The provided financial support is gratefully acknowledged by the authors.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Braided composites
KW - Global sensitivity analysis
KW - Homogenization
KW - Multiscale analysis
KW - Probabilistic analysis
UR - http://www.scopus.com/inward/record.url?scp=85021169529&partnerID=8YFLogxK
U2 - 10.1016/j.compstruct.2017.06.037
DO - 10.1016/j.compstruct.2017.06.037
M3 - Article
AN - SCOPUS:85021169529
VL - 183
SP - 550
EP - 562
JO - Composite Structures
JF - Composite Structures
SN - 0263-8223
ER -