Details
| Original language | English |
|---|---|
| Pages (from-to) | 155-171 |
| Number of pages | 17 |
| Journal | Computational mechanics |
| Volume | 72 |
| Issue number | 1 |
| Early online date | 27 Apr 2023 |
| Publication status | Published - Jul 2023 |
Abstract
Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model.
Keywords
- Computational micro-to-macro approach, Convolutional neural networks, Deep learning, Heterogeneous materials
ASJC Scopus subject areas
- Engineering(all)
- Computational Mechanics
- Engineering(all)
- Ocean Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computational Theory and Mathematics
- Mathematics(all)
- Computational Mathematics
- Mathematics(all)
- Applied Mathematics
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In: Computational mechanics, Vol. 72, No. 1, 07.2023, p. 155-171.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Efficient multiscale modeling of heterogeneous materials using deep neural networks
AU - Aldakheel, Fadi
AU - Elsayed, Elsayed S.
AU - Zohdi, Tarek I.
AU - Wriggers, Peter
N1 - Funding Information: Fadi Aldakheel (FA) gratefully acknowledges support for this research by the “German Research Foundation” (DFG) in the Priority Program SPP 2020 within its second funding phase. FA would like to thank N. Noii and A. Khodadadian for the support with mesh generation related to our paper [].
PY - 2023/7
Y1 - 2023/7
N2 - Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model.
AB - Material modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the well-established homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the data-set used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element RVE . The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model.
KW - Computational micro-to-macro approach
KW - Convolutional neural networks
KW - Deep learning
KW - Heterogeneous materials
UR - http://www.scopus.com/inward/record.url?scp=85153709994&partnerID=8YFLogxK
U2 - 10.1007/s00466-023-02324-9
DO - 10.1007/s00466-023-02324-9
M3 - Article
AN - SCOPUS:85153709994
VL - 72
SP - 155
EP - 171
JO - Computational mechanics
JF - Computational mechanics
SN - 0178-7675
IS - 1
ER -