Efficient multiscale modeling of heterogeneous materials using deep neural networks
- verfasst von
- Fadi Aldakheel, Elsayed S. Elsayed, Tarek I. Zohdi, Peter Wriggers
- 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.
- Organisationseinheit(en)
-
Institut für Baumechanik und Numerische Mechanik
Institut für Kontinuumsmechanik
- Externe Organisation(en)
-
University of California (UCLA)
- Typ
- Artikel
- Journal
- Computational mechanics
- Band
- 72
- Seiten
- 155-171
- Anzahl der Seiten
- 17
- ISSN
- 0178-7675
- Publikationsdatum
- 07.2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Numerische Mechanik, Meerestechnik, Maschinenbau, Theoretische Informatik und Mathematik, Computational Mathematics, Angewandte Mathematik
- Elektronische Version(en)
-
https://doi.org/10.1007/s00466-023-02324-9 (Zugang:
Offen)
