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Efficient multiscale modeling of heterogeneous materials using deep neural networks

authored by
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.

Organisation(s)
Institute of Mechanics and Computational Mechanics
Institute of Continuum Mechanics
External Organisation(s)
University of California (UCLA)
Type
Article
Journal
Computational mechanics
Volume
72
Pages
155-171
No. of pages
17
ISSN
0178-7675
Publication date
07.2023
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Computational Mechanics, Ocean Engineering, Mechanical Engineering, Computational Theory and Mathematics, Computational Mathematics, Applied Mathematics
Electronic version(s)
https://doi.org/10.1007/s00466-023-02324-9 (Access: Open)
 

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