Aldakheel, F.; Elsayed, E.S.; Zohdi, T.I.; Wriggers, P.: Efficient multiscale modeling of heterogeneous materials using deep neural networks. In: Computational Mechanics 72 (2023), Nr. 1, S. 155-171. DOI: https://doi.org/10.1007/s00466-023-02324-9
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. | |
License of this version: | CC BY 4.0 Unported |
Document Type: | Article |
Publishing status: | publishedVersion |
Issue Date: | 2023 |
Appears in Collections: | Fakultät für Bauingenieurwesen und Geodäsie Fakultät für Maschinenbau |
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