In:
Computational Mechanics, Springer Science and Business Media LLC, Vol. 72, No. 1 ( 2023-07), p. 155-171
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 $$\mathcal {RVE}$$ 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.
Type of Medium:
Online Resource
ISSN:
0178-7675
,
1432-0924
DOI:
10.1007/s00466-023-02324-9
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
1458937-0
detail.hit.zdb_id:
799787-5
SSG:
11
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