Augmentation of scarce data—A new approach for deep-learning modeling of composites
Journal article, 2024

High-fidelity full-field micro-mechanical modeling of the non-linear path-dependent materials demands a substantial computational effort. Recent trends in the field incorporates data-driven Artificial Neural Networks (ANNs) as surrogate models. However, ANNs are inherently data-hungry, functioning as a bottleneck for the development of high-fidelity data-driven models. This study introduces a novel approach for data augmentation, expanding an original dataset without additional computational simulations. A Recurrent Neural Network (RNN) was trained and validated on high-fidelity micro-mechanical simulations of elasto-plastic short fiber reinforced composites. The obtained results showed a considerable improvement of the network predictions trained on expanded datasets using the proposed data augmentation approach. The proposed method for augmentation of scarce data may be used not only for other kind of composites, but also for other materials and at different length scales, and hence, opening avenues for innovative data-driven models in materials science and computational mechanics.

Deep learning

Elasto-plastic behavior

Recurrent neural networks

Short fiber composites

Data augmentation

Author

Hon Lam Cheung

Student at Chalmers

Petter Uvdal

University of Gothenburg

Mohsen Mirkhalaf

University of Gothenburg

Composites Science and Technology

0266-3538 (ISSN)

Vol. 249 110491

Subject Categories (SSIF 2011)

Applied Mechanics

DOI

10.1016/j.compscitech.2024.110491

More information

Latest update

3/1/2024 8