#7858. Estimation of Local Strain Fields in Two-Phase Elastic Composite Materials Using UNet-Based Deep Learning

October 2026publication date
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Journal’s subject area:
Industrial and Manufacturing Engineering;
Materials Science (all);
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Abstract:
The knowledge of the distribution of local micromechanical fields is crucial in the design of composite materials. Traditionally full-field methods (such as finite element methods) and fast Fourier transformation-based methods are used to obtain the local fields. However, full-field simulations are computationally expensive and time-consuming. Recently, there has been a push toward using the big-data-driven machine learning approaches to estimate the local fields and establish the structure–property linkages. In this work, we use one of the deep learning-based algorithms known as the UNet to predict the local strain fields in a two-phase composite material subjected to uniaxial tensile load.
Keywords:
Convolutional neural network; Material informatics; Strain localization; Structure–property linkages; UNet architecture

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