Chengkan Xu1,2,4, Xiaofei Wang3, Yixuan Li2, Guannan Wang2,*, He Zhang2,4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 957-974, 2024, DOI:10.32604/cmes.2024.047327
Abstract Structural damage in heterogeneous materials typically originates from microstructures where stress concentration occurs. Therefore, evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial. Repeating unit cells (RUCs) are commonly used to represent microstructural details and homogenize the effective response of composites. This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs. The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters, including volume fraction, fiber/matrix property ratio, fiber shapes, and loading direction. Subsequently, the conditional generative adversarial network… More >
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