mlsanityData to 7 Oct 2026

A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content

arXiv 2305.08102 ↗First posted 14 May 2023cs.LG cs.CEcited by 1

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Abstract (arXiv)

In this work, we propose a deep learning (DL)-based constitutive model for investigating the cyclic viscoelastic-viscoplastic-damage behavior of nanoparticle/epoxy nanocomposites with moisture content. For this, a long short-term memory network is trained using a combined framework of a sampling technique and a perturbation method. The training framework, along with the training data generated by an experimentally validated viscoelastic-viscoplastic model, enables the DL model to accurately capture the rate-dependent stress-strain relationship and consistent tangent moduli. In addition, the DL-based constitutive model is implemented into finite element analysis. Finite element simulations are performed to study the effect of load rate and moisture content on the force-displacement response of nanoparticle/ epoxy samples. Numerical examples show that the computational efficiency of the DL model depends on the loading condition and is significantly higher than the conventional constitutive model. Furthermore, comparing numerical results and experimental data demonstrates good agreement with different nanoparticle and moisture contents.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 80 references that are in the map. First posted on arXiv 14 May 2023.

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13 of 80 references.

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1 more
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  1. 2024A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocompositescited by 0