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

TL;DR

This paper introduces a deep learning-based constitutive model for nanoparticle/epoxy nanocomposites that accurately captures complex viscoelastic-viscoplastic behavior influenced by moisture, improving computational efficiency and matching experimental results.

Contribution

It develops a novel DL-based model trained with experimental data to simulate rate-dependent behavior of nanocomposites with moisture content, integrating into finite element analysis.

Findings

DL model accurately predicts stress-strain behavior

Computational efficiency surpasses conventional models

Good agreement with experimental data across conditions

Abstract

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.