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.

本研究提出了一种基于深度学习 (DL) 的本构模型，用于研究含水量的纳米颗粒 / 环氧纳米复合材料的循环粘弹 - 粘塑 - 损伤行为。通过采用抽样技术和扰动方法的组合框架训练长短期记忆网络，使其可以准确捕捉速率依赖的应力应变关系，并且一致的切线形变模量，从而实现 DL 模型的高效计算。此外，将 DL 模型实现到有限元分析中，用于研究加载速率和含水量对纳米颗粒 / 环氧样品力 - 位移响应的影响，数字例子表明，DL 模型的计算效率取决于加载条件，并且比传统的本构模型高得多。此外，将数值结果和实验数据进行比较，可以证明在不同的纳米颗粒和含水量下，数值结果与实验数据具有良好的一致性。