A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content
This work addresses the need for efficient and accurate simulation of nanocomposite materials under environmental conditions, though it is incremental as it applies an existing deep learning method to a specific domain problem.
The authors tackled the problem of modeling the complex cyclic viscoelastic-viscoplastic-damage behavior of epoxy nanocomposites with moisture content by developing a deep learning-based constitutive model, which achieved significantly higher computational efficiency than conventional models and showed good agreement with experimental data across varying nanoparticle and moisture contents.
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.