A machine learning-based viscoelastic-viscoplastic model for epoxy
  nanocomposites with moisture content
Betim Bahtiri Behrouz Arash Sven Scheffler Maximilian Jux Raimund Rolfes
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.