  Predicting material properties of 3D printed polymer products is a challenge
in additive manufacturing due to the highly localized and complex manufacturing
process. The microstructure of such products is fundamentally different from
the ones obtained by using conventional manufacturing methods, which makes the
task even more difficult. As a first step of a systematic multiscale approach,
in this work, we have developed an artificial neural network (ANN) to predict
the mechanical properties of the crystalline form of Polyamide12 (PA12) based
on data collected from molecular dynamics (MD) simulations. Using the machine
learning approach, we are able to predict the stress-strain relations of PA12
once the macroscale deformation gradient is provided as an input to the ANN. We
have shown that this is an efficient and accurate approach, which can provide a
three-dimensional molecular-level anisotropic stress-strain relation of PA12
for any macroscale mechanics model, such as finite element modeling at
arbitrary quadrature points. This work lays the foundation for a multiscale
finite element method for simulating semicrystalline polymers, which will be
published as a separate study.
