  Laser Powder Bed Fusion has become a widely adopted method for metal Additive
Manufacturing (AM) due to its ability to mass produce complex parts with
increased local control. However, AM produced parts can be subject to
undesirable porosity, negatively influencing the properties of printed
components. Thus, controlling porosity is integral for creating effective
parts. A precise understanding of the porosity distribution is crucial for
accurately simulating potential fatigue and failure zones. Previous research on
generating synthetic porous microstructures have succeeded in generating parts
with high density, isotropic porosity distributions but are often inapplicable
to cases with sparser, boundary-dependent pore distributions. Our work bridges
this gap by providing a method that considers these constraints by
deconstructing the generation problem into its constitutive parts. A framework
is introduced that combines Generative Adversarial Networks with Mallat
Scattering Transform-based autocorrelation methods to construct novel
realizations of the individual pore geometries and surface roughness, then
stochastically reconstruct them to form realizations of a porous printed part.
The generated parts are compared to the existing experimental porosity
distributions based on statistical and dimensional metrics, such as nearest
neighbor distances, pore volumes, pore anisotropies and scattering transform
based auto-correlations.
