  In context of the universal presence of defects in additively manufactured
(AM) metals, efficient computational tools are required to rapidly screen AM
microstructures for mechanical integrity. To this end, a deep learning approach
is used to predict the elastic stress fields in images of defect-containing
metal microstructures. A large dataset consisting of the stress response of
100,000 random microstructure images is generated using high-resolution Fast
Fourier Transform-based finite element (FFT-FE) calculations, which is then
used to train a modified U-Net style convolutional neural network (CNN) model.
The trained U-Net model more accurately predicted the stress response compared
to alternative CNN architectures, exceeded the accuracy of low-resolution
FFT-FE calculations, and was generalizable to microstructures with complex
defect geometries. The model was applied to images of real AM microstructures
with severe lack of fusion defects, and predicted a strong linear increase of
maximum stress as a function of pore fraction. Together, the proposed CNN
offers an efficient and accurate way to predict the structural response of
defect-containing AM microstructures.
