  We present a deep learning approach for quantifying and localizing ex-situ
porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ
thermal image monitoring data. Our goal is to build the real time porosity map
of parts based on thermal images acquired during the build. The quantification
task builds upon the established Convolutional Neural Network model
architecture to predict pore count and the localization task leverages the
spatial and temporal attention mechanisms of the novel Video Vision Transformer
model to indicate areas of expected porosity. Our model for porosity
quantification achieved a $R^2$ score of 0.57 and our model for porosity
localization produced an average IoU score of 0.32 and a maximum of 1.0. This
work is setting the foundations of part porosity "Digital Twins" based on
additive manufacturing monitoring data and can be applied downstream to reduce
time-intensive post-inspection and testing activities during part qualification
and certification. In addition, we seek to accelerate the acquisition of
crucial insights normally only available through ex-situ part evaluation by
means of machine learning analysis of in-situ process monitoring data.
