  Insufficient overlap between the melt pools produced during Laser Powder Bed
Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical
and fatigue performance. In-situ monitoring of the melt pool subsurface
morphology requires specialized equipment that may not be readily accessible or
scalable. Therefore, we introduce a machine learning framework to correlate
in-situ two-color thermal images observed via high-speed color imaging to the
two-dimensional profile of the melt pool cross-section. Specifically, we employ
a hybrid CNN-Transformer architecture to establish a correlation between single
bead off-axis thermal image sequences and melt pool cross-section contours
measured via optical microscopy. In this architecture, a ResNet model embeds
the spatial information contained within the thermal images to a latent vector,
while a Transformer model correlates the sequence of embedded vectors to
extract temporal information. Our framework is able to model the curvature of
the subsurface melt pool structure, with improved performance in high energy
density regimes compared to analytical melt pool models. The performance of
this model is evaluated through dimensional and geometric comparisons to the
corresponding experimental melt pool observations.
