  Defects in laser powder bed fusion (L-PBF) parts often result from the
meso-scale dynamics of the molten alloy near the laser, known as the melt pool.
For instance, the melt pool can directly contribute to the formation of
undesirable porosity, residual stress, and surface roughness in the final part.
Experimental in-situ monitoring of the three-dimensional melt pool physical
fields is challenging, due to the short length and time scales involved in the
process. Multi-physics simulation methods can describe the three-dimensional
dynamics of the melt pool, but are computationally expensive at the mesh
refinement required for accurate predictions of complex effects, such as the
formation of keyhole porosity. Therefore, in this work, we develop a generative
deep learning model based on the probabilistic diffusion framework to map
low-fidelity, coarse-grained simulation information to the high-fidelity
counterpart. By doing so, we bypass the computational expense of conducting
multiple high-fidelity simulations for analysis by instead upscaling
lightweight coarse mesh simulations. Specifically, we implement a 2-D diffusion
model to spatially upscale cross-sections of the coarsely simulated melt pool
to their high-fidelity equivalent. We demonstrate the preservation of key
metrics of the melting process between the ground truth simulation data and the
diffusion model output, such as the temperature field, the melt pool dimensions
and the variability of the keyhole vapor cavity. Specifically, we predict the
melt pool depth within 3 $\mu m$ based on low-fidelity input data 4$\times$
coarser than the high-fidelity simulations, reducing analysis time by two
orders of magnitude.
