  Additive manufacturing (AM) technology is being increasingly adopted in a
wide variety of application areas due to its ability to rapidly produce,
prototype, and customize designs. AM techniques afford significant
opportunities in regard to nuclear materials, including an accelerated
fabrication process and reduced cost. High-fidelity modeling and simulation
(M\&S) of AM processes is being developed in Idaho National Laboratory (INL)'s
Multiphysics Object-Oriented Simulation Environment (MOOSE) to support AM
process optimization and provide a fundamental understanding of the various
physical interactions involved. In this paper, we employ Bayesian inverse
uncertainty quantification (UQ) to quantify the input uncertainties in a
MOOSE-based melt pool model for AM. Inverse UQ is the process of inversely
quantifying the input uncertainties while keeping model predictions consistent
with the measurement data. The inverse UQ process takes into account
uncertainties from the model, code, and data while simultaneously
characterizing the uncertain distributions in the input parameters--rather than
merely providing best-fit point estimates. We employ measurement data on melt
pool geometry (lengths and depths) to quantify the uncertainties in several
melt pool model parameters. Simulation results using the posterior
uncertainties have shown improved agreement with experimental data, as compared
to those using the prior nominal values. The resulting parameter uncertainties
can be used to replace expert opinions in future uncertainty, sensitivity, and
validation studies.
