  The present paper aims at applying uncertainty quantification methodologies
to process simulations of powder bed fusion of metal. In particular, for a
part-scale thermomechanical model of an Inconel 625 super-alloy beam, we study
the uncertainties of three process parameters, namely the activation
temperature, the powder convection coefficient and the gas convection
coefficient. First, we perform a variance-based global sensitivity analysis to
study how each uncertain parameter contributes to the variability of the beam
displacements. The results allow us to conclude that the gas convection
coefficient has little impact and can therefore be fixed to a constant value
for subsequent studies. Then, we conduct an inverse uncertainty quantification
analysis, based on a Bayesian approach on synthetic displacements data, to
quantify the uncertainties of the two remaining parameters, namely the
activation temperature and the powder convection coefficient. Finally, we use
the results of the inverse uncertainty quantification analysis to perform a
data-informed forward uncertainty quantification analysis of the residual
strains. Crucially, we make use of surrogate models based on sparse grids to
keep to a minimum the computational burden of every step of the uncertainty
quantification analysis. The proposed uncertainty quantification workflow
allows us to substantially ease the typical trial-and-error approach used to
calibrate power bed fusion part-scale models, and to greatly reduce
uncertainties on the numerical prediction of the residual strains. In
particular, we demonstrate the possibility of using displacement measurements
to obtain a data-informed probability density function of the residual strains,
a quantity much more complex to measure than displacements.
