  We present an efficient approach to quantify the uncertainties associated
with the numerical simulations of the laser-based powder bed fusion of metals
processes. Our study focuses on a thermomechanical model of an Inconel 625
cantilever beam, based on the AMBench2018-01 benchmark proposed by the National
Institute of Standards and Technology (NIST). The proposed approach consists of
a forward uncertainty quantification analysis of the residual strains of the
cantilever beam given the uncertainty in some of the parameters of the
numerical simulation, namely the powder convection coefficient and the
activation temperature. The uncertainty on such parameters is modelled by a
data-informed probability density function obtained by a Bayesian inversion
procedure, based on the displacement experimental data provided by NIST. To
overcome the computational challenges of both the Bayesian inversion and the
forward uncertainty quantification analysis we employ a multi-fidelity
surrogate modelling technique, specifically the multi-index stochastic
collocation method. The proposed approach allows us to achieve a 33\% reduction
in the uncertainties on the prediction of residual strains compared with what
we would get basing the forward UQ analysis on a-priori ranges for the
uncertain parameters, and in particular the mode of the probability density
function of such quantities (i.e., its ``most likely value'', roughly speaking)
results to be in good agreement with the experimental data provided by NIST,
even though only displacement data were used for the Bayesian inversion
procedure.
