Sparse-grids uncertainty quantification of part-scale additive manufacturing processes

TL;DR

This paper applies sparse-grid surrogate models to efficiently perform uncertainty quantification in powder bed fusion simulations, reducing computational costs and improving residual strain predictions through sensitivity analysis, Bayesian calibration, and data-informed modeling.

Contribution

It introduces a workflow combining sparse-grid surrogates with sensitivity and Bayesian analysis for efficient uncertainty quantification in additive manufacturing.

Findings

Gas convection coefficient has minimal impact on displacements.

Bayesian calibration reduces uncertainties in activation temperature and powder convection.

Surrogate models enable data-informed residual strain predictions.

Abstract

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.