Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing

TL;DR

This paper introduces a data-informed uncertainty quantification method for laser-based powder bed fusion simulations, reducing prediction uncertainties and aligning well with experimental data using Bayesian inversion and multi-fidelity surrogate modeling.

Contribution

It develops a novel approach combining Bayesian inversion and multi-fidelity surrogate modeling for uncertainty quantification in additive manufacturing simulations.

Findings

33% reduction in residual strain prediction uncertainty

Good agreement between most likely parameter values and experimental data

Efficient uncertainty quantification with reduced computational cost

Abstract

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.