Modeling strength and failure variability due to porosity in additively manufactured metals

To model and quantify the variability in plasticity and failure of additively manufactured metals due to imperfections in their microstructure, we have developed uncertainty quantification methodology based on pseudo marginal likelihood and embedded variability techniques. We account for both the porosity resolvable in computed tomography scans of the initial material and the sub-threshold distribution of voids through a physically motivated model. Calibration of the model indicates that the sub-threshold population of defects dominates the yield and failure response. The technique also allows us to quantify the distribution of material parameters connected to microstructural variability created by the manufacturing process, and, thereby, make assessments of material quality and process control. •We developed a Bayesian calibration method to quantify microstructural material variability.•The stress response was influenced by both a resolvable pore population and a sub-threshold porosity density.•The method draws upon pseudo–marginal and embedded likelihood techniques to handle the irreducible uncertainty of the visible microstructure and model the parametric variability of the implicit pore distribution.•We demonstrate the method on a dataset with 105 stress–strain curves and 18 computed tomography scans.