Additive manufacturing (AM) features many advantages over traditional casting and wrought methods, but our understanding of the technique is still limited. Computational models are useful to study and isolate underlying physics and improve our understanding of the process-microstructure-property relations. However, these models rely on simplifications and parameters of uncertain values. These assumptions reduce the overall reliability of the predictive capabilities of these models, so it is important to calculate the uncertainty in model output. In doing so, we quantify the effect of model limitations and identify potential areas of improvement, a process made possible by uncertainty quantification (UQ). Here we highlight recent work which coupled and propagated statistical and systematic uncertainties from a melt pool transport model based in OpenFOAM, through a grain growth cellular automaton code, and finally to a crystal plasticity model for mechanical property predictions. We demonstrate how a UQ framework can identify though all three models model parameters which most significantly impact the reliability of model predictions and thus provide insight for future improvements in the models and suggest measurements to reduce output uncertainty.

Moderated by: Mark Schneider / Jakob Olofsson