Understanding Uncertainty in Microstructure Evolution and Constitutive Properties in Additive Process Modeling

Coupled process-microstructure-property modeling, and understanding the sources of uncertainty and their propagation toward error in part property prediction, are key steps toward full utilization of additive manufacturing (AM) for predictable quality part development. The OpenFOAM model for process conditions, the ExaCA model for as-solidified grain structure, and the ExaConstit model for constitutive mechanical properties are used as part of the ExaAM modeling framework to examine a few of the various sources of uncertainty in the modeling workflow. In addition to random uncertainty (due to random number generation in the orientations and locations of grains present), the heterogeneous nucleation density N0 and the mean substrate grain spacing S0 are varied to examine their impact of grain area development as a function of build height in the simulated microstructure. While mean grain area after 1 mm of build is found to be sensitive to N0 and S0, particularly at small N0 and large S0 (despite some convergence toward similar values), the resulting grain shapes and overall textures develop in a reasonably similar manner. As a result of these similar textures, ExaConstit simulation using ExaCA representative volume elements (RVEs) from various permutations of N0, S0, and location within the build resulted in similar yield stress, stress-strain curve shape, and stress triaxiality distributions. It is concluded that for this particular material and scan pattern, 15 layers is sufficient for ExaCA texture and ExaConstit predicted properties to become relatively independent of additional layer simulation, provided that reasonable estimates for N0 and S0 are used. However, additional layers of ExaCA will need to be run to obtain mean grain areas independent of build height and baseplate structure.

Automated summary

Coupled process-microstructure-property modeling and understanding of uncertainty sources are crucial for reliable additive manufacturing (AM) part development. This study investigates several sources of uncertainty in the modeling workflow and finds that mean grain area is sensitive to nucleation density and substrate grain spacing, while grain shapes and overall textures develop similarly. Simulation results show that 15 layers are sufficient for independent predictions of mechanical properties, but additional layers are needed for mean grain area independent of build height.