  Citation: Rolchigo, M.; Carson, R.; Belak, J. Understanding Uncertainty in Microstructure Evolution and Constitutive Properties in Additive Process Modeling. Metals 2022, 12, 324. https://doi.org/10.3390/ met12020324 Academic Editors: Francesco De Bona, Jelena Srnec Novak and Francesco Mocera Received: 1 January 2022 Accepted: 8 February 2022 Published: 12 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). metals Article Understanding Uncertainty in Microstructure Evolution and Constitutive Properties in Additive Process Modeling Matthew Rolchigo 1, * , Robert Carson 2 and James Belak 2 1 Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA 2 Lawrence Livermore National Laboratory, Livermore, CA 94550, USA; carson16@llnl.gov (R.C.); belak1@llnl.gov (J.B.) * Correspondence: rolchigomr@ornl.gov Abstract: 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 Open- FOAM 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 addi- tion to “random” uncertainty (due to random number generation in the orientations and locations of grains present), the heterogeneous nucleation density N 0 and the mean substrate grain spacing S 0 are varied to examine their impact of grain area development as a function of build height in the simu- lated microstructure. While mean grain area after 1 mm of build is found to be sensitive to N 0 and S 0 , particularly at small N 0 and large S 0 (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 N 0 , S 0 , 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 N 0 and S 0 are used. However, additional layers of ExaCA will need to be run to obtain mean grain areas independent of build height and baseplate structure. Keywords: additive manufacturing; microstructure; properties 1. Introduction Additive manufacturing (AM) processes for alloys are an exciting frontier in metallur- gical and materials science and engineering, in part due to the ability to manufacture unique geometries and compositions not possible via traditional metallurgical processing [1,2], as well as the potential for local control over microstructure and properties. However, realizing this potential for local microstructure and property control is difficult in practice. As discussed extensively in recent review articles on the subject, microstructure and prop- erties within AM builds result from complex multiscale relationships between material parameters and processing conditions [3–7]. By taking advantage of these relationships, a large number of studies have attempted to control features of AM microstructure (and resulting properties) for various alloys and specific AM processes. One method of control- ling microstructure is through altering aspects of the energy source itself, such as changing scan rotation between layers [8], modifying the beam shape [9], using nonlinear spot or island-based scan strategies [10–12], varying volumetric energy density [13], and using a pulsed beam with varied frequency and energy [14]. Other aspects of the process can also be altered to affect microstructure and properties; for example, the application of Metals 2022, 12, 324. https://doi.org/10.3390/met12020324 https://www.mdpi.com/journal/metals