DOE OSTI · 3015166
Quantification and prediction of solidification textures under additive manufacturing conditions
Abstract
Crystallographic textures are a major determinant of the macroscale anisotropic properties of polycrystalline metallic alloys produced in a wide range of additive manufacturing (AM) processes. Here, we introduce a statistical method that can accurately quantify the degree of orientational order of textures despite the large random fluctuations in the orientation of individual grains inherent in AM processes. The method, demonstrated for laser and resolidification of AlSi thin films, extends Z-scoring to a dynamical regime to assess the statistical significance of observed textures compared to randomly generated ones at different stages of solidification. We further show that, combined with phase-field modeling, this method can be used to infer fundamental anisotropic properties of the solid-liquid interface that are essential for texture prediction, and are compared here to the results of atomistic simulations. In addition, phase-field modeling reveals that, even at rapid AM solidification rates, the observed 〈110〉-dominated textures in the AlSi thin films are controlled predominantly by the anisotropy of the interface free-energy and sheds light on the physical mechanism of grain competition. These results significantly enhance both the existing tools for the quantification and prediction of AM crystallographic textures and our basic understanding of their formation.
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Zhong, Mingwang [Northeastern Univ., Boston, MA (United States)], Eres-Castellanos, Adriana [Colorado School of Mines, Golden, CO (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000213248676), Ji, Kaihua [Northeastern Univ., Boston, MA (United States)] (ORCID:0009000089322396), Saville, Alec I. [Colorado School of Mines, Golden, CO (United States)], Rodgers, Brian [Colorado School of Mines, Golden, CO (United States)] (ORCID:0000000212035783), Coughlin, Dan R. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Gibbs, John W. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000202311318), Roehling, John D. [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], McKeown, Joseph T. [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)] (ORCID:0000000289214798), Clarke, Amy Jean [Colorado School of Mines, Golden, CO (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000188373503), Karma, Alain [Northeastern Univ., Boston, MA (United States)] (ORCID:0000000170329862). 2025-11-28. Quantification and prediction of solidification textures under additive manufacturing conditions. https://doi.org/10.1038/s41467-025-66655-2
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