UC Santa Barbara

Additive manufacturing (AM) of metallic components has become a full-fledged manufacturing technique as the space and aerospace industries require reduced weight, increased design and property control, and reduced lead times. The advantages of additive manufacturing directly correlate to challenges in characterization, property prediction, and component reliability, as printed parts display remarkable variability compared to traditionally qualified components. Regarding characterization, additively manufactured components represent a departure from traditional assumptions such as isotropic (same in all directions) material properties. Particularly for properties such as crystallographic texture, the variable solidification conditions of AM require consideration of arbitrary anisotropy and symmetry on the bulk scale.This work focuses on advancing the quantification of the properties of additively manufactured components with the bulk ultrasonic technique, resonant ultrasound spectroscopy (RUS). Critically, this technique is non-destructive, low-cost, and can be performed in a matter of minutes. However, existing frameworks to characterize properties such as the elastic constants from ultrasonic data only account for either isotropic or single crystalline compounds, with assumptions made to simplify the elastic symmetry of polycrystalline textures. To account for arbitrarily textured microstructures, a novel framework is developed to quantify texture directly from the resonant frequencies, with a novel cobalt-nickel-base superalloy (SB-CoNi-10C) specimen used to demonstrate the effectiveness of the technique. The determination of elastic constants from the resonant frequencies requires inverse problem solving to iteratively calculate the resonant frequencies and compare them to those measured in the laboratory. Given the complexity of the solution space and a need for robust parameter estimates on the independently determined texture, a CPU-parallelizable Bayesian inference technique, Sequential Monte Carlo (SMC) is employed to reduce computational costs to under 24h on a 10-core system. Looking toward crystal scale property prediction, the single crystal elastic constants are critical to understand the behavior of novel AM alloys. Therefore, a framework is developed to determine the single crystal elastic constants from the resonant frequencies of textured AM specimens, provided their texture is known by EBSD or neutron diffraction measurements. Agreement with reported single crystal constants measured on grown single crystal specimens is demonstrated, circumventing the need to grow single crystals. This framework provides an end-to-end determination of the single crystal elastic constants that quantifies error directly from the measured resonant frequency measurements. The determination of single crystal elastic constants from AM specimens has been extended to the lab scale, with 2 mm x 2 mm EBSD scans demonstrated as sufficient to inform the texture within a specimen, in contrast to the prior need for neutron diffraction or large-scale EBSD data. The incorporation of texture coefficient variability from an EBSD measurement in the model enables the single crystal elastic constants to be determined much more accurately. The single crystal elastic constants are determined with EBSD and RUS data of additively manufactured SB-CoNi-10C, Inconel 625, and Ti-6Al-4V, demonstrating agreement with previously reported literature values for each material. Finally, RUS is extended to arbitrary geometry specimens of AM Inconel 625, demonstrating the capability of RUS to quantify microstructure variability by leveraging finite-element forward models of the resonant frequencies and correlating to experimental frequencies. Insightful examples are provided to demonstrate the limitations and constraints of determining the single crystal elastic constants and texture coefficients from resonant frequencies.