<p>Additive manufacturing (AM) has enabled design and processing advantages for metallic components. Despite the advantages of AM, qualification of&nbsp;AM components&nbsp;for&nbsp;critical applications has limited&nbsp;its&nbsp;widespread usage. Resonant ultrasound spectroscopy (RUS) is capable of&nbsp;determining the elastic constants through inversion techniques, and has shown promise to characterize AM components. However, determining the elastic constants of built AM specimens with unknown anisotropy due to polycrystalline texture is problematic, as any of the 21 possible unknown elastic constants can be non-zero. Independently determining each of the 21 elastic constants by RUS inversion is not computationally feasible or numerically viable. Instead, texture (ODF) coefficients are directly parametrized in the inversion, then&nbsp;transformed into the second-order Hashin-Shtrikman bounds and the self-consistent average of the aggregate elastic constants. The&nbsp;calculated elastic constants are used to perform RUS inversion with the experimental resonant frequencies. The parallelizable and open-source SMCPy Python package enables Bayesian inference for this computationally expensive forward model, resulting in an order of magnitude reduction of the computation time. Residual stress induced shifts on the resonant frequencies were explicitly&nbsp;parametrized during the inversion, enabling the determination of their effect on the resonant frequencies without a stress-relief heat treatment. Additively manufactured cobalt-nickel-base superalloy (SB-CoNi-10C) specimens were sectioned at multiple inclinations relative to the build direction and scanned with resonant ultrasound spectroscopy to demonstrate the characterization of any textured cubic microstructure through the framework. This is a novel use of RUS inversion to quantify the elastic constants and texture coefficients of arbitrarily anisotropic AM polycrystals.</p>

Bayesian Inference of Elastic Constants and Texture Coefficients in Additively Manufactured Alloys Using Resonant Ultrasound Spectroscopy

Paper Proceedings: https://asmedigitalcollection.asme.org/qnde/QNDE2021/volume/85529

ASME 2021 48th Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE2021)

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