@article {1741, title = {Bayesian inference of elastic constants and texture coefficients in additively manufactured cobalt-nickel superalloys using resonant ultrasound spectroscopy}, journal = {Acta Materialia}, volume = {220}, year = {2021}, pages = {117287}, abstract = {

Bayesian inference with sequential Monte Carlo is used to quantify the orientation distribution function coefficients and to calculate the fully anisotropic elastic constants of additively manufactured specimens from only the experimentally-measured resonant frequencies. The parallelizable and open-source SMCPy Python package enabled Bayesian inference within this new modeling framework, resulting in an order of magnitude reduction of the computation time for an 8-core machine. Residual stress-induced shifts on the resonant frequencies were explicitly accounted for during the Bayesian inference, enabling the estimation 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 characterization of any arbitrarily textured cubic microstructure through the resonant frequencies. The orientation distribution function coefficients of the textured polycrystalline microstructure were estimated in tensorial form to calculate both the 2nd order Hashin-Shtrikman bounds and the self-consistent estimate of the elastic constants, enabling accurate determination of all 21 possible independent elastic constants through the convergence constraints of the texture. Pole figures generated directly from the calculated texture coefficients showed good agreement with experimentally measured textures.

}, keywords = {Additive manufacturing, Bayesian inference, Parallel computing, Residual stress, Resonance ultrasound spectroscopy, Sequential monte carlo, Texture characterization}, issn = {1359-6454}, doi = {https://doi.org/10.1016/j.actamat.2021.117287}, url = {https://www.sciencedirect.com/science/article/pii/S1359645421006674}, author = {Jeff Rossin and Patrick Leser and Kira Pusch and Carolina Frey and Sean P. Murray and Chris J. Torbet and Stephen Smith and Samantha Daly and Tresa M. Pollock} } @article {1671, title = {Temperature dependence of single crystal elastic constants in a CoNi-Base alloy: A new methodology}, journal = {Materials Science and Engineering A}, volume = {803}, year = {2021}, pages = {140507}, abstract = {

A novel experimental setup combining induction heating, contactless (infrared thermometer) temperature measurements, and custom 15 cm length SiC-tipped piezoelectric transducers has been developed to rapidly collect resonant ultrasound spectroscopy (RUS) data from metallic rectangular parallelepiped (RP) specimens heated in air. This setup is used to collect over 70 of the lowest-frequency resonance modes from a single crystal of the novel CoNi-based superalloy SB-CoNi-10+\—an alloy identified for further study due to a promising set of high temperature properties. RUS spectra from room temperature (RT) to 927 \°C (1200 K) along with dilatometry measurements of the thermal expansion behavior are supplied to a robust, open-sourced, Bayesian inference code for simultaneous estimation of: three single crystal elastic constants, three crystal axis misorientation parameters, and a noise parameter. This study is the first application of the Bayesian RUS methodology at elevated temperatures, and the first to characterize SB-CoNi-10+. A monotonic decrease in elastic constants (C11,C12,C44) from (236.4, 150.8, 134.1) GPa to (190.7, 138.1, 95.2) GPa and a 15\% increase in elastic anisotropy from A = 3.131 \→ 3.618 is observed upon heating from RT to 927 \°C, with an increased rate of softening above 600 \°C (873 K).

}, keywords = {Bayesian inference, Elastic constants, High-temperature, Resonant ultrasound spectroscopy, Single crystal, Superalloy}, issn = {09215093}, doi = {10.1016/j.msea.2020.140507}, url = {https://doi.org/10.1016/j.msea.2020.140507}, author = {Goodlet, Brent R. and Murray, Sean P. and Bales, Ben and Rossin, Jeff and Torbet, Chris J. and Pollock, Tresa M.} } @article {1806, title = {Assessment of grain structure evolution with resonant ultrasound spectroscopy in additively manufactured nickel alloys}, journal = {Materials Characterization}, volume = {167}, year = {2020}, pages = {110501}, abstract = {

Despite the advantages of metal additive manufacturing (AM), ensuring integrity and reproducibility for built components is a barrier to the implementation of AM components in critical applications. Component qualification necessitates Non-Destructive Evaluation (NDE), but existing NDE frameworks are insufficient for the rapid and cost effective screening of variable AM components. In this study, alterations in laser powder bed fusion (LPBF) AM process parameters were characterized using resonant ultrasound spectroscopy (RUS). Samples that were subjected to a Hot Isostatic Press (HIP) and Heat Treatment (HT) post-processing step exhibited changes in resonance frequencies that varied in magnitude and direction with the type of resonance mode. The initial build direction prior to HIP and HT had a negligible effect on resonant frequency changes after recrystallization. The change in resonant frequencies at each process condition was predicted using Finite Element Modeling (FEM) informed with Electron Backscatter Diffraction (EBSD) data. FEM identified that the experimentally measured change in resonant response between the initially textured state and the recrystallized state was dominated by grain orientation dependent changes in elasticity. The EBSD-estimated elastic constants and FEM results were validated using experimental laser vibrometry and RUS inversion of elastic constants. RUS Inversion by Bayesian inference and Hamiltonian Monte Carlo has not been used to characterize an AM nickel-base alloy prior to this work. These results demonstrate that RUS is capable of detecting part to part microstructure variability between built AM components.

}, keywords = {Additive manufacturing, Bayesian inference, Elastic constant inversion, Finite element modeling, Recrystallization, Resonance ultrasound spectroscopy}, issn = {1044-5803}, doi = {https://doi.org/10.1016/j.matchar.2020.110501}, url = {https://www.sciencedirect.com/science/article/pii/S1044580320319720}, author = {Jeff Rossin and Brent Goodlet and Chris Torbet and William Musinski and Marie Cox and Jonathan Miller and Michael Groeber and Alexander Mayes and Eric Biedermann and Stephen Smith and Samantha Daly and Tresa Pollock} }