@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 {1386, title = {Computational homogenization for multiscale forward modeling of resonant ultrasound spectroscopy of heterogeneous materials}, journal = {Materials Characterization}, volume = {158}, year = {2019}, pages = {109945}, abstract = {

We present a computational framework for multiscale forward modeling of ultrasound resonance in heterogeneous materials that accounts for microstructure. The approach includes two steps. The first step is the accurate determination of the elastic properties of heterogeneous materials with finite element simulations on a representative volume element of the microstructure at the mesoscopic length scale. The second step is modeling resonance frequencies of a macroscopic component made of an effective homogeneous medium having the same elastic properties as the actual material with microstructure. The approach is validated in a case study on a Cu\–W two-phase composite, for which resonance frequencies predicted with the proposed framework are compared against the experimental measurements. The present multiscale modeling approach, involving computational homogenization and leveraging 3D microstructure data, showed better accuracy compared to classical Voigt/Reuss bounds often used for forward modeling of resonant ultrasound spectroscopy.

}, keywords = {Computational homogenization, Finite elements, Forward modeling, Non-destructive evaluation, Resonant ultrasound spectroscopy}, issn = {10445803}, doi = {10.1016/j.matchar.2019.109945}, url = {https://doi.org/10.1016/j.matchar.2019.109945}, author = {Latypov, Marat I. and Charpagne, Marie Agathe and Souther, Mason and Goodlet, Brent R. and Echlin, Mclean P. and Beyerlein, Irene J. and Pollock, Tresa M.} } @article {1426, title = {Bayesian inference of elastic properties with resonant ultrasound spectroscopy}, journal = {The Journal of the Acoustical Society of America}, volume = {143}, year = {2018}, pages = {71{\textendash}83}, abstract = {

\© 2018 Acoustical Society of America. Bayesian modeling and Hamiltonian Monte Carlo (HMC) are utilized to formulate a robust algorithm capable of simultaneously estimating anisotropic elastic properties and crystallographic orientation of a specimen from a list of measured resonance frequencies collected via Resonance Ultrasound Spectroscopy (RUS). Unlike typical optimization procedures which yield point estimates of the unknown parameters, computing a Bayesian posterior yields probability distributions for the unknown parameters, and HMC is an efficient way to compute this posterior. The algorithms described are demonstrated on RUS data collected from two parallelepiped specimens of structural metal alloys. First, the elastic constants for a specimen of fine-grain polycrystalline Ti-6Al-4 V with random crystallographic texture and isotropic elastic symmetry are estimated. Second, the elastic constants and crystallographic orientation for a single crystal Ni-based superalloy CMSX-4 specimen are accurately determined, using only measurements of the specimen geometry, mass, and resonance frequencies. The unique contributions of this paper are as follows: the application of HMC for sampling the Bayesian posterior of a probabilistic RUS model, and the procedure for simultaneous estimation of elastic constants and lattice-specimen misorientation. Compared to previous approaches these algorithms demonstrate superior convergence behavior, particularly when the initial parameterization is unknown, and enable substantially simplified experimental procedures.

}, issn = {0001-4966}, doi = {10.1121/1.5017840}, author = {Bales, Ben and Petzold, Linda and Goodlet, Brent R. and Lenthe, William C. and Pollock, Tresa M.} }