@article {1986, title = {Boundary characterization using 3D mapping of geometrically necessary dislocations in AM Ta microstructure}, journal = {Journal of Materials Science}, volume = {57}, year = {2022}, month = {06}, doi = {10.1007/s10853-022-07074-2}, author = {Witzen, Wyatt and Polonsky, Andrew and Rottmann, Paul and Pusch, Kira and Echlin, Mclean and Pollock, Tresa and Beyerlein, Irene} } @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 {1351, title = {BisQue for 3D Materials Science in the Cloud: Microstructure{\textendash}Property Linkages}, journal = {Integrating Materials and Manufacturing Innovation}, volume = {8}, year = {2019}, pages = {52{\textendash}65}, abstract = {

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing\–structure\–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing\–structure\–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.

}, keywords = {Cloud-based computing, Homogenization, Materials cyberinfrastructure, Reduced order models}, isbn = {4019201900}, issn = {21939772}, doi = {10.1007/s40192-019-00128-5}, author = {Latypov, Marat I. and Khan, Amil and Lang, Christian A. and Kvilekval, Kris and Polonsky, Andrew T. and Echlin, McLean L.P. and Beyerlein, Irene J. and Manjunath, B. S. 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.} } @article {jorgensen2016bond, title = {Bond coatings with high rumpling resistance: Design and characterization}, journal = {Surface and Coatings Technology}, volume = {300}, year = {2016}, pages = {25{\textendash}34}, publisher = {Elsevier}, author = {Jorgensen, David J and Suzuki, Akane and Lipkin, Don M and Pollock, Tresa M} } @article {steuer2015benefits, title = {Benefits of high gradient solidification for creep and low cycle fatigue of AM1 single crystal superalloy}, journal = {Materials Science and Engineering: A}, volume = {645}, year = {2015}, pages = {109{\textendash}115}, publisher = {Elsevier}, author = {Steuer, S and Villechaise, P and Pollock, TM and Cormier, J} } @article {carpenter2015bulk, title = {Bulk texture evolution of nanolamellar Zr{\textendash}Nb composites processed via accumulative roll bonding}, journal = {Acta Materialia}, volume = {92}, year = {2015}, pages = {97{\textendash}108}, publisher = {Pergamon}, author = {Carpenter, JS and Nizolek, T and McCabe, RJ and Knezevic, M and Zheng, SJ and Eftink, BP and Scott, JE and Vogel, SC and Pollock, TM and Mara, NA and others} } @article {rhein2011bimetallic, title = {Bimetallic low thermal-expansion panels of Co-base and silicide-coated Nb-base alloys for high-temperature structural applications}, journal = {Materials Science and Engineering: A}, volume = {528}, number = {12}, year = {2011}, pages = {3973{\textendash}3980}, publisher = {Elsevier}, author = {Rhein, RK and Novak, MD and Levi, CG and Pollock, TM} } @article {pollock1996breakdown, title = {The breakdown of single-crystal solidification in high refractory nickel-base alloys}, journal = {Metallurgical and Materials Transactions A}, volume = {27}, number = {4}, year = {1996}, pages = {1081{\textendash}1094}, publisher = {Springer}, author = {Pollock, TM and Murphy, WH} }