@inbook {1531, title = {Acquisition of 3D Data for Prediction of Monotonic and Cyclic Properties of Superalloys}, booktitle = {Integrated Computational Materials Engineering (ICME): Advancing Computational and Experimental Methods}, year = {2020}, pages = {1{\textendash}18}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

The component design and fatigue life prediction of turbine disk alloys are critically dependent on the thermomechanical properties of the material. The TriBeam microscope provides a pathway to capture large, targeted 3D microstructural data volumes from turbine disk alloys in order to instantiate models that are used to simulate mechanical loading and assess material properties. The TriBeam experiments used to capture the microstructure, the analysis methods, data requirements, and future needs for 3D infrastructure will be discussed.

}, isbn = {978-3-030-40562-5}, doi = {10.1007/978-3-030-40562-5_1}, url = {https://doi.org/10.1007/978-3-030-40562-5_1}, author = {Echlin, Mclean P. and Lenthe, William C. and Stinville, Jean-Charles and Pollock, Tresa M.}, editor = {Ghosh, Somnath and Woodward, Christopher and Przybyla, Craig} } @inbook {1536, title = {Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys}, booktitle = {Integrated Computational Materials Engineering (ICME): Advancing Computational and Experimental Methods}, year = {2020}, pages = {55{\textendash}90}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

This chapter discusses fundamental aspects of the development of statistically equivalent virtual microstructures (SEVMs) and microstructure and property-based statistically equivalent representative volume elements (M-SERVE and P-SERVE) of the Ni-based superalloy at multiple scales. The two specific scales considered for this development are the subgrain scale of intragranular \γ\thinspace\−\thinspace\γ\textasciiacutex microstructures and the polycrystalline scale of grain ensembles with annealing twins. A comprehensive suite of computational methods that can translate microstructural data in experimental methods to optimally defined representative volumes for effective micromechanical modeling is the objective of this study. The framework involves a sequence of tasks, viz., serial sectioning, image processing, feature extraction, and statistical characterization, followed by micromechanical analysis and convergence tests for statistical functions. A principal motivation behind this paper is to translate high-fidelity microstructural image data into statistics of parametric descriptors in constitutive laws governing material performance.

}, isbn = {978-3-030-40562-5}, doi = {10.1007/978-3-030-40562-5_3}, url = {https://doi.org/10.1007/978-3-030-40562-5_3}, author = {Ghosh, Somnath and Weber, George and Pinz, Maxwell and Bagri, Akbar and Pollock, Tresa M. and Lenthe, Will and Stinville, Jean-Charles and Uchic, Michael D. and Woodward, Christopher}, editor = {Ghosh, Somnath and Woodward, Christopher and Przybyla, Craig} } @article {1491, title = {Microstructure and Property-Based Statistically Equivalent Representative Volume Elements for Polycrystalline Ni-Based Superalloys Containing Annealing Twins}, journal = {Metallurgical and Materials Transactions A: Physical Metallurgy and Materials Science}, volume = {49}, year = {2018}, pages = {5727{\textendash}5744}, abstract = {

This paper has three major objectives related to the development of computational micromechanics models of Ni-based superalloys, containing a large number of annealing twins. The first is the development of a robust methodology for generating 3D statistically equivalent virtual polycrystalline microstructures (3D-SEVPM) of Ni-based superalloys. Starting from electron backscattered diffraction (EBSD) images of sections, the method develops distributions and correlation functions of various morphological and crystallographic parameters. To incorporate twins in the parent grain microstructure, the joint probability of the number of twins and parent grain size, and the conditional probability distributions of twin thickness and twin distance are determined. Subsequently, a method is devised for inserting twins following the distribution functions. The overall methodology is validated by successfully comparing various statistics of the virtual microstructures with 3D EBSD data. The second objective is to establish the microstructure-based statistically equivalent representative volume element or M-SERVE that corresponds to the minimum SERVE size at which the statistics of any morphological or crystallographic feature converge to that of the experimental data. The Kolmogorov\–Smirnov (KS) test is conducted to assess the convergence of the M-SERVE size. The final objective is to estimate the property-based statistically equivalent RVE or P-SERVE, defined as the smallest SERVE, which should be analyzed to predict effective material properties. The crystal plasticity finite-element model is used to simulate SERVEs, from which the overall material response is computed. Convergence plots of material properties including the yield strength and hardening rate are used to assess the P-SERVE. A smaller P-SERVE compared to the M-SERVE indicates that the characteristic features of twins implemented in determining the M-SERVE are more stringent than those for determining material properties.

}, issn = {10735623}, doi = {10.1007/s11661-018-4858-y}, url = {https://doi.org/10.1007/s11661-018-4858-y}, author = {Bagri, Akbar and Weber, George and Stinville, Jean Charles and Lenthe, William and Pollock, Tresa and Woodward, Christopher and Ghosh, Somnath} }