|Title||Multi-scale Microstructure and Property-Based Statistically Equivalent RVEs for Modeling Nickel-Based Superalloys|
|Publication Type||Book Chapter|
|Year of Publication||2020|
|Authors||Ghosh S, Weber G, Pinz M, Bagri A, Pollock TM, Lenthe W, Stinville J-C, Uchic MD, Woodward C|
|Editor||Ghosh S, Woodward C, Przybyla C|
|Book Title||Integrated Computational Materials Engineering (ICME): Advancing Computational and Experimental Methods|
|Publisher||Springer International Publishing|
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\thinspaceâ\thinspaceγ\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.