Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. (1st May 2017)

• Record Type: Journal Article
• Authors: Paulson, Noah H.; Priddy, Matthew W.; McDowell, David L.; Kalidindi, Surya R.
• Is Part Of: Acta materialia. Volume 129(2017)
• Page Start: 428
• Page End: 438
• Publication Date: 2017-05-01
• Subjects: 2-Point correlations -- Microstructure -- Computational model -- Crystal plasticity -- Reduced-order model; Materials -- Periodicals; Materials science -- Periodicals; Materials -- Mechanical properties -- Periodicals; Metallurgy -- Periodicals; Chemistry, Inorganic -- Periodicals; 620.112
• DOI: 10.1016/j.actamat.2017.03.009
• Languages: English
• ISSNs: 1359-6454

Abstract

Abstract: Computationally efficient structure-property (S-P) linkages (i.e., reduced order models) are a necessary key ingredient in accelerating the rate of development and deployment of structural materials. This need represents a major challenge for polycrystalline materials, which exhibit rich heterogeneous microstructure at multiple structure/length scales, and exhibit a wide range of properties. In this study, a novel framework is described for extracting S-P linkages in polycrystalline microstructures that are obtained using 2-point spatial correlations (also called 2-point statistics) to quantify the material's microstructure, and principal component analysis (PCA) to represent this information in a reduced dimensional space. Additionally, it is demonstrated that the use of generalized spherical harmonics (GSH) as a Fourier basis for functions defined on the orientation space leads to a compact and computationally efficient representation of the desired S-P linkages. In this study, these novel protocols are developed and demonstrated for elastic stiffness and yield strength predictions for α − Ti microstructures using a dataset produced through microscale finite element simulations. Graphical abstract: Image 1