Microstructural design of textile composites

Ganesh, Venkata; Ramakrishna, Sikhakolli; Teoh, Swee Hin; Naik, N.K.

doi:10.1016/s0261-3069(97)00099-x

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…(12) need to be suitably modified. Recognizing that T 1 (g) = 1 it can be shown thatF Q1 j=0 =F 1Q j=0 = C Q ,F 11 j=0 = |S|,F Q1 j =0 =F 1Q j =0 = 0 (13) where C Q are related to the 1-point statistics of the microstructure for single-phase polycrystals and contain the same information as the ODF. At this point it is useful to introduce two different arrays of GSH-based 2-pt correlations:…”

Section: Discussionmentioning

confidence: 99%

“…with L = 1 and K = 1 excluded (14) Equations (12) and (13) imply that the independent set of correlations consists of the first row or column of A in addition to any row or column of B (for 2L − 1 total correlations).…”

Section: Discussionmentioning

confidence: 99%

“…Materials development and deployment efforts require evaluation of the properties of large sets of potential material internal structures (generically referred to as microstructures; this description also includes information regarding chemical composition at the relevant length scales) [1][2][3][4][5][6][7][8][9][10][11][12][13]. Currently, experiments and simulations are used for such analyses at a high cost, severely limiting the extent of the materials design spaces explored [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics

et al. 2017

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%