Gradient-based microstructure reconstructions from distributions using fast Fourier transforms

Fullwood, David T.; Kalidindi, Surya R.; Niezgoda, Stephen R.; Fast, Anthony Nathan; Hampson, Naomi A.

doi:10.1016/j.msea.2007.10.087

Cited by 110 publications

(50 citation statements)

References 19 publications

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“…The n-point statistics described above are most efficiently computed on digital datasets using fast Fourier transform (FFT) techniques [13,35,36,41]. An implicit benefit of treating the material structure function as a stochastic process is that it allows a rigorous quantification of the associated variance [13,14].…”

Section: Introductionmentioning

confidence: 99%

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Cecen

Fast

Kalidindi

2016

Integr Mater Manuf Innov

105

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This paper presents a generalized framework along with the associated computational strategies for a rigorous quantification of the material structure in a range of different applications using the framework of 2-point spatial correlations. In particular, we focus on applications requiring different assumptions about the periodicity and/or involving irregular domain shapes and potentially extremely large datasets. Important details of the computational algorithms needed to address these challenges are developed and illustrated with example case studies. Algorithms developed and presented in this work are available at http://dx.doi.org/10.5281/zenodo.31329. Background

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Section: Introductionmentioning

confidence: 99%

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Cecen

Fast

Kalidindi

2016

Integr Mater Manuf Innov

105

View full text Add to dashboard Cite

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“…The local state can include any information needed to define a material's properties at the length scale of the spatial voxels and can include chemical composition, phase identifiers, orientations (e.g., in crystalline phases), and defect densities (e.g., dislocation density). In addition to transforming a material's structure into a versatile digital signal, this approach inherently treats the material's structure as a stochastic process because of the probabilistic interpretation of the variable m. The digital signal representation of structure offers many advantages, including fast computation of spatial correlations (38,(116)(117)(118); automated identification of salient structure features in large data sets (119); extraction of representative volume elements from an ensemble of data sets (120)(121)(122); reconstructions of structures from measured statistics (117,(123)(124)(125); building of real-time, searchable structure databases (126,127); and mining of high-fidelity, multiscale structure-performance-structure evolution correlations from physics-based models (128-132).…”

Section: Materials Data Analyticsmentioning

confidence: 99%

Materials Data Science: Current Status and Future Outlook

Kalidindi¹,

Graef

2015

Annu. Rev. Mater. Res.

213

125

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The field of materials science and engineering is on the cusp of a digital data revolution. After reviewing the nature of data science and Big Data, we discuss the features of materials data that distinguish them from data in other fields. We introduce the concept of process-structure-property (PSP) linkages and illustrate how the determination of PSPs is one of the main objectives of materials data science. Then we review a

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“…Methods such as simulated annealing, gradient-based schemes and Gaussian random fields are usually applicable for isotropic and twophase media (Torquato, 2002;Jiao et al, 2007;Jiao et al, 2008;Fullwood et al, 2008a;Lanzini et al, 2009;Jiang et al, 2013;Ballani & Stoyan, 2015). Methods such as simulated annealing, gradient-based schemes and Gaussian random fields are usually applicable for isotropic and twophase media (Torquato, 2002;Jiao et al, 2007;Jiao et al, 2008;Fullwood et al, 2008a;Lanzini et al, 2009;Jiang et al, 2013;Ballani & Stoyan, 2015).…”

Section: Modification Of Phase-recovery Algorithm For Three-phase Recmentioning

confidence: 99%

Efficient three‐phase reconstruction of heterogeneous material from 2D cross‐sections via phase‐recovery algorithm

Hasanabadi

Baniassadi

Abrinia

et al. 2016

Journal of Microscopy

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Digital reconstruction of a complex heterogeneous media from the limited statistical information, mostly provided by different imaging techniques, is the key to the successful computational analysis of this important class of materials. In this study, a novel approach is presented for three-dimensional (3D) reconstruction of a three-phase microstructure from its statistical information provided by two-dimensional (2D) cross-sections. In this three-step method, first two-point correlation functions (TPCFs) are extracted from the cross-section(s) using a spectral method suitable for the three-phase media. In the next step, 3D TPCFs are approximated for all vectors in a representative volume element (RVE). Finally, the 3D microstructure is realized from the full-set TPCFs obtained in the previous step, using a modified phase-recovery algorithm. The method is generally applicable to any complex three-phase media, here illustrated for an SOFC anode microstructure. The capabilities and shortcomings of the method are then investigated by performing a qualitative comparison between example cross-sections obtained computationally and their experimental equivalents. Finally, it is shown that the method almost conserves key microstructural properties of the media including tortuosity, percolation and three-phase boundary length (TPBL).

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Gradient-based microstructure reconstructions from distributions using fast Fourier transforms

Cited by 110 publications

References 19 publications

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Materials Data Science: Current Status and Future Outlook

Efficient three‐phase reconstruction of heterogeneous material from 2D cross‐sections via phase‐recovery algorithm

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