Microstructure reconstructions from 2-point statistics using phase-recovery algorithms

Fullwood, David T.; Niezgoda, Stephen R.; Kalidindi, Surya R.

doi:10.1016/j.actamat.2007.10.044

Cited by 283 publications

(142 citation statements)

References 13 publications

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“…In other words, m h s takes values of 0 or 1 depending on the local state occupying each voxel in each MVE. Similar descriptions have been successfully implemented in prior work for microstructure classification [9,10,25], microstructure reconstructions [47], and establishing process-structure-property linkages [28,30,34,44].…”

Section: Machine Learning Problem Definitionmentioning

confidence: 93%

“…Each MVE has 21 × 21 × 21 = 9261 voxels. In all the MVEs used in this study, each voxel is completely occupied by one of the microscale constituents (i.e., no voxel is occupied by mixtures of the two constituent phases; also called eigen-microstructures [47]). The microscale elastic strain distributions in each MVE were computed using finite element (FE) models executed using the commercial software package, ABAQUS [48].…”

Section: Localization: Problem and Data Descriptionmentioning

confidence: 99%

“…More precisely, m h s denotes the volume fraction of local state h (i.e., black or white phases in Fig. 1) in the spatial voxel of interest, s, where s = 1, 2, ..., S. For the case studies shown here, MVEs are generated as eigenmicrostructures [47], where each voxel is occupied by a single discrete local state and they have a size of 21×21×21 resulting in total of S = 9261 voxels. In other words, m h s takes values of 0 or 1 depending on the local state occupying each voxel in each MVE.…”

Section: Machine Learning Problem Definitionmentioning

confidence: 99%

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Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Liu

Yabansu

Yang

et al. 2017

Integr Mater Manuf Innov

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The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

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Section: Machine Learning Problem Definitionmentioning

confidence: 93%

Section: Localization: Problem and Data Descriptionmentioning

confidence: 99%

Section: Machine Learning Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Liu

Yabansu

Yang

et al. 2017

Integr Mater Manuf Innov

Self Cite

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show abstract

“…When working with the correlations each point in the spanned space represents a set of potential microstructure correlations, and new microstructure realizations must be reconstructed from the statistics. Reconstruction from statistics is an active area of research, and significant advances have been made in recent years [30,31]. In the opinion of the authors the benefits of a more compact linear (PCA) representation and a cohesive framework based on the formalism of stochastic processes outweigh the added abstraction.…”

Section: Visualizations In the Microstructure Spacementioning

confidence: 99%

Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data

Niezgoda

Kanjarla

Kalidindi

2013

Integr Mater Manuf Innov

115

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The study of microstructure and its relation to properties and performance is the defining concept in the field of materials science and engineering. Despite the paramount importance of microstructure to the field, a rigorous systematic framework for the quantitative comparison of microstructures from different material classes has yet to be adopted. In this paper, the authors develop and present a novel microstructure quantification framework that facilitates the visualization of complex microstructure relationships, both within a material class and across multiple material classes. This framework, based on the stochastic process representation of microstructure, serves as a natural environment for developing relational statistical analyses, for establishing quantitative microstructure descriptors. In addition, it will be shown that this new framework can be used to link microstructure visualizations with properties to develop reduced-order microstructure-property linkages and performance models.

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“…Note that the framework for the description of the microstructure presented above is fairly general and can accommodate any combination of material features by simply increasing the dimensionality of the local state space; it is also not limited to any specific length or time scales. The discretized representation of microstructure offers many advantages in fast computation of microstructure measures/metrics [9,89], automated identification of salient microstructure features in large datasets [138], extraction of representative volume elements from an ensemble of datasets [110,111], reconstructions of microstructures from measured statistics [139,140], building of real-time searchable microstructure databases [44], and mining of high-fidelity multiscale structure-performance-structure evolution linkages from physics-based models (the main focus of this paper) [13,[125][126][127][128].…”

Section: Isrn Materials Sciencementioning

confidence: 99%

Computationally Efficient, Fully Coupled Multiscale Modeling of Materials Phenomena Using Calibrated Localization Linkages

Kalidindi

2012

ISRN Materials Science

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Most modern physics-based multiscale materials modeling and simulation tools aim to take into account the important details of the material internal structure at multiple length scales. However, they are extremely computationally expensive. In recent years, a novel data science enabled framework has been formulated for effective scale-bridging that is central to practical multiscaling. A salient feature of this new approach is its ability to capture heterogeneity of fields of interest at different length scales. In this approach, the computations at the mesoscale are handled using a novel data science approach called materials knowledge systems (MKS). The MKS approach has enjoyed tremendous success in building highly accurate and computationally efficient metamodels for localization (i.e., mesoscale spatial distribution of a macroscale imposed field such as stress or strain rate) in simulating a number of different multiscale materials phenomena. MKS derives its accuracy from the fact that it is calibrated to results from previously established numerical models for the phenomena of interest, while its computational efficiency comes from the use of fast Fourier transforms. The current capabilities and the future outlook for the MKS framework are expounded in this paper.

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Microstructure reconstructions from 2-point statistics using phase-recovery algorithms

Cited by 283 publications

References 13 publications

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data

Computationally Efficient, Fully Coupled Multiscale Modeling of Materials Phenomena Using Calibrated Localization Linkages

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