Formulation and calibration of higher-order elastic localization relationships using the MKS approach

Fast, Tony; Kalidindi, Surya R.

doi:10.1016/j.actamat.2011.04.005

Cited by 71 publications

(77 citation statements)

References 27 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: 94%

“…Historically, the multiscale materials modeling efforts have addressed either homogenization (communication of information from the lower length scale to the higher length scale) [30][31][32][33] or localization (communication of information from the higher length scale to the lower length scale) [8,13,14,[34][35][36]. Although both homogenization and localization have been studied extensively in literature using physically based approaches [31][32][33]37], recent work has identified the tremendous benefits of fusing these approaches with data-driven approaches [8,13,14,30,[34][35][36]. However, most of the prior effort has only addressed a limited number of the multiscale features.…”

Section: Introductionmentioning

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: 94%

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…MKS is a statistical tool using a response variable of the local material state to estimate the local phase or stress status of the microstructure in an applied thermal or strain field [28,29,46]. MKS provides efficient calculations to simulate the elastic deformation of the microstructure.…”

Section: Elastic Deformationmentioning

confidence: 99%

A Computational Framework for Material Design

Kattner

Campbell

2017

Integr Mater Manuf Innov

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A computational framework is proposed that enables the integration of experimental and computational data, a variety of user-selected models, and a computer algorithm to direct a design optimization. To demonstrate this framework, a sample design of a ternary Ni-Al-Cr alloy with a high work-to-necking ratio is presented. This design example illustrates how CALPHAD phase-based, composition and temperature-dependent phase equilibria calculations and precipitation models are coupled with models for elastic and plastic deformation to calculate the stress-strain curves. A genetic algorithm then directs the search within a specific set of composition and processing constraints for the ideal composition and processing profile to optimize the mechanical properties. The initial demonstration of the framework provides a potential solution to initiate the material design process in a large space of composition and processing conditions. This framework can also be used in similar material systems or adapted for other material classes.Keywords Ni-based superalloy · Integrated computational materials engineering · Genetic algorithm · Material design

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“…However, the MKS approach dramatically improves the accuracy of these expressions by calibrating the convolution kernels in these expressions to results from previously validated physics-based models. In recent work [126], the MKS approach was demonstrated to successfully capture the tails of the microscale stress and strain distributions in composite systems with relatively high contrast, using the higher-order terms in the localization relationships. It was also demonstrated that the MKS approach can be applied to problems involving nonlinear material behaviour such as spinodal decomposition [125] and rigid plastic deformation [127].…”

Section: Introductionmentioning

confidence: 99%

“…This is the central idea behind the recently formulated scale-bridging framework called microstructure knowledge systems (MKS) [13,[125][126][127][128]. Building on the statistical continuum theories developed by Kroner [129,130], MKS establishes high-fidelity microstructure propertyprocessing relationships that are amenable for bidirectional exchange of information between the constituent hierarchical length scales.…”

Section: Introductionmentioning

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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Formulation and calibration of higher-order elastic localization relationships using the MKS approach

Cited by 71 publications

References 27 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

A Computational Framework for Material Design

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

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