Generation of statistically representative synthetic three-dimensional microstructures

Mandal, Subinay; Lao, Jacky; Donegan, Sean P.; Rollett, Anthony D.

doi:10.1016/j.scriptamat.2017.11.034

Cited by 23 publications

(15 citation statements)

References 24 publications

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“…A fundamental challenge of RVE modeling lies in the selection of appropriate volume elements that are truly representative of the microstructure under investigation. [471][472][473] This criterion means that the employed model structure must contain all relevant microstructural features according to a prescribed (i.e., measurable) distribution. [474,475] In this context it should be mentioned that microstructure representativeness depends on the targeted properties under consideration.…”

Section: B Representative Volume Element Simulations Of Microstructumentioning

confidence: 99%

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Raabe

Sun

Silva

et al. 2020

Metall Mater Trans A

125

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This is a viewpoint paper on recent progress in the understanding of the microstructure–property relations of advanced high-strength steels (AHSS). These alloys constitute a class of high-strength, formable steels that are designed mainly as sheet products for the transportation sector. AHSS have often very complex and hierarchical microstructures consisting of ferrite, austenite, bainite, or martensite matrix or of duplex or even multiphase mixtures of these constituents, sometimes enriched with precipitates. This complexity makes it challenging to establish reliable and mechanism-based microstructure–property relationships. A number of excellent studies already exist about the different types of AHSS (such as dual-phase steels, complex phase steels, transformation-induced plasticity steels, twinning-induced plasticity steels, bainitic steels, quenching and partitioning steels, press hardening steels, etc.) and several overviews appeared in which their engineering features related to mechanical properties and forming were discussed. This article reviews recent progress in the understanding of microstructures and alloy design in this field, placing particular attention on the deformation and strain hardening mechanisms of Mn-containing steels that utilize complex dislocation substructures, nanoscale precipitation patterns, deformation-driven transformation, and twinning effects. Recent developments on microalloyed nanoprecipitation hardened and press hardening steels are also reviewed. Besides providing a critical discussion of their microstructures and properties, vital features such as their resistance to hydrogen embrittlement and damage formation are also evaluated. We also present latest progress in advanced characterization and modeling techniques applied to AHSS. Finally, emerging topics such as machine learning, through-process simulation, and additive manufacturing of AHSS are discussed. The aim of this viewpoint is to identify similarities in the deformation and damage mechanisms among these various types of advanced steels and to use these observations for their further development and maturation.

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Section: B Representative Volume Element Simulations Of Microstructumentioning

confidence: 99%

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Raabe

Sun

Silva

et al. 2020

Metall Mater Trans A

125

View full text Add to dashboard Cite

show abstract

“…Finally, the coordinates of the centers of the spheres and their diameter are provided as output for the Laguerre tesselation. In other cases, Monte Carlo algorithms were used to obtain the spatial distribution of the set points for the tessellation, so the final cell size distribution coincides with the experimental grain size distribution (Cruzado et al, 2015;Mandal et al, 2018).…”

Section: Digital Representation Of the Microstructurementioning

confidence: 99%

Computational Homogenization of Polycrystals

Segurado

Lebensohn

LLorca

2018

Advances in Applied Mechanics

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This paper reviews the current state-of-the-art in the simulation of the mechanical behavior of polycrystalline materials by means of computational homogenization. The key ingredients of this modelling strategy are presented in detail starting with the parameters needed to describe polycrystalline microstructures and the digital representation of such microstructures in a suitable format to perform computational homogenization. The different crystal plasticity frameworks that can describe the physical mechanisms of deformation in single crystals (dislocation slip and twinning) at the microscopic level are presented next. This is followed by the description of computational homogenization methods based on mean-field approximations by means of the viscoplastic self-consistent approach, or on the full-field simulation of the mechanical response of a representative polycrystalline volume element by means of the finite element method or the fast Fourier transform-based method. Multiscale frameworks based on the combination of mean-field homogenization and the finite element method are presented next to model the plastic deformation of polycrystalline specimens of arbitrary geometry under complex mechanical loading. Examples of application to predict the strength, fatigue life, damage, and texture evolution under different conditions are presented to illustrate the capabilities of the different models. Finally, current challenges and future research directions in this field are summarized.

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“…Generating an ensemble of stochastic Syn S that stand in for the Real S is a challenging task. In general, Syn S can be generated using several different approaches 42 , 44 . Of these, approaches based on shape descriptors have been used in the past; objects are inserted into a computational domain using shape packing algorithms constrained by global shape descriptors, such as volume fractions and particle size distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Of these, approaches based on shape descriptors have been used in the past; objects are inserted into a computational domain using shape packing algorithms constrained by global shape descriptors, such as volume fractions and particle size distributions. While packing algorithms 44 can be used to generate microstructures with specified morphometric characteristics (e.g., porosity, particle size distributions), these methods are limited to regular/analytical shapes such as spheres, ellipsoids, and polygons 45 . It is also difficult to pack regular shapes for theoretical maximum densities (TMDs) significantly higher than the close-packing limit; typical pressed energetic materials of the type simulated in the current work can have TMD values greater than 90% 46 .…”

mentioning

confidence: 99%

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

Chun

Roy

Nguyen

et al. 2020

Sci Rep

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the sensitivity of heterogeneous energetic (He) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. emerging multi-scale predictive models of He response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAn) to spawn ensembles of synthetic heterogeneous energetic material microstructures. the method generates qualitatively and quantitatively realistic microstructures by learning from images of He microstructures. We show that the proposed GAn method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer He materials for targeted performance in a materials-by-design framework.

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Generation of statistically representative synthetic three-dimensional microstructures

Cited by 23 publications

References 24 publications

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Current Challenges and Opportunities in Microstructure-Related Properties of Advanced High-Strength Steels

Computational Homogenization of Polycrystals

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

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