Three-dimensional (3D) microstructure visualization and finite element modeling of the mechanical behavior of SiC particle reinforced aluminum composites

Chawla, Nikhilesh; Ganesh, V. V.; Wunsch, Benjamin H.

doi:10.1016/j.scriptamat.2004.03.043

Cited by 214 publications

(90 citation statements)

References 8 publications

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“…Three Vickers hardness indentations were then placed in each specimen to act as fiduciary marks during serial polishing. [33][34][35] All observations in the optical microscope were made viewing along the sheet normal direction with the tensile axis horizontal. Specimens were polished to a final finish with 1-lm diamond abrasive prior to serial polishing.…”

Section: Methodsmentioning

confidence: 99%

Effect of Microstructure on Cavitation during Hot Deformation of a Fine-Grained Aluminum-Magnesium Alloy as Revealed through Three-Dimensional Characterization

Chang

Taleff

Krajewski

2009

Metall Mater Trans A

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The effect of microstructure on cavitation developed during hot deformation of a fine-grained AA5083 aluminum-magnesium alloy is investigated. Two-point correlation functions and threedimensional (3-D) microstructure characterization reveal that cavitation depends strongly on the mechanism that controls plastic deformation. Grain-boundary-sliding (GBS) creep produces large, interconnected cavities rapidly during plastic straining. Solute-drag (SD) creep produces isolated cavities with less total volume fraction at a given strain. The 3-D microstructure data reveal adjacency between various microstructural features. Cavities are observed to be preferentially adjacent to large Al 6 (Mn,Fe) particles and to Mg-Si particles of all observed sizes. These data suggest that cavities preferentially nucleate at Mg-Si particles and at large Al 6 (Mn,Fe) particles. This result may be applied to reduce cavitation in commercial hot-forming operations utilizing aluminum-magnesium alloys.

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Section: Methodsmentioning

confidence: 99%

Effect of Microstructure on Cavitation during Hot Deformation of a Fine-Grained Aluminum-Magnesium Alloy as Revealed through Three-Dimensional Characterization

Chang

Taleff

Krajewski

2009

Metall Mater Trans A

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“…(2) Specific Imaging Problems for 3D Particle Analysis: Representativity versus Resolution A major problem for particle analysis with 3D microscopy is the inherent conflict between resolution and representativity. [16][17][18] The ideal resolution depends mainly on the average particle size, although the resolution should be high enough to also characterize accurately the smallest objects of interest. In addition, a large number of particles should be enclosed within the data volume in order to yield statistically representative analyses.…”

Section: Three-dimensional Particle Analysis With Fib-ntmentioning

confidence: 99%

FIB‐Nanotomography of Particulate Systems—Part I: Particle Shape and Topology of Interfaces

Holzer

Muench

Wegmann

et al. 2006

Journal of the American Ceramic Society

125

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A new 3D-microscopy method, focused ion beam-nanotomography (FIB-nt), has been applied to the statistical particle shape analysis and for topological characterization of granular textures in cement samples. Because of its high resolution (15 nm), FIB-nt reveals precise microstructural information at the submicrometer scale, which cannot be obtained with conventional tomography methods. It is demonstrated that even from complex granular textures with dense agglomerates, it is possible to identify the individual sub-grains. This is the basis for reliable statistical shape analysis. For this purpose, moments of inertia were determined for particles from five different grain size fractions of a given cement, which provides important input data for future modeling of rheology and hydration processes. In addition, FIB-nt was used for topological characterization of the particle-particle interfaces in the dense and fine-grained granular textures. The unique 3D-data obtained with FIB-nt thus open new possibilities for quantitative microstructure analysis and the data can be used as structural input for object-oriented modeling.

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“…Pettifor maps, one of the earliest graphical representation techniques, is exceedingly efficient except that it requires a thorough understanding and intuition about the materials. Recent progress in computational capabilities has seen the advent of more complicated paradigms -socalled virtual interrogation techniques -which span from first-principles calculations to multi-scale models [3][4][5][6][7]. These complex multi-physics and/or statistical techniques and simulations [8,9] result in an integrated set of tools which can predict the relationships between chemical, microstructural, and mechanical properties producing an exponentially large collection of data.…”

Section: Introductionmentioning

confidence: 99%

A software framework for data dimensionality reduction: application to chemical crystallography

Samudrala

Balachandran

Żola

et al. 2014

Integr Mater Manuf Innov

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Materials science research has witnessed an increasing use of data mining techniques in establishing process-structure-property relationships. Significant advances in high-throughput experiments and computational capability have resulted in the generation of huge amounts of data. Various statistical methods are currently employed to reduce the noise, redundancy, and the dimensionality of the data to make analysis more tractable. Popular methods for reduction (like principal component analysis) assume a linear relationship between the input and output variables. Recent developments in non-linear reduction (neural networks, self-organizing maps), though successful, have computational issues associated with convergence and scalability. Another significant barrier to use dimensionality reduction techniques in materials science is the lack of ease of use owing to their complex mathematical formulations. This paper reviews various spectral-based techniques that efficiently unravel linear and non-linear structures in the data which can subsequently be used to tractably investigate process-structure-property relationships. In addition, we describe techniques (based on graph-theoretic analysis) to estimate the optimal dimensionality of the low-dimensional parametric representation. We show how these techniques can be packaged into a modular, computationally scalable software framework with a graphical user interface -Scalable Extensible Toolkit for Dimensionality Reduction (SETDiR). This interface helps to separate out the mathematics and computational aspects from the materials science applications, thus significantly enhancing utility to the materials science community. The applicability of this framework in constructing reduced order models of complicated materials dataset is illustrated with an example dataset of apatites described in structural descriptor space. Cluster analysis of the low-dimensional plots yielded interesting insights into the correlation between several structural descriptors like ionic radius and covalence with characteristic properties like apatite stability. This information is crucial as it can promote the use of apatite materials as a potential host system for immobilizing toxic elements.

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Three-dimensional (3D) microstructure visualization and finite element modeling of the mechanical behavior of SiC particle reinforced aluminum composites

Cited by 214 publications

References 8 publications

Effect of Microstructure on Cavitation during Hot Deformation of a Fine-Grained Aluminum-Magnesium Alloy as Revealed through Three-Dimensional Characterization

Effect of Microstructure on Cavitation during Hot Deformation of a Fine-Grained Aluminum-Magnesium Alloy as Revealed through Three-Dimensional Characterization

FIB‐Nanotomography of Particulate Systems—Part I: Particle Shape and Topology of Interfaces

A software framework for data dimensionality reduction: application to chemical crystallography

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