Measurement of average grain volume and certain topological parameters by serial section analysis

Rhines, F. N.; Craig, Kenneth R.; Rousse, D. A.

doi:10.1007/bf02817891

Cited by 66 publications

(19 citation statements)

References 4 publications

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“…The data collection process is somewhat time consuming and thus, limited in scale. Therefore, in addition to developing direct conversion processes [14,15], here we discuss a new methodology for creating synthetic microstructures that are statistically-equivalent to Tessellation and growth models have been used by a number of researchers to create synthetic microstructures [6,8]. However, the statistical inputs used in these efforts have often been generated from a 2D analysis.…”

Section: Synthetic Microstructures For Computational Modelingmentioning

confidence: 98%

“…For example, most grain structures are known to have non-uniform distributions for size or shape, and can also exhibit large variations in morphology and crystallography [4]. This realization clearly identifies a shortcoming when grain morphology is represented by a uniform array of basic shapes like spheres, cubes, or simple polyhedrons to fill space, as is typically done in many simulation studies [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A framework for automated 3D microstructure analysis & representation

Groeber

Uchic

Dimiduk

et al. 2007

J Computer-Aided Mater Des

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Over the past 5 years there have been significant advances in developing serial-sectioning methods that provide quantitative data describing the structure and crystallography of grain-level microstructures in three dimensions (3D). The subsequent analysis and representation of this information can provide modeling and simulation efforts with a highly-refined and unbiased characterization of specific microstructural features. For example, the grain structure and crystallography of an engineering alloy could be characterized and then translated directly into a 3D volume mesh for subsequent Finite Element Analysis. However, this approach requires a multitude of data sets in order to appropriately sample the intrinsic heterogeneity observed in typical microstructures. One way to circumvent this issue is to develop computation tools that create synthetic microstructures that are statisticallyequivalent to the measured structure. This study will discuss the development of software programs that take as input a series of Electron Backscatter Diffraction Patterns from a serial sectioning experiment, and output a robust statistical analysis of the 3D data, as well as generate a host of synthetic structures which are analogous to the real microstructure. Importantly, the objective of this study is to provide a framework towards complete microstructure representation that is consistent with quantifiable experimental data.

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Section: Synthetic Microstructures For Computational Modelingmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

A framework for automated 3D microstructure analysis & representation

Groeber

Uchic

Dimiduk

et al. 2007

J Computer-Aided Mater Des

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“…The affinities for involvement of i-class grains in type II and III events are given below in Eqs. [4] and [5], respectively:…”

Section: E Rates Of Topological Eventsmentioning

confidence: 99%

“…This description of grain growth is more than a theoretical model; it is a necessary sequence of steps that must occur for the coarsening of a space-filling aggregate of cells with faces, edges, and corners under the constraint of surface tension. Although the topological model of grain growth has been of interest for a number of decades [1][2][3][4][5][6][7] and the types of transitions that take place are understood, many of the details about how they occur remain to be determined. This missing information is required to move beyond the viewpoint of grain topology as ''the result of'' curvature-driven grain growth to the more accurate position of ''that which controls grain growth.''…”

Section: Introductionmentioning

confidence: 99%

Topological Characteristics of Two-Dimensional Grain Growth-Simulation and Analysis

Sprague

Patterson

Grandhi

2009

Metall Mater Trans A

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A two-dimensional (2-D) grain growth simulation, using a curvature-driven vertex model applied to an initially Monte Carlo (MC)-generated microstructure, has been developed for analysis of the topological characteristics of the process, including the rates of different topological events and the effect of these event frequencies on the evolving grain structure. Findings include a constant ratio of the number fraction of disappearing grains to the area fraction swept by the grain boundary of 4/3; constant fractions of boundary sweeping due to grain disappearance (~2 pct), boundary-switching events (~8 pct), and simple boundary motion (~90 pct); and a constant ratio of 1.34 boundary-switching events per grain disappearance. An affinity term was developed to describe the tendency of grains of different edge classes to contact each other or to be involved in different topological events, relative to random behavior. The highest and lowest edge classes, 3 and 12, respectively, exhibited the highest affinity for mutual contact, a 16 times random occurrence, but an affinity of~0 for contact with themselves. Intermediate edge classes showed an affinity of~1, random contact, with other classes or with themselves. Few-edged grains showed~0 affinity for contacting a disappearing trigon or gaining an edge in an edge-switching event, but had a high affinity, approaching 6, for losing an edge in a switching event. Many-edged grains showed the opposite trends and intermediate edge classes showed a random or less tendency for participation in any topological event. It was shown statistically that the growth rate of individual grains is controlled solely by the edge class, with essentially no direct effect from the grain size. The evolution of the grain area and edge class distributions were monitored throughout the transition from transient to steady-state grain growth, with a steady state achieved after loss of approximately 1/3 of the initial grains. The steady-state values of the coefficients of variation (CVs) of the edge class and grain area distributions were~0.2 and 0.7, respectively.

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“…The 3D microstructure of a relatively small microstructural volume of an opaque material can be reconstructed using the classic serial sectioning technique developed in the 1970s. [16] In the classic serial sectioning, an image of a field of view (FOV) is captured, a small material thickness (~0.5 to 5 lm, depending on the length scale of microstrucutral features of interest) is removed by metallographic techniques, and in the new metallographic plane (serial section), the image of the FOV below the first FOV in the previous sectioning plane is captured. This procedure is repeated to obtain a stack of serial section images as shown in Figure 1(a) from which a small segment of 3D microstrucutral volume is reconstructed.…”

Section: Background On Serial Sectioningmentioning

confidence: 99%

Reconstruction and Quantitative Characterization of Multiphase, Multiscale Three-Dimensional Microstructure of a Cast Al-Si Base Alloy

Singh

Gokhale

Mao

et al. 2009

Metall Mater Trans B

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The serial sectioning technique is well known for the reconstruction of three-dimensional (3D) microstructures of opaque materials. In recent years, techniques also have been developed for the reconstruction of high-fidelity, large-volume segments of 3D microstructures that use montage serial sections and robot-assisted automated acquisitions of montage serial sections. This article reports the reconstruction of the multiphase, multiscale 3D microstructure of a permanent mold cast unmodified Al-12 wt pct Si-1 wt pct Ni base alloy that contains eutectic Si platelets, coarse primary polyhedral Si particles, Fe-rich script intermetallic particles, and pores. These constituents are segmented, reconstructed, rendered, and characterized in three dimensions. The estimated 3D microstrucutral attributes include the distribution of eutectic platelet thickness; the mean volume, mean surface area, and mean thickness of the eutectic Si platelets; the mean volume and the mean surface area of the polyhedral primary Si particles; and the mean number of faces, edges, and corners on the polyhedral primary Si particles.

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Measurement of average grain volume and certain topological parameters by serial section analysis

Cited by 66 publications

References 4 publications

A framework for automated 3D microstructure analysis & representation

A framework for automated 3D microstructure analysis & representation

Topological Characteristics of Two-Dimensional Grain Growth-Simulation and Analysis

Reconstruction and Quantitative Characterization of Multiphase, Multiscale Three-Dimensional Microstructure of a Cast Al-Si Base Alloy

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