EBSD image segmentation using a physics-based forward model

Park, Se Un; Wei, Dennis; Graef, Marc De; Shah, Megna; Simmons, Jeff; Hero, Alfred O.

doi:10.1109/icip.2013.6738779

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Poisson noise was added to the pattern to reflect noise in electron interactions and camera electronics, in line with previous studies of noise in EBSPs (Cizmar et al, 2008; Pinard et al, 2011; Park et al, 2013; Wright et al, 2015). Although others have cited Poisson noise as a representative noise type for some factors in the imaging process, its application to specific aspects in EBSD imaging within this paper have been inferred based on statements in the referenced papers.…”

Section: Methodsmentioning

confidence: 99%

Influence of Noise-Generating Factors on Cross-Correlation Electron Backscatter Diffraction (EBSD) Measurement of Geometrically Necessary Dislocations (GNDs)

et al. 2017

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Studies of dislocation density evolution are fundamental to improved understanding in various areas of deformation mechanics. Recent advances in cross-correlation techniques, applied to electron backscatter diffraction (EBSD) data have particularly shed light on geometrically necessary dislocation (GND) behavior. However, the framework is relatively computationally expensive-patterns are typically saved from the EBSD scan and analyzed offline. A better understanding of the impact of EBSD pattern degradation, such as binning, compression, and various forms of noise, is vital to enable optimization of rapid and low-cost GND analysis. This paper tackles the problem by setting up a set of simulated patterns that mimic real patterns corresponding to a known GND field. The patterns are subsequently degraded in terms of resolution and noise, and the GND densities calculated from the degraded patterns using cross-correlation ESBD are compared with the known values. Some confirmation of validity of the computational degradation of patterns by considering real pattern degradation is also undertaken. The results demonstrate that the EBSD technique is not particularly sensitive to lower levels of binning and image compression, but the precision is sensitive to Poisson-type noise. Some insight is also gained concerning effects of mixed patterns at a grain boundary on measured GND content.

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Section: Methodsmentioning

confidence: 99%

Influence of Noise-Generating Factors on Cross-Correlation Electron Backscatter Diffraction (EBSD) Measurement of Geometrically Necessary Dislocations (GNDs)

et al. 2017

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“…Electron backscatter diffraction (EBSD) microscopy acquires crystal orientation at multiple locations within a grain by capturing the Kikuchi diffraction patterns of the backscatter electrons [11]. A Kikuchi pattern can be translated to crystal orientation through Hough Transformation analysis [12] or Dictionary-Based indexing [13]. The process of assigning mean orientation values to each grain is known as indexing.…”

Section: Crystallographic Orientation Estimationmentioning

confidence: 99%

“…We next illustrate the proposed EM-ML orientation estimator on a real IN100 sample acquired from US Air Force Research Laboratory (AFRL) [13]. The IN100 sample is a polycrystalline Ni superalloy which has cubic symmetry in the m3m point symmetry group.…”

Section: B Em-ml Orientation Estimator For In100 Nickel Samplementioning

confidence: 99%

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Parameter Estimation in Spherical Symmetry Groups

Chen

Wei

Newstadt

et al. 2015

IEEE Signal Process. Lett.

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This paper considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group. It is shown that any such distribution must satisfy a restricted finite mixture representation. When specialized to the case of distributions over the sphere that are invariant to the actions of a finite spherical symmetry group G, a groupinvariant extension of the Von Mises Fisher (VMF) distribution is obtained. The G-invariant VMF is parameterized by location and scale parameters that specify the distribution's mean orientation and its concentration about the mean, respectively. Using the restricted finite mixture representation these parameters can be estimated using an Expectation Maximization (EM) maximum likelihood (ML) estimation algorithm. This is illustrated for the problem of mean crystal orientation estimation under the spherically symmetric group associated with the crystal form, e.g., cubic or octahedral or hexahedral. Simulations and experiments establish the advantages of the extended VMF EM-ML estimator for data acquired by Electron Backscatter Diffraction (EBSD) microscopy of a polycrystalline Nickel alloy sample. 1

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“…As different sets of inputs might cause the same result, forward modeling usually learns a many-toone mapping. Forward modeling has been widely studied in various fields of machine learning, such as object detection [3,4], image segmentation [5,6], machine translation [7,8] and some prediction tasks in scientific computing [9][10][11][12][13][14][15][16]. Inverse modeling is the process to infer the causes based on results or observations, e.g., exploring the relationships from performance to processing in materials.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Mao

Yang

Jha

et al. 2022

Integr Mater Manuf Innov

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There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

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EBSD image segmentation using a physics-based forward model

Cited by 5 publications

References 15 publications

Influence of Noise-Generating Factors on Cross-Correlation Electron Backscatter Diffraction (EBSD) Measurement of Geometrically Necessary Dislocations (GNDs)

Influence of Noise-Generating Factors on Cross-Correlation Electron Backscatter Diffraction (EBSD) Measurement of Geometrically Necessary Dislocations (GNDs)

Parameter Estimation in Spherical Symmetry Groups

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

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