Alpha Shape Analysis (ASA) Framework for Post- Clustering Property Determination in Atom Probe Tomographic Data

Still, Evan K.; Schreiber, Daniel K.; Wang, Jing; Hosemann, Peter

doi:10.1017/s1431927620024939

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Especially, the description of the volume based on α-shapes is sensitive. These results add quantitative substantiation to previous studies [65,77,111,114]. Volume here refers to either the interior volume of the closed polyhedron (in the case of convex hull, tessellation, and iso-surfaces eventually), or the accumulated volume of interior tetrahedra (in the case of α-shapes).…”

Section: Comparative Study Of Methods For Modeling the Edge Of A Datasetsupporting

confidence: 86%

“…We modified the paraprobe-surfacer tool to support convex hull computations and added diagnostics for α-shapes [75,76] such as returning the geometry of interior tetrahedra for given α-values, mesh closure tests, and the option to characterize the set of possible α-shapes as a function of α-value, also to offer a bridge to related work on α-shapes for cluster analysis [77]. Paraprobe-surfacer can store the geometry of all created objects as triangle and tetrahedra sets.…”

Section: The Paraprobe-toolbox Is Modularizedmentioning

confidence: 99%

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On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Kühbach¹,

Rielli²,

Primig³

et al. 2022

Preprint

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Characterizing microstructure-material-property relations calls for software tools which extract point-cloud-and continuum-scale-based representations of microstructural objects. Application examples include atom probe, electron, and computational microscopy experiments. Mapping between atomic-and continuum-scale representations of microstructural objects results often in representations which are sensitive to parameterization; however assessing this sensitivity is a tedious task in practice.Here, we show how combining methods from computational geometry, collision analyses, and graph analytics yield software tools for automated analyses of point cloud data for reconstruction of three-dimensional objects, characterization of composition profiles, and extraction of multi-parameter correlations via evaluating graph-based relations between sets of meshed objects. Implemented for point clouds with mark data, we discuss use cases in atom probe microscopy that focus on interfaces, precipitates, and coprecipitation phenomena observed in different alloys. The methods are expandable for spatio-temporal analyses of grain fragmentation, crystal growth, or precipitation.

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Section: Comparative Study Of Methods For Modeling the Edge Of A Datasetsupporting

confidence: 86%

Section: The Paraprobe-toolbox Is Modularizedmentioning

confidence: 99%

On Strong-Scaling and Open-Source Tools for High-Throughput Quantification of Material Point Cloud Data: Composition Gradients, Microstructural Object Reconstruction, and Spatial Correlations

Kühbach¹,

Rielli²,

Primig³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The test case is one example of recent efforts from the PNNL group to develop FAIR methods for analyzing solute clustering on APT dataset (Still et al, 2021). 2.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, the real APT dataset is intended to test the algorithm's capability to detect clusters with varying effective atomic densities, where conventional single threshold-based methods are challenged. The test case is one example of recent efforts from the PNNL group to develop FAIR methods for analyzing solute clustering on APT dataset (Still et al, 2021). Clustering UoM (University of Michigan) contains a synthetic dataset with compositional gradients and instantiated clusters.…”

Section: Resultsmentioning

confidence: 99%