SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data

Angelidakis, Vasileios; Nadimi, Sadegh; Utili, Stefano

doi:10.1016/j.cpc.2021.107983

Cited by 33 publications

(19 citation statements)

References 43 publications

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“…Shape characterization of 3D images of an ellipsoid with various levels of noise and blur, varied in a parametric manner, showed that image quality affected the obtained morphological results by as much as 25%, for the analyzed range of parameters. [ 1 ]…”

Section: Resultsmentioning

confidence: 99%

“…The labeled image of the iron pyrite particles was imported in the open‐source software SHAPE: Shape Analyzer for Particle Engineering , [ 1 ] where they were processed in an automated manner. Each labeled voxelated image was imported in SHAPE, where it was transformed into a surface mesh, i.e., a triangulated tessellation of the particle surface, using a refined Delaunay triangulation algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…Additional to image resolution in terms of pixels per diameter, there are other sources of deficiency to characterise particles. Shape characterisation of 3D images of an ellipsoid with various levels of noise and blur, varied in a parametric manner, showed that image quality affected the obtained morphological results by as much as 25%, for the analysed range of parameters [1].…”

Section: Effect Of Image Resolution On Shape Parametersmentioning

confidence: 99%

“…Particulate materials appear across the scales (nano to macro) and in many industrial applications, our daily life as well as in nature. They come with different physical attributes (size, shape and surface properties) amongst which morphology, while very challenging to characterise, presents very special importance in numerous applications [1]. In particular, the morphology of nano-particles is known to significantly influence their mechanical, chemical, rheological, and hydrologic behaviour [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…They come with different physical attributes (size, shape, and surface properties) among which morphology, while very challenging to characterize, presents very special importance in numerous applications. [1] In particular, the morphology of nano-particles is known to significantly influence their mechanical, chemical, rheological, and hydrologic behavior. [2][3][4][5][6][7] For example, in nano biomaterials-cell interaction, particle shape affects the cellular functions in which effective morphology characterization would be central.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Angelidakis

Nadimi

Garum

et al. 2022

Part & Part Syst Charact

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This study analyses the morphology of iron pyrite particles within a shale sample, captured using nano-computed tomography (Nano-CT). The complex, framboidal morphology of the iron pyrite particles is characterised using various metrics, and comparisons are drawn on their effectiveness to quantify their observed morphological characteristics. Then, simplified representations of selected iron pyrite particles are generated to facilitate a sensitivity analysis of the effect of imaging resolution on morphological parameters of particle form. A discussion is developed on the required number of pixels per particle diameter for particle shape characterisation. It is shown that shape indices that rely on the simplified main particle dimensions can be accurately calculated even for low fidelity levels of 10 pixels per particle diameter. More complex shape indices, that use vertices, volume and surface area, are more sensitive to image resolution even for 40 pixels per particle diameter.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Effect Of Image Resolution On Shape Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Angelidakis

Nadimi

Garum

et al. 2022

Part & Part Syst Charact

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Investigation techniques and physical aspects of the angle of repose of granular matter

Duverger,

Angelidakis,

Nadimi

et al. 2024

Granular Matter

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The repose of granular materials is investigated via two different Discrete Element Method (DEM) implementations in comparison with an experimental reference from a recently proposed benchmark setup. On a methodological standpoint, a rigorous measurement method of the angle of repose (AOR) is first proposed for plane-strain and axisymmetric conditions as encountered in the reference experiments. Additionally, two systematic procedures are designed in order to also determine the void ratio of the heap, as a fundamental property of granular matter possibly influencing the AOR. A physical discussion is then developed on the role of particle shape, considering the nonspherical nature of reference particles with a convexity value of C = 0.954. Adoping non-convex multi-spheres aggregates (i.e. clumps), the first DEM modelling approach successfully predicts the AOR within a 8% tolerance. After a convex simplification that neglects local concavities, another approach based on potential particles underestimates to a greater extent the AOR, bringing it down from 35.95 ± 0.88 • to 31.26 ± 0.95 • . For the loading setup(s) at hand, the AOR is eventually shown to bear no constitutive nature. It is for instance independent of initial void ratio but is still different than the critical friction angle. The latter may actually serve as a lower bound for the process-dependent AOR. These conclusions are drawn from a statistical analysis of a large set of results, accounting for the random nature of the microscopic arrangement in the studied process.

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Structural fluctuations in thin cohesive particle layers in powder-based additive manufacturing

Roy,

Xiao,

Angelidakis

et al. 2024

Granular Matter

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Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion influences the characteristics of the packing structure with a focus on the fluctuation of the local morphology. As cohesion increases, the overall packing density decreases, and the free surface roughness increases, which is calculated from digitized surface height distributions. Local structural fluctuations for both quantities are examined through the local packing anisotropy on the particle scale, obtained from Voronoï tessellation. The distributions of these particle-level metrics quantify the increasingly heterogeneous packing structure with clustering and changing surface morphology.

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SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data

Cited by 33 publications

References 43 publications

Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Nano‐Scale Characterization of Particulate Iron Pyrite Morphology in Shale

Investigation techniques and physical aspects of the angle of repose of granular matter

Structural fluctuations in thin cohesive particle layers in powder-based additive manufacturing

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