SHAPE: SHape Analyser for Particle Engineering

버전 2021.08 (41.9 MB) 작성자: Vasileios Angelidakis
Morphological characterisation and simplification of three-dimensional particle geometries

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업데이트 날짜: 2022/3/3

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SHape Analyser for Particle Engineering

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What SHAPE doesArchitectural featuresFile treeSimple exampleCreditsBYOSAcknowledging SHAPE

What SHAPE does

SHAPE implements morphological characterisation of three-dimensional particles from imaging data, such as point clouds, surface and tetrahedral meshes or segmented voxelated images (derived using Computed Tomography). Characterisation of morphology is performed for all three aspects of shape, namely form, roundness and surface texture (roughness). The code also supports shape simplification, using edge-collapse techniques, to reduce the number of triangular faces of each particle to user-defined fidelity levels. The particle shapes can be exported to several formats, compatible with various FEA and DEM solvers.

Architectural features

SHAPE is built using an object-oriented architecture, where each particle has the following set of attributes:

-Particle % e.g. 1, 2, 3, etc.
  -Particle_type % e.g. Original, Convex_hull, Face_No_100, Face_No_50, etc.
    -Mesh % Surface_mesh, Tetrahedral_mesh, Voxelated_image, Surface_texture
    -Auxiliary_geometries % AABB, OBB, Fitted_ellipsoid, Minimal_bounding_sphere, Maximal_inscribed_sphere
    -Geometrical_features % Volume, Centroid, Surface_area, Current_inertia_tensor, Principal_inertia_tensor, Principal_orientations
    -Morphological_features % Form, Roundness, Roughness

File tree

Simple example

This example demonstrates different ways to define Particle objects and characterise their morphology.

addpath(genpath('functions'));	% Load in-house functions
addpath(genpath('lib'));	% Load external functions (dependencies)
addpath(genpath('classes'));	% Load object-oriented architecture

% Define particle from Point Cloud
p1=Particle(P,[],[],[],options); % P (Nv x 3): List of Vertices; options (struct): options for shape characterisation and/or simplification

% Define particle from Surface/Tetrahedral Mesh and Texture profile
p2=Particle(P,F,[],Texture,options); % P (Nv x 3): List of Vertices; F (Nf x 3) or (Nf x 4): List of Faces/Elements; Texture (Nx x Ny): Planar roughness profile

% Define particle from voxelated (volumetric) image
p3=Particle([],[],Vox,[],options); % Vox.img (Nx x Ny x Nz): Segmented voxelated (3-D) image of particle geometry;

New users are advised to start from running the available examples, to get familiarised with the syntax and functionalities of SHAPE.


SHAPE uses several external functions available within the Matlab FEX community. We want to acknowledge the work of the following contributions, for making our lives easier:

These external dependencies are added within the source code of SHAPE, to provide an out-of-the-box implementation. The licensing terms of each external dependency can be found inside the lib folder.

BYOS (Bring Your Own Scripts)!

If you enjoy using SHAPE and you are interested in shape characterisation, you are welcome to ask for the implementation of new morphological descriptors and features or even better contribute and share your implementations. SHAPE was created out of our excitement and curiosity around the characterisation of irregular particle morphologies and we share this tool hoping that members of the community will find it useful. So, feel free to expand the code, propose improvements and report issues.

Acknowledging SHAPE

Angelidakis, V., Nadimi, S. and Utili, S., 2021. SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data. Computer Physics Communications 265, p.107983.

Download BibTeX entry

2021 © Vasileios Angelidakis, Sadegh Nadimi, Stefano Utili. Newcastle University, UK

인용 양식

Angelidakis, Vasileios, et al. “SHape Analyser for Particle Engineering (SHAPE): Seamless Characterisation and Simplification of Particle Morphology from Imaging Data.” Computer Physics Communications, vol. 265, Elsevier BV, Aug. 2021, p. 107983, doi:10.1016/j.cpc.2021.107983.

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