SHAPE: SHape Analyser for Particle Engineering
Updated 3 Mar 2022
SHape Analyser for Particle Engineering
What SHAPE does • Architectural features • File tree • Simple example • Credits • BYOS • Acknowledging 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.
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
- classes (Definition of objects)
- lib (External dependencies)
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:
- Qianqian Fang - Iso2Mesh
- Luigi Giaccari - Surface Reconstruction From Scattered Points Cloud
- Johaness Korsawe - Minimal Bounding Box
- Pau Micó - stlTools
- Yury Petrov - Ellipsoid fit
- Anton Semechko - Exact minimum bounding spheres and circles
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.
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.
2021 © Vasileios Angelidakis, Sadegh Nadimi, Stefano Utili. Newcastle University, UK
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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Platform CompatibilityWindows macOS Linux
Inspired by: Iso2Mesh, Surface Reconstruction From Scattered Points Cloud, Minimal Bounding Box, stlTools, Ellipsoid fit, Exact minimum bounding spheres and circles
Inspired: CLUMP: Code Library to generate Multi-sphere Particles
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