Characterising 3D spherical packings by principal component analysis
ISSN: 0264-4401
Article publication date: 28 November 2019
Issue publication date: 8 April 2020
Abstract
Purpose
The purpose of this paper is to extend the previous study [Computer Methods in Applied Mechanics and Engineering 340: 70-89, 2018] on the development of a novel packing characterising system based on principal component analysis (PCA) to quantitatively reveal some fundamental features of spherical particle packings in three-dimensional.
Design/methodology/approach
Gaussian quadrature is adopted to obtain the volume matrix representation of a particle packing. Then, the digitalised image of the packing is obtained by converting cross-sectional images along one direction to column vectors of the packing image. Both a principal variance (PV) function and a dissimilarity coefficient (DC) are proposed to characterise differences between different packings (or images).
Findings
Differences between two packings with different packing features can be revealed by the PVs and DC. Furthermore, the values of PV and DC can indicate different levels of effects on packing caused by configuration randomness, particle distribution, packing density and particle size distribution. The uniformity and isotropy of a packing can also be investigated by this PCA based approach.
Originality/value
Develop an alternative novel approach to quantitatively characterise sphere packings, particularly their differences.
Keywords
Acknowledgements
The study was partially supported by the National Natural Science Foundation of China (Project No. 11772135) and the special fund from the Institute of Manufacturing Engineering of Huaqiao University, China. This support is greatly acknowledged.
Citation
Zhao, T., Feng, Y.T. and Tan, Y. (2020), "Characterising 3D spherical packings by principal component analysis", Engineering Computations, Vol. 37 No. 3, pp. 1023-1041. https://doi.org/10.1108/EC-05-2019-0225
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited