Computations of Particle Surface Characteristics Using Optical and X-Ray CT Images

Published

October 12, 2021

Author(s)

E Masad, S Saadeh, T Al-Rousan, Edward Garboczi, D Little

Abstract

The description of particle surface characteristics is of interest in many disciplines in science and engineering such as powder technology, geology, soil science, and infrastructure materials. Consequently, there is a need to develop methods to quantify particle surface characteristics rapidly and accurately. In this study, a number of computational methods were utilized to describe the surface characteristics (form, angularity and texture) of three different granular materials. These computational methods are the radius method, gradient method, form index, Foruier series, spherical harmonic series, and sphericity index. To illustrate the validity of these methods, they were first used to analyze the shape of standard images that were used in the past by geologists for visual classification of particles. Subsequently, the methods were used to analyze images captured using nondestructive X-ray computed tomography (CT), and an automated computer controlled system known as the Aggregate Imaging System (AIMS). The results demonstrated the capabilities of the analysis methods to quantify the multiscale nature of form, angularity, and texture. In general, the images captured using AIMS can be used to develop a particle shape classification system, while the X-ray CT images, in conjunction with spherical harmonic analysis, a powerful technique to represent and reconstruct three dimensional images of particles, can be used to mathematically represent the shape of particles in computational models.

Citation

Journal of Computational Geoscience

Pub Type

Journals

Keywords

computation, imaging, particle shape, spherical harmonics, X-ray computed tomography

Citation

Masad, E. , Saadeh, S. , Al-Rousan, T. , Garboczi, E. and Little, D. (2021), Computations of Particle Surface Characteristics Using Optical and X-Ray CT Images, Journal of Computational Geoscience (Accessed December 5, 2024)