Quantifying three-dimensional sphericity indices of irregular fine particles from 2D images through sequential sieving tests

Abstract

This study aims to suggest a new method for predicting 3D sphericity through traditional 2D image processing through a novel sieving analysis. The 3D sphericity indices of grains (over 3000 particles for each material) from seven irregular granular materials are determined using μCT slices. These indices are then compared with existing 2D indices obtained through SEM image processing. Additionally, seven synthetic materials (semi-regular in size and shape) are also assessed to account for unusual particle shapes. The findings shed light on the role of sphericity in the rate at which particles pass through sieve openings. The results indicate that the initial passing rate of grains is strongly correlated with the 3D sphericity indices, which significantly decrease as sphericity decreases. The proposed method involves a sequential sieving test, performed similarly to the conventional sieving test but conducted sequentially at different time steps. Several correlations between 3D sphericity and its 2D counterparts are presented, which can successfully predict the 3D sphericity indices. Additionally, two empirical equations are proposed to predict the most frequent flatness and elongation aspect ratios, used in the Zingg diagram. Furthermore, the grading analysis derived from both 2D and 3D image processing is compared with sieve analysis. The results show that, unlike the 2D results, the grading curves obtained from 3D image processing are in excellent agreement with the sieve analysis. A corrected grading curve, derived from traditional 2D image processing, is proposed to align with 3D grading curves.