Machine vision based particle size distribution of particulate minerals and its experimental verification

Abstract

Reliable and accurate measurement of particle size and subsequent analysis of particle size distribution (PSD) is central to characterization of particulate minerals. Traditionally PSD of particulate materials was determined using standard mechanical sieving, but it was shown to be deficient in accurate particle size measurements, hence PSD analysis. In this paper a highly inexpensive machine vision based approach was proposed as an alternative to mechanical sieving. The test mineral used was ball-milled celestite. This machine vision approach used a document scanner as imaging device and a user-coded Java ImageJ plugin performed the image processing and automated the PSD analysis. The acquired color images were preprocessed to binary images and the particles analyzed after grouping them based on their distinct length. Volumes of all particles were evaluated assuming a prolate spheroid geometry from measured lengths and widths. A new approach of using sum of volumes as weighting factor (?volume) was utilized for particle grouping in ASABE Standard PSD analysis. The plugin also evaluated several significant dimensional (16) and distributional (27) parameters that characterize the PSD of samples. Standard mechanical sieving of samples was performed for experimental verification and results compared with machine vision method. The cumulative undersize PSD followed a log-normal distribution, and the plot against particle size exhibited a linear trend. Shapes of log-normal plot of cumulative undersize PSD were similar between both methods; however, the mechanical sieving curves lagged by 0.2-0.5 mm. The deviation was attributed to the "falling- through" effect of longer particles through sieve openings.