Image analytical determination of particle size distribution characteristics of natural and industrial bulk aggregates

Image Analysis combined with multivariate regression on Angle Measure Technique (AMT) transformed imagery and the Theory of Sampling (TOS) is here presented as a comprehensive for Image Analysis Sampling (IAS), which takes all aspects of sampling representativity into account — especially 2-dimensional image versus 3-dimensioanl bulk compositions issues. Every IAS application has to be based on optimized image acquisition parameters: camera and illumination type, illumination angle, sample thickness as well as image post-processing, which are all examined here in order to delineate the general requirements for optimal prediction models for particle size distribution of natural and industrial bulk solid aggregates. We present a complete optimization study in order to show its intrinsic problem-dependent nature. This optimization allowed an original 60-sample data set to be compressed to an essential 22 natural coastal sands array with equally varying composition ranges — which was subjected to IAS in order to characterize the specific particle size distribution curves. In addition to D50 (50%-tile), six other size classes were successfully predicted, while extreme size classes (extreme low or high particle sizes) showed a too narrow training data set span, illustrating a critical grain size contrast which will always bracket successful models of particulate matter being imaged for grain size characterization. All classes with a satisfactory (representative) calibration interval span can be quantitatively predicted due to the powerful scale-dependency of the central AMT feature extraction combined with PLS multivariate calibration. The present application to natural sand aggregate size distributions forms a vehicle to illustrate the full potential of image analysis in general, IAS in particular, also for technological/industrial manufacturing on-line product and process monitoring applications, or quality control purposes, with similar grain-size prediction objectives. There is a significant carrying-over potential to parallel industrial scenarios.