Particle Size Distribution
Particle size distribution (PSD) analysis aims to determine the proportions of different particle sizes within a material, a crucial parameter impacting material properties and behavior across diverse fields. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) and other AI-driven approaches, to analyze images (e.g., from microscopy) and rapidly predict PSDs, often outperforming traditional methods. These advancements enable high-throughput analysis of complex systems, improving efficiency and accuracy in applications ranging from catalyst design to environmental monitoring and material science.
Papers
PSDNet: Determination of Particle Size Distributions Using Synthetic Soil Images and Convolutional Neural Networks
Javad Manashti, Pouyan Pirnia, Alireza Manashty, Sahar Ujan, Matthew Toews, François Duhaime
Comparing PSDNet, pretrained networks, and traditional feature extraction for predicting the particle size distribution of granular materials from photographs
Javad Manashti, François Duhaime, Matthew F. Toews, Pouyan Pirnia, Jn Kinsonn Telcy