Identification of particle mixtures using machine-learning-assisted laser diffraction analysis

TL;DR

This paper introduces a machine-learning-assisted laser diffraction method that accurately identifies particle mixture properties using a single diffraction pattern, simplifying the process and enhancing industrial particle analysis.

Contribution

The study presents a novel approach combining machine learning with laser diffraction to identify particle mixtures from a single pattern, reducing detector requirements.

Findings

Achieved over 92% efficiency in particle mixture identification.

Utilized a small-angle diffraction pattern for analysis.

Demonstrated potential for industrial application in sample classification.

Abstract

We demonstrate a smart laser-diffraction analysis technique for particle mixture identification. We retrieve information about the size, geometry, and ratio concentration of two-component heterogeneous particle mixtures with an efficiency above 92%. In contrast to commonly-used laser diffraction schemes -- in which a large number of detectors is needed -- our machine-learning-assisted protocol makes use of a single far-field diffraction pattern, contained within a small angle ($\sim 0.26^{\circ}$) around the light propagation axis. Because of its reliability and ease of implementation, our work may pave the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.