New Paper in Combustion and Flame - Machine Learning Analysis of Al-Zr Composite Powder Combustion
📄 New Collaborative Paper Published in Combustion and Flame
Our collaborative study, “Machine Learning-Assisted Analysis of Ignition and Combustion Properties in As-Milled and Annealed Al-Zr Composite Powders,” has been published in Combustion and Flame.
Research Summary
Metal-based composite powders can exhibit complex ignition and combustion behavior that is difficult to quantify using conventional analysis alone.
In this work, we investigated how ball milling, composition, and annealing influence the ignition and combustion behavior of Al-Zr composite powders. A machine-learning-assisted image-analysis framework was used to quantify combustion phenomena, including the occurrence of microexplosions during particle burning.
The study demonstrates how materials processing, combustion experiments, high-speed imaging, and machine learning can be integrated to understand dynamic materials behavior.
Publication Details
Journal: Combustion and Flame
Volume: 284
Article: 114660
Year: 2026
DOI: 10.1016/j.combustflame.2025.114660
This collaboration was a useful opportunity to apply data-driven analysis beyond my primary work in structural and biomedical materials, while still asking the same underlying question: how does processing control material behavior?