High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots over time of the evolving microstructure and attributes. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. Here we present a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. Our technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to 9 times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data into compact, semantic-rich representations of visually salient characteristics (e.g., peak shapes). These characteristics can be a rapid indicator of anomalous events such as changes in diffraction peak shapes. We anticipate that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods that span many decades of length scales.
@article{arxiv.2312.03989,
title = {Rapid detection of rare events from in situ X-ray diffraction data using machine learning},
author = {Weijian Zheng and Jun-Sang Park and Peter Kenesei and Ahsan Ali and Zhengchun Liu and Ian T. Foster and Nicholas Schwarz and Rajkumar Kettimuthu and Antonino Miceli and Hemant Sharma},
journal= {arXiv preprint arXiv:2312.03989},
year = {2023}
}