|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Spatiotemporal Feature Extraction Using Deep Learning for Stress Corrosion Cracking in X-ray Computed Tomography Scans of Al-Mg Alloys
||Thomas Ciardi, Pawan Tripathi, John Lewandowski, Roger French
|On-Site Speaker (Planned)
Spatiotemporal studies of material degradation have rapidly improved with the high resolution imaging capabilities of X-ray computed tomography (XCT). Materials science, however, lacks the tooling to analyze the scale of data produced. As a result, analysis is limited to manually segmented features and subsets of data which is time consuming and results in large information loss. We propose leveraging computer vision and deep learning to develop automated frameworks for full-scale feature extraction and analysis. Slow strain rate tension tests were conducted with collaborators at the Diamond Light Source on field-retrieved Al-Mg plate material removed after 42-years of service exposure. A sample at 50%RH and a sample in dry air were tested to determine the effects of long-term service on stress corrosion cracking. We developed an automated deep learning pipeline that segments features of interest, quantifies their properties, and builds a complete spatiotemporal microstructural degradation profile of the dataset.