|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Deep Learning-based Algorithms for X-ray Microtomography Analysis: Unravelling Challenges for 4D Experiments
||Hamidreza T-Sarraf, Hanyu Zhu, Swapnil Morankar, Amey Luktuke, Sridhar Niverty, Nikhilesh Chawla
|On-Site Speaker (Planned)
3D characterization is used to understand the relationships between materials microstructure and function. X-ray microtomography is an important 3D characterization technique due to its non-destructive nature which provides time-dependent (4D) information. However, image processing and segmentation of 4D tomographic data is extremely time intensive. Moreover, factors such as phase transformation or defect propagation during a time-evolved tomography experiment limits the scan time and/or number of scan iterations. Thus, a robust algorithm needs to be established that can render x-ray datasets accurately and efficiently. In this talk, we describe the application of Deep Convolutional Neural Network algorithms for X-ray image quality enhancement and segmentation. Using a modified Generative Adversarial Network algorithm we provide a workflow to transform low quality x-ray tomograph acquired by a fast scan to a high quality dataset. Our results point to the ability to drastically reduce x-ray data acquisition times,thereby opening a window for efficient 4D experiments.