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27 April 2020 3D damage detection in porous materials via advanced X-ray phase tomography (Conference Presentation)
Yunhui Zhu, Ziling Wu, Ting Yang, Ling Li
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Proceedings Volume 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V; 114040D (2020) https://doi.org/10.1117/12.2558215
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
Porous structures are widely found in natural and engineered material systems. To study the defect initialization and damage evolution in the complex 3D network structures, we explore advanced X-ray phase tomography to provide holistic and high-resolution 3D data. A pipeline of deep learning-based phase retrieval, computer vision, and damage identification algorithms are implemented to extract various types of damage for large volumetric tomography data. We first obtain high-quality phase tomography reconstruction from noisy and insufficient CT acquisition. Based on a hybrid approach using both model-based feature filtering and data-driven machine learning, we then identifies the defects and damaged regions from the background of porous structures. This method is applied to an in-situ X-ray tomography measurement on a natural cellular material; the accurate and comprehensive defects detection reveals insight into 3D damage evolution modes for porous material systems.
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Yunhui Zhu, Ziling Wu, Ting Yang, and Ling Li "3D damage detection in porous materials via advanced X-ray phase tomography (Conference Presentation)", Proc. SPIE 11404, Anomaly Detection and Imaging with X-Rays (ADIX) V, 114040D (27 April 2020); https://doi.org/10.1117/12.2558215
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KEYWORDS
Tomography
X-rays
Damage detection
3D acquisition
Computer vision technology
Data modeling
Machine vision
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