View Video Presentation: https://doi. org/10.2514/6.2021-2024. vid We present here the
development and evaluation of a deep learning (artificial intelligence)-based computer …

Four‐dimensional quantitative characterization of heterogeneous materials using in situ
synchrotron radiation computed tomography can reveal 3D sub‐micrometer features …

The greatest challenge when using deep convolutional neural networks (DCNNs) for
automatic segmentation of microstructural X-ray computed tomography (XCT) data is the …

Advanced composite laminates comprised of carbon (micro) fiber reinforced polymer
(CFRP) have become widespread in modern high-performance aerospace structures …

X-Ray Computed Tomography (XCT) techniques have evolved to a point that high-
resolution data can be acquired so fast that classic segmentation methods are prohibitively …

View Video Presentation: https://doi. org/10.2514/6.2022-0375. vid In this study, the effects
on 3D strengthening and toughening mechanisms of interlaminar nanoreinforcement …

The quantitative analysis of microstructural features is a key to understanding the
micromechanical behavior of metal matrix composites (MMCs), which is a premise for their …

While X-ray microtomography has become indispensable in 3D inspections of materials,
efficient processing of such volumetric datasets continues to be a challenge. This paper …

It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is
critical to achieve high‐precision segmentation scores. The challenging experimental XCT …

Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes
in morphological descriptors in a sample (eg, porosity, particle size, and crack width) during …