Zobrazeno 1 - 10
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pro vyhledávání: '"Withers, P. J. A."'
Autor:
White, Michael D., Haribabu, Gowtham Nimmal, Jegadeesan, Jeyapriya Thimukonda, Basu, Bikramjit, Withers, Philip J., Race, Chris P.
Microstructure is key to controlling and understanding the properties of metallic materials, but traditional approaches to describing microstructure capture only a small number of features. To enable data-centric approaches to materials discovery, al
Externí odkaz:
http://arxiv.org/abs/2401.11967
Autor:
Chen, Y., Tang, Y. T., Collins, D. M., Clark, S. J., Ludwig, W., Rodriguez-Lamas, R., Detlefs, C., Reed, R. C., Lee, P. D., Withers, P. J., Yildirim, C.
The industrialization of Laser Additive Manufacturing (LAM) is challenged by the undesirable microstructures and high residual stresses originating from the fast and complex solidification process. Non-destructive assessment of the mechanical perform
Externí odkaz:
http://arxiv.org/abs/2303.04764
Autor:
White, Michael D., Tarakanov, Alexander, Race, Christopher P., Withers, Philip J., Law, Kody J. H.
Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. A statistical framework is systematically developed for compressed characterisation of a population
Externí odkaz:
http://arxiv.org/abs/2203.13718
Autor:
Fogarty, Kyle, Ametova, Evelina, Burca, Genoveva, Korsunsky, Alexander M., Schmidt, Søren, Withers, Philip J., Lionheart, William R. B.
Point by point strain scanning is often used to map the residual stress (strain) in engineering materials and components. However, the gauge volume and hence spatial resolution is limited by the beam defining apertures and can be anisotropic for very
Externí odkaz:
http://arxiv.org/abs/2201.09669
Autor:
Warr, Ryan, Ametova, Evelina, Cernik, Robert J., Fardell, Gemma, Handschuh, Stephan, Jørgensen, Jakob S., Papoutsellis, Evangelos, Pasca, Edoardo, Withers, Philip J.
Here we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (800 eV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic
Externí odkaz:
http://arxiv.org/abs/2103.04796
Autor:
Wang, Ying, Xu, Xu, Zhao, Wenxia, Li, Nan, McDonald, Samuel A., Chai, Yuan, Atkinson, Michael, Dobson, Katherine J., Michalik, Stefan, Fan, Yingwei, Withers, Philip J., Zhou, Xiaorong, Burnett, Timothy L.
The damage mechanisms and load redistribution of high strength TC17 titanium alloy/unidirectional SiC fibre composite (fibre diameter = 100 $\mu$m) under high temperature (350 {\deg}C) fatigue cycling have been investigated in situ using synchrotron
Externí odkaz:
http://arxiv.org/abs/2102.13575
Autor:
Ametova, Evelina, Burca, Genoveva, Chilingaryan, Suren, Fardell, Gemma, Jørgensen, Jakob S., Papoutsellis, Evangelos, Pasca, Edoardo, Warr, Ryan, Turner, Martin, Lionheart, William R. B., Withers, Philip J.
Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (time of flight)
Externí odkaz:
http://arxiv.org/abs/2102.06706
Autor:
Papoutsellis, Evangelos, Ametova, Evelina, Delplancke, Claire, Fardell, Gemma, Jørgensen, Jakob S., Pasca, Edoardo, Turner, Martin, Warr, Ryan, Lionheart, William R. B., Withers, Philip J.
The newly developed Core Imaging Library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularised reconstruction algorithms and explicit
Externí odkaz:
http://arxiv.org/abs/2102.06126
Autor:
Jørgensen, Jakob S., Ametova, Evelina, Burca, Genoveva, Fardell, Gemma, Papoutsellis, Evangelos, Pasca, Edoardo, Thielemans, Kris, Turner, Martin, Warr, Ryan, Lionheart, William R. B., Withers, Philip J.
We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for h
Externí odkaz:
http://arxiv.org/abs/2102.04560
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