Deep-Learning Detection of Cracks in In-Situ Computed Tomograms of Nano-Engineered Composites
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Deep-Learning Detection of Cracks in In-Situ Computed Tomograms of Nano-Engineered Composites. / Mehdikhani, Mahoor; Upadhyay, Shailee; Soete, Jeroen; Swolfs, Yentl; Smith, Abraham George; Aravand, M. Ali; Liotta, Andrew H.; Wicks, Sunny S.; Wardle, Brian L.; Lomov, Stepan V.; Gorbatikh, Larissa.
Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022. ed. / Olesya Zhupanska; Erdogan Madenci. DEStech Publications, Inc., 2022.Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review
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TY - ABST
T1 - Deep-Learning Detection of Cracks in In-Situ Computed Tomograms of Nano-Engineered Composites
AU - Mehdikhani, Mahoor
AU - Upadhyay, Shailee
AU - Soete, Jeroen
AU - Swolfs, Yentl
AU - Smith, Abraham George
AU - Aravand, M. Ali
AU - Liotta, Andrew H.
AU - Wicks, Sunny S.
AU - Wardle, Brian L.
AU - Lomov, Stepan V.
AU - Gorbatikh, Larissa
N1 - Publisher Copyright: © Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022. All rights reserved.
PY - 2022
Y1 - 2022
N2 - The deformation and damage development of nano-engineered composites have not yet been investigated in 3D, although it can provide a deeper insight into their damage behavior. To fill this gap, we perform a tensile test on a nano-engineered composite with in-situ X-ray micro-Computed Tomography (micro-CT). The composite is made from woven alumina fibers with grafted carbon nanotubes (CNTs) and epoxy. More diffuse damage seems to exist for the materials with CNTs compared to the baseline material. However, at such resolution where individual fibers are vaguely visible, grayscale thresholding does not accurately characterize the matrix cracks due to their small opening and low contrast with the material itself. Thus, we employ a deep-learning tool, called RootPainter, for segmentation of cracks with small opening in relation to the voxel size, in the 3D images. The results show that RootPainter can reliably identify these small cracks. In addition to the investigation of the mechanical performance of the nano-engineered composite, this study provides a novel and reliable method for the characterization of micro-cracks in in-situ tomograms of these composites.
AB - The deformation and damage development of nano-engineered composites have not yet been investigated in 3D, although it can provide a deeper insight into their damage behavior. To fill this gap, we perform a tensile test on a nano-engineered composite with in-situ X-ray micro-Computed Tomography (micro-CT). The composite is made from woven alumina fibers with grafted carbon nanotubes (CNTs) and epoxy. More diffuse damage seems to exist for the materials with CNTs compared to the baseline material. However, at such resolution where individual fibers are vaguely visible, grayscale thresholding does not accurately characterize the matrix cracks due to their small opening and low contrast with the material itself. Thus, we employ a deep-learning tool, called RootPainter, for segmentation of cracks with small opening in relation to the voxel size, in the 3D images. The results show that RootPainter can reliably identify these small cracks. In addition to the investigation of the mechanical performance of the nano-engineered composite, this study provides a novel and reliable method for the characterization of micro-cracks in in-situ tomograms of these composites.
UR - http://www.scopus.com/inward/record.url?scp=85139567528&partnerID=8YFLogxK
M3 - Conference abstract in proceedings
AN - SCOPUS:85139567528
BT - Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022
A2 - Zhupanska, Olesya
A2 - Madenci, Erdogan
PB - DEStech Publications, Inc.
T2 - 37th Technical Conference of the American Society for Composites, ASC 2022
Y2 - 19 September 2022 through 21 September 2022
ER -
ID: 322792396