Deep-Learning Detection of Cracks in In-Situ Computed Tomograms of Nano-Engineered Composites
Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning › fagfællebedømt
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.
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022 |
| Redaktører | Olesya Zhupanska, Erdogan Madenci |
| Forlag | DEStech Publications, Inc. |
| Publikationsdato | 2022 |
| ISBN (Elektronisk) | 9781605956909 |
| Status | Udgivet - 2022 |
| Begivenhed | 37th Technical Conference of the American Society for Composites, ASC 2022 - Tucson, USA Varighed: 19 sep. 2022 → 21 sep. 2022 |
Konference
| Konference | 37th Technical Conference of the American Society for Composites, ASC 2022 |
|---|---|
| Land | USA |
| By | Tucson |
| Periode | 19/09/2022 → 21/09/2022 |
Bibliografisk note
Funding Information:
We acknowledge the support of the KU Leuven Research Council (project C24/17/052), FWO Postdoc Fellowship (project ToughImage 1263421N), and FWO large infrastructure (project I013518N). The KU Leuven XCT Core facility is acknowledged for the 3D image acquisition and quantitative post-processing tools (www.xct.kuleuven.be).ThisworkwaspartiallysupportedbyAirbus,ANSYS,Boeing, Embraer,LockheedMartin,SaabAB,andTeijinCarbonAmericathroughMIT'sNano-EngineeredCompositeaerospaceSTructures(NECST)Consortium.
Funding Information:
We acknowledge the support of the KU Leuven Research Council (project C24/17/052), FWO Postdoc Fellowship (project ToughImage 1263421N), and FWO large infrastructure (project I013518N). The KU Leuven XCT Core facility is acknowledged for the 3D image acquisition and quantitative post-processing tools (www.xct.kuleuven.be). This work was partially supported by Airbus, ANSYS, Boeing, Embraer, Lockheed Martin, Saab AB, and Teijin Carbon America through MIT's Nano-Engineered Composite aerospace STructures (NECST) Consortium.
Publisher Copyright:
© Proceedings of the American Society for Composites - 37th Technical Conference, ASC 2022. All rights reserved.
ID: 322792396