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28 February 2024 Research on defect detection and wall thickness analysis of fully-wrapped carbon fiber reinforced hydrogen storage cylinder for unmanned aerial vehicles by industrial computed tomography
Yan Shi, Zitao Shen, Li Zhao, Shijie Xie, Cunjian Miao, Ping Tang
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Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130710S (2024) https://doi.org/10.1117/12.3025473
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China
Abstract
The thin-walled structure of fully-wrapped carbon fiber reinforced hydrogen storage cylinders for unmanned aerial vehicles elevates the requirements for mechanical processing and non-destructive testing. Industrial computed tomography technology can offer valuable information on the distribution of the linear attenuation coefficient inside the tested object, enabling intuitive defect detection and size measurement. Here, we conducted defect detection and wall thickness analysis by industrial computed tomography on the hydrogen storage cylinder for unmanned aerial vehicles and found that the thickness of delamination defects at the cylinder neck and cylindrical shell was approximately equivalent. The wall thickness of the Al liner and carbon fiber layer exhibited relative minimum and maximum values at the Al liner abnormal area, respectively, likely resulting from that the sudden thickening of the carbon fiber layer caused the Al liner to shift inward under the additional pressure from the carbon fiber layer. These results proved that industrial computed tomography technology could effectively inspect the quality of hydrogen storage cylinders for unmanned aerial vehicles.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yan Shi, Zitao Shen, Li Zhao, Shijie Xie, Cunjian Miao, and Ping Tang "Research on defect detection and wall thickness analysis of fully-wrapped carbon fiber reinforced hydrogen storage cylinder for unmanned aerial vehicles by industrial computed tomography", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130710S (28 February 2024); https://doi.org/10.1117/12.3025473
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KEYWORDS
Hydrogen
Carbon fibers
Aluminum
Unmanned aerial vehicles
Computed tomography
X-ray computed tomography
Defect detection
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