Applications of neural networks for crack initiation and propagation monitoring in aircraft structures
Timely detection of fatigue cracks on aircraft structural elements is the main task in damage tolerance principle approach. In this regard, much attention in aviation is paid to the methods of non-destructive testing which requires special equipment with the involvement of highly qualified personnel...
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National Aerospace University «Kharkiv Aviation Institute»
2021
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oai:doaj.org-article:44066452b9be424d9c22613f0be70b362021-11-09T07:53:52ZApplications of neural networks for crack initiation and propagation monitoring in aircraft structures1727-73372663-221710.32620/aktt.2021.4sup2.13https://doaj.org/article/44066452b9be424d9c22613f0be70b362021-08-01T00:00:00Zhttp://nti.khai.edu/ojs/index.php/aktt/article/view/1468https://doaj.org/toc/1727-7337https://doaj.org/toc/2663-2217Timely detection of fatigue cracks on aircraft structural elements is the main task in damage tolerance principle approach. In this regard, much attention in aviation is paid to the methods of non-destructive testing which requires special equipment with the involvement of highly qualified personnel. Nowadays we can see that technologies that can learn to identify defects are preferred to simplify the gap process and minimize human factor errors. A self-learning technology is incorporated in the crack detection program. This makes it possible to increase the sensitivity of defects in the mode of the used technically false equipment. Unlike the detection methods of other machine learning detection systems, the system developed in this paper can also measure the cracks without the use of sophisticated sensors. However, the proposed system requires a photo-capturing device. Compared to similar visual systems, the developed system can work with very noisy images and detect cracks up to 0.3 mm. To do this, the webcam from the mid-range segment with 1920×1080 resolutions is used, that makes such technology easy to access. All modifications in the design of the camera scheme were associated with a change in the focal length, implemented by shifting the lens relative to the matrix. It allows the camera to focus on close distance less than 50 cm. For the fatigue tests compact specimens of duralumin alloy D16T with edge stress concentrator were used. The specimens were cycle tested by cantilever banding with stress ratio R=-1. Loading bogie apply force to specimens in direction normal to specimen surface. A loading value depends on the length of the loading crank and can be adjusted if needed. To measure cracks in the processed images, a visual control program on a convolutional neural network and a sliding window algorithm were used. About 4,000 images were used to train the algorithm. The sliding window algorithm analyzes small images sequentially. One by one, image regions were selected and monitored for cracks using a convolutional neural network. Areas with detected cracks are memorized by the sliding window algorithm.Надія Іванівна БурауСвятослав Сергійович ЮцкевичАндрій Ігорович КомпанецьNational Aerospace University «Kharkiv Aviation Institute»articlefatiguecracknon-destructive testing methodsneural networksMotor vehicles. Aeronautics. AstronauticsTL1-4050ENRUUKАвіаційно-космічна техніка та технологія, Vol 0, Iss 4sup2, Pp 99-103 (2021) |
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DOAJ |
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EN RU UK |
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fatigue crack non-destructive testing methods neural networks Motor vehicles. Aeronautics. Astronautics TL1-4050 |
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fatigue crack non-destructive testing methods neural networks Motor vehicles. Aeronautics. Astronautics TL1-4050 Надія Іванівна Бурау Святослав Сергійович Юцкевич Андрій Ігорович Компанець Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| description |
Timely detection of fatigue cracks on aircraft structural elements is the main task in damage tolerance principle approach. In this regard, much attention in aviation is paid to the methods of non-destructive testing which requires special equipment with the involvement of highly qualified personnel. Nowadays we can see that technologies that can learn to identify defects are preferred to simplify the gap process and minimize human factor errors. A self-learning technology is incorporated in the crack detection program. This makes it possible to increase the sensitivity of defects in the mode of the used technically false equipment. Unlike the detection methods of other machine learning detection systems, the system developed in this paper can also measure the cracks without the use of sophisticated sensors. However, the proposed system requires a photo-capturing device. Compared to similar visual systems, the developed system can work with very noisy images and detect cracks up to 0.3 mm. To do this, the webcam from the mid-range segment with 1920×1080 resolutions is used, that makes such technology easy to access. All modifications in the design of the camera scheme were associated with a change in the focal length, implemented by shifting the lens relative to the matrix. It allows the camera to focus on close distance less than 50 cm. For the fatigue tests compact specimens of duralumin alloy D16T with edge stress concentrator were used. The specimens were cycle tested by cantilever banding with stress ratio R=-1. Loading bogie apply force to specimens in direction normal to specimen surface. A loading value depends on the length of the loading crank and can be adjusted if needed. To measure cracks in the processed images, a visual control program on a convolutional neural network and a sliding window algorithm were used. About 4,000 images were used to train the algorithm. The sliding window algorithm analyzes small images sequentially. One by one, image regions were selected and monitored for cracks using a convolutional neural network. Areas with detected cracks are memorized by the sliding window algorithm. |
| format |
article |
| author |
Надія Іванівна Бурау Святослав Сергійович Юцкевич Андрій Ігорович Компанець |
| author_facet |
Надія Іванівна Бурау Святослав Сергійович Юцкевич Андрій Ігорович Компанець |
| author_sort |
Надія Іванівна Бурау |
| title |
Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| title_short |
Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| title_full |
Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| title_fullStr |
Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| title_full_unstemmed |
Applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| title_sort |
applications of neural networks for crack initiation and propagation monitoring in aircraft structures |
| publisher |
National Aerospace University «Kharkiv Aviation Institute» |
| publishDate |
2021 |
| url |
https://doaj.org/article/44066452b9be424d9c22613f0be70b36 |
| work_keys_str_mv |
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