Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network
Abstract The damage process of composite materials, such as short fiber-reinforced plastics (SFRP), is complex. Therefore, it is necessary to accurately represent the damage process in fatigue life prediction. Herein, fatigue life prediction was conducted by combining the digital image correlation m...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-75884-2 |
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| author | Yuta Mizuno Atsushi Hosoi Hiroyuki Koshita Dai Tsunoda Hiroyuki Kawada |
| author_facet | Yuta Mizuno Atsushi Hosoi Hiroyuki Koshita Dai Tsunoda Hiroyuki Kawada |
| author_sort | Yuta Mizuno |
| collection | DOAJ |
| description | Abstract The damage process of composite materials, such as short fiber-reinforced plastics (SFRP), is complex. Therefore, it is necessary to accurately represent the damage process in fatigue life prediction. Herein, fatigue life prediction was conducted by combining the digital image correlation method, which is a non-destructive testing technique, with a convolutional neural network (CNN), using Xception as the network architecture. High prediction accuracy was obtained when training and testing were performed on the same SFRP specimens. In contrast, using different specimens for training and testing resulted in lower accuracy. This issue may be improved by increasing the number of specimens. The regions of interest in the model were visualized by Gradient-weighted Class Activation Mapping. Notably, the model indicated the breaking point as the region of interest from the early stages of the test. The breaking point was identified at an earlier stage by the CNN than by visual inspection, demonstrating the potential for a new method of damage observation. |
| format | Article |
| id | doaj-art-bfeed30e5eca46b0a82a061accc0e979 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bfeed30e5eca46b0a82a061accc0e9792025-08-20T02:11:17ZengNature PortfolioScientific Reports2045-23222024-10-0114111410.1038/s41598-024-75884-2Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural networkYuta Mizuno0Atsushi Hosoi1Hiroyuki Koshita2Dai Tsunoda3Hiroyuki Kawada4Department of Applied Mechanics and Aerospace Engineering, Waseda UniversityDepartment of Applied Mechanics and Aerospace Engineering, Waseda UniversityMobility Business Headquarters Mobility R&D Center, Resonac CorporationMobility Business Headquarters Mobility R&D Center, Resonac CorporationDepartment of Applied Mechanics and Aerospace Engineering, Waseda UniversityAbstract The damage process of composite materials, such as short fiber-reinforced plastics (SFRP), is complex. Therefore, it is necessary to accurately represent the damage process in fatigue life prediction. Herein, fatigue life prediction was conducted by combining the digital image correlation method, which is a non-destructive testing technique, with a convolutional neural network (CNN), using Xception as the network architecture. High prediction accuracy was obtained when training and testing were performed on the same SFRP specimens. In contrast, using different specimens for training and testing resulted in lower accuracy. This issue may be improved by increasing the number of specimens. The regions of interest in the model were visualized by Gradient-weighted Class Activation Mapping. Notably, the model indicated the breaking point as the region of interest from the early stages of the test. The breaking point was identified at an earlier stage by the CNN than by visual inspection, demonstrating the potential for a new method of damage observation.https://doi.org/10.1038/s41598-024-75884-2 |
| spellingShingle | Yuta Mizuno Atsushi Hosoi Hiroyuki Koshita Dai Tsunoda Hiroyuki Kawada Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network Scientific Reports |
| title | Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| title_full | Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| title_fullStr | Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| title_full_unstemmed | Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| title_short | Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| title_sort | fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network |
| url | https://doi.org/10.1038/s41598-024-75884-2 |
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