Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김학성 | - |
dc.date.accessioned | 2022-11-24T08:05:28Z | - |
dc.date.available | 2022-11-24T08:05:28Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | COMPOSITE STRUCTURES, v. 267, article no. 113871 | en_US |
dc.identifier.issn | 0263-8223;1879-1085 | en_US |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0263822321003317?via%3Dihub | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/177398 | - |
dc.description.abstract | In this work, damage sensing of carbon fiber reinforced polymer composite (CFRP) was conducted based on an addressable conducting network (ACN). To improve the accuracy of damage detection, a deep learning-based damage sensing system was developed. The data for deep learning were generated using a resist network model based on Kirchhoff's law. The generated data was verified through finite element analysis. Then, the Artificial Neural Network (ANN) deep learning algorithm was used for damage detection and evaluation. The accuracy of damage sensing was improved by applying the resist network model that considered not only delamination but also the damage of the carbon fiber. As a result, damage detection of CFRP was performed with a high accuracy rate of about 95%. | en_US |
dc.description.sponsorship | This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20206910100160). This research was also supported by a Semiconductor Industry Collaborative Project between Hanyang University and Samsung Electronics Co. Ltd. | en_US |
dc.language | en | en_US |
dc.publisher | ELSEVIER SCI LTD | en_US |
dc.subject | Carbon fiber polypropylene composite | en_US |
dc.subject | Addressable conducting network | en_US |
dc.subject | Damage sensing | en_US |
dc.subject | Deep learning and artificial neural network | en_US |
dc.title | Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network | en_US |
dc.type | Article | en_US |
dc.relation.volume | 267 | - |
dc.identifier.doi | 10.1016/j.compstruct.2021.113871 | en_US |
dc.relation.journal | COMPOSITE STRUCTURES | - |
dc.contributor.googleauthor | Yu, Myeong-Hyeon | - |
dc.contributor.googleauthor | Kim, Hak-Sung | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 기계공학부 | - |
dc.identifier.pid | kima | - |
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