Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yu, Myeong-Hyeon | - |
dc.contributor.author | Kim, Hak-Sung | - |
dc.date.accessioned | 2021-07-30T04:43:08Z | - |
dc.date.available | 2021-07-30T04:43:08Z | - |
dc.date.created | 2021-07-14 | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 0263-8223 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1017 | - |
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%. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Deep-learning based damage sensing of carbon fiber/polypropylene composite via addressable conducting network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Hak-Sung | - |
dc.identifier.doi | 10.1016/j.compstruct.2021.113871 | - |
dc.identifier.scopusid | 2-s2.0-85103298537 | - |
dc.identifier.wosid | 000652603100014 | - |
dc.identifier.bibliographicCitation | COMPOSITE STRUCTURES, v.267, pp.1 - 9 | - |
dc.relation.isPartOf | COMPOSITE STRUCTURES | - |
dc.citation.title | COMPOSITE STRUCTURES | - |
dc.citation.volume | 267 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 9 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mechanics | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Mechanics | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Composites | - |
dc.subject.keywordPlus | NONDESTRUCTIVE EVALUATION | - |
dc.subject.keywordPlus | ELECTRICAL-RESISTIVITY | - |
dc.subject.keywordPlus | MULTI-DELAMINATION | - |
dc.subject.keywordPlus | RESISTANCE | - |
dc.subject.keywordPlus | GFRP | - |
dc.subject.keywordAuthor | Carbon fiber polypropylene composite | - |
dc.subject.keywordAuthor | Addressable conducting network | - |
dc.subject.keywordAuthor | Damage sensing | - |
dc.subject.keywordAuthor | Deep learning and artificial neural network | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0263822321003317?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.