Residual neural network-based fully convolutional network for microstructure segmentation
DC Field | Value | Language |
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dc.contributor.author | Jang, Junmyoung | - |
dc.contributor.author | Van, Donghyun | - |
dc.contributor.author | Jang, Hyojin | - |
dc.contributor.author | Baik, Dae Hyun | - |
dc.contributor.author | Yoo, Sang Duk | - |
dc.contributor.author | Park, Jaewoong | - |
dc.contributor.author | Mhin, Sungwook | - |
dc.contributor.author | Mazumder, Jyoti | - |
dc.contributor.author | Lee, Seung Hwan | - |
dc.date.accessioned | 2021-07-30T05:00:33Z | - |
dc.date.available | 2021-07-30T05:00:33Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 1362-1718 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/2584 | - |
dc.description.abstract | In this study, microstructures of weldment produced using carbon steel A516 grade 60 were analysed via a deep learning approach to measure the fraction of acicular ferrite which considerably influences on mechanical properties of carbon steel. The fully convolutional network was used to conduct the image segmentation. Submerged arc welding was used for welding, and the dataset was constructed using optical microscope. The model was compiled with ResNet, which is the state-of-the-art classifier used as an encoder. The model is trained to distinguish acicular ferrite from microstructures of dataset images and then estimate its accuracy. As a result, the mean intersection over union, which is a metric commonly used to evaluate image segmentation, was shown to be higher than 85%. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Residual neural network-based fully convolutional network for microstructure segmentation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seung Hwan | - |
dc.identifier.doi | 10.1080/13621718.2019.1687635 | - |
dc.identifier.scopusid | 2-s2.0-85074971243 | - |
dc.identifier.wosid | 000494916700001 | - |
dc.identifier.bibliographicCitation | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, v.25, no.4, pp.282 - 289 | - |
dc.relation.isPartOf | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.title | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.volume | 25 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 282 | - |
dc.citation.endPage | 289 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Metallurgy & Metallurgical Engineering | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Metallurgy & Metallurgical Engineering | - |
dc.subject.keywordPlus | ACICULAR FERRITE | - |
dc.subject.keywordPlus | STEEL | - |
dc.subject.keywordPlus | MN | - |
dc.subject.keywordAuthor | Submerged arc welding | - |
dc.subject.keywordAuthor | carbon steel | - |
dc.subject.keywordAuthor | acicular ferrite | - |
dc.subject.keywordAuthor | fraction | - |
dc.subject.keywordAuthor | segmentation | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | fully convolutional network | - |
dc.subject.keywordAuthor | ResNet | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/13621718.2019.1687635 | - |
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