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dc.contributor.authorKOO, GYOGWON-
dc.contributor.authorSHIN, CRINO-
dc.contributor.authorHYEYEON, CHOI-
dc.contributor.authorLEE, JONG-HAK-
dc.contributor.authorKIM, SANG WOO-
dc.contributor.authorYUN, JONG PIL-
dc.date.available2019-12-05T12:30:03Z-
dc.date.created2019-12-05-
dc.date.issued2020-08-19-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://oasis.postech.ac.kr/handle/2014.oak/100373-
dc.description.abstractThis study proposes an automated brittle fracture rate (BFR) estimator using deep learning. As the demand for line-pipes increases in various industries, the need for BFR estimation through dropweight tear test (DWTT) increases to evaluate steel's property. Conventional BFR or ductile fracture rate (DFR) estimation methods require an expensive 3D scanner. Alternatively, a rule-based approach is used with a single charge-coupled device (CCD) camera. However, it is sensitive to the hyper-parameter. To solve these problems, we propose an approach based on deep learning that has recently been successful in the fields of computer vision and image processing. The method proposed in this study is the first to use deep learning approach for BFR estimation. The proposed method consists of a VGG-based U-Net (VU-Net) which is inspired by U-Net and fully convolutional network (FCN). VU-Net includes a deep encoder and a decoder. The encoder is adopted from VGG19 and transferred with a pre-trained model with ImageNet. In addition, the structure of the decoder is the same as that of the encoder, and the decoder uses the feature maps of the encoder through concatenation operation to compensate for the reduced spatial information. To analyze the proposed VU-Net, we experimented with different depths of networks and various transfer learning approaches. In terms of accuracy used in real industrial application, we compared the proposed VU-Net with U-Net and FCN to evaluate the performance. The experiments showed that VU-Net was the accuracy of approximately 94.9 %, and was better than the other two, which had the accuracies of about 91.8 % and 93.7 %, respectively.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectLOW-TEMPERATURE TOUGHNESS-
dc.subjectX70-
dc.subjectMICROSTRUCTURE-
dc.subjectSEGMENTATION-
dc.subjectPREDICTION-
dc.titleAutomated Brittle Fracture Rate Estimator for Steel Property Evaluation Using Deep Learning After Drop-Weight Tear Test-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.bibliographicCitationIEEE ACCESS, v.7, pp.145095 - 145103-
dc.identifier.wosid000498841600001-
dc.citation.endPage145103-
dc.citation.startPage145095-
dc.citation.titleIEEE ACCESS-
dc.citation.volume7-
dc.contributor.affiliatedAuthorKOO, GYOGWON-
dc.contributor.affiliatedAuthorHYEYEON, CHOI-
dc.contributor.affiliatedAuthorKIM, SANG WOO-
dc.identifier.scopusid2-s2.0-85073609209-
dc.description.journalClass1-
dc.description.isOpenAccessY-
dc.type.docTypeArticle-

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