맞대기 V형 그루브의 GMA 초층용접에서 합성곱 신경망을 이용한 이면비드 발생 예측 모델 개발
DC Field | Value | Language |
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dc.contributor.author | 이형원 | - |
dc.contributor.author | 유지영 | - |
dc.contributor.author | 김광국 | - |
dc.contributor.author | 김영민 | - |
dc.contributor.author | 황인성 | - |
dc.contributor.author | 이승환 | - |
dc.contributor.author | 김동윤 | - |
dc.date.accessioned | 2022-07-06T11:58:42Z | - |
dc.date.available | 2022-07-06T11:58:42Z | - |
dc.date.created | 2021-12-08 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 2466-2232 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140732 | - |
dc.description.abstract | Gas metal arc (GMA) welding is widely used in the machinery industry. The quality of a welded joint is affected by the penetration of root pass welding in the V-groove joint. Automation using GMA welding is continuously re- quired, and root pass welding automation is required to automate the entire welding process. In particular, the devel- opment of a prediction model that can ensure full penetration back-bead is required for the automation of root pass welding. In this study, a convolutional neural network (CNN) model was applied to predict the occurrence of back-bead in V-groove butt joint GMA root pass welding. The bead profile was measured using a laser vision sensor system and it was used as the input data for the prediction model, and the bead occurrence was used as the output data for the model. A total of 12,873 bead profiles were extracted and pre-processed through cutting, resizing, and thresholding. The CNN model consists of nine layers, and performs three convolution and two pooling operations. The accuracy of the prediction model was 99.5%, and through this study, it was demonstrated that the quality of root-pass welding can be controlled by using convolutional neural network and it can contribute to automation. | - |
dc.language | 한국어 | - |
dc.language.iso | ko | - |
dc.publisher | 대한용접접합학회 | - |
dc.title | 맞대기 V형 그루브의 GMA 초층용접에서 합성곱 신경망을 이용한 이면비드 발생 예측 모델 개발 | - |
dc.title.alternative | Convolutional Neural Network Model for the Prediction of Back-Bead Occurrence in GMA Root Pass Welding of V-groove Butt Joint | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 이승환 | - |
dc.identifier.doi | 10.5781/JWJ.2021.39.5.1 | - |
dc.identifier.bibliographicCitation | 대한용접접합학회지, v.39, no.5, pp.463 - 470 | - |
dc.relation.isPartOf | 대한용접접합학회지 | - |
dc.citation.title | 대한용접접합학회지 | - |
dc.citation.volume | 39 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 463 | - |
dc.citation.endPage | 470 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002771202 | - |
dc.description.journalClass | 2 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Gas metal arc welding (GMAW) | - |
dc.subject.keywordAuthor | V-Groove | - |
dc.subject.keywordAuthor | Back-bead | - |
dc.subject.keywordAuthor | Root pass | - |
dc.subject.keywordAuthor | Full penetration | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | Laser vision | - |
dc.identifier.url | https://e-jwj.org/journal/view.php?doi=10.5781/JWJ.2021.39.5.1 | - |
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