Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 73372 | - |
dc.citation.startPage | 73359 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.contributor.author | Oh, Sehyeok | - |
dc.contributor.author | Ki, Hyungson | - |
dc.date.accessioned | 2023-12-21T17:41:37Z | - |
dc.date.available | 2023-12-21T17:41:37Z | - |
dc.date.created | 2020-05-25 | - |
dc.date.issued | 2020-04 | - |
dc.description.abstract | A deep learning model was applied for predicting a cross-sectional bead image from laser welding process parameters. The proposed model consists of two successive generators. The first generator produces a weld bead segmentation map from laser intensity and interaction time, which is subsequently translated into an optical microscopic (OM) image by the second generator. Both generators exhibit an encoder & x2013;decoder structure based on a convolutional neural network (CNN). In the second generator, a conditional generative adversarial network (cGAN) was additionally employed with multiscale discriminators and residual blocks, considering the size of the OM image. For a training dataset, laser welding experiments with AISI 1020 steel were conducted on a large process window using a 2 KW fiber laser, and a total of 39 process conditions were used for the training. High-resolution OM images were successfully generated, and the predicted bead shapes were reasonably accurate (R-Squared: 89.0 & x0025; for penetration depth, 93.6 & x0025; for weld bead area). | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.73359 - 73372 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2987858 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85084760214 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/32211 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9066982 | - |
dc.identifier.wosid | 000530829100007 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Engineering; Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Welding | - |
dc.subject.keywordAuthor | Laser beams | - |
dc.subject.keywordAuthor | Laser modes | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Generators | - |
dc.subject.keywordAuthor | Power lasers | - |
dc.subject.keywordAuthor | Fiber lasers | - |
dc.subject.keywordAuthor | Laser welding | - |
dc.subject.keywordAuthor | weld-bead prediction | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | image-to-image translation | - |
dc.subject.keywordPlus | PENETRATION DEPTH | - |
dc.subject.keywordPlus | PARAMETERS | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | SHAPE | - |
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