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DC Field | Value | Language |
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dc.citation.endPage | 116267 | - |
dc.citation.startPage | 116254 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.contributor.author | Oh, Sehyeok | - |
dc.contributor.author | Kim, Hyun Kyung | - |
dc.contributor.author | Jeong, Taek-Eon | - |
dc.contributor.author | Kam, Dong-Hyuck | - |
dc.contributor.author | Ki, Hyungson | - |
dc.date.accessioned | 2023-12-21T17:19:23Z | - |
dc.date.available | 2023-12-21T17:19:23Z | - |
dc.date.created | 2020-07-29 | - |
dc.date.issued | 2020-06 | - |
dc.description.abstract | Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)-based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness. | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.116254 - 116267 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3004337 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85087833827 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/47386 | - |
dc.identifier.wosid | 000547512100001 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Deep-Learning-Based Predictive Architectures for Self-Piercing Riveting Process | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
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 | Cross-sectional shape prediction | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | segmentation prediction | - |
dc.subject.keywordAuthor | scalar-to-segmentation generator | - |
dc.subject.keywordAuthor | self-piercing riveting | - |
dc.subject.keywordPlus | SPR JOINTS | - |
dc.subject.keywordPlus | SIMULATION | - |
dc.subject.keywordPlus | ALUMINUM | - |
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