Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jang, Junmyoung | - |
dc.contributor.author | Lee, Jaeheon | - |
dc.contributor.author | Lee, Jaeyoung | - |
dc.contributor.author | Park, Sang Rin | - |
dc.contributor.author | Kim, Jin-young | - |
dc.contributor.author | Kim, Young-Beom | - |
dc.contributor.author | Lee, Seung Hwan | - |
dc.date.accessioned | 2023-11-24T05:18:26Z | - |
dc.date.available | 2023-11-24T05:18:26Z | - |
dc.date.created | 2023-06-19 | - |
dc.date.issued | 2023-11 | - |
dc.identifier.issn | 1362-1718 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/193095 | - |
dc.description.abstract | A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Young-Beom | - |
dc.contributor.affiliatedAuthor | Lee, Seung Hwan | - |
dc.identifier.doi | 10.1080/13621718.2023.2219081 | - |
dc.identifier.scopusid | 2-s2.0-85161470321 | - |
dc.identifier.wosid | 000999997200001 | - |
dc.identifier.bibliographicCitation | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, v.28, no.8, pp.738 - 746 | - |
dc.relation.isPartOf | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.title | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.volume | 28 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 738 | - |
dc.citation.endPage | 746 | - |
dc.type.rims | ART | - |
dc.type.docType | Article in press | - |
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 | ALUMINUM-ALLOY | - |
dc.subject.keywordPlus | SEAM TRACKING | - |
dc.subject.keywordPlus | QUALITY | - |
dc.subject.keywordPlus | PENETRATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | POOL | - |
dc.subject.keywordAuthor | Flux-cored arc welding | - |
dc.subject.keywordAuthor | root-pass welding | - |
dc.subject.keywordAuthor | gap monitoring | - |
dc.subject.keywordAuthor | electrical signals | - |
dc.subject.keywordAuthor | time-series | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | random convolutional kernel transform (ROCKET) | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/13621718.2023.2219081 | - |
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