Optimisation of hybrid tandem metal active gas welding using Gaussian process regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Dae Young | - |
dc.contributor.author | Leifsson, Leifur | - |
dc.contributor.author | Kim, Jin-Young | - |
dc.contributor.author | Lee, Seung Hwan | - |
dc.date.accessioned | 2021-07-30T05:00:34Z | - |
dc.date.available | 2021-07-30T05:00:34Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.issn | 1362-1718 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/2589 | - |
dc.description.abstract | In this paper, an additional filler wire with opposite polarity was inserted in tandem flux cored arc welding process to increase the welding speed and deposition rate. In this hybrid welding, the optimisation of welding parameters is required to improve the bead geometry which directly indicates the welding quality. However, the correlation between the parameters and the bead geometry is hard to identify, so the process parameters are usually selected intuitively by the experienced engineers. Therefore, welding process modelling is constructed with the Gaussian process regression model, and parameter optimisation is performed with sequential quadratic programming optimisation algorithm. The proposed modelling optimisation process is verified by performing the welding experiment using the parameters that are optimised by the proposed process. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.title | Optimisation of hybrid tandem metal active gas welding using Gaussian process regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seung Hwan | - |
dc.identifier.doi | 10.1080/13621718.2019.1666222 | - |
dc.identifier.scopusid | 2-s2.0-85073955694 | - |
dc.identifier.wosid | 000487219500001 | - |
dc.identifier.bibliographicCitation | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, v.25, no.3, pp.208 - 217 | - |
dc.relation.isPartOf | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.title | SCIENCE AND TECHNOLOGY OF WELDING AND JOINING | - |
dc.citation.volume | 25 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 208 | - |
dc.citation.endPage | 217 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
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 | PARAMETER OPTIMIZATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordAuthor | Tandem flux cored arc welding | - |
dc.subject.keywordAuthor | hot-wire | - |
dc.subject.keywordAuthor | hybrid tandem metal active gas welding | - |
dc.subject.keywordAuthor | Gaussian process regression | - |
dc.subject.keywordAuthor | parameter optimisation | - |
dc.subject.keywordAuthor | fillet welding | - |
dc.subject.keywordAuthor | machine learning | - |
dc.identifier.url | https://www.tandfonline.com/doi/full/10.1080/13621718.2019.1666222 | - |
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