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기계학습을 기반으로 한 자외선 경화형 도장의 부착성 불량 위험수준 정량화
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 윤주호 | - |
| dc.contributor.author | 추병하 | - |
| dc.contributor.author | 김병훈 | - |
| dc.date.accessioned | 2022-07-18T01:33:50Z | - |
| dc.date.available | 2022-07-18T01:33:50Z | - |
| dc.date.created | 2021-08-25 | - |
| dc.date.issued | 2021-08 | - |
| dc.identifier.issn | 1225-0988 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/108239 | - |
| dc.language | 한국어 | - |
| dc.language.iso | ko | - |
| dc.publisher | 대한산업공학회 | - |
| dc.title | 기계학습을 기반으로 한 자외선 경화형 도장의 부착성 불량 위험수준 정량화 | - |
| dc.title.alternative | Quantification of the Risk Level of Adhesion Defect of Ultraviolet Ray Curable Coating based on a Machine Learning Technique | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | 김병훈 | - |
| dc.identifier.doi | 10.7232/JKIIE.2021.47.4.406 | - |
| dc.identifier.bibliographicCitation | 대한산업공학회지, v.47, no.4, pp.406 - 413 | - |
| dc.relation.isPartOf | 대한산업공학회지 | - |
| dc.citation.title | 대한산업공학회지 | - |
| dc.citation.volume | 47 | - |
| dc.citation.number | 4 | - |
| dc.citation.startPage | 406 | - |
| dc.citation.endPage | 413 | - |
| dc.type.rims | ART | - |
| dc.identifier.kciid | ART002743800 | - |
| dc.description.journalClass | 2 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | kci | - |
| dc.description.journalRegisteredClass | other | - |
| dc.subject.keywordAuthor | Penetration Film Thickness | - |
| dc.subject.keywordAuthor | Quality Management | - |
| dc.subject.keywordAuthor | Classification | - |
| dc.subject.keywordAuthor | XGBoost | - |
| dc.subject.keywordAuthor | Risk Level of Adhesion Defect | - |
| dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10592172 | - |
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Related Researcher
- Kim, Byunghoon
- ERICA 공학대학 (DEPARTMENT OF INDUSTRIAL & MANAGEMENT ENGINEERING)