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딥 러닝 접근법을 사용한 AM의 최적 내부구조 예측
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 구혜지 | - |
| dc.contributor.author | 문대환 | - |
| dc.contributor.author | 김경환 | - |
| dc.contributor.author | 한석영 | - |
| dc.date.accessioned | 2021-08-02T11:51:20Z | - |
| dc.date.available | 2021-08-02T11:51:20Z | - |
| dc.date.created | 2021-05-14 | - |
| dc.date.issued | 2019-05 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/14103 | - |
| dc.language | 한국어 | - |
| dc.language.iso | ko | - |
| dc.publisher | 한국생산제조학회 | - |
| dc.title | 딥 러닝 접근법을 사용한 AM의 최적 내부구조 예측 | - |
| dc.title.alternative | Predicting optimal infill for additive manufacturing using deep learning approach | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | 한석영 | - |
| dc.identifier.bibliographicCitation | 2019 한국생산제조학회 춘계학술대회 논문집, pp.137 - 137 | - |
| dc.relation.isPartOf | 2019 한국생산제조학회 춘계학술대회 논문집 | - |
| dc.citation.title | 2019 한국생산제조학회 춘계학술대회 논문집 | - |
| dc.citation.startPage | 137 | - |
| dc.citation.endPage | 137 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Proceeding | - |
| dc.description.journalClass | 3 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | other | - |
| dc.subject.keywordAuthor | Additive manufacturing | - |
| dc.subject.keywordAuthor | Infill | - |
| dc.subject.keywordAuthor | Optimal design | - |
| dc.subject.keywordAuthor | Deep learning | - |
| dc.subject.keywordAuthor | Conditional generative adversarial networks | - |
| dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE08741234 | - |
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