Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이성환 | - |
dc.date.accessioned | 2019-10-30T04:35:47Z | - |
dc.date.available | 2019-10-30T04:35:47Z | - |
dc.date.issued | 2005-10 | - |
dc.identifier.citation | 공학기술논문집, v. 14, Page. 43-51 | en_US |
dc.identifier.uri | http://riet.hanyang.ac.kr/journal/174 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/111672 | - |
dc.description.abstract | Detection of exit burr is very important in manufacturing automation. In this paper, acoustic emission(AE) was used to detect the burr formation during milling. By using wavelet transformation, AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net. In order to validate the proposed scheme, the wavelet based ANN results were compared with cutting condition (cutting speed, feed, depth of cut, etc.) based ANN results. | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 한양대학교 공학기술연구소 | en_US |
dc.title | 웨이브렛 변환을 이용한 밀링 버 생성 음향방출 모니터링 | en_US |
dc.title.alternative | Acoustic Emission Monitoring of Milling Burr Formation Using Wavelet Transform | en_US |
dc.type | Article | en_US |
dc.relation.journal | 공학기술논문집 | - |
dc.contributor.googleauthor | 마채훈 | - |
dc.contributor.googleauthor | 이성환 | - |
dc.contributor.googleauthor | 조용원 | - |
dc.relation.code | 2012210021 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF MECHANICAL ENGINEERING | - |
dc.identifier.pid | sunglee | - |
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