웨이브렛 변환과 신경망 알고리즘을 이용한 드릴링 버 생성 음향방출 모니터링
- Title
- 웨이브렛 변환과 신경망 알고리즘을 이용한 드릴링 버 생성 음향방출 모니터링
- Other Titles
- Acoustic Emission Monitoring of Drilling Burr Formation Using Wavelet Transform and an Artificial Neural Network
- Author
- 이성환
- Keywords
- DriIling Burr (드릴링 버); Artificial Neural Network (인공지능신경망); AE Monitoring (음향방줄 모니터링); Wavelet Transform (웨이브렛 변환)
- Issue Date
- 2005-04
- Publisher
- Korean Society for Precision Engineering
- Citation
- Journal of the Korean Society for Precision Engineering v.22 No.4, Page. 37-43
- Abstract
- Real time monitoring of exit burr formation is critical in manufacturing automation. In this paper, acoustic emission (AE) was used to detect the burr formation during drilling. By using wavelet transform (WT), AE data were compressed without unnecessary details. Then the transformed data were used as selected features (inputs) of a back-propagation artificial neural net (ANN). In order to validate the in process AE monitoring system, both WT -based ANN and cutting condition (cutting speed, feed, drill diameter, etc.) based ANN outputs were compared with experimental data.
- URI
- http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00855879&language=ko_KRhttps://repository.hanyang.ac.kr/handle/20.500.11754/152104
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MECHANICAL ENGINEERING(기계공학과) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML