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웨이브렛 변환과 신경망 알고리즘을 이용한 드릴링 버 생성 음향방출 모니터링Acoustic Emission Monitoring of Drilling Burr Formation Using Wavelet Transform and an Artificial Neural Network
- Other Titles
- Acoustic Emission Monitoring of Drilling Burr Formation Using Wavelet Transform and an Artificial Neural Network
- Issue Date
- Apr-2005
- Publisher
- 한국정밀공학회
- Keywords
- Drilling Burr; AE Monitoring; Wavelet Transform; Artificial Neural Network; 드릴링 버; 음향방출 모니터링; 웨이브렛 변환; 인공지능신경망; Drilling Burr; AE Monitoring; Wavelet Transform; Artificial Neural Network
- Citation
- 한국정밀공학회지, v.22, no.4, pp 37 - 43
- Pages
- 7
- Indexed
- KCI
- Journal Title
- 한국정밀공학회지
- Volume
- 22
- Number
- 4
- Start Page
- 37
- End Page
- 43
- ISSN
- 1225-9071
2287-8769
- 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.
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Related Researcher
- Lee, Seoung Hwan
- ERICA 공학대학 (DEPARTMENT OF MECHANICAL ENGINEERING)