Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Acoustic Emission Monitoring of Drilling Burr Formation Using Wavelet Transform and an Artificial Neural Network

웨이브렛 변환과 신경망 알고리즘을 이용한 드릴링 버 생성 음향방출 모니터링

  • 이성환 (한양대학교 기계공학과) ;
  • 김태은 (한양대학교 대학원 정밀기계공학과) ;
  • 라광렬 (한양대학교 대학원 정밀기계공학과)
  • Published : 2005.04.01
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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.

Keywords

References

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