웨이브렛 변환을 이용한 밀링 버 생성 음향방출 모니터링

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.