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In-Process Monitoring of Drilling Burr Formation using Acoustic Emission and a Wavelet-based Artificial Neural Network

Title
In-Process Monitoring of Drilling Burr Formation using Acoustic Emission and a Wavelet-based Artificial Neural Network
Author
이성환
Keywords
drilling burr; in process sensor monitoring; acoustic emission; wavelet transform; artificial neural network; COMPOSITES; PARTS
Issue Date
2008-09
Publisher
TAYLOR & FRANCIS LTD
Citation
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, v. 46, No. 17, Page. 4871-4888
Abstract
Prediction/detection of exit burrs is critical in manufacturing automation. In this research, an intelligent burr sensing/monitoring scheme is proposed. Acoustic emission (AE) was selected to detect burr formation during drilling. For effective extraction of information contained in the collected AE signals, wavelet transform (WT) was adopted and the selected features through WT were fed into a back-propagation artificial neural net (ANN) as input vectors. To validate the in-process AE monitoring system, both WT-based ANN and cutting condition-based ANN outputs (cutting speed, feed, drill diameter, etc.) were compared with experimental data. The results show that the proposed scheme is not only efficient with fewer inputs, but more reliable in predicting drilling burr types over cutting condition-based ANN.
URI
https://www.tandfonline.com/doi/abs/10.1080/00207540601152040https://repository.hanyang.ac.kr/handle/20.500.11754/80677
ISSN
0020-7543
DOI
10.1080/00207540601152040
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MECHANICAL ENGINEERING(기계공학과) > Articles
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