|
| << previous | next >> |
Journal description
All volumes of the corresponding journal
All issues of the corresponding volume
All articles of the corresponding issues
Details for article 102 of 183 found articles
| Title: |
In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network |
| Author: |
Lee, S. H. Lee, D. |
| Appeared in: |
International journal of production research |
| Paging: | Volume 46 (2008) nr. 17 pages 4871-4888 |
| Year: |
2008-09-01 |
| Contents: |
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. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 102 of 183 found articles
| << previous | next >> |