| contributor author | S. T. S. Bukkapatnam | |
| contributor author | S. R. T. Kumara | |
| contributor author | A. Lakhtakia | |
| date accessioned | 2017-05-09T00:00:09Z | |
| date available | 2017-05-09T00:00:09Z | |
| date copyright | November, 1999 | |
| date issued | 1999 | |
| identifier issn | 1087-1357 | |
| identifier other | JMSEFK-27351#568_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/122424 | |
| description abstract | Acoustic emission (AE) signals are emerging as promising means for monitoring machining processes, but understanding their generation is presently a topic of active research; hence techniques to analyze them are not completely developed. In this paper, we present a novel methodology based on chaos theory, wavelets and neural networks, for analyzing AE signals. Our methodology involves a thorough signal characterization, followed by signal representation using wavelet packets, and state estimation using multilayer neural networks. Our methodology has yielded a compact signal representation, facilitating the extraction of a tight set of features for flank wear estimation. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Analysis of Acoustic Emission Signals in Machining | |
| type | Journal Paper | |
| journal volume | 121 | |
| journal issue | 4 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.2833058 | |
| journal fristpage | 568 | |
| journal lastpage | 576 | |
| identifier eissn | 1528-8935 | |
| keywords | Machining | |
| keywords | Acoustic emissions | |
| keywords | Signals | |
| keywords | Wavelets | |
| keywords | Artificial neural networks | |
| keywords | Chaos theory | |
| keywords | Wear AND State estimation | |
| tree | Journal of Manufacturing Science and Engineering:;1999:;volume( 121 ):;issue: 004 | |
| contenttype | Fulltext | |