| タイトル | Neural-Net Processed Characteristic Patterns for Measurement of Structural Integrity of Pressure Cycled Components |
| 本文（外部サイト） | http://hdl.handle.net/2060/20010050735 |
| 著者（英） | Decker, A. J. |
| 著者所属（英） | NASA Glenn Research Center |
| 発行日 | 2001-03-01 |
| 言語 | eng |
| 内容記述 | A neural-net inspection process has been combined with a bootstrap training procedure and electronic holography to detect changes or damage in a pressure-cycled International Space Station cold plate to be used for cooling instrumentation. The cold plate was excited to vibrate in a normal mode at low amplitude, and the neural net was trained by example to flag small changes in the mode shape. The NDE (nondestructive-evaluation) technique is straightforward but in its infancy; its applications are ad-hoc and uncalibrated. Nevertheless previous research has shown that the neural net can detect displacement changes to better than 1/100 the maximum displacement amplitude. Development efforts that support the NDE technique are mentioned briefly, followed by descriptions of electronic holography and neural-net processing. The bootstrap training procedure and its application to detection of damage in a pressure-cycled cold plate are discussed. Suggestions for calibrating and quantifying the NDE procedure are presented. |
| NASA分類 | Quality Assurance and Reliability |
| レポートNO | NASA/TM-2001-210812
E-12727
NAS 1.15:210812 |
| 権利 | No Copyright |
