The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3.
Pattern recognition for nondestructive evaluation
Mustererkennung für die zerstörungsfreie Prüfung
1991
, 11 Quellen
Aufsatz (Konferenz)
Englisch
An Adaptive Algorithm for Pattern Recognition in a Nondestructive Control of Ferromagnets
British Library Online Contents | 1999
|NTRS | 1977
|AIAA | 2014
|Quantitative nondestructive evaluation
Tema Archiv | 1979
|