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.
S. Morris et al., "Pattern Recognition for Nondestructive Evaluation," Proceedings of the IEEE Aerospace Applications Conference, 1991, Institute of Electrical and Electronics Engineers (IEEE), Jan 1991.
The definitive version is available at https://doi.org/10.1109/AERO.1991.154534
IEEE Aerospace Applications Conference, 1991
Keywords and Phrases
A-Scan; Douglas Aircraft; NDT; Aircraft Components; Artificial Intelligence; Automatic Testing; Classification Algorithm; Composite Materials; Computerised Pattern Recognition; Data Preprocessing; Feature Extraction; Knowledge Based Systems; Knowledge-Based Systems; Learning Systems; Mechanical Engineering Computing; Nondestructive Evaluation; Nondestructive Testing; Nondestructive Tests; Pattern Recognition; Scale-Space Filtering; Symbolic Machine Learning Algorithm; Test Waveforms
Article - Conference proceedings
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