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.


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    Title :

    Pattern recognition for nondestructive evaluation


    Additional title:

    Mustererkennung für die zerstörungsfreie Prüfung


    Contributors:
    Amirfathi, M.M. (author) / Morris, S. (author) / O'Rorke, P. (author) / Bond, W.E. (author) / St. Clair, D.C. (author)


    Publication date :

    1991


    Size :

    , 11 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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