There are various nondestructive testing (NDT) inspection methods, such as vision, eddy current, and ultrasonic used for crack or corrosion detection of the skin or the structures of aircraft around rivets and fasteners. These methods require a skilled technician to identify the existence of the cracks. Despite the training that a technician goes through, human error is identified to be one of the major contributing factors to problems in the determination of the safety of aircraft. There has been some effort to develop expert systems that can be used by technicians. However, there is currently no expert system developed that can learn and improve its capability as it encounters new situations. The neural network and its possibility for aiding the technician in detecting defects is reviewed.


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    Titel :

    Neural Networks for Detecting Defects in Aircraft Structures


    Beteiligte:
    B. Bahr (Autor:in) / T. M. Nabeel (Autor:in)

    Erscheinungsdatum :

    1990


    Format / Umfang :

    14 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




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