STP1165

    Artificial Intelligence for Twin Identification

    Published: Jan 1993


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    Abstract

    The presence of twin bands in some alloys makes grain sizing by automatic image analysis difficult. Twin bands appear similar to grain boundaries; therefore, the measured grain size is too small. Although an experienced metallographer can ignore twins while measuring grain size manually, it is often desirable to use automatic image analysis to save time and increase reproducibility. Because twin bands are so similar to grain boundaries, traditional image processing techniques cannot distinguish them. However, the techniques of artificial intelligence (AI) can be used to impart knowledge to a computer that enables it to distinguish among microstructural features. This method was tested on standard grain size charts and on real specimens. The results showed that the use of artificial intelligence for twin identification and removal works sufficiently well to lead to an accurate measure of grain size.

    Keywords:

    twin bands, artificial intelligence, image processing, image analysis, grain boundary, microstructure, metallography, metallurgical specimens, metallographic techniques


    Author Information:

    Friel, JJ
    Technical director and product manager, Princeton Gamma-Tech, Princeton, NJ

    Prestridge, EB
    Technical director and product manager, Princeton Gamma-Tech, Princeton, NJ


    Paper ID: STP25106S

    Committee/Subcommittee: E04.08

    DOI: 10.1520/STP25106S


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