SYMPOSIA PAPER Published: 01 January 1993

Artificial Intelligence for Twin Identification


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.

Author Information

Friel, JJ
Princeton Gamma-Tech, Princeton, NJ
Prestridge, EB
Princeton Gamma-Tech, Princeton, NJ
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Developed by Committee: E04
Pages: 243–253
DOI: 10.1520/STP25106S
ISBN-EB: 978-0-8031-5226-7
ISBN-13: 978-0-8031-1484-5