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The micro-structure of most of the engineering alloys contains someinclusions and precipitates, which may affect their properties, therefore it iscrucial to characterize them. In this work we focus on the development of astate-of-the-art artificial intelligence model for Anomaly Detection namedMLography to automatically quantify the degree of anomaly of impurities inalloys. For this purpose, we introduce several anomaly detection measures:Spatial, Shape and Area anomaly, that successfully detect the most anomalousobjects based on their objective, given that the impurities were alreadylabeled. The first two measures quantify the degree of anomaly of each objectby how each object is distant and big compared to its neighborhood, and by theabnormally of its own shape respectively. The last measure, combines the formertwo and highlights the most anomalous regions among all input images, for later(physical) examination. The performance of the model is presented and analyzedbased on few representative cases. We stress that although the models presentedhere were developed for metallography analysis, most of them can be generalizedto a wider set of problems in which anomaly detection of geometrical objects isdesired. All models as well as the data-set that was created for this work, arepublicly available at: https://github.com/matanr/MLography.

MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach