  Metallography is crucial for a proper assessment of material's properties. It
involves mainly the investigation of spatial distribution of grains and the
occurrence and characteristics of inclusions or precipitates. This work
presents an holistic artificial intelligence model for Anomaly Detection that
automatically quantifies the degree of anomaly of impurities in alloys. We
suggest the following examination process: (1) Deep semantic segmentation is
performed on the inclusions (based on a suitable metallographic database of
alloys and corresponding tags of inclusions), producing inclusions masks that
are saved into a separated database. (2) Deep image inpainting is performed to
fill the removed inclusions parts, resulting in 'clean' metallographic images,
which contain the background of grains. (3) Grains' boundaries are marked using
deep semantic segmentation (based on another metallographic database of
alloys), producing boundaries that are ready for further inspection on the
distribution of grains' size. (4) Deep anomaly detection and pattern
recognition is performed on the inclusions masks to determine spatial, shape
and area anomaly detection of the inclusions. Finally, the system recommends to
an expert on areas of interests for further examination. The performance of the
model is presented and analyzed based on few representative cases. Although the
models presented here were developed for metallography analysis, most of them
can be generalized to a wider set of problems in which anomaly detection of
geometrical objects is desired. All models as well as the data-sets that were
created for this work, are publicly available at
https://github.com/Scientific-Computing-Lab-NRCN/MLography.

Materials Science;Computer Vision and Pattern Recognition;Machine Learning;Data Analysis, Statistics and Probability