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.
翻译:对正确评估材料特性而言,冶金学至关重要,主要涉及调查谷物的空间分布以及包容或沉淀物的发生和特征;这项工作为异常检测提供了一个整体人工智能模型,自动量化合金中杂质异常的程度;我们建议采用以下检验程序:(1) 在包含物上进行深语义分解(基于合金和相应的包容标记的适当合金数据库),生成被保存在分离数据库中的隐含面罩。 (2) 进行深图像涂抹以填补被删除的包含物部分,从而产生含有谷物背景的“清洁”美all图象。(3) Grains的边界使用深语义分解(基于其他合金的合金数据数据库)进行标示,为进一步检查谷物的分布进行深度语义分解;(4) 在包含物面面面罩上进行深度异常检测和图案识别,以确定包容物的空间、形状和地区异常度检测。最后,系统建议专家了解含有谷物背景背景的“清洁”图象图像。 (3) Grains的边界是使用深语义分法分解(基于另一个合金数据库),可以评估所有典型/图解的模型。