Prediction of Casting Mechanical Parameters Based on Direct Microstructure Image Analysis Using Deep Neural Network and Graphite Forms Classification.

This paper presents methods of prediction of casting mechanical parameters based on direct microstructure image analysis using deep neural networks and graphite forms recognition and classification. These methods are applied to predict tensile strength of iron-carbon alloys based on microstructure photos taken with the light-optical microscopy technique, but are general and can be adapted to other applications. In the first approach EfficientNet architecture is used. In the second approach graphite structures are separated, recognized using VGG19 network, counted and classified using support vector machines, decision trees, random forest, logistic regression, multi-layer perceptron and AdaBoost. Accuracy of the first approach is better. However, the second allows to create a classifier, for which the accuracy is also high, and can be easily analyzed by human expert.