Data Augmentation of Micrographs and Prediction of Impact Toughness for Cast Austenitic Steel by Machine Learning - Gang Xu; Xiaotong Zhang; Jinwu Xu (Metals, 2023)
The Material Genome Initiative has been driven by high-throughput calculations, experiments, characterizations, and machine learning, which has accelerated the efficiency of the discovery of novel materials. However, the precise quantification of the material microstructure features and the construction of microstructure–property models are still challenging in optimizing the performance of materials. In this study, we proposed a new model based on machine learning to enhance the power of the data augmentation of the micrographs and construct a microstructure–property linkage for cast austenitic steels. The developed model consists of two modules: the data augmentation module and microstructure–property linkage module. The data augmentation module used a multi-layer convolution neural network architecture with diverse size filter to extract the microstructure features from irregular micrographs and generate new augmented microstructure images. The microstructure–property linkage module used a modified VGG model to establish the relationship between the microstructure and material property. Taking cast austenitic stainless steels after solution treating in different temperatures as an example, the results showed that the prediction accuracy of the developed machine learning model had been improved. The coefficient R2 of the model was 0.965, and the medians were only ±2 J different with the measured impact toughness.