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We performed a machine learning-aided analysis of the rolling and recrystallization textures in pure iron with different cold reduction ratios and cold-rolling directions. Five types of specimens with different cold reduction ratios and cold-rolling directions were prepared. The effect of two-way cold-rolling on the rolling texture was small at cold reduction ratios different from 60%. The cold reduction ratio in each stage hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. In the case of long-term annealing, although abnormal grain growth occurred, the crystal orientation of the grains varied. Moreover, the direction of cold-rolling in each stage also hardly affected the texture evolution during cold-rolling and subsequent short-term annealing. During long-term annealing, sheets with the same cold-rolling direction in the as-received state and in the first stage showed the texture evolution of conventional one-way cold-rolled pure iron. Additionally, we conducted a machine learning-aided analysis of rolling and recrystallization textures. Using cold-rolling and annealing conditions as the input data and the degree of Goss orientation development as the output data, we constructed high-accuracy regression models using artificial neural networks and XGBoost. We also revealed that the annealing temperature is the dominant factor in the nucleation of Goss grains.

An image regression analysis using a convolutional neural network (CNN) was conducted to correlate microstructural images with the mechanical properties of tempered martensitic steel. The proposed model could link the microstructure with a property without any manual feature extraction. To demonstrate the mechanism of the CNN−based image regression algorithm, its convoluted output was dimensionally reduced using uniform manifold approximation and projection (UMAP) and then plotted.