Insights on the mechanical behavior in materials highly depend upon sufficiently characterizing microstructure details at the relevant length scales. In this study, the side-branching dynamics of dendritic structures formation in pure substances is studied upon the phase-field simulations of crystallization with applied direct currents. The effect of heat diffusion (including thermoelectric effect and undercooling) on the dendritic development is investigated, and the characteristics of the primary arms and side-branches are identified by implementing the image recognition technique. Results indicate that increasing the latent heat release would firstly enhance the side-branching and then cause the side-branches re-melting with large heat extraction form the solid. The side-branching could be tailored by rationally controlling the applied electric filed as well the heat treatment, which could be a potential way to improve the mechanical properties in metallic materials via optimizing the microstructure.

Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolutional neural networks (CNNs) with extreme learning machine (ELM) is proposed. First, the image enhancement and data enhancement are performed on a sample set, and GAN is used to expand the sample data volume. Second, CNN is used to extract the feature vectors of the processed images and label eigenvectors with one-hot encoding. Third, the feature matrix is input to the map in the ELM network to minimize the error and obtain the optimal weight by training. Finally, the image to be detected is input to the trained model, and the image with the greatest probability is selected as the final category. Through model comparison experiments, the results show that the hemolysis image detection method based on the GAN-CNN-ELM model is better than GAN-CNN, GAN-ELM, GAN-ELM-L1, GAN-SVM, GAN-CNN-SVM, and CNN-ELM in accuracy and speed, and the accuracy rate is 98.91%.

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