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Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods. [ABSTRACT FROM AUTHOR]

This research aims to provide a solution to the scarcity and fragmentation of industrial data on die casting aluminum alloys. Quantifying the coupling between die casting process-composition-properties of aluminum alloys through small datasets, is a critical step in predicting part properties and optimizing process selection. To visualize the connections and discuss the effect of the interaction between different parameters on the property, data is fed into a self-organizing mapping model. Whereafter, an innovative data extension method is proposed to predict both yield and tensile strengths with more than 96% accuracy using a small data set. Moreover, two novel methods of multi-parameter combined range selection, the multi-objective optimization based on the agent model and the superimposition of the contour map, are guided by being informed in which region the mechanical properties fall. Finally, the feasibility of application range selection method is verified experimentally. Mapping based on data-driven process-composition-properties relationships and the free combination of application ranges are reliable theoretical solutions which are valuable for practical applications. [ABSTRACT FROM AUTHOR]