An Explainable Deep Learning Model Based on Multi-scale Microstructure Information for Establishing Composition–Microstructure–Property Relationship of Aluminum Alloys

Establishing a quantitative composition–microstructure–property relationship is crucial in material design and process optimization. With the advent of big data technology, deep learning models, as a machine learning method that can automatically extract information from images, have been widely used in microstructure image identification and property prediction. However, most deep learning models only use single-scale images for property prediction, ignoring the multi-scale microstructure information of materials. In this study, an explainable deep learning model was developed based on a multi-modal and multi-scale dataset for predicting the tensile properties of aluminum alloys. Three different kinds of aluminum alloys, each incorporating various trace elements, were prepared to evaluate the adaptation of the model. The predicted results demonstrate that the integration of multi-scale microstructure information significantly improves the model’s prediction ability. Furthermore, the intrinsic mechanisms of the deep learning model were elucidated through the application of a visualization technique, greatly improving the explicability of the model. In addition, the effect of data redundancy on model performance was analyzed. The proposed deep learning model breaks the traditional deep learning strategy with the single-scale image as input and effectively establishes the composition–microstructure–property relationship.