Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning

Abstract

Exploring the “composition-microstructure-property” relationship is a long-standing theme in materials science. However, complex interactions make this area of research challenging. Based on the image processing and machine learning techniques, this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength (UTS) of Al-Si alloys. Firstly, the composition and image information are collected from the literature and supplementary experiments, followed by the image segmentation and quantitative analysis of eutectic Si images. Subsequently, the quantitative analysis results are combined with other features for three-step feature screening, and 12 key features are obtained. Finally, four machine-learning models (i.e., decision tree, random forest, adaptive boosting, and extreme gradient boosting [XGBoost]) are used to predict the UTS of Al-Si alloys. The results show that the quantitative analysis method proposed in this paper is superior to Image-Pro Plus (IPP) software in some aspects. The XGBoost model has the best prediction performance with R² = 0.94. Furthermore, five mixed features and their critical values that significantly affect UTS are identified. Our study provides enlightenment for the prediction of UTS of Al-Si alloys from composition and microstructure, and would be applicable to other alloys.