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

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 experi-ments,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 R^(2)=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.