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As-cast irons and aluminum alloys are used in various industrial fields and their phase and microstructure properties are strongly affected by the undercooling degree. However, existing studies regarding the undercooling degree are mostly limited to qualitative analyses. In this paper, a quantitative analysis of the undercooling degree is performed by collecting experimental data and employing machine learning. Nine machining learning models including Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), Ridge Regression (RIDGE) and Gradient Boosting Regressor (GBDT) methods are used to predict the undercooling degree via six features, which include the cooling rate (CR), mean atomic covalence radius (MAR) and mismatch (MM). Four additional effective models of machine learning algorithms are then selected for a further analysis and cross-validation. Finally, the optimal machine learning model is selected for the dataset and the best combination of features is found by comparing the prediction accuracy of all possible feature combinations. It is found that RF model with CR and MAR features has the optimal performance results for predicting the undercooling degree.

The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning