Machine Learning-Assisted Hardness Prediction of Dispersion-Strengthened Tungsten Alloy

Hardness, as a typical mechanical property of dispersion-strengthened tungsten alloy, is influenced by various coupled factors. This paper aims to identify the key factors affecting the hardness of the dispersion-strengthened tungsten alloys with different carbides and oxides as the reinforcement phase in order to enable the high-throughput prediction of hardness. A dataset was established with alloy hardness as the target variable, and the features included the content of reinforcement phase, the Vickers hardness of reinforcement phase, the melting point of the reinforcement phase, the valence electron number of the reinforcement phase, the sintering temperature, the sintering time, pressure, relative density, and grain size. Seven regression models were trained, and we selected random forest, support vector regression, and XGBoost regression machine learning models with better performance to construct a hardness prediction model of the dispersion-strengthened tungsten alloy. SHAP analysis, based on random forests, shows that the content of reinforcement phase, grain size, and relative density have the most significant impact on the hardness. A random forest model is the most suitable machine learning method for predicting the hardness of dispersion-strengthened tungsten alloys in this work. The R2 values of the training and test sets are 0.93 and 0.80, and the MAE values of the training and test sets are 22.72 and 38.37. The influence of the most important features on the hardness was also discussed based on the random forest model. This study provides a data-driven approach for the accurate and efficient prediction of the hardness of dispersion-strengthened tungsten alloys, offering an important reference for the design and development of high-performance tungsten alloy materials.