Machine Learning Assisted Design of Isothermal Decomposition Parameters of U-Mo Alloy

A machine learning method was applied to the rapid design of isothermal decomposition parameters of U-Mo alloys. With the hardness of the alloy as a design index, a machine learning support vector machine (SVM) model between the alloy hardness and the above parameters was established based on a small amount of data. Based on the prediction of hardness, the differences in optimization efficiency between the two types of experimental design algorithms based on predicted values and based on expected improvement were compared. The results show that the experimental design algorithm based on the expected improvement can significantly improve the hardness through a small number of iterative experiments, while the design algorithm based on the predicted value does not significantly improve the hardness. Using the above-mentioned machine learning aided design method, the optimal parameter combination for isothermal decomposition of the alloy was successfully determined through four experiments. When the aging temperature is 565 degrees C, the aging time is more than 20 h, the homogenization temperature is 900 similar to 950 degrees C, and the Mo content is 6wt%, the hardness of the alloy processed is the highest, and the powder rate is the highest. This study makes a preliminary attempt to use machine learning methods to quickly optimize U-based alloy process parameters. Such data-based methods can effectively improve the efficiency of material development.