Machine Learning Design of Aluminum-Lithium Alloys with High Strength

Abstract

Due to the large unexplored compositional space, long development cycle, and high cost of traditional trial-and-error experiments, designing high strength aluminum-lithium alloys is a great challenge. This work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development cycle. The calculation results indicate that radial basis function (RBF) neural networks exhibit better predictive ability than back propagation (BP) neural networks. The RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96, root mean square errors of 30.68 and 25.30, and mean absolute errors of 28.15 and 19.08, respectively. In the validation experiment, the comparison between experimental data and predicted data demonstrated the robustness of the two neural network models. The tensile and yield strengths of Al-2Li-1Cu-3Mg-0.2Zr (wt.%) alloy are 17.8 and 3.5 MPa higher than those of the Al-1Li-4.5Cu-0.2Zr (wt.%) alloy, which has the best overall performance, respectively. It demonstrates the reliability of the neural network model in designing high strength aluminum-lithium alloys, which provides a way to improve research and development efficiency.