Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning

In this study, a small dataset of 370 datapoints of Mg alloys are selected for machine learning (ML), in which each datapoint includes five rare‐earth‐free alloying elements (Ca, Zn, Al, Mn and Sn), three extrusion parameters (extrusion speed, temperature and ratio), and three mechanical properties (yield strength [YS], ultimate tensile strength [UTS] and elongation [EL]). The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. The SVR model combined with multi‐objective genetic algorithm are successfully used to optimize mechanical properties of four extruded alloys from Mg‐Ca, Mg‐Ca‐Zn, Mg‐Ca‐Mn‐Sn, and Mg‐Ca‐Al‐Zn‐Mn series alloys, respectively, and the corresponding experimental results are in good agreement with the designed ones. Furthermore, new composition schemes are proposed from a wider range of elements and processing features to match the objectives of high‐strength, strength–ductility balanced, and high‐ductility Mg alloys, and the four‐, five‐ and six‐element alloying schemes are provided for the candidates of new‐generation wrought Mg alloys. In this work, we use machine learning algorithms combined with multi‐objective genetic algorithm to achieve rapidly reverse optimization design of high‐performance rare‐earth‐free wrought magnesium alloys, with high reliability.