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Abstract

The design of complex multi-component superalloys has always been challenging due to the interaction of multiple elements and stringent requirements for various properties. In this study, an integrated approach to designing the high-component (>7) ??-strengthened Co-based superalloys with well-balanced properties is developed by combining the diffusion-multiples and machine-learning models. A ?cross-component? prediction is achieved by the machine-learning models, where two types of novenary superalloys are screened out for aero-engine and industrial gas turbine blades, respectively, based on the experimental database mainly consisting of 6-7 elements. The method is verified to be effective or slightly more favorable than the Calculation of Phase Diagram (CALPHAD) in predicting the ?? solvus temperature (T??) and phase constituent of the high-component alloys when reasonable data of low-component alloys is just provided. Furthermore, the oxidation resistance and hardness of polycrystal superalloys as well as the compressive strength of single crystal superalloys are tested. Finally, some factors affecting the accuracy of ?cross-component? prediction are discussed. Expanding the compositional range and supplementing the critical interaction data of multiple elements in the database are beneficial for improving the accuracy of the ?cross-component? prediction. This article is protected by copyright. All rights reserved.