Abstract
Designing a material with multiple desired properties is a great challenge, especially in a complex material system. Here, we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine learning. The microstructural stability, γ′ solvus temperature, γ′ volume fraction, density, processing window, freezing range, and oxidation resistance were simultaneously optimized. A series of novel Co-base superalloys were successfully selected and experimentally synthesized from >210,000 candidates. The best performer, Co-36Ni-12Al-2Ti-4Ta-1W-2Cr, possesses the highest γ′ solvus temperature of 1266.5 °C without the precipitation of any deleterious phases, a γ′ volume fraction of 74.5% after aging for 1000 h at 1000 °C, a density of 8.68 g cm−3 and good high-temperature oxidation resistance at 1000 °C due to the formation of a protective alumina layer. Our approach paves a new way to rapidly design multi-component materials with desired multi-performance functionality.
Highlights
As the key materials of the twenty-first century, aerospace alloys are of great economic value and developmental potential
Superalloys, the γ′ solvus temperature is of the greatest importance, as it determines the upper temperature capability limit
In order to further understand the elemental distribution to the γ and γ′ phases of the experimental alloys, elemental mapping was performed with high-angle annular dark field (HAADF) scanning transmission electron microscope (STEM) nanoprobe and accurate phase compositions were measured using atom probe npj Computational Materials (2020) 62
Summary
As the key materials of the twenty-first century, aerospace alloys are of great economic value and developmental potential. Great efforts have been put forth to balance different alloying elements such as Al, Ti, Fe, Cr, Nb, Mo, W, and Ta in order to promote precipitation of stable γ′-Ni3(Al, Ti), or a combination of γ′ and γ′′-Ni3Nb, optimizing different aspects of the material performance[4]. These alloys are reaching their temperature capability limits, and novel design ideas are required to further improve engine performance and efficiency. As these various properties often compete, improving one is often at the expense of the other, and further improvement and optimization is needed[8]
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