A physics-based machine-learning approach for modeling the temperature-dependent yield strengths of medium- or high-entropy alloys AITranslate

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DOI：https://doi.org/10.1016/j.apmt.2023.101747

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Machine learning is becoming a powerful tool to accurately predict temperature-dependent yield strengths (YS) of structural materials, particularly for multi-principal-element systems. However, successful machine-learning predictions depend on the use of reasonable machine-learning models. Here, we present a comprehensive and up-to-date overview of a bilinear log model for predicting temperature-dependent YS of medium-entropy or high-entropy alloys (MEAs or HEAs). In this model, a break temperature, Tbreak, is introduced, which can guide the design of MEAs or HEAs with attractive high-temperature properties. Unlike assuming black-box structures, our model is based on the underlying physics, incorporated in the form of a priori information. A technique for the unconstrained global optimization is employed to enable the concurrent optimization of model parameters over low- and high-temperature regimes, showing that the break temperature is consistent across the YS and ultimate strength for a variety of HEA compositions. A high-level comparison between YS of MEAs/HEAs and those of Nickel-based superalloys reveals superior strength properties of selected refractory HEAs. For reliable operations, the temperature of a structural component, such as a turbine blade, made from refractory alloys, may need to stay below Tbreak. Once above Tbreak, phase transformations may start taking place, and the alloy may begin losing structural integrity. Graphical abstract Download : Download high-res image (396KB) Download : Download full-size image