Machine Learning Guided Insights into the Effects of Nb/Ta and Ti/Ta Ratios on Microstructure and Creep Rupture Life in Nickel-Based Single-Crystal Superalloys

This study provides a comprehensive analysis of the effects of modulating the Nb/Ta and Ti/Ta ratios on the microstructure and creep rupture life of nickel-based single-crystal superalloys by integrating a machine learning model with experimental validation. The findings indicate that optimizing the Ti/Ta ratio significantly enhances the creep life of the alloy, while increasing the Nb/Ta ratio negatively impacts creep performance. The predictive accuracy of the model is substantiated by a comparative analysis of the machine learning predictions and the experimental results, clarifying the mechanisms by which alloying elements affect creep behavior. These findings advance the understanding of performance regulation in nickel-based single-crystal superalloys and establish a robust theoretical and experimental foundation for future research and applications in high-temperature materials.