摘要
The high-temperature performance of Co-based superalloys is primarily dictated by the coarsening kinetics and volume fraction of theγ′phase.To simultaneously optimize these two interrelated microstructural parameters,we propose a dual-objective design framework that integrates explainable machine learning(XML),multi-fidelity data augmentation,and SHapley Additive exPlanations(SHAP)-based interpretability.Forγ′phase coarsening rate constant(Kr),a small experimental dataset was expanded using medium-fidelity simulations and further balanced with low-fidelity synthetic samples.Forγ′volume fraction(V_(γ′)),synthetic oversampling was applied to a larger dataset to mitigate distribution imbalance.ML models trained on these augmented datasets achieved high predictive accuracy,with SHAP analysis providing interpretable insights.Guided by these insights,several new compositions were proposed and validated.The optimal composition,Co-30Ni-10Al-3Ti-4Ta-5Cr-2Mo-1V(at.%),achieves a low Kr of 0.756±0.06 nm^(2)·s^(-1)and a high V_(γ′)of exceeding 70%at 1000°C,while also fulfills multiple other critical design criteria,offering a promising route for next-generation Co-based superalloys.
基金
supports from the National Natural Science Foundation of China (Grant Nos. 52471004, 52171107, 52201203, 52401015)
the Industry-University-Research Cooperation Project of Hebei Based Universities and Shijiazhuang City (Grant No. 241791237A) are gratefully acknowledged. We also greatly appreciate Dr. Bing Zhang from Yanshan University and Dr. Chun-He Chu from Henan University of Science and Technology for his insightful proposition and valuable guidance.
