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Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning
Acta Materialia ( IF 8.3 ) Pub Date : 2024-12-16 , DOI: 10.1016/j.actamat.2024.120656
Yancheng Li, Jingyu Pang, Zhen Li, Qing Wang, Zhenhua Wang, Jinlin Li, Hongwei Zhang, Zengbao Jiao, Chuang Dong, Peter K. Liaw
Acta Materialia ( IF 8.3 ) Pub Date : 2024-12-16 , DOI: 10.1016/j.actamat.2024.120656
Yancheng Li, Jingyu Pang, Zhen Li, Qing Wang, Zhenhua Wang, Jinlin Li, Hongwei Zhang, Zengbao Jiao, Chuang Dong, Peter K. Liaw
Design of novel superalloys with low density, high strength, and great microstructural stability is a big challenge. This work used an automated machine learning (ML) model to explore high-entropy superalloys (HESAs) with coherent γ' nanoprecipitates in the FCC-γ matrix. The database samples were firstly preprocessed via the domain-knowledge before ML. Both autogluon and genetic algorithm methods were applied to establish the relationship between the alloy composition and yield strength and to deal with the optimization problem in ML. Thus, the ML model cannot only predict the strength with a high accuracy (R 2 > 95 %), but also design compositions efficiently with desired property in multi-component systems. Novel HESAs with targeted strengths and densities were predicted by ML and then validated by a series of experiments. It is found that the experimental results are well consistent with the predicted properties, as evidenced by the fact that the designed Ni-5.82Fe-15.34Co-2.53Al-2.99Ti-2.90Nb-15.97Cr-2.50Mo (wt.%) HESA has a yield strength of 1346 MPa at room temperature and 1061 MPa at 1023 K and a density of 7.98 g/cm3 . Moreover, it exhibits superior creep resistance with a rupture lifetime of 149 h under 480 MPa at 1023 K, outperforming most conventional wrought superalloys. Additionally, the coarsening rate of γ' nanoprecipitates in these alloys is extremely slow at 1023 K, showing a prominent microstructural stability. The strengthening and deformation mechanisms were further discussed. This framework provides a new pathway to realize the property-oriented composition design for high-performance complex alloys via ML.
中文翻译:
在自动化机器学习的指导下,开发具有高强度和卓越抗蠕变性的新型低密度高熵高温合金
设计具有低密度、高强度和出色微观结构稳定性的新型高温合金是一项巨大的挑战。这项工作使用自动化机器学习 (ML) 模型来探索 FCC-γ 基体中具有相干 γ' 纳米沉淀物的高熵高温合金 (HESA)。首先在 ML 之前通过 domain-knowledge 对数据库样本进行预处理。应用自胶和遗传算法方法建立合金成分与屈服强度之间的关系,并处理 ML 中的优化问题。因此,ML 模型不仅可以高精度地预测强度 (R2 > 95 %),还可以在多组分系统中有效地设计具有所需特性的成分。通过 ML 预测具有目标强度和密度的新型 HESA,然后通过一系列实验进行验证。结果表明,实验结果与预测的性能非常吻合,所设计的Ni-5.82Fe-15.34Co-2.53Al-2.99Ti-2.90Nb-15.97Cr-2.50Mo (wt.%) HESA在室温下的屈服强度为1346 MPa,在1023 K下为1061 MPa,密度为7.98 g/cm3。此外,它还表现出优异的抗蠕变性,在 1023 K 下,在 480 MPa 下断裂寿命为 149 小时,优于大多数传统的锻造高温合金。此外,这些合金中 γ' 纳米沉淀物的粗化速率在 1023 K 时非常缓慢,显示出突出的微观结构稳定性。进一步讨论了加固和变形机制。该框架为通过 ML 实现高性能复杂合金的面向性能的成分设计提供了一条新途径。
更新日期:2024-12-16
中文翻译:
在自动化机器学习的指导下,开发具有高强度和卓越抗蠕变性的新型低密度高熵高温合金
设计具有低密度、高强度和出色微观结构稳定性的新型高温合金是一项巨大的挑战。这项工作使用自动化机器学习 (ML) 模型来探索 FCC-γ 基体中具有相干 γ' 纳米沉淀物的高熵高温合金 (HESA)。首先在 ML 之前通过 domain-knowledge 对数据库样本进行预处理。应用自胶和遗传算法方法建立合金成分与屈服强度之间的关系,并处理 ML 中的优化问题。因此,ML 模型不仅可以高精度地预测强度 (R2 > 95 %),还可以在多组分系统中有效地设计具有所需特性的成分。通过 ML 预测具有目标强度和密度的新型 HESA,然后通过一系列实验进行验证。结果表明,实验结果与预测的性能非常吻合,所设计的Ni-5.82Fe-15.34Co-2.53Al-2.99Ti-2.90Nb-15.97Cr-2.50Mo (wt.%) HESA在室温下的屈服强度为1346 MPa,在1023 K下为1061 MPa,密度为7.98 g/cm3。此外,它还表现出优异的抗蠕变性,在 1023 K 下,在 480 MPa 下断裂寿命为 149 小时,优于大多数传统的锻造高温合金。此外,这些合金中 γ' 纳米沉淀物的粗化速率在 1023 K 时非常缓慢,显示出突出的微观结构稳定性。进一步讨论了加固和变形机制。该框架为通过 ML 实现高性能复杂合金的面向性能的成分设计提供了一条新途径。