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先进航空发动机追求的更高推重比和更长服役寿命对热端部件材料提出了前所未有的挑战。作为涡轮叶片的关键材料，镍基单晶高温合金的超高温蠕变抗性与持久寿命直接关乎航空发动机的性能与可靠性。然而，随着高代次航空发动机涡轮进口温度的不断提升，即使应用高效气冷与涂层技术，单晶叶片的服役温度也已逼近镍基合金熔点的80%。在这种极端条件下，镍基单晶合金表现出有别于中高温的蠕变行为机制、更显著的成分敏感性和更复杂的合金化元素交互作用，这使得基于传统合金设计原则、采用实验试错与本构方程相结合的方式开展高承温单晶合金成分精准设计难度大、成本高且周期长，亟需探索单晶高温合金快速研发的新途径与新方法。
来自北京航空航天大学材料科学与工程学院的赵文月副研究员团队，近年来致力于探索AI驱动的高承温单晶高温合金智能设计策略，力求突破材料数据稀少、内在物理机制复杂所带来的机器学习模型性能瓶颈，从而助力面向航发涡轮叶片的新型单晶高温合金快速研发。他们在先前开发的一种具有先验物理约束且可解释的深度神经网络基础上[1]，应用迁移学习技术在仅有极少单晶高温合金超高温蠕变数据的情况下，有效将微调训练后的深度学习模型“外推”泛化至超高温下具有更高蠕变抗性的合金成分空间，实现了对合金超高温持久寿命的准确可靠预测，进而利用该模型成功设计了一种具有优异超高温蠕变抗性的新型单晶高温合金，并揭示了其超高温蠕变机制。
研究发现，基于中高温镍基单晶合金蠕变数据的预训练深度学习模型所捕获的局域成分敏感性规律，在一定程度上亦适用于超高温蠕变条件，这一关键先验信息有效降低了模型对数据量的依赖性，促使模型可以通过有限数据的微调训练即可精准捕捉单晶高温合金成分与其超高温蠕变性能间的内在关系。在可靠预测单晶高温合金超高温持久寿命基础上，研究进一步利用模型蕴含的复杂元素交互作用信息，探索出调控Mo/Ta成分比的合金成分设计新路径，成功跨越了合金成分设计空间的局部最优“陷阱”，设计了一种具有更高超高温蠕变性能的单晶合金。经实验验证，该新型单晶高温合金在1200°C/80MPa条件下的持久寿命达到了170小时，超过已报道最先进水平约30%。进一步结合微观组织分析与第一性原理计算，研究深入揭示了该新型合金优异超高温蠕变抗性的来源，即：通过调控Mo/Ta成分比，新型单晶合金在超高温蠕变过程中形成了更长、更平直的γ'形筏组织以及更强的界面键合稳定了γ/γ'界面，该界面强化机制大幅提升了合金的超高温蠕变抗性，延长了其持久寿命。此外，研究所构建模型的卓越“外推”能力，也为降低后续主动学习对初始数据集规模的要求提供了新的可能，这对快速迭代开发具有更高承温能力(>1200℃)的新型单晶高温合金具有重要意义。上述研究为AI加速设计先进航发涡轮叶片用单晶高温合金提供了一种创新性思路方法和一个成功应用案例。
该文近期发表于
npj Computationa
l
Materials
10
: 149 (2024)
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Fig. 1 | Deep learning model construction strategy for improving extrapolation efficiency and alloy design flow.
Fig. 2 | Characteristics of traditional SX superalloys and IC SX superalloys in compositional distribution.
Transfer learning enables the rapid design of single crystal superalloys with superior creep resistances at ultrahigh temperature
Fan Yang, Wenyue Zhao & Yi Ru
Accelerating the design of Ni-based single crystal (SX) superalloys with superior creep resistance at ultrahigh temperatures is a desirable goal but extremely challenging task. In the present work, a deep transfer learning neural network with physical constraints for creep rupture life prediction at ultrahigh temperatures is constructed. Transfer learning enables deep learning model breaks through the generalization performance barrier in the extrapolation space of ultrahigh temperature creep properties in the case of a very small dataset, which is the key to achieving the above design goal. Transfer learning is demonstrated to be effective in utilizing the prior compositional sensitivities information contained in the pre-trained model, and motivates the fine-tuned model to capture the particular relationship between composition and creep rupture life at ultrahigh temperature. Aiming to find advanced SX superalloys applied at 1200 °C, the proposed transfer learning-based model guides us to design a superalloy with a verified creep rupture life of ~170 h at 80 MPa, which exceeds the state-of-art value by 30%. The improved γ/γ′ interface strengthening, which is effectively regulated by the Mo/Ta ratio to form γ′ rafting with longer, flatter interfaces and achieve stronger interfacial bonding, is revealed as the dominant mechanism behind combining experiments and first-principles calculations. Moreover, the excellent extrapolation ability of the proposed model is further confirmed to enhance the efficiency of active learning by reducing its dependence on the initial dataset size. This study provides a pioneering AI-driven approach for the rapid development of Ni-based SX superalloys applied in advanced aero-engine blades.
