Wide applications of powder metallurgy superalloys in mechanical engineering imply the great significance of studying their mechanical properties. Fatigue property of superalloys is complicated because of many influencing factors with variability. This means the difficulty to correctly predict a real-valued fatigue life due to the propagation of error. Specially, extreme dispersion of multiaxial fatigue life is an important challenge of fatigue life prediction for superalloys. In this paper, we focus on the difficulty and dispersion by developing machine learning (ML) models for predicting interval-valued fatigue life of FGH96 superalloy under multiaxial loadings. Some data extracted from fatigue failure process of FGH96 superalloy is used to develop ML methods such as back-propagation neural network (BP), support vector regression (SVR) and random forest (RF). Then considering the randomness in the data originating from multiaxial loading paths, measures of sample geometry and mechanical loading, testing system error and others, the low cycle fatigue life of FGH96 superalloy is predicted as an interval with a probability distribution. The obtained results are analyzed and compared with the fatigue experimental observations of FGH96 superalloy under six loading paths. It is found that the multiaxial fatigue failure behavior of FGH96 superalloy can be effectively described, and the developed ML models exhibit some advantages to tackle the difficulty and dispersion in predicting multiaxial fatigue life of alloys.

粉末冶金高温合金在机械工程中的广泛应用意味着研究其力学性能具有重要意义。高温合金的疲劳性能复杂，影响因素众多，具有可变性。这意味着由于误差传播而难以正确预测实值疲劳寿命。特别地，多轴疲劳寿命的极端离散是高温合金疲劳寿命预测的重要挑战。在本文中，我们通过开发用于预测 FGH96 高温合金在多轴载荷下的区间值疲劳寿命的机器学习 (ML) 模型来关注难度和分散性。从 FGH96 高温合金的疲劳失效过程中提取的一些数据用于开发ML 方法，例如反向传播神经网络 (BP)、支持向量回归 (SVR) 和随机森林 (RF)。然后考虑来自多轴加载路径的数据的随机性、样品几何和机械加载的测量、测试系统误差等，将FGH96高温合金的低周疲劳寿命预测为具有概率分布的区间。将所得结果与六种加载路径下FGH96高温合金的疲劳实验观察结果进行分析比较。发现可以有效地描述 FGH96 高温合金的多轴疲劳失效行为，所开发的 ML 模型在解决合金多轴疲劳寿命预测的困难和分散方面具有一定的优势。