Traditional probabilistic fatigue life test requires long time, a large number of samples and high cost due to dispersion, randomness and complexity. A new data-driven probabilistic fatigue life prediction framework informed by experiments and multiscale simulation, was proposed and applied to a Ni-based superalloy FGH96. It exhibits the advantages of less tests and fully consideration of the effects of microstructure dispersion on fatigue crack initiation, small fatigue crack (SFC) and long fatigue crack (LFC) propagation. The fatigue crack initiation life was accurately predicted by a stored energy model of persistent slip band (PSB) based on microscale crystal plasticity (CP) theory. The usage of artificial neural network (ANN) leads to the capability of rapidly obtaining CP parameters through the macroscopic Ramberg-Osgood (RO) relationship. Bayesian neural networks (BNN) was trained to predict probabilistic fatigue crack initiation life, SFC and LFC growth rates based on results obtained from CP modellings and carefully designed experiments. With BNN as a probabilistic distribution generator, a Monte Carlo (MCs) model is developed to capture the complete fatigue failure processes, including the crack initiation, SFC and LFC propagation. The total fatigue life and the proportions of life at different stages of superalloy FGH96 were predicted by MCs, which agree well with the experimental data. This work provides a new pathway for structural safety assessment and damage tolerance design.

传统的概率疲劳寿命试验由于分散性、随机性和复杂性，耗时长、样本量大、成本高。提出了一种基于实验和多尺度模拟的新数据驱动概率疲劳寿命预测框架，并将其应用于镍基高温合金 FGH96。具有试验少、充分考虑微观组织分散对疲劳裂纹萌生、小疲劳裂纹(SFC)和长疲劳裂纹(LFC)扩展影响的优点。基于微尺度晶体塑性 (CP) 理论的持久滑移带 (PSB) 储能模型可准确预测疲劳裂纹萌生寿命。人工神经网络 (ANN) 的使用导致能够通过宏观 Ramberg-Osgood (RO) 关系快速获取 CP 参数。贝叶斯神经网络 (BNN) 经过训练，可以根据从 CP 模型和精心设计的实验中获得的结果来预测概率疲劳裂纹萌生寿命、SFC 和 LFC 增长率。以 BNN 作为概率分布生成器，开发了蒙特卡罗 (MCs) 模型来捕获完整的疲劳失效过程，包括裂纹萌生、SFC 和 LFC 扩展。通过MCs预测了FGH96高温合金各阶段的总疲劳寿命和各阶段寿命所占比例，与试验数据吻合较好。这项工作为结构安全评估和损伤容限设计提供了一条新途径。以 BNN 作为概率分布生成器，开发了蒙特卡罗 (MCs) 模型来捕获完整的疲劳失效过程，包括裂纹萌生、SFC 和 LFC 扩展。通过MCs预测了FGH96高温合金各阶段的总疲劳寿命和各阶段寿命所占比例，与试验数据吻合较好。这项工作为结构安全评估和损伤容限设计提供了一条新途径。以 BNN 作为概率分布生成器，开发了蒙特卡罗 (MCs) 模型来捕获完整的疲劳失效过程，包括裂纹萌生、SFC 和 LFC 扩展。通过MCs预测了FGH96高温合金各阶段的总疲劳寿命和各阶段寿命所占比例，与试验数据吻合较好。这项工作为结构安全评估和损伤容限设计提供了一条新途径。