Fretting fatigue (FF) involves multiaxial and non-proportional stress states in addition to strong stress gradients, making crack propagation modeling challenging. In this work, we evaluate different models based on stress intensity factors (SIFs) to predict crack propagation kink angles under FF conditions, including both theoretical and artificial neural network (ANN) models. To train and test the ANN model, we collected FF data on Al alloys from six different sources in the literature, where the crack paths observed in the experiments were available. Testing data was also used to assess the performance of the theoretical models in predicting crack propagation kink angles. We demonstrate that the ANN model outperforms the theoretical models in predicting crack propagation kink angles. Finally, we compared the investigated models by simulating the crack propagation path under FF conditions using finite element modeling. The results indicate that, although the ANN model was more accurate in predicting the kink angle for a given crack configuration observed in the experiments, its performance was comparable to the theoretical models that account for non-proportional effects when estimating the crack propagation path. However, under high compressive states, the ANN model predicted more stable crack paths than the theoretical models.

中文翻译：

---

ANN 与预测微动疲劳中裂纹路径的理论模型

微动疲劳 （FF） 除了涉及强应力梯度外，还涉及多轴和非比例应力状态，这使得裂纹扩展建模具有挑战性。在这项工作中，我们基于应力强度因子 （SIF） 评估了不同的模型，以预测 FF 条件下的裂纹扩展扭结角，包括理论和人工神经网络 （ANN） 模型。为了训练和测试 ANN 模型，我们从文献中的六个不同来源收集了 Al 合金的 FF 数据，其中实验中观察到的裂纹路径是可用的。测试数据还用于评估理论模型在预测裂纹扩展扭结角方面的性能。我们证明 ANN 模型在预测裂纹扩展扭结角度方面优于理论模型。最后，我们通过使用有限元建模模拟 FF 条件下的裂纹扩展路径来比较所研究的模型。结果表明，尽管 ANN 模型在预测实验中观察到的给定裂纹配置的扭结角度方面更准确，但其性能与在估计裂纹扩展路径时考虑非比例效应的理论模型相当。然而，在高压缩状态下，ANN 模型预测的裂纹路径比理论模型更稳定。