The propagation of small cracks contributes to the majority of the fatigue lifetime for structural components. Despite significant interest, criteria for the growth of small cracks, in terms of the direction and speed of crack advancement, have not yet been determined. In this work, a new approach to identify the microstructurally small fatigue crack driving force is presented. Bayesian network and machine learning techniques are utilized to identify relevant micromechanical and microstructural variables that influence the direction and rate of the fatigue crack propagation. A multimodal dataset, combining results from a high-resolution 4D experiment of a small crack propagating in situ within a polycrystalline aggregate and crystal plasticity simulations, is used to provide training data. The relevant variables form the basis for analytical expressions thus representing the small crack driving force in terms of a direction and a rate equation. The ability of the proposed expressions to capture the observed experimental behavior is quantified and compared to the results directly from the Bayesian network and from fatigue metrics that are common in the literature. Results indicate that the direction of small crack propagation can be reliably predicted using the proposed analytical model and compares more favorably than other fatigue metrics.

小裂纹的蔓延有助于延长结构部件的疲劳寿命。尽管引起了极大的兴趣，但尚未确定关于裂纹扩展的方向和速度的小裂纹增长的标准。在这项工作中，提出了一种识别微观结构小的疲劳裂纹驱动力的新方法。利用贝叶斯网络和机器学习技术来识别影响疲劳裂纹扩展方向和速率的相关微机械和微结构变量。多模态数据集结合了高分辨率的4D实验结果（在多晶骨料中就地传播小裂纹）和晶体可塑性模拟相结合，来提供训练数据。相关变量构成了解析表达式的基础，因此就方向和速率方程而言代表了较小的裂纹驱动力。量化提出的表达式捕获观测到的实验行为的能力，并将其与直接来自贝叶斯网络和文献中常见的疲劳度量的结果进行比较。结果表明，使用所提出的分析模型可以可靠地预测小裂纹扩展的方向，并且比其他疲劳度量标准更有利。量化提出的表达式捕获观测到的实验行为的能力，并将其与直接来自贝叶斯网络和文献中常见的疲劳度量的结果进行比较。结果表明，使用所提出的分析模型可以可靠地预测小裂纹扩展的方向，并且比其他疲劳度量标准更有利。量化提出的表达式捕获观测到的实验行为的能力，并将其与直接来自贝叶斯网络和文献中常见的疲劳度量的结果进行比较。结果表明，使用所提出的分析模型可以可靠地预测小裂纹扩展的方向，并且比其他疲劳度量标准更有利。