小裂紋的擴展是導致結構部件進入疲勞期的主要因素。盡管人們對此有很大的興趣，但就裂縫擴展的方向和速度而言，小裂縫的生長指標尚未確定。

由美國普渡大學Michael Sangid領導的團隊，提出了一種識別微結構小疲勞裂紋驅動力的新方法。他們采用機器學習建立了兩個獨立的貝葉斯網絡，分析了鈦合金原位疲勞循環過程中獲得的衍射數據和X線斷層影像數據。該方法能識別影響疲勞裂紋擴展方向和擴展速率的微機械和微結構變量。他們發現，第一主應力軸在特定方向上的取向和最大分辨剪切應力與裂紋擴展最為相關，將其納入關系分析中可以描述裂紋擴展方向的概率。他們的分析模型可靠地預測了小裂紋擴展的方向，再現了實驗結果，比之前文獻的預測更為可靠。這種半監督機器學習方法不僅可以識別鈦合金中小裂紋擴展方向背后的復雜變量，也有望識別其他復雜工程問題中的驅動力。

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

Andrea Rovinelli, Michael D. Sangid, Henry Proudhon & Wolfgang Ludwig

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.