小裂紋的擴(kuò)展是導(dǎo)致結(jié)構(gòu)部件進(jìn)入疲勞期的主要因素。盡管人們對(duì)此有很大的興趣，但就裂縫擴(kuò)展的方向和速度而言，小裂縫的生長(zhǎng)指標(biāo)尚未確定。

由美國(guó)普渡大學(xué)Michael Sangid領(lǐng)導(dǎo)的團(tuán)隊(duì)，提出了一種識(shí)別微結(jié)構(gòu)小疲勞裂紋驅(qū)動(dòng)力的新方法。他們采用機(jī)器學(xué)習(xí)建立了兩個(gè)獨(dú)立的貝葉斯網(wǎng)絡(luò)，分析了鈦合金原位疲勞循環(huán)過(guò)程中獲得的衍射數(shù)據(jù)和X線斷層影像數(shù)據(jù)。該方法能識(shí)別影響疲勞裂紋擴(kuò)展方向和擴(kuò)展速率的微機(jī)械和微結(jié)構(gòu)變量。他們發(fā)現(xiàn)，第一主應(yīng)力軸在特定方向上的取向和最大分辨剪切應(yīng)力與裂紋擴(kuò)展最為相關(guān)，將其納入關(guān)系分析中可以描述裂紋擴(kuò)展方向的概率。他們的分析模型可靠地預(yù)測(cè)了小裂紋擴(kuò)展的方向，再現(xiàn)了實(shí)驗(yàn)結(jié)果，比之前文獻(xiàn)的預(yù)測(cè)更為可靠。這種半監(jiān)督機(jī)器學(xué)習(xí)方法不僅可以識(shí)別鈦合金中小裂紋擴(kuò)展方向背后的復(fù)雜變量，也有望識(shí)別其他復(fù)雜工程問(wèn)題中的驅(qū)動(dòng)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.