International Journal of Fatigue最新文献_第9页

An automated method for interpreting fatigue fracture surfaces with marker load based on computer vision and artificial neural networks 基于计算机视觉和人工神经网络的标记载荷疲劳断口自动判读方法

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-28 DOI: 10.1016/j.ijfatigue.2025.109471

Jinyu Wang, Xiaofan He, Hao Xin, Zhongwen Tao, Zhen Jia

The marker load method, recognized as one of the most effective approaches for reproducing the three-dimensional fatigue crack growth process in alloys, has been widely applied in theoretical and experimental studies of fatigue crack gross in reusable flight vehicle structures. However, this method suffers from low efficiency and poor accuracy in interpreting marker lines on fatigue fracture surfaces. In this study, an automatic method for marker line recognition and local defect completion is developed based on computer vision and artificial neural network techniques to enhance the efficiency and accuracy of crack interpretation, thereby strengthening the capability of the marker load method in extracting crack front (effective only for single-source cracks, multi-source cracks require further investigation.). Specifically, a convolutional neural network algorithm (constructed on the you only look once (YOLO) v8 framework) is first employed to identify continuous marker lines as a series of discrete coordinate points according to their geometric features. Subsequently, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, combined with a newly developed scatter cluster matching algorithm, is used to cluster and match the points belonging to the same crack front. Finally, a long short term memory (LSTM) neural network model is utilized to reconstruct incomplete marker lines, establishing an automatic interpretation method for fatigue fracture marker lines and compensating for the loss of crack information caused by marker line defects (crack source need to be selected manually).

标记载荷法被认为是再现合金三维疲劳裂纹扩展过程最有效的方法之一，已广泛应用于可重复使用飞行器结构疲劳裂纹总量的理论和实验研究中。然而，该方法在解释疲劳断口标记线时效率低、精度差。本研究基于计算机视觉和人工神经网络技术，开发了一种基于标记线识别和局部缺陷补全的自动方法，提高了裂缝解释的效率和准确性，从而增强了标记载荷法提取裂缝前沿的能力（仅对单源裂缝有效，多源裂缝有待进一步研究）。具体来说，首先使用卷积神经网络算法（构建在you only look once (YOLO) v8框架上）根据连续标记线的几何特征将其识别为一系列离散坐标点。随后，采用基于密度的带噪声应用空间聚类（DBSCAN）算法，结合新开发的散点聚类匹配算法，对属于同一裂纹前沿的点进行聚类匹配。最后，利用长短期记忆（LSTM）神经网络模型重构不完整的标记线，建立疲劳断裂标记线的自动解释方法，补偿标记线缺陷造成的裂纹信息丢失（需要人工选择裂纹源）。

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引用次数: 0

Phase boundary and defect dependent high cycle fatigue behavior in AlCoCrFeNi2.1 eutectic high-entropy alloy cocrfeni2.1共晶高熵合金的相界和缺陷高周疲劳行为

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-27 DOI: 10.1016/j.ijfatigue.2025.109462

Xiaochuan Yang , Tianxin Li , Jiang Yang , Mingpan Wan , Zhong Zhang , Chaowen Huang

The AlCoCrFeNi_2.1 (Ni21) eutectic high-entropy alloy (EHEA) exhibits a dual-phase heterogeneous microstructure that contributes to its high strength and ductility. However, its high cycle fatigue (HCF) damage mechanisms remain insufficiently understood and restrict its practical application. In this study, the HCF behavior of the as-cast Ni21 alloy was systematically investigated. The alloy exhibits a fatigue strength of approximately 297 MPa (σ_-1 (10⁷)), corresponding to a fatigue ratio (σ_-1 (10⁷)/YS) of 0.512. Under cyclic loading, dislocations are preferentially activated in the face-centered cubic (FCC) phase and accumulate at phase boundaries (PBs), where they induce stress concentration and trigger fatigue microcrack initiation. Meanwhile, nano-precipitates within the ordered body-centered cubic (B2) phase effectively hinder dislocations transmission across PBs, thereby enhancing resistance to fatigue crack initiation. The combined contribution of the dual-phase microstructure and nano- precipitates plays a critical role in extending fatigue life. Furthermore, fatigue crack propagation is most effectively suppressed when the crack growth direction intersects PBs at angles between 40° and 70°. Considerable scatter in the HCF data is observed, primarily resulting from casting defects such as blowholes. Overall, these findings highlight that further improvements in the HCF performance of Ni21 alloy will require improved casting quality or appropriate thermo-mechanical treatments.

AlCoCrFeNi2.1 （Ni21）共晶高熵合金（EHEA）具有双相非均相组织，具有较高的强度和塑性。然而，对其高周疲劳损伤机理的认识尚不充分，制约了其实际应用。本研究系统地研究了铸态Ni21合金的HCF行为。合金的疲劳强度约为297 MPa (σ-1(107))，对应的疲劳比（σ-1 (107)/YS）为0.512。在循环加载下，位错优先在面心立方（FCC）相中激活，并在相界（PBs）处积累，从而引起应力集中并引发疲劳微裂纹萌生。同时，有序体心立方（B2）相内的纳米沉淀有效地阻碍了位错在PBs中的传播，从而增强了抗疲劳裂纹萌生的能力。双相组织和纳米析出相的共同作用对延长疲劳寿命起着至关重要的作用。当裂纹扩展方向与疲劳裂纹扩展方向成40°~ 70°夹角相交时，疲劳裂纹扩展得到最有效的抑制。在HCF数据中观察到相当大的分散，主要是由于铸造缺陷，如气孔。总之，这些研究结果表明，进一步提高Ni21合金的HCF性能需要提高铸造质量或适当的热机械处理。

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引用次数: 0

Defect-sensitive fatigue assessment of heavy-section ductile cast irons: a comparative study of pearlitic and high-silicon ferritic grades 大断面球墨铸铁的缺陷敏感疲劳评估：珠光体和高硅铁素体等级的比较研究

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-27 DOI: 10.1016/j.ijfatigue.2025.109455

M. Benedetti , M. Pedranz , D. Lusuardi , F. Zanini , S. Carmignato , V. Fontanari

Heavy-section castings of ductile cast iron (DCI) unavoidably contain micro shrinkage porosity due to non-uniform, slow cooling, and service components also feature geometric stress raisers. This study quantifies how these two realities—intrinsic defects and notches—jointly control fatigue resistance and formalizes a design approach that accounts for their interaction. We compare a pearlitic EN-GJS-600–3 (GJS-600–3) and a high-silicon solid solution strengthened ferritic (HSi) DCI, which exhibit different matrix ductility and distinct pore populations. Pore size distributions are characterized (via X-ray computed tomography, CT), and extreme-value statistics are used to estimate the most critical defect expected in the highly stressed region of notched specimens. This defect measure is then coupled to a strain energy density (SED) criterion to predict fatigue limits. Fatigue tests on plain and V-notched specimens with varying notch severity reveal a systematic transition from pore-dominated initiation (plain and mildly notched) to notch-dominated initiation (severe notches). The proposed CT–statistics–SED framework reproduces both the fatigue limits and the observed switch in the governing initiation site. Compared with GJS-600–3, the HSi grade shows lower intrinsic fatigue strength but greater tolerance to distributed microporosity, leading to improved reliability in geometries with large highly stressed volumes. The approach provides a practical route to defect-aware fatigue design of DCI components, suggesting material-and-geometry selection: pearlitic grades for smaller, sharper features where notch control prevails; high-silicon ferritic grades for large, blunt features where defect tolerance is paramount. Overall, the method supports lighter, more reliable cast designs without resorting to overly conservative safety factors.

球墨铸铁（DCI）的大断面铸件由于冷却不均匀、速度慢，不可避免地存在微收缩孔隙，而且服务部件也具有几何应力升高的特点。本研究量化了这两种现实——内在缺陷和缺口——如何共同控制抗疲劳性，并形式化了一种解释它们相互作用的设计方法。我们比较了珠光体EN-GJS-600-3 （GJS-600-3）和高硅固溶体强化铁素体（HSi） DCI，它们具有不同的基体延展性和不同的孔隙数量。表征孔径分布（通过x射线计算机断层扫描，CT），并使用极值统计来估计缺口样品高应力区域中预期的最关键缺陷。然后将该缺陷测量与应变能密度（SED）准则耦合以预测疲劳极限。对不同缺口严重程度的平原和v形缺口试样进行疲劳试验，揭示了从孔隙主导起始（平原和轻度缺口）到缺口主导起始（严重缺口）的系统转变。提出的ct -统计- sed框架再现了疲劳极限和在控制起始位置观察到的开关。与GJS-600-3相比，HSi等级具有较低的固有疲劳强度，但对分布微孔隙的容错性更强，从而提高了高应力体积几何形状的可靠性。该方法为DCI组件的缺陷感知疲劳设计提供了一条实用的途径，建议选择材料和几何形状：珠光体级用于更小、更锋利的特征，其中缺口控制占优；高硅铁素体牌号用于大而钝的特征，其中缺陷容忍度是至关重要的。总体而言，该方法支持更轻，更可靠的铸造设计，而无需诉诸过于保守的安全因素。

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引用次数: 0

An effective approach for identifying fatigue-critical defects from X-ray 3D reconstruction: Example in L-PBF AlSil0Mg alloys x射线三维重建识别疲劳临界缺陷的有效方法：以L-PBF AlSil0Mg合金为例

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-25 DOI: 10.1016/j.ijfatigue.2025.109457

Zhengkai Wu , Tianyu Qin , Jianguang Bao , Weijian Qian , Enrico Salvati , Shengchuan Wu , Rihan Da , Jiang Dong , Hiroyuki Toda

Internal defects have been widely believed as the physical origins of fatigue crack initiation and growth in additively manufactured (AM) metals, and the specific defect that triggers final failure typically dictates the overall fatigue life of critical safety equipment. To this regard, reliable identification of such critical defects among numerous of these imperfections is essential for accurate fatigue life prediction and defect-tolerant design. In this study, we investigate the geometric and spatial nature of porosity defects in laser powder bed fusion AlSi10Mg alloys, including their size, position, morphology, and orientation, and attempt elucidating their effect on fatigue resistance using high-resolution X-ray computed tomography (X-CT) and post-mortem fractographic analysis. Recognizing that image-based finite element analysis can be computationally intensive and that conventional defect descriptor may not fully capture the complexity of defect geometry and spatial context, we propose an effective Critical Defect Ranking Function (CDRF) metric that quantitatively integrates defect size, location, morphology, and orientation directly from large 3D X-CT imaging data. An effective defect size is adopted to ensure physical consistency, with the enhanced detrimental effect of near-surface defects considered. The CDRF enables direct, automated identification and ranking of fatigue-critical defects, and demonstrates predictive correlation with post-mortem experimental results. This robust, non-destructive framework facilitates defect-based reliability assessment and quality assurance in AM components.

内部缺陷被广泛认为是增材制造（AM）金属疲劳裂纹萌生和扩展的物理根源，而引发最终失效的特定缺陷通常决定了关键安全设备的整体疲劳寿命。在这方面，在众多缺陷中可靠地识别这些关键缺陷对于准确的疲劳寿命预测和缺陷容忍度设计至关重要。在这项研究中，我们研究了激光粉末床熔合AlSi10Mg合金孔隙缺陷的几何和空间性质，包括它们的大小、位置、形态和取向，并试图通过高分辨率x射线计算机断层扫描（X-CT）和尸检断口分析来阐明它们对抗疲劳性能的影响。认识到基于图像的有限元分析可能需要大量的计算，并且传统的缺陷描述符可能无法完全捕获缺陷几何和空间背景的复杂性，我们提出了一种有效的关键缺陷排序函数（CDRF）度量，该度量可以直接从大型3D X-CT成像数据中定量地集成缺陷尺寸、位置、形态和方向。采用有效的缺陷尺寸来保证物理一致性，并考虑了近表面缺陷增强的有害影响。CDRF能够直接、自动地识别疲劳临界缺陷并对其进行排序，并证明了与死后实验结果的预测相关性。这种强大的、非破坏性的框架促进了基于缺陷的可靠性评估和增材制造组件的质量保证。

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引用次数: 0

Stress intensity factor – Life: Improving high cycle fatigue understanding of laser powder bed fusion 316L stainless steel by combining effects of stress, defect size, and defect shape 应力强度因子-寿命：通过结合应力、缺陷尺寸和缺陷形状的影响，提高对激光粉末床熔合316L不锈钢高周疲劳的认识

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-25 DOI: 10.1016/j.ijfatigue.2025.109454

Edwin Glaubitz , Orion Kafka , Nik Hrabe , Joy Gockel

As-built components made using laser powder bed fusion have a variety of both surface connected and internal defects, which reduce fatigue life. This work manufactures 316L stainless steel fatigue bars with two outer contour parameter sets, one which produces surface notch defects, and one that produces near surface keyhole porosity. Two fully reversed (R = −1) high cycle fatigue testing methods: axial and four-point rotating bending (RBF), produced different stress distributions in the gauge section: axial fatigue produced a uniform distribution while RBF produced a variable distribution with zero stress in the center and maximum stress on the surface. The parameter set with near surface keyhole porosity was found to have lower fatigue life than the parameter set that produced surface notches across test methods at stresses above 125 MPa. Stress intensity factors of the failure initiating defects were characterized by fractography and the relationship between stress intensity factor and cycles to failure was analyzed. The stress intensity-fatigue life relationship was found to be consistent across three of the four combinations of defect types and test methods. The deepest valley measured with optical profilometry (Sv) was a good direct measurement of fractography measured notch depth but unrelated to notch area. Another method of estimating defect area based on fitting a half ellipse showed good correlation with the true measured area though, the two were not identical. An improved understanding of appropriate defect measurement will lead to surface roughness measurements that accurately characterize notch area and improve fatigue predictions.

采用激光粉末床熔接技术制造的成品部件存在各种表面连接缺陷和内部缺陷，从而降低了疲劳寿命。本工作生产的316L不锈钢疲劳棒具有两组外轮廓参数，一组产生表面缺口缺陷，一组产生近表面锁孔孔隙。两种完全反向（R =−1）的高周疲劳试验方法：轴向和四点旋转弯曲（RBF）在规截面产生不同的应力分布：轴向疲劳产生均匀分布，而RBF产生中心应力为零，表面应力最大的变量分布。在125 MPa以上的应力下，具有近表面锁孔孔隙度的参数组的疲劳寿命低于具有表面缺口的参数组。用断口学方法对破坏起始缺陷的应力强度因子进行了表征，分析了应力强度因子与破坏循环的关系。发现应力强度-疲劳寿命关系在缺陷类型和测试方法的四种组合中有三种是一致的。用光学轮廓测量法（Sv）测量的最深谷是断口测量缺口深度的一个很好的直接测量，但与缺口面积无关。另一种基于半椭圆拟合的缺陷面积估计方法与真实测量面积具有较好的相关性，但两者并不完全相同。对适当缺陷测量的更好理解将导致表面粗糙度测量准确表征缺口区域和改进疲劳预测。

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引用次数: 0

Analytical and machine learning-based fatigue life prediction of welded joints under multiaxial loading 多轴载荷下焊接接头疲劳寿命分析与机器学习预测

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-24 DOI: 10.1016/j.ijfatigue.2025.109459

Marten Beiler , Niklas Michael Bauer , Jörg Baumgartner , Moritz Braun

Evaluating the fatigue life of welded joints under multiaxial loading is a key challenge in structural engineering. This study explores machine learning (ML) methods for predicting fatigue life and compares their performance against the novel super ellipse criterion, which is an analytical approach that aims to improve current design standard methods (e.g., Eurocode 3, IIW). Using a dataset of uniaxial and multiaxial fatigue tests with varying phase angles, ML models—including artificial neural networks and extreme gradient boosting (XGBoost)—are trained on features like stress amplitudes, phase differences, and material properties. Artificial neural networks provide high accuracy, while tree-based models like XGBoost offer better interpretability via model agnostic interpretation using Explainable Artificial Intelligence. Results show ML models can outperform traditional criteria, especially under non-proportional loading, but face limitations near the edges of the training data. This work highlights the potential and challenges of ML in fatigue prediction and highlights their value for enhancing the safety and reliability of welded structures.

多轴载荷作用下焊接接头的疲劳寿命评估是结构工程中的一个关键问题。本研究探索了用于预测疲劳寿命的机器学习（ML）方法，并将其性能与新型超椭圆准则进行了比较，超椭圆准则是一种旨在改进当前设计标准方法（例如，Eurocode 3， IIW）的分析方法。使用不同相角的单轴和多轴疲劳测试数据集，ML模型（包括人工神经网络和极端梯度增强（XGBoost））可以根据应力幅值、相位差和材料特性等特征进行训练。人工神经网络提供了很高的准确性，而像XGBoost这样基于树的模型通过使用可解释的人工智能（explable Artificial Intelligence）进行模型不可知的解释，提供了更好的可解释性。结果表明，机器学习模型可以优于传统的标准，特别是在非比例负载下，但在训练数据的边缘附近面临限制。这项工作强调了机器学习在疲劳预测中的潜力和挑战，并强调了它们在提高焊接结构的安全性和可靠性方面的价值。

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引用次数: 0

Multiaxial fatigue life assessment of welded joints using the super ellipse criterion under consideration of support effects 考虑支撑效应的超椭圆准则焊接接头多轴疲劳寿命评价

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-24 DOI: 10.1016/j.ijfatigue.2025.109458

Niklas Michael Bauer, Annina Wöhle, Jörg Baumgartner

The super ellipse criterion has recently been developed as an easy-to-use method reliably predicting multiaxial fatigue life of welded joints based on experimentally determined S-N curves. This paper evaluates the application of the super ellipse criterion using design S-N curves on the basis of so-called effective stresses. In contrast to the established stress concepts such as the nominal, the structural, or the notch stress concept, effective stresses incorporate both the local weld geometry and the support effect of the material surrounding the failure critical weld notch allowing fatigue assessment using S-N curves independent of the weld geometry, such as the weld toe or the weld root. Based on a comprehensive database of welded steel joints under multiaxial loading, the super ellipse criterion is found to accurately and precisely predict multiaxial fatigue life using effective normal and effective shear stresses derived by the critical distance or the stress averaging approach, achieving root mean square logarithmic errors of 0.36 and 0.35. Moreover, the assessment procedure is unified by providing parameters for deriving effective stresses for different stress components and plate thicknesses.

超椭圆准则是基于实验确定的S-N曲线可靠预测焊接接头多轴疲劳寿命的一种简便易行的方法。本文以有效应力为基础，用设计S-N曲线对超椭圆准则的应用进行了评价。与已建立的应力概念（如标称应力、结构应力或缺口应力概念）相反，有效应力结合了局部焊缝几何形状和失效临界焊缝缺口周围材料的支撑效应，允许使用独立于焊缝几何形状（如焊缝脚趾或焊缝根部）的S-N曲线进行疲劳评估。基于综合的多轴载荷下焊接钢接头数据库，发现超椭圆准则能够准确准确地预测多轴疲劳寿命，利用临界距离法和应力平均法分别得到有效法向和有效剪应力，其对数均方根误差分别为0.36和0.35。此外，通过提供不同应力分量和板厚的有效应力计算参数，统一了评估程序。

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引用次数: 0

Predicting the whole-life stress amplitude evolution of high-Mn TWIP steel under complex loading conditions using fatigue failure criteria and machine learning 基于疲劳失效准则和机器学习的高mn TWIP钢在复杂载荷条件下的全寿命应力幅值演化预测

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-22 DOI: 10.1016/j.ijfatigue.2025.109449

Di Song , Jinze Pei , Ye Xiao , Ronghai Wu , Heng Li

Previous research on fatigue performance has primarily focused on fatigue life, with limited emphasis on the evolution of stress amplitude. However, stress amplitude is a critical parameter in strain-controlled fatigue failure analysis. The interaction of deformation mechanisms during cyclic loading complicates the evolution of stress amplitude, and this challenge is further amplified when considering the applicability of models across diverse loading conditions. This study employs both failure criteria-based and machine learning approaches to develop predictive models for the entire fatigue life stress amplitude evolution under varying loading orientations, pre-strains, temperatures, and strain amplitudes. The failure criteria-based model introduces a novel combined prediction framework of fatigue life and stress amplitude, enabling the prediction of four representative stress amplitudes: initial, maximum, half-life, and failure. The fatigue life prediction achieves an accuracy of 96.1 % within a 2 × error band, while stress amplitude predictions attain 96.4 % within a 1.2 × error band. The machine learning model, based on symbolic regression, utilizes these four amplitudes as training data to derive an interpretable formula for the entire evolution curve, achieving 99.7 % data within the 1.2 × error band. The overall curve exhibits a high R² of 0.95 and a mean absolute percentage error (MAPE) of 1.87 %, demonstrating robust predictive capability across diverse conditions.

以往对疲劳性能的研究主要集中在疲劳寿命上，对应力幅值的演化研究较少。而应力幅值是应变控制疲劳失效分析中的一个关键参数。循环加载过程中变形机制的相互作用使应力幅值的演化变得复杂，考虑到模型在不同加载条件下的适用性，这一挑战进一步放大。本研究采用基于失效准则的方法和机器学习方法，建立了在不同加载方向、预应变、温度和应变幅值下的整个疲劳寿命应力幅值演变的预测模型。基于失效准则的模型引入了一种新的疲劳寿命和应力幅值组合预测框架，能够预测4种具有代表性的应力幅值：初始、最大、半衰期和失效。疲劳寿命预测精度在2 ×误差范围内达到96.1%，应力幅值预测精度在1.2 ×误差范围内达到96.4%。机器学习模型基于符号回归，利用这四个幅值作为训练数据，推导出整个进化曲线的可解释公式，在1.2 ×误差范围内实现99.7%的数据。总体曲线的R2为0.95，平均绝对百分比误差（MAPE）为1.87%，显示出在不同条件下的稳健预测能力。

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引用次数: 0

Multi-mechanism crystal plasticity-based finite element framework uncovering the tensile and fatigue behavior of Ni-based single crystal superalloy 基于多机制晶体塑性的有限元框架揭示了ni基单晶高温合金的拉伸和疲劳行为

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-22 DOI: 10.1016/j.ijfatigue.2025.109456

Yuanyuan Wang , Donghai Yu , Changjian Geng , Xianglin Zhou , Shilei Li , Yan-Dong Wang

A multi-mechanism crystal plasticity finite element (CPFE) model is developed to capture the micromechanical deformation and fatigue behavior of γ′-strengthened [001]-oriented Ni-based single-crystal (Ni-based SX) superalloy across a wide temperature range. The model integrates lattice friction and dislocation resistance for both γ and γ′ phases to reproduce hardening characteristics. In the γ matrix, microstructual features such as γ channel width are introduced to determine the initial slip resistance, while the strengthening effect associated with dislocation bypass of γ′ precipitate is incorporated through an Orowan mechanism. The critical γ/γ′ phase interaction is represented by back stress arising from dislocation pileup at the phase boundary. For the γ′ phase, the resistance to anti-phase boundary (APB) shearing is explicitly considered to account for the additional stress required for dislocation motion within the ordered structure. Model parameters are calibrated through temperature-dependent tensile tests using the genetic algorithm (GA) to minimize discrepancies between simulated and experimental stress–strain responses. The model reliably reproduces the stress–strain response, clarifying the contribution of distinct deformation mechanisms, and accurately captures the evolution of hysteresis loops, which exhibit a plastic-to-elastic transition at 760 ℃ and a fully elastic regime at 900 ℃. Finally, an empirical energy-based approach is employed to predict fatigue life across different temperatures and strain amplitudes, achieving good agreement with our experimental results.

建立了一种多机制晶体塑性有限元（CPFE）模型，以捕获γ′强化[001]取向镍基单晶（Ni-based SX）高温合金在宽温度范围内的微观力学变形和疲劳行为。该模型综合了γ相和γ′相的晶格摩擦和位错阻力，再现了硬化特征。在γ基体中，引入了γ通道宽度等微观结构特征来确定初始抗滑性，而与γ′沉淀的位错旁路相关的强化效应通过Orowan机制被纳入。γ/γ′相的临界相互作用表现为位错堆积在相边界处产生的背应力。对于γ′相，明确考虑了反相边界（APB）剪切的阻力，以解释有序结构内位错运动所需的附加应力。模型参数通过使用遗传算法（GA）的温度相关拉伸测试进行校准，以尽量减少模拟和实验应力-应变响应之间的差异。该模型可靠地再现了应力-应变响应，明确了不同变形机制的贡献，并准确地捕捉了迟滞回线的演变过程，在760℃时表现为塑性-弹性转变，在900℃时表现为完全弹性状态。最后，采用基于经验能量的方法预测了不同温度和应变幅值下的疲劳寿命，与实验结果吻合较好。

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引用次数: 0

Competitive propagation of multiple surface fatigue cracks in railheads: compact tension tests and peridynamic simulations 铁路道头多个表面疲劳裂纹的竞争扩展：紧致张力试验和周围动力学模拟

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-21 DOI: 10.1016/j.ijfatigue.2025.109453

Xiaoming Wang , Yitong Shi , Weijia Dong , Qing He , Boyang An , Bing Yang , Jun Huang , Ping Wang

Significant interactions exist among multiple surface cracks in railheads. This study investigates the competitive propagation behavior of rail surface cracks using the compact tension (CT) tests and a peridynamic (PD) model. Four sets of CT tests for multi-crack propagation were designed with U75V railhead material, and corresponding PD fatigue models were established. Significant shielding effects were observed among cracks during propagation, with the PD model accurately replicating crack propagation paths and fatigue lives from CT tests. A PD model was constructed to simulate the dynamic crack propagation on rail surfaces under rolling wheel loading, revealing significant promotion and suppression effects among cracks dominantly influenced by crack number and spacing. PD-predicted crack branching and coalescence align with field rail damage patterns.

铁路道头多个表面裂缝之间存在着显著的相互作用。本文研究了钢轨表面裂纹的竞争扩展行为，采用了致密拉伸（CT）试验和周动力（PD）模型。采用U75V轨道口材料设计了4套多裂纹扩展CT试验，建立了相应的PD疲劳模型。在裂纹扩展过程中观察到明显的屏蔽效应，PD模型准确地复制了CT试验的裂纹扩展路径和疲劳寿命。建立了滚动车轮荷载作用下钢轨表面裂纹动态扩展的PD模型，发现裂纹数量和裂纹间距对钢轨表面裂纹扩展具有显著的促进和抑制作用。pd预测的裂纹分支和合并与现场钢轨损伤模式一致。

引用次数: 0