International Journal of Fatigue最新文献_第2页

Influence of preprocessing on the fatigue life prediction of scanned weld topologies 预处理对扫描焊缝拓扑疲劳寿命预测的影响

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ijfatigue.2026.109536

Georg Veile , Julius Lotz , Daniel Klöss , Stefan Weihe

This work quantifies the influence of preprocessing scanned weld topologies on the accuracy and scatter of fatigue life prediction. Based on 13 welded fatigue specimens, 99 weld topologies were created using different settings in preprocessing. Mesh convergence was achieved in FEA using elastic plastic material models. Common fatigue damage parameters (FDP), such as von Mises (vM), Smith-Watson-Topper (SWT), Fatemi-Socie (FS), and their gradient-based extensions were used to create 1089 fatigue life predictions for comparison with experimental fatigue life. The deviation of fatigue life prediction is defined as the logarithmic fraction of experimental and predicted fatigue life. This paper examines the influence of different approaches when scan data is transformed to a solid in FEA. Increasing the number of Non-Uniform Rational B-Splines (NURBS) patches in preprocessing to 250 resulted in smaller radii r (in mm) {r| 0.05 ≤ r ≤ 0.19}. Reducing this number to 10 increased the interval to {r| 0.07 ≤ r ≤ 0.44}. With a reduction of NURBS nodes the deviation of common FDP of ca. 3 decreased by a third. Gradient based FDP were not affected to a comparable magnitude with a deviation closer to null. By manual displacement of NURBS nodes the radii increased to {r| 0.76 ≤ r ≤ 2.24}. Here, deviation of common FDP decreased over 50 % while gradient based extension of SWT resulted in the best deviation of 0.042. It is evident that the user-influence on the preprocessing stage, whether conscious or unconscious, has a substantial impact on the fatigue life prediction.

本工作量化了预处理扫描焊缝拓扑对疲劳寿命预测精度和离散度的影响。基于13个焊接疲劳试样，采用不同的预处理设置创建了99个焊缝拓扑结构。采用弹塑性材料模型进行有限元分析，实现了网格收敛。利用von Mises （vM）、Smith-Watson-Topper （SWT）、Fatemi-Socie （FS）等常用疲劳损伤参数及其基于梯度的扩展，建立了1089个疲劳寿命预测模型，并与实验疲劳寿命进行了比较。疲劳寿命预测的偏差定义为试验疲劳寿命与预测疲劳寿命的对数分数。本文探讨了有限元分析中扫描数据转化为实体时不同处理方法的影响。将预处理的非均匀有理b样条（NURBS）斑块数量增加到250个，半径r（单位mm）更小{r| 0.05≤r≤0.19}。将这个数减小到10，区间增大到{r| 0.07≤r≤0.44}。随着NURBS节点的减少，大约3的共同FDP的偏差减少了三分之一。基于梯度的FDP不受影响，其偏差接近于零。通过手动位移NURBS节点，半径增加到{r| 0.76≤r≤2.24}。在这里，普通FDP的偏差减小了50%以上，而基于梯度的SWT扩展的最佳偏差为0.042。显然，用户在预处理阶段的影响，无论是有意识的还是无意识的，都对疲劳寿命预测有实质性的影响。

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引用次数: 0

Experimental investigation and modeling of the superalloy crack growth behavior under combined high and low cycle fatigue 高温合金高、低周复合疲劳下裂纹扩展行为的实验研究与建模

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-07 DOI: 10.1016/j.ijfatigue.2026.109553

Han Yan , Dawei Huang , Aofei Li , Zhenyu He , Heming Xu , Naixian Hou , Xiaojun Yan

The disk groove structure of aero engines is affected by combined high and low cycle fatigue (CCF) loads during service, and the crack growth rate model is a critical input condition for the damage tolerance analysis of the disk. In this study, the crack propagation behavior of the Inconel 718 superalloy, a commonly used material for disks, is investigated under the CCF loads. Firstly, crack propagation tests are conducted on the superalloy under three loading conditions: pure low cycle fatigue (LCF), pure high cycle fatigue (HCF), and CCF. The influence of different loads on the crack growth rate is analyzed. Then, considering the coupling effect of the HCF and LCF loads, a CCF equivalent stress intensity factor K_eq is proposed. A crack growth rate model is developed based on the K_eq. The predicted crack growth rates fall within the 1.7-fold dispersion band. Furthermore, the influence mechanism of the stress ratio and CCF loads on crack propagation is explored through fracture analysis, revealing that under CCF loads, the high cycle component significantly governs the crack propagation process. This study can provide valuable methods and data support for evaluating the crack propagation life of the groove structure.

航空发动机盘面槽结构在使用过程中受到高、低周联合疲劳载荷的影响，裂纹扩展速率模型是盘面损伤容限分析的重要输入条件。本文研究了盘片常用材料Inconel 718高温合金在CCF载荷作用下的裂纹扩展行为。首先，对高温合金在纯低周疲劳（LCF）、纯高周疲劳（HCF）和CCF三种载荷下进行了裂纹扩展试验。分析了不同载荷对裂纹扩展速率的影响。在此基础上，考虑高、低载荷的耦合效应，提出了CCF等效应力强度因子Keq。在此基础上建立了裂纹扩展速率模型。预测的裂纹扩展速率落在1.7倍色散带内。通过断裂分析，探索应力比和CCF载荷对裂纹扩展的影响机制，发现在CCF载荷下，高周分量显著控制裂纹扩展过程。该研究可为评价沟槽结构裂纹扩展寿命提供有价值的方法和数据支持。

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引用次数: 0

Influence of inelastic strain in meso-structure on fatigue behavior of polyamide glass fiber woven composites 细观结构非弹性应变对聚酰胺玻璃纤维编织复合材料疲劳性能的影响

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ijfatigue.2026.109551

Ebrahim Ebrahimi , Mohammad Nazmus Saquib , Edwing Chaparro-Chavez , Diego Pedrazzoli , Mingfu Zhang , Oleksandr G. Kravchenko

This study investigates the fatigue behavior and evolution of inelastic effects in 2/2 twill PA6 woven glass fiber composites under low-cycle fatigue (LCF) and high-cycle fatigue (HCF) regimes. A hybrid fatigue life model combining power-law and exponential components was developed to improve fatigue life prediction. To characterize the role of viscoplasticity and damage in fatigue mechanisms, a comprehensive experimental and modeling approach was adopted. Cyclic stress–strain responses, secant modulus evolution, and energy dissipation were used to capture stiffness degradation and inelastic effects. Digital image correlation (DIC) was employed to map local strain distributions and identify regions of elevated viscoplastic deformation within the woven meso-structure, particularly between the longitudinal and transverse yarns. Finite element analysis (FEA) provided meso-scale insights into local stress and strain fields, while micro-computed tomography (microCT) was used to assess internal damage accumulation and validate the modeling framework. The integration of DIC, FEA, and microCT enabled a detailed investigation of the relationships between local deformation, meso-structural features, and global fatigue response. At a high level, the results reveal distinct damage mechanisms governing LCF and HCF, highlighting the roles of viscoplasticity, yarn orientation, and gradual stiffness degradation.

研究了2/2斜纹PA6编织玻璃纤维复合材料在低周疲劳（LCF）和高周疲劳（HCF）状态下的疲劳行为和非弹性效应的演变。为了提高疲劳寿命的预测精度，提出了一种结合幂律分量和指数分量的混合疲劳寿命模型。为了表征粘塑性和损伤在疲劳机制中的作用，采用了综合实验和建模方法。循环应力-应变响应、割线模量演化和能量耗散用于捕获刚度退化和非弹性效应。采用数字图像相关（DIC）来绘制局部应变分布，并识别织造细观结构中粘塑性变形升高的区域，特别是在纵向和横向纱线之间。有限元分析（FEA）提供了对局部应力和应变场的细观见解，而微计算机断层扫描（microCT）用于评估内部损伤积累并验证建模框架。DIC、FEA和microCT的集成可以详细研究局部变形、细观结构特征和整体疲劳响应之间的关系。在较高的水平上，结果揭示了不同的损伤机制控制LCF和HCF，突出粘塑性，纱线取向和逐渐的刚度退化的作用。

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引用次数: 0

A Bayesian physics-informed neural network model for probabilistic fatigue life assessment considering the size effect in additively manufactured materials 考虑尺寸效应的增材制造材料概率疲劳寿命评估贝叶斯神经网络模型

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijfatigue.2026.109526

Qia Zhao , Jing Cao , Boda Wang , Yuan Tao , Xiang Xie , Weixing Yao

This study investigates the pronounced size effect in high-cycle fatigue of additively manufactured metallic specimens and proposes a probabilistic fatigue life assessment approach that combines a size-effect physical model with a Bayesian neural network (BNN). The model takes two physical-model parameters and the applied stress level as inputs, with these two parameters jointly capturing the influences of specimen size, defect characteristics, and Vickers hardness. By normalizing these inputs, the BNN is able to learn fatigue response patterns in a concise yet comprehensive manner. Statistical fatigue test data are used to construct training, validation, and test sets, followed by a systematic hyperparameter search to determine the optimal model configuration for the dataset in this study. Cross-validation is then performed on three different materials with a total of eight specimen sizes. The results show that the proposed Bayesian Physics-Informed Neural Network model delivers reliable fatigue life predictions across multi-material and multi-size conditions, demonstrating strong generalization capability.

研究了增材制造金属试样在高周疲劳中的显著尺寸效应，提出了一种将尺寸效应物理模型与贝叶斯神经网络（BNN）相结合的概率疲劳寿命评估方法。该模型以两个物理模型参数和外加应力水平作为输入，这两个参数共同捕捉了试样尺寸、缺陷特征和维氏硬度的影响。通过将这些输入归一化，BNN能够以简洁而全面的方式学习疲劳响应模式。统计疲劳试验数据用于构建训练集、验证集和测试集，然后进行系统的超参数搜索，以确定本研究数据集的最佳模型配置。然后对三种不同的材料进行交叉验证，共有八种样品尺寸。结果表明，所提出的贝叶斯物理信息神经网络模型可以在多材料和多尺寸条件下提供可靠的疲劳寿命预测，具有很强的泛化能力。

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引用次数: 0

Progressive fatigue damage modeling for FRP in high cycle and very high cycle fatigue regimes FRP在高周和甚高周疲劳状态下的渐进疲劳损伤建模

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijfatigue.2026.109543

Gen Li , Xiaorui Liu , Hao Li , Tianyu Chen , Zhengmao Yang , Huan Tu , Rubing Zhang

A progressive fatigue damage model is proposed in this paper to model the FRP fatigue behaviors and damage progression in high cycle and very high cycle fatigue (HCF and VHCF) regimes. Firstly, a new stiffness degradation model is proposed and validated for FRP stiffness degradation behaviors in HCF and VHCF regimes. Then the progressive fatigue damage model is generated by combining anisotropic elastic equations, the new stiffness degradation model, strength degradation model and failure criterion. The progressive fatigue damage model is applicable to model uniaxial and bending fatigue behavior of FRP, and determine different failure modes as fiber tensile failure, matrix tensile failure and interface shear failure. The HCF and VHCF fatigue behaviors of GFRP and CFRP under uniaxial and bending fatigue loads are modeled by the progressive fatigue damage model on finite element software. The full field stress and stiffness data for the simulated fatigue specimens are provided, and the damage progression in HCF and VHCF regimes is quantified efficiently. The S-N curves, stiffness degradation process and failure behavior achieve good consistency with the uniaxial and bending fatigue test results. The proposed progressive fatigue damage model effectively extends the analysis approach for FRP fatigue behaviors and damage progression in HCF and VHCF regimes.

本文提出了一种渐进疲劳损伤模型来模拟高周疲劳和甚高周疲劳（HCF和VHCF）状态下FRP的疲劳行为和损伤进展。首先，提出了一种新的FRP在HCF和VHCF下的刚度退化模型，并对其进行了验证。然后将各向异性弹性方程、新的刚度退化模型、强度退化模型和破坏准则相结合，建立了渐进疲劳损伤模型。渐进疲劳损伤模型适用于模拟FRP的单轴和弯曲疲劳行为，并确定纤维拉伸破坏、基体拉伸破坏和界面剪切破坏的不同破坏模式。采用有限元软件中的渐进疲劳损伤模型，模拟了GFRP和CFRP在单轴和弯曲疲劳载荷下的HCF和VHCF疲劳行为。提供了模拟疲劳试样的全应力场和刚度数据，有效地量化了HCF和VHCF状态下的损伤进程。S-N曲线、刚度退化过程和破坏行为与单轴和弯曲疲劳试验结果具有较好的一致性。所提出的累进疲劳损伤模型有效地扩展了FRP在HCF和VHCF状态下的疲劳行为和损伤进展分析方法。

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引用次数: 0

Prediction of residual life and critical crack length using the forward/inverse machine learning based on the configurational force fatigue model 基于构形力疲劳模型的正反向机器学习预测残余寿命和临界裂纹长度

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ijfatigue.2026.109525

Yingxuan Dong , Ran Liu , Qun Li

For mixed-mode fatigue cracks commonly found in engineering structures, classical empirical fatigue models struggle to accurately assess nonlinear propagation characteristics and fatigue life under complex stress states. To address this limitation, this study develops a physics-informed neural network (PINN) integrated with a configurational force fatigue model to predict mixed-mode fatigue crack propagation and remaining life in ductile metallic materials via a forward/inverse machine-learning framework. The proposed methodology overcomes the limited applicability of traditional fatigue models to mixed-mode cracking while enhancing the extrapolation capability of purely data-driven methods through physics-based constraints derived from fatigue crack growth mechanisms. Numerical results demonstrate accurate remaining life predictions for both mode-I and I-II mixed-mode fatigue cracks. Furthermore, based on the established correlation between the crack length and the residual life, a genetic algorithm is employed to perform the inverse machine learning process. The critical fatigue crack lengths for I-II mixed-mode cracks at various deflection angles are inversely identified from the zero remaining life. This work establishes a novel PINN framework based on the configurational force theory, achieving integrated and accurate predictions of both the propagation and remaining life of mixed-mode fatigue cracks.

对于工程结构中常见的混合模态疲劳裂纹，传统的经验疲劳模型难以准确评估复杂应力状态下的非线性扩展特性和疲劳寿命。为了解决这一限制，本研究开发了一个物理信息神经网络（PINN），该网络与配置力疲劳模型相结合，通过正/逆机器学习框架预测延性金属材料的混合模式疲劳裂纹扩展和剩余寿命。该方法克服了传统疲劳模型对混合模式裂纹适用性的局限性，同时通过疲劳裂纹扩展机制的物理约束增强了纯数据驱动方法的外推能力。数值结果表明，i型和I-II型混合模态疲劳裂纹的剩余寿命预测是准确的。此外，基于已建立的裂纹长度与剩余寿命之间的相关性，采用遗传算法进行逆机器学习。在剩余寿命为零的情况下，可以反求出I-II型混合模裂纹在不同挠度下的临界疲劳裂纹长度。本文基于构形力理论建立了一种新的PINN框架，实现了对混合模态疲劳裂纹扩展和剩余寿命的综合准确预测。

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引用次数: 0

Physical information-enhanced machine learning method for high cycle fatigue strength prediction of foreign object damaged aeroengine blades 基于物理信息增强的机器学习方法的航空发动机叶片外物损伤高周疲劳强度预测

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-09 DOI: 10.1016/j.ijfatigue.2026.109540

Yuming Huang , Yibo Shang , Yu Fu , Chen Wang , Qiang Chen , Yun He , Qingyang Shen , Weisi Gao , Shifeng Wen , Weifeng He , Ming Li , Zhifen Zhang , Liucheng Zhou , Zhenhua Zhao

Foreign object damage (FOD) induced fatigue strength attenuation of aeroengine blades is a critical challenge in reliability design, as it is affected by the coupling of multiple factors such as foreign object characteristics, impact angle, and damage morphology. Experimental data in this context often suffer from limited sample size, significant noise, and incomplete feature coverage. Traditional physical empirical methods rely on simplified assumptions, struggling to adapt to nonlinear evolution of complex damage, while pure data-driven models lack physical constraints, tending to overfit or produce physically implausible predictions. To address these limitations, this study proposes a physics-informed enhanced machine learning method for FOD fatigue strength prediction. The core of physical information enhanced-XGBoost (PIE-XGBoost) lies in embedding physical priors from the Peterson empirical formula into the XGBoost loss function and precomputing theoretical fatigue strength using parameters such as damage depth and foreign object diameter as physical constraints throughout training. Additionally, an adaptive physical constraint strength mechanism is introduced to dynamically adjust regularization coefficients via training error, balancing physical constraint guidance in the early stages with data-driven optimization in the later stages. Finally, based on the simulated blade experimental data verification and analysis of FOD blades, the average error of PIE-XGBoost is 3.2%. Compared with the traditional physical empirical formula’s average error of 41.47%, PIE-XGBoost reduces the error by 38.27%, thus verifying the effectiveness of the method. Further application of this method to actual aeroengine blades can provide technical support for aeroengine maintenance, which has high engineering practical significance and application prospects.

航空发动机叶片的外来物损伤疲劳强度衰减受外来物特性、冲击角度和损伤形态等多种因素的耦合影响，是可靠性设计中的一个关键问题。在这种情况下，实验数据往往受到样本量有限，显著噪声和不完整的特征覆盖的影响。传统的物理经验方法依赖于简化的假设，难以适应复杂损伤的非线性演化，而纯粹的数据驱动模型缺乏物理约束，容易过拟合或产生物理上不合理的预测。为了解决这些限制，本研究提出了一种基于物理的增强机器学习方法，用于FOD疲劳强度预测。物理信息增强-XGBoost （PIE-XGBoost）的核心在于将Peterson经验公式中的物理先验嵌入到XGBoost损失函数中，并在整个训练过程中使用损伤深度和异物直径等参数作为物理约束，预计算理论疲劳强度。引入自适应物理约束强度机制，通过训练误差动态调整正则化系数，平衡前期物理约束指导和后期数据驱动优化。最后，基于FOD叶片的模拟叶片实验数据验证和分析，PIE-XGBoost的平均误差为3.2%。与传统物理经验公式的平均误差41.47%相比，PIE-XGBoost将误差降低了38.27%，验证了该方法的有效性。将该方法进一步应用于实际的航空发动机叶片，可为航空发动机维修提供技术支持，具有较高的工程实际意义和应用前景。

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引用次数: 0

Achieving superior high-cycle fatigue resistance of an extruded TiAl alloy 实现了挤压TiAl合金优异的高周抗疲劳性能

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ijfatigue.2026.109532

Jiaqi Sheng , Junke Ren , Xiaodong Wang , Hongwei Wang , Yongfeng Liang , Junpin Lin

Herein, we present a significant breakthrough in processing TiAl alloys by demonstrating that a large-ratio hot extrusion process alone, without any subsequent heat treatment or surface treatment, can yield an exceptional strength-ductility, superior creep and fatigue resistance. The as-extruded TiAl alloy exhibits remarkably high fatigue strength of 700 MPa at room temperature and 648 MPa at 700°C, with a minimal performance gap between these temperatures indicating outstanding microstructural stability. Contrary to conventional wisdom, the absence of post-processing treatment did not compromise performance; instead, the finely tuned as-extruded microstructure provided superior resistance to thermal–mechanical degradation. The results showed that surface and subsurface crack nucleation failures were identified as two competing mechanisms that influenced the fatigue life of TiAl alloys. When failure was dominated by the subsurface cracks, the TiAl alloys exhibited a significantly longer fatigue life compared to failures initiated by surface cracks. The deformation mechanisms of dislocations and intersecting nanotwins in the γ phase were observed to play crucial roles in the fatigue fracture process. Concurrently, dislocations and antiphase domains within equiaxed α₂ grains were found to provide additional deformation capacity. There was a sharp drop in fatigue limit in high-temperature high-cycle fatigue. Transmission electron microscopy analysis revealed that this scatter primarily correlates with the degradation of the α₂ laths and the transformation of the ω₀ phase, when the critical stress value was exceeded.

在此，我们提出了加工TiAl合金的重大突破，通过证明单独使用大比例热挤压工艺，无需任何后续热处理或表面处理，可以产生卓越的强度-延展性，卓越的蠕变和抗疲劳性。挤压态TiAl合金在室温和700℃下分别表现出700 MPa和648 MPa的高疲劳强度，且两者之间的性能差距很小，具有良好的组织稳定性。与传统观点相反，没有后处理并不会影响性能；相反，精心调整的挤压微观结构提供了优越的耐热性。结果表明，表面和亚表面裂纹形核失效是影响TiAl合金疲劳寿命的两种相互竞争的机制。当破坏主要由亚表面裂纹引起时，TiAl合金的疲劳寿命明显长于表面裂纹引起的破坏。在疲劳断裂过程中，观察到γ相位错和相交纳米孪晶的变形机制起着至关重要的作用。同时，等轴α2晶粒内的位错和反相域提供了额外的变形能力。高温高周疲劳时，疲劳极限急剧下降。透射电镜分析表明，这种散射主要与超过临界应力值时α2板条的降解和ω0相的转变有关。

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引用次数: 0

Fatigue of additively manufactured 18Ni300 maraging steel 增材制造18Ni300马氏体时效钢的疲劳性能

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-01-30 DOI: 10.1016/j.ijfatigue.2026.109529

Paschalis Adamidis, Antonios Tsakiris, Georgios Savaidis

This study investigates the fatigue behavior of 18Ni300 maraging steel fabricated via Laser Powder Bed Fusion (LPBF) and subjected to a novel, cost-effective two-stage heat treatment performed in an air atmosphere. The specimens underwent solution annealing at 940°C for 1 h followed by aging at 490°C for 6 h. Monotonic tensile tests revealed that this thermal treatment significantly enhances mechanical strength, increasing the yield strength by 86% and the ultimate tensile strength by 70% compared to the as-built condition, although ductility decreases from 4.7% to 2.6%. Fatigue test results demonstrated superior fatigue resistance compared to similar datasets from literature for both as-built and conventionally heat-treated conditions. Microstructural analysis confirmed that the studied air-atmosphere thermal process effectively dissolved the laser-induced melt pool boundaries, resulting in a homogenized martensitic matrix, but with a notable fraction of reverted austenite. Fractographic examination identified that fatigue failure was driven predominantly by non-metallic inclusions located just beneath the surface. The findings suggest that while air-furnace heat treatment is a viable, low-cost method for restoring static strength, the fatigue life of AM maraging steel remains sensitive to oxide inclusions which persist as stress concentrators within the hardened matrix.

本研究研究了激光粉末床熔合（LPBF）制备的18Ni300马氏体时效钢的疲劳行为，并在空气气氛中进行了一种新颖的、经济有效的两阶段热处理。试样在940℃固溶退火1 h，然后在490℃时效6 h。单调拉伸试验表明，这种热处理显著提高了机械强度，与原状相比，屈服强度提高了86%，极限抗拉强度提高了70%，但延展性从4.7%下降到2.6%。疲劳测试结果表明，与文献中类似的数据集相比，无论是在预制条件下还是在常规热处理条件下，该材料都具有更好的抗疲劳性能。显微组织分析证实，所研究的空气-大气热过程有效地溶解了激光诱导的熔池边界，产生均匀的马氏体基体，但有显著比例的还原奥氏体。断口学检查发现，疲劳失效主要是由位于表面以下的非金属夹杂物引起的。研究结果表明，虽然空气炉热处理是一种可行的、低成本的恢复静态强度的方法，但AM马氏体时效钢的疲劳寿命仍然对氧化物夹杂物敏感，这些夹杂物在硬化基体中作为应力集中剂存在。

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引用次数: 0

Effects of Microstructure and Notch on the Fatigue Failure Behavior and Fatigue Strength of Cu-Cr-Zr Alloys Used for High-Speed Railway Contact Wires 显微组织和缺口对高速铁路接触线用Cu-Cr-Zr合金疲劳失效行为和疲劳强度的影响

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-07-01 Epub Date: 2026-02-01 DOI: 10.1016/j.ijfatigue.2026.109524

Jie Huang , Jinfa Guan , Jiwang Zhang , Dongdong Ji , Renhui Li

This study investigates the effects of microstructure and V-notch geometry on the fatigue failure behavior and fatigue strength of Cu-Cr-Zr alloys used for high-speed railway contact wires. The results indicate that dislocation strengthening and precipitation strengthening are the primary contributors to the alloy’s yield strength. The introduction of an annular V-notch with an elastic stress concentration factor of K_t = 4.9 reduces the fatigue strength at 10⁷ cycles from 240.2 MPa to 60.9 MPa and is accompanied by a transition in fracture mode from shear-dominated to tension-dominated failure. For smooth specimens, fatigue damage is governed by multiscale interactions involving crystallographic texture, dislocations, precipitate distributions, and geometric anisotropy of grains. In notched specimens, the highly localized stress field at the notch root suppresses the barrier effects of the microstructure and forces the crack to propagate perpendicular to the loading direction. Furthermore, while both the Theory of Critical Distances (PM) and the Neuber-Kuhn approach exhibit high accuracy in predicting notched fatigue strength, the latter proves more practical for the engineering design of contact wires. This work elucidates the competing mechanisms between microstructural features and local stress fields in fatigue failure and provides guidance for performance optimization of Cu-Cr-Zr alloys.

研究了高速铁路接触线用Cu-Cr-Zr合金的微观组织和v形缺口几何形状对其疲劳失效行为和疲劳强度的影响。结果表明，位错强化和析出强化是提高合金屈服强度的主要因素。引入弹性应力集中系数为Kt = 4.9的环形v形缺口，使107次循环时的疲劳强度从240.2 MPa降至60.9 MPa，并伴随着断裂模式从剪切为主向拉伸为主的转变。对于光滑试样，疲劳损伤是由晶体结构、位错、沉淀分布和晶粒几何各向异性等多尺度相互作用控制的。在缺口试件中，缺口根部高度局部化的应力场抑制了微观组织的阻挡作用，迫使裂纹沿垂直加载方向扩展。此外，虽然临界距离理论和Neuber-Kuhn方法在预测缺口疲劳强度方面都表现出较高的精度，但后者在接触丝的工程设计中更为实用。本研究阐明了Cu-Cr-Zr合金疲劳失效时微观组织特征与局部应力场之间的竞争机制，为Cu-Cr-Zr合金的性能优化提供了指导。

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引用次数: 0