International Journal of Fatigue最新文献_第9页

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

Fatigue life prediction of multi jet fusion-manufactured polyamide12 lattice structures using the average strain energy density method 用平均应变能密度法预测多喷流熔接聚酰胺12晶格结构的疲劳寿命

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.2025.109452

Raffaele De Biasi , Lorenzo Romanelli , Ciro Santus , Matteo Perini , Filippo Berto , Matteo Benedetti

The industrial sector continues to explore innovative strategies to exploit the full potential of Additive Manufacturing (AM). Among its many advantages, AM enables the fabrication of lattice structures; these are lightweight metamaterials with tunable mechanical properties and excellent energy absorption capabilities. Despite their promise, the widespread industrial use of such structures is limited by the difficulty in accurately assessing their fatigue behavior. This study presents a methodology aimed at predicting the fatigue life of polymer-based lattice components, with a specific focus on PA12 manufactured using the Multi Jet Fusion (MJF) process. This is an industrially relevant technology offering large production volumes, high printing quality and low production costs. The approach begins with fatigue testing of bulk PA12 specimens to establish baseline material behavior. Based on these results, a predictive algorithm is developed to estimate the fatigue performance of lattice structures. The model adopts an energy-based framework inspired by the Average Strain Energy Density (ASED) method, previously used for metallic materials, and adapts it to the characteristics of polymer lattices. The proposed methodology contributes to the development of efficient fatigue assessment tools, supporting the broader adoption of lattice structures in cost-sensitive industrial applications where polymer-based materials are effective.

工业部门继续探索创新战略，以充分利用增材制造（AM）的潜力。在其众多优点中，增材制造可以制造晶格结构；这些都是重量轻的超材料，具有可调的机械性能和出色的能量吸收能力。尽管它们很有前途，但由于难以准确评估其疲劳行为，这种结构的广泛工业应用受到了限制。本研究提出了一种方法，旨在预测基于聚合物的晶格部件的疲劳寿命，特别关注使用多喷射融合（MJF）工艺制造的PA12。这是一项工业相关技术，可提供大批量，高印刷质量和低生产成本。该方法首先进行散装PA12试样的疲劳试验，以建立基准材料性能。在此基础上，提出了一种预测网格结构疲劳性能的算法。该模型采用了一种基于能量的框架，其灵感来自于之前用于金属材料的平均应变能密度（ASED）方法，并使其适应于聚合物晶格的特性。提出的方法有助于开发有效的疲劳评估工具，支持在成本敏感的工业应用中更广泛地采用晶格结构，其中聚合物基材料是有效的。

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引用次数: 0

Investigation on fatigue crack growth behavior and remaining useful life prediction of 6005A-T6 aluminum alloy under fatigue aging 6005A-T6铝合金疲劳老化疲劳裂纹扩展行为及剩余使用寿命预测研究

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.2025.109448

Zhe Zhang, Bing Yang, Shiqi Zhou, Jinbang Liu, Long Yang, Shoune Xiao, Guangwu Yang, Tao Zhu

To address the fatigue aging of 6005A-T6 aluminum alloy—widely used in rail transit structures—under long-term service, this study investigates its crack growth behavior and remaining useful life (RUL) prediction under different fatigue aging conditions. The simulation covered 4 fatigue aging states, achieved by applying different numbers of pre-fatigue cycles. Compact-tension-shear specimens were tested under mixed-mode I + II fatigue crack growth at 4 loading angles (0°, 30°, 45°, and 60°). Digital image correlation was employed to capture crack tip strain fields for analyzing crack growth behavior. Experimental results show that fatigue aging significantly reduces the material’s resistance to crack growth. While increasing the loading angle suppresses crack growth rate, this suppressive effect is weakened under severe fatigue aging conditions. The antagonistic interplay between fatigue aging and increased loading angle in determining RUL is investigated for the first time. Fractographic analysis reveals that the reduction in fatigue striations and the increase in microcrack formation are the key microstructural mechanisms responsible for the fatigue aging-induced decline in crack resistance. Furthermore, an extended finite element model based on an energy release rate attenuation mechanism was developed. The simulation results show high agreement with experimental data, with a maximum standard deviation of 1.3887 and a maximum life prediction error within 7.5 %. These findings provide theoretical support and technical guidance for service life prediction and failure assessment of aluminum alloy structures.

为解决轨道交通结构中广泛使用的6005A-T6铝合金在长期使用条件下的疲劳老化问题，研究了不同疲劳老化条件下6005A-T6铝合金的裂纹扩展行为和剩余使用寿命预测。模拟涵盖了4种疲劳老化状态，通过应用不同数量的预疲劳循环来实现。在4种加载角度（0°、30°、45°和60°）下，对压实拉伸-剪切试件进行混合模式I + II疲劳裂纹扩展试验。采用数字图像相关技术捕获裂纹尖端应变场，分析裂纹扩展行为。实验结果表明，疲劳老化显著降低了材料的抗裂纹扩展能力。增大加载角度对裂纹扩展速率有抑制作用，但在严重疲劳老化条件下，这种抑制作用减弱。本文首次研究了疲劳老化与加载角增大之间的拮抗作用。断口分析表明，疲劳条痕的减少和微裂纹形成的增加是疲劳老化导致抗裂性能下降的关键组织机制。在此基础上，建立了基于能量释放速率衰减机理的扩展有限元模型。仿真结果与实验数据吻合较好，最大标准差为1.3887，最大寿命预测误差在7.5 %以内。研究结果为铝合金结构寿命预测和失效评估提供了理论支持和技术指导。

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引用次数: 0

A framework for fatigue life prediction of fiber reinforced composites with limited testing data 基于有限试验数据的纤维增强复合材料疲劳寿命预测框架

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.2025.109446

Lu Yubin , Wu Zhen

To ensure structural integrity, it is essential to establish an accurate fatigue life prediction model. Traditional regression models are constrained by predefined functional forms, which often neglect the effects of material properties. However, purely data-driven methods require large datasets and exhibit poor extrapolation ability. Therefore, this study develops a novel framework to accurately predict fatigue life using limited testing data. The framework consists of two main parts, namely feature selection and iterative generation-estimation process (IGEP). Based on Pearson correlation coefficient, Variance inflation factors and Shapley additive explanations, the stress level, strength, and stiffness are selected as critical features. The IGEP uniquely integrates two synergistic neural networks, namely a generative model L (mapping stress to life) and an estimated model D (mapping life to stress). Seven neural architectures are evaluated, and then Convolutional Neural Network (CNN) and a combined model including Convolutional Neural Network, Long Short-Term Memory, and Attention module (CNN-LSTM-Attention) are selected to construct L and D, respectively. Models L and D form a closed-loop system that iteratively refines life predictions under the constraint of the fundamental S-N relationship. Compared with experimental data, the predictive accuracy of the IGEP has been verified. Despite the paucity of available experimental data, IGEP can generate reliable fatigue life curves across a wide range of stress levels. Moreover, when applied to stress levels, laminate configurations and material systems beyond those represented in the training data, the IGEP demonstrates robust extrapolation capability. The proposed framework provides a practical and generalizable tool for fatigue life prediction in FRPs under data-limited conditions.

为了保证结构的完整性，建立准确的疲劳寿命预测模型至关重要。传统的回归模型受到预定义函数形式的约束，往往忽略了材料特性的影响。然而，纯数据驱动的方法需要大型数据集，并且表现出较差的外推能力。因此，本研究开发了一种新的框架，可以使用有限的测试数据准确预测疲劳寿命。该框架由特征选择和迭代生成估计过程（IGEP）两个主要部分组成。基于Pearson相关系数、方差膨胀因子和Shapley加性解释，选择应力水平、强度和刚度作为关键特征。IGEP独特地集成了两个协同神经网络，即生成模型L（将压力映射到生活）和估计模型D（将生活映射到压力）。评估了7种神经结构，然后选择卷积神经网络（CNN）和卷积神经网络、长短期记忆和注意模块（CNN- lstm -Attention）的组合模型分别构建L和D。模型L和D形成了一个闭环系统，在基本S-N关系的约束下迭代地细化寿命预测。通过与实验数据的比较，验证了IGEP预测的准确性。尽管缺乏可用的实验数据，但IGEP可以在广泛的应力水平范围内生成可靠的疲劳寿命曲线。此外，当应用于应力水平、层压板结构和材料系统时，IGEP显示出强大的外推能力。提出的框架为数据有限条件下frp的疲劳寿命预测提供了一个实用的、可推广的工具。

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引用次数: 0

Study on fatigue crack growth characteristics and microscopic damage evolution of ER8 wheel steel with different microstructures 不同组织ER8车轮钢疲劳裂纹扩展特征及细观损伤演化研究

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2025-12-20 DOI: 10.1016/j.ijfatigue.2025.109444

Yuqi Qiao , Xiaohui Shi , Fengfeng Huo , Minhao Li , Junwei Qiao

In the present study, the fatigue crack growth characteristics of ER8 wheel steel were examined. Five billets with different microstructures were prepared by the hot working process. The correlation between fatigue crack growth rate (da/dN) and stress intensity factor range (ΔK) was derived based on stress-controlled fatigue experiments. Fatigue crack growth paths were analyzed with the assistance of electron backscatter diffraction (EBSD) technology. EBSD analysis revealed that cracks were deflected when encountering high-angle grain boundaries and tend to propagate along low-energy paths, with low-angle grain boundaries being such paths. Finally, based on the tensile property parameters of the materials, two prediction models were established to assess the effect of microstructural morphologies on the fatigue crack growth rate.

对ER8车轮钢的疲劳裂纹扩展特性进行了研究。采用热加工工艺制备了5种不同组织的钢坯。基于应力控制疲劳试验，推导了疲劳裂纹扩展速率（da/dN）与应力强度因子范围（ΔK）的相关关系。利用电子背散射衍射（EBSD）技术分析了疲劳裂纹的扩展路径。EBSD分析表明，裂纹在遇到高角度晶界时会发生偏转，并倾向于沿低能路径扩展，其中低角度晶界是低能路径。最后，基于材料的拉伸性能参数，建立了两种预测模型来评估微观组织形态对疲劳裂纹扩展速率的影响。

引用次数: 0