Pub Date : 2025-12-24DOI: 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.
{"title":"Multiaxial fatigue life assessment of welded joints using the super ellipse criterion under consideration of support effects","authors":"Niklas Michael Bauer, Annina Wöhle, Jörg Baumgartner","doi":"10.1016/j.ijfatigue.2025.109458","DOIUrl":"10.1016/j.ijfatigue.2025.109458","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109458"},"PeriodicalIF":6.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 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.
{"title":"Predicting the whole-life stress amplitude evolution of high-Mn TWIP steel under complex loading conditions using fatigue failure criteria and machine learning","authors":"Di Song , Jinze Pei , Ye Xiao , Ronghai Wu , Heng Li","doi":"10.1016/j.ijfatigue.2025.109449","DOIUrl":"10.1016/j.ijfatigue.2025.109449","url":null,"abstract":"<div><div>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 <em>R</em><sup>2</sup> of 0.95 and a mean absolute percentage error (MAPE) of 1.87 %, demonstrating robust predictive capability across diverse conditions.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109449"},"PeriodicalIF":6.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 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.
{"title":"Multi-mechanism crystal plasticity-based finite element framework uncovering the tensile and fatigue behavior of Ni-based single crystal superalloy","authors":"Yuanyuan Wang , Donghai Yu , Changjian Geng , Xianglin Zhou , Shilei Li , Yan-Dong Wang","doi":"10.1016/j.ijfatigue.2025.109456","DOIUrl":"10.1016/j.ijfatigue.2025.109456","url":null,"abstract":"<div><div>A multi-mechanism crystal plasticity finite element (CPFE) model is developed to capture the micromechanical deformation and fatigue behavior of <em>γ</em>′-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 <em>γ</em> and <em>γ</em>′ phases to reproduce hardening characteristics. In the <em>γ</em> matrix, microstructual features such as <em>γ</em> channel width are introduced to determine the initial slip resistance, while the strengthening effect associated with dislocation bypass of <em>γ</em>′ precipitate is incorporated through an Orowan mechanism. The critical <em>γ</em>/<em>γ</em>′ phase interaction is represented by back stress arising from dislocation pileup at the phase boundary. For the <em>γ</em>′ 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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109456"},"PeriodicalIF":6.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-21DOI: 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.
{"title":"Competitive propagation of multiple surface fatigue cracks in railheads: compact tension tests and peridynamic simulations","authors":"Xiaoming Wang , Yitong Shi , Weijia Dong , Qing He , Boyang An , Bing Yang , Jun Huang , Ping Wang","doi":"10.1016/j.ijfatigue.2025.109453","DOIUrl":"10.1016/j.ijfatigue.2025.109453","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109453"},"PeriodicalIF":6.8,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-21DOI: 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.
{"title":"Fatigue life prediction of multi jet fusion-manufactured polyamide12 lattice structures using the average strain energy density method","authors":"Raffaele De Biasi , Lorenzo Romanelli , Ciro Santus , Matteo Perini , Filippo Berto , Matteo Benedetti","doi":"10.1016/j.ijfatigue.2025.109452","DOIUrl":"10.1016/j.ijfatigue.2025.109452","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109452"},"PeriodicalIF":6.8,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-21DOI: 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.
{"title":"Investigation on fatigue crack growth behavior and remaining useful life prediction of 6005A-T6 aluminum alloy under fatigue aging","authors":"Zhe Zhang, Bing Yang, Shiqi Zhou, Jinbang Liu, Long Yang, Shoune Xiao, Guangwu Yang, Tao Zhu","doi":"10.1016/j.ijfatigue.2025.109448","DOIUrl":"10.1016/j.ijfatigue.2025.109448","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109448"},"PeriodicalIF":6.8,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-21DOI: 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.
{"title":"A framework for fatigue life prediction of fiber reinforced composites with limited testing data","authors":"Lu Yubin , Wu Zhen","doi":"10.1016/j.ijfatigue.2025.109446","DOIUrl":"10.1016/j.ijfatigue.2025.109446","url":null,"abstract":"<div><div>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 <em>L</em> (mapping stress to life) and an estimated model <em>D</em> (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 <em>L</em> and <em>D</em>, respectively. Models <em>L</em> and <em>D</em> form a closed-loop system that iteratively refines life predictions under the constraint of the fundamental <em>S</em>-<em>N</em> 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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109446"},"PeriodicalIF":6.8,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-20DOI: 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.
{"title":"Study on fatigue crack growth characteristics and microscopic damage evolution of ER8 wheel steel with different microstructures","authors":"Yuqi Qiao , Xiaohui Shi , Fengfeng Huo , Minhao Li , Junwei Qiao","doi":"10.1016/j.ijfatigue.2025.109444","DOIUrl":"10.1016/j.ijfatigue.2025.109444","url":null,"abstract":"<div><div>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 (d<em>a</em>/d<em>N</em>) and stress intensity factor range (Δ<em>K</em>) 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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109444"},"PeriodicalIF":6.8,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1016/j.ijfatigue.2025.109445
Peng Liu, Haoyuan Li, Hailong Tian, Lai Wei, Yunshenghao Qiu
Dynamic load spectra for electric-drive assemblies are difficult to estimate from road tests because the signals are non-stationary, non-Gaussian, and noisy. We propose a pseudo-damage-constrained data–model fusion framework that reconstructs torque/load histories while preserving rainflow counting and fatigue consistency. The approach combines trend extraction with nonlinear state estimation and an innovation-based adaptive step that enforces pseudo-damage equivalence to the raw signal within a controlled tolerance. Extreme-value fits are used only as tail diagnostics to verify that rare high-load behavior is preserved; they are not involved in cycle counting. On representative road data, the method achieved a Peak–Valley Preservation Rate ≈93% and the lowest weighted-MAPE (26.2%) among EKF, PF, KalmanNet, and LSTM baselines, with clear gains in fatigue-critical mid–high levels and no inflation of the spectrum tail. The results indicate that the proposed framework yields higher-fidelity spectra for durability analysis and test-bench replay while keeping established fatigue rules (four-point rainflow with Goodman correction) unchanged.
{"title":"A pseudo-damage-constrained data–model fusion method for dynamic load spectrum estimation in electric-drive assemblies","authors":"Peng Liu, Haoyuan Li, Hailong Tian, Lai Wei, Yunshenghao Qiu","doi":"10.1016/j.ijfatigue.2025.109445","DOIUrl":"10.1016/j.ijfatigue.2025.109445","url":null,"abstract":"<div><div>Dynamic load spectra for electric-drive assemblies are difficult to estimate from road tests because the signals are non-stationary, non-Gaussian, and noisy. We propose a pseudo-damage-constrained data–model fusion framework that reconstructs torque/load histories while preserving rainflow counting and fatigue consistency. The approach combines trend extraction with nonlinear state estimation and an innovation-based adaptive step that enforces pseudo-damage equivalence to the raw signal within a controlled tolerance. Extreme-value fits are used only as tail diagnostics to verify that rare high-load behavior is preserved; they are not involved in cycle counting. On representative road data, the method achieved a Peak–Valley Preservation Rate ≈93% and the lowest weighted-MAPE (26.2%) among EKF, PF, KalmanNet, and LSTM baselines, with clear gains in fatigue-critical mid–high levels and no inflation of the spectrum tail. The results indicate that the proposed framework yields higher-fidelity spectra for durability analysis and test-bench replay while keeping established fatigue rules (four-point rainflow with Goodman correction) unchanged.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109445"},"PeriodicalIF":6.8,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.ijfatigue.2025.109451
Lu Zhang
The estimation of fatigue life is vital for ensuring structural durability and safety in engineering applications. To address the limitations of current nonlinear fatigue cumulative damage models, which often overlook the interplay between load sequence and material properties, a novel nonlinear fatigue damage accumulation model is developed in this work. By comprehensively reviewing and analyzing nonlinear action coefficients in prior enhanced models, a new function for the action coefficient is formulated, incorporating three key elements: adjacent stress ratio, material S-N curve slope, and equivalent fatigue damage. The key parameters of the model are determined based on two-level stress test data of multiple materials. Furthermore, using fatigue test data of various metal materials under two-level to five-level stress spectra, the prediction accuracy of the new model is compared and verified against 8 typical models. The results show that the new model exhibits better adaptability and prediction accuracy under different stress levels and load sequences, demonstrating good potential for engineering applications.
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