Pub Date : 2025-12-30DOI: 10.1016/j.ijfatigue.2025.109476
Jiasen Gu , Deqiao Xie , Xuwen Gu , Shuang Liu , Kai Zhou , Chen Jiao , Rong Jiang , Xinfeng Lv , Juan Hu , Zongjun Tian , Dongsheng Wang , Lida Shen
Fatigue life prediction of laser powder bed fusion (LPBF) components remains challenging because critical defects cannot be reliably identified before service, resulting in large scatter and limited applicability of existing methods. In this study, an integrated framework combining quasi in-situ X-ray computed tomography (XCT), finite element method (FEM), and machine learning (ML) was developed to rapidly screen critical defects and predict fatigue life prior to loading. The results revealed the early-stage evolution of critical defects during crack initiation, and a Murakami-Basquin model was established to quantitatively link defect features with fatigue life. Moreover, the FEM-driven ML approach achieved high-accuracy life prediction within a 1.5× error band, with identified as the dominant factor, followed by defect depth () and , in agreement with classical fatigue criteria. Demonstrated with Ti6Al4V, this work establishes a critical-defect-driven pathway for fatigue life prediction, providing a broadly applicable methodology for defect-sensitive design and life assessment of LPBF components.
{"title":"Critical defect-driven fatigue evolution mechanism and life prediction of Ti6Al4V part built by laser powder bed fusion","authors":"Jiasen Gu , Deqiao Xie , Xuwen Gu , Shuang Liu , Kai Zhou , Chen Jiao , Rong Jiang , Xinfeng Lv , Juan Hu , Zongjun Tian , Dongsheng Wang , Lida Shen","doi":"10.1016/j.ijfatigue.2025.109476","DOIUrl":"10.1016/j.ijfatigue.2025.109476","url":null,"abstract":"<div><div>Fatigue life prediction of laser powder bed fusion (LPBF) components remains challenging because critical defects cannot be reliably identified before service, resulting in large scatter and limited applicability of existing methods. In this study, an integrated framework combining quasi <em>in-situ</em> X-ray computed tomography (XCT), finite element method (FEM), and machine learning (ML) was developed to rapidly screen critical defects and predict fatigue life prior to loading. The results revealed the early-stage evolution of critical defects during crack initiation, and a Murakami-Basquin model was established to quantitatively link defect features with fatigue life. Moreover, the FEM-driven ML approach achieved high-accuracy life prediction within a 1.5× error band, with <span><math><msub><mi>σ</mi><mrow><mi>FEM</mi></mrow></msub></math></span> identified as the dominant factor, followed by defect depth (<span><math><mi>h</mi></math></span>) and <span><math><msqrt><mrow><mi>area</mi></mrow></msqrt></math></span>, in agreement with classical fatigue criteria. Demonstrated with Ti6Al4V, this work establishes a critical-defect-driven pathway for fatigue life prediction, providing a broadly applicable methodology for defect-sensitive design and life assessment of LPBF components.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109476"},"PeriodicalIF":6.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894536","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 : 2025-12-30DOI: 10.1016/j.ijfatigue.2025.109475
Wenyue Zhang , Yong Chen , Xing He , Fang Xue , Peng Xu , Wentao He
This paper proposes a dynamic digital twin framework driven by real-time physical information, which integrates a Radial Basis Function (RBF) neural network and a Dynamic Bayesian Network (DBN). A time-varying fatigue crack growth program is developed to update uncertain crack growth parameters and to enable real-time life prediction under welding residual stress and variable-amplitude loading conditions. A finite element model of welding residual stress is established based on thermo-elastic–plastic theory, and the associated stress intensity factor is calculated using the weight function method. A nonlinear mapping between the stress intensity factor and crack length is constructed using the RBF neural network, accounting for both welding residual stress and variable-amplitude loading. The physics-informed digital twin framework, where the Particle Filter (PF) algorithm drives the DBN, is applied to predict fatigue crack growth and update uncertain parameters in Middle Tension (MT) specimens. Under conditions of periodic multiple overloads, the predicted fatigue life closely matches the experimental results, with an error under 1%. The crack growth process is validated through the co-simulation of ABAQUS and FRANC3D using the updated parameters, with the error between simulated and experimental results remaining below 1%, which demonstrates the high accuracy and robustness of the proposed digital twin framework for fatigue life prediction.
{"title":"Real-Time fatigue crack growth prediction for welded structures based on digital twin framework considering residual stress and variable amplitude loading","authors":"Wenyue Zhang , Yong Chen , Xing He , Fang Xue , Peng Xu , Wentao He","doi":"10.1016/j.ijfatigue.2025.109475","DOIUrl":"10.1016/j.ijfatigue.2025.109475","url":null,"abstract":"<div><div>This paper proposes a dynamic digital twin framework driven by real-time physical information, which integrates a Radial Basis Function (RBF) neural network and a Dynamic Bayesian Network (DBN). A time-varying fatigue crack growth program is developed to update uncertain crack growth parameters and to enable real-time life prediction under welding residual stress and variable-amplitude loading conditions. A finite element model of welding residual stress is established based on thermo-elastic–plastic theory, and the associated stress intensity factor is calculated using the weight function method. A nonlinear mapping between the stress intensity factor and crack length is constructed using the RBF neural network, accounting for both welding residual stress and variable-amplitude loading. The physics-informed digital twin framework, where the Particle Filter (PF) algorithm drives the DBN, is applied to predict fatigue crack growth and update uncertain parameters in Middle Tension (MT) specimens. Under conditions of periodic multiple overloads, the predicted fatigue life closely matches the experimental results, with an error under 1%. The crack growth process is validated through the co-simulation of ABAQUS and FRANC3D using the updated parameters, with the error between simulated and experimental results remaining below 1%, which demonstrates the high accuracy and robustness of the proposed digital twin framework for fatigue life prediction.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109475"},"PeriodicalIF":6.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894537","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 : 2025-12-30DOI: 10.1016/j.ijfatigue.2025.109477
J.H. Du, P. Chen, Z.P. Jia, X.W. Li
This study systematically investigates the tension–tension fatigue behavior and deformation mechanisms of solution-treated and aging-treated Fe-30.5Mn-8Al-1C (wt%) austenitic low-density steels, focusing on the critical role of κ-carbide precipitation state in controlling fatigue properties. In aged samples, intragranular κ-carbides induce planar dislocation slip through a “glide plane softening” mechanism, enhancing slip reversibility under cyclic loading and thereby improving fatigue life. Strengthening is primarily due to the interaction between dislocations and intragranular κ-carbides. An appropriate increase in the size of intragranular κ-carbides significantly enhances fatigue life and fatigue strength at low stress amplitudes. Conversely, intergranular κ-carbide precipitation impedes slip transmission, intensifies localized stress concentration, and accelerates damage, thus reducing fatigue life at high stress amplitudes. These findings strongly demonstrate that accelerating the precipitation of intragranular κ-carbides while suppressing intergranular precipitation is an effective microstructural pathway to concurrently enhance fatigue performance of Fe-Mn-Al-C austenitic low-density steels across the entire range of stress amplitudes.
{"title":"Fatigue properties of Fe-30.5Mn-8Al-1C austenitic low-density steel: Critical impact of κ-carbide precipitation state","authors":"J.H. Du, P. Chen, Z.P. Jia, X.W. Li","doi":"10.1016/j.ijfatigue.2025.109477","DOIUrl":"10.1016/j.ijfatigue.2025.109477","url":null,"abstract":"<div><div>This study systematically investigates the tension–tension fatigue behavior and deformation mechanisms of solution-treated and aging-treated Fe-30.5Mn-8Al-1C (wt%) austenitic low-density steels, focusing on the critical role of κ-carbide precipitation state in controlling fatigue properties. In aged samples, intragranular κ-carbides induce planar dislocation slip through a “glide plane softening” mechanism, enhancing slip reversibility under cyclic loading and thereby improving fatigue life. Strengthening is primarily due to the interaction between dislocations and intragranular κ-carbides. An appropriate increase in the size of intragranular κ-carbides significantly enhances fatigue life and fatigue strength at low stress amplitudes. Conversely, intergranular κ-carbide precipitation impedes slip transmission, intensifies localized stress concentration, and accelerates damage, thus reducing fatigue life at high stress amplitudes. These findings strongly demonstrate that accelerating the precipitation of intragranular κ-carbides while suppressing intergranular precipitation is an effective microstructural pathway to concurrently enhance fatigue performance of Fe-Mn-Al-C austenitic low-density steels across the entire range of stress amplitudes.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109477"},"PeriodicalIF":6.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894539","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 : 2025-12-29DOI: 10.1016/j.ijfatigue.2025.109460
Ali Rauf , Indrajit Nandi , Kim Vanmeensel , Reza Talemi
Inconel 718 (IN-718) is a precipitation-strengthened nickel-based superalloy that is widely explored for its applicability in fatigue-critical applications when fabricated using additive manufacturing (AM) at an industrial scale. Among the various factors influencing its performance, the choice of shielding gas during laser powder bed fusion (L-PBF) plays a crucial yet often overlooked role in determining the material’s microstructure and mechanical behaviour. This study investigates the critical influence of shielding gases like argon and nitrogen on the microstructure, defect distribution and the very high cycle fatigue (VHCF) durability of heat-treated L-PBF fabricated IN-718. Defect quantification was undertaken using a combination of optical microscopy, Archimedes density measurements, X-ray computed tomography (XCT), revealing higher defect contents in samples processed under nitrogen shielding. Microstructural analysis through scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS) revealed pronounced variations in grain morphology and inclusion content between the two gas environments. VHCF tests were performed under fully reversed, uniaxial, stress-controlled loading at 20 kHz using dog-bone specimens with larger risk volumes to capture a conservative fatigue life assessment. Fatigue life distributions were analysed using a Weibull accelerated failure time model, revealing similar median lives but narrower scatter for argon-shielded specimens. Fractographic analysis revealed distinct crack-initiation mechanisms, microstructure driven initiation in argon-shielded specimens leaving facets at initiation sites versus defect-assisted initiation often involving inclusions along with pores and lack-of-fusion (LOF) defects in nitrogen-shielded counterparts. Although nitrogen shielding produced a refined microstructure, the elevated porosity and inclusion density-controlled crack initiation and degraded fatigue performance.
{"title":"Process gas influence on Very-High-Cycle fatigue response of Inconel 718 fabricated by laser powder bed fusion","authors":"Ali Rauf , Indrajit Nandi , Kim Vanmeensel , Reza Talemi","doi":"10.1016/j.ijfatigue.2025.109460","DOIUrl":"10.1016/j.ijfatigue.2025.109460","url":null,"abstract":"<div><div>Inconel 718 (IN-718) is a precipitation-strengthened nickel-based superalloy that is widely explored for its applicability in fatigue-critical applications when fabricated using additive manufacturing (AM) at an industrial scale. Among the various factors influencing its performance, the choice of shielding gas during laser powder bed fusion (L-PBF) plays a crucial yet often overlooked role in determining the material’s microstructure and mechanical behaviour. This study investigates the critical influence of shielding gases like argon and nitrogen on the microstructure, defect distribution and the very high cycle fatigue (VHCF) durability of heat-treated L-PBF fabricated IN-718. Defect quantification was undertaken using a combination of optical microscopy, Archimedes density measurements, X-ray computed tomography (XCT), revealing higher defect contents in samples processed under nitrogen shielding. Microstructural analysis through scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and energy-dispersive X-ray spectroscopy (EDS) revealed pronounced variations in grain morphology and inclusion content between the two gas environments. VHCF tests were performed under fully reversed, uniaxial, stress-controlled loading at 20 kHz using dog-bone specimens with larger risk volumes to capture a conservative fatigue life assessment. Fatigue life distributions were analysed using a Weibull accelerated failure time model, revealing similar median lives but narrower scatter for argon-shielded specimens. Fractographic analysis revealed distinct crack-initiation mechanisms, microstructure driven initiation in argon-shielded specimens leaving facets at initiation sites versus defect-assisted initiation often involving inclusions along with pores and lack-of-fusion (LOF) defects in nitrogen-shielded counterparts. Although nitrogen shielding produced a refined microstructure, the elevated porosity and inclusion density-controlled crack initiation and degraded fatigue performance.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109460"},"PeriodicalIF":6.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894569","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 : 2025-12-29DOI: 10.1016/j.ijfatigue.2025.109470
Kangnan Zhu, Jiajun Shi, Anji Wang, Guijun Xian, Chenggao Li
Carbon/glass hybrid fiber reinforced polymer (C/GFRP) tubes, which offer both high performance and cost-effectiveness, are often subjected to the synergistic effects of fatigue and creep during their service life as transportation carriers, which reduces the safety of the structure. This study investigates the tension–tension fatigue behavior of C/GFRP tubes under constant stress ratio at different stress levels. The influence of a hygrothermal environment on fatigue failure modes, fatigue life, and stiffness degradation was examined via laboratory accelerated aging (150 days of immersion in distilled water at 60 °C). The creep displacement evolution was investigated by experimental and analytical means. Finally, a modified fatigue stiffness degradation model accounting for creep effects was proposed based on the creep growth curve. During fatigue loading, the primary load-bearing responsibility gradually shifts from the resin to the fibers as the resin deforms. This transition alters the material’s viscoelastic behavior, evolving from resin-dominated viscoelasticity toward fiber-dominated elasticity. Consequently, the total energy dissipated per loading cycle significantly decreases. Hygrothermal aging alters the failure mode, causing irregular serrated matrix fractures due to interface degradation, and significantly reduces fatigue life. After 150 days of accelerated aging, the fatigue life retention rates of the C/GFRP tubes at stress levels of 0.50, 0.45, 0.40, and 0.38 were 16.3 %, 61.6 %, 57.1 %, and 45.8 %, respectively. Creep effects lead to increased stiffness during fatigue in tubes. The modified stiffness degradation model effectively characterizes the actual stiffness evolution of C/GFRP tubes during fatigue process by separating the cyclic creep.
{"title":"Creep-fatigue interaction and hygrothermal aging effect on the fatigue behavior of carbon/glass hybrid fiber filament-wound tubes","authors":"Kangnan Zhu, Jiajun Shi, Anji Wang, Guijun Xian, Chenggao Li","doi":"10.1016/j.ijfatigue.2025.109470","DOIUrl":"10.1016/j.ijfatigue.2025.109470","url":null,"abstract":"<div><div>Carbon/glass hybrid fiber reinforced polymer (C/GFRP) tubes, which offer both high performance and cost-effectiveness, are often subjected to the synergistic effects of fatigue and creep during their service life as transportation carriers, which reduces the safety of the structure. This study investigates the tension–tension fatigue behavior of C/GFRP tubes under constant stress ratio at different stress levels. The influence of a hygrothermal environment on fatigue failure modes, fatigue life, and stiffness degradation was examined via laboratory accelerated aging (150 days of immersion in distilled water at 60 °C). The creep displacement evolution was investigated by experimental and analytical means. Finally, a modified fatigue stiffness degradation model accounting for creep effects was proposed based on the creep growth curve. During fatigue loading, the primary load-bearing responsibility gradually shifts from the resin to the fibers as the resin deforms. This transition alters the material’s viscoelastic behavior, evolving from resin-dominated viscoelasticity toward fiber-dominated elasticity. Consequently, the total energy dissipated per loading cycle significantly decreases. Hygrothermal aging alters the failure mode, causing irregular serrated matrix fractures due to interface degradation, and significantly reduces fatigue life. After 150 days of accelerated aging, the fatigue life retention rates of the C/GFRP tubes at stress levels of 0.50, 0.45, 0.40, and 0.38 were 16.3 %, 61.6 %, 57.1 %, and 45.8 %, respectively. Creep effects lead to increased stiffness during fatigue in tubes. The modified stiffness degradation model effectively characterizes the actual stiffness evolution of C/GFRP tubes during fatigue process by separating the cyclic creep.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109470"},"PeriodicalIF":6.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881308","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 : 2025-12-29DOI: 10.1016/j.ijfatigue.2025.109469
Luohuan Zou , Yu Gong , Dingli Tian , Sizhuo Hao , Jianyu Zhang , Libin Zhao , Ning Hu
Delamination usually occurs and grows in composite laminates under fatigue loading. The stress ratio is an important factor, while its influence law has no consensus yet. In this paper, to fully investigate the influence of fiber bridging and stress ratio on the fatigue delamination behavior, mode I fatigue delamination tests under two stress ratios (0.1 and 0.5) are conducted. Test results reveal that, the initial and steady-state values of the fatigue R-curve are consistent with those of quasi-static ones, while there are significant differences in the growth stage of fiber bridging. Furthermore, it is found that, the slope and intercept of the da/dN-Gmax curves vary under different stress ratios. A novel four-parameter fatigue model considering fiber bridging and stress ratio effects is proposed. The proposed model is compared with other classical models in literatures using the fatigue data of two stress ratios (0.1 and 0.5). It is found that the proposed model can well characterize fatigue delamination behavior. To further verify the model applicability, fatigue tests under stress ratio of 0.3 are supplemented. The predicted da/dN-Gmax curves by the model and experimental results are compared with a 95% confidence interval, which indicates that the proposed model has good applicability and can provide an effective method for fatigue delamination prediction.
{"title":"A new empirical model for mode I fatigue delamination of composite laminates considering fiber bridging and stress ratio effects","authors":"Luohuan Zou , Yu Gong , Dingli Tian , Sizhuo Hao , Jianyu Zhang , Libin Zhao , Ning Hu","doi":"10.1016/j.ijfatigue.2025.109469","DOIUrl":"10.1016/j.ijfatigue.2025.109469","url":null,"abstract":"<div><div>Delamination usually occurs and grows in composite laminates under fatigue loading. The stress ratio is an important factor, while its influence law has no consensus yet. In this paper, to fully investigate the influence of fiber bridging and stress ratio on the fatigue delamination behavior, mode I fatigue delamination tests under two stress ratios (0.1 and 0.5) are conducted. Test results reveal that, the initial and steady-state values of the fatigue R-curve are consistent with those of quasi-static ones, while there are significant differences in the growth stage of fiber bridging. Furthermore, it is found that, the slope and intercept of the d<em>a</em>/d<em>N</em>-<em>G<sub>max</sub></em> curves vary under different stress ratios. A novel four-parameter fatigue model considering fiber bridging and stress ratio effects is proposed. The proposed model is compared with other classical models in literatures using the fatigue data of two stress ratios (0.1 and 0.5). It is found that the proposed model can well characterize fatigue delamination behavior. To further verify the model applicability, fatigue tests under stress ratio of 0.3 are supplemented. The predicted d<em>a</em>/d<em>N</em>-<em>G<sub>max</sub></em> curves by the model and experimental results are compared with a 95% confidence interval, which indicates that the proposed model has good applicability and can provide an effective method for fatigue delamination prediction.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109469"},"PeriodicalIF":6.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895491","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}
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）。
{"title":"An automated method for interpreting fatigue fracture surfaces with marker load based on computer vision and artificial neural networks","authors":"Jinyu Wang, Xiaofan He, Hao Xin, Zhongwen Tao, Zhen Jia","doi":"10.1016/j.ijfatigue.2025.109471","DOIUrl":"10.1016/j.ijfatigue.2025.109471","url":null,"abstract":"<div><div>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 <em>you only look once</em> (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).</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109471"},"PeriodicalIF":6.8,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881309","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 : 2025-12-27DOI: 10.1016/j.ijfatigue.2025.109462
Xiaochuan Yang , Tianxin Li , Jiang Yang , Mingpan Wan , Zhong Zhang , Chaowen Huang
The AlCoCrFeNi2.1 (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.
{"title":"Phase boundary and defect dependent high cycle fatigue behavior in AlCoCrFeNi2.1 eutectic high-entropy alloy","authors":"Xiaochuan Yang , Tianxin Li , Jiang Yang , Mingpan Wan , Zhong Zhang , Chaowen Huang","doi":"10.1016/j.ijfatigue.2025.109462","DOIUrl":"10.1016/j.ijfatigue.2025.109462","url":null,"abstract":"<div><div>The AlCoCrFeNi<sub>2.1</sub> (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 (<em>σ<sub>-1</sub></em> (10<sup>7</sup>)), corresponding to a fatigue ratio (<em>σ<sub>-1</sub></em> (10<sup>7</sup>)/<em>YS</em>) 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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109462"},"PeriodicalIF":6.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845099","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 : 2025-12-27DOI: 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.
{"title":"Defect-sensitive fatigue assessment of heavy-section ductile cast irons: a comparative study of pearlitic and high-silicon ferritic grades","authors":"M. Benedetti , M. Pedranz , D. Lusuardi , F. Zanini , S. Carmignato , V. Fontanari","doi":"10.1016/j.ijfatigue.2025.109455","DOIUrl":"10.1016/j.ijfatigue.2025.109455","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109455"},"PeriodicalIF":6.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845098","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 : 2025-12-25DOI: 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.
{"title":"An effective approach for identifying fatigue-critical defects from X-ray 3D reconstruction: Example in L-PBF AlSil0Mg alloys","authors":"Zhengkai Wu , Tianyu Qin , Jianguang Bao , Weijian Qian , Enrico Salvati , Shengchuan Wu , Rihan Da , Jiang Dong , Hiroyuki Toda","doi":"10.1016/j.ijfatigue.2025.109457","DOIUrl":"10.1016/j.ijfatigue.2025.109457","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"206 ","pages":"Article 109457"},"PeriodicalIF":6.8,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845101","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}
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