Search Results (530)

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Under long-term marine environmental loading, deep-water risers are highly susceptible to fatigue damage, and the accumulation of local damage may lead to global structural failure. In this study, the fatigue damage mechanism and crack growth behavior of a girth-welded riser are systematically investigated. Full-scale radial fatigue test results of risers are referenced, and the experimental process is reproduced through numerical simulation. A finite element model of a girth-welded riser is established. The fatigue crack growth process is subsequently simulated, yielding the crack propagation path and crack growth rate curves. By comparison with experimental results, the characteristics of the crack growth process are analyzed, and the feasibility and accuracy of numerical simulations in predicting fatigue crack growth in riser girth welds are verified. A relatively accurate prediction model for fatigue crack growth in risers is proposed. To further improve the accuracy of crack growth prediction, a machine learning-based correction model is developed. On the basis of available in-service inspection data, a correction strategy is proposed in which the predicted crack growth process is dynamically updated with measured crack growth data. The proposed approach establishes a theoretical foundation for accurate and forward prediction of fatigue fracture damage in riser structures. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

This article comprehensively reviews the fatigue crack growth (FCG) models applied to Ti6Al4V alloy manufactured by electron beam melting (EBM) powder bed fusion (PBF). The current progress in FCG analytical and numerical models and their application to EBM Ti6Al4V are reviewed. Much experimental data for the creation of historical FCG models was based on conventionally manufactured (CM) aluminum alloys and various steels. With the growth of additive manufacturing (AM), recent studies have applied traditional models and modified new models to EBM Ti6Al4V and validated their use as accurate predictive models for the da/dN-ΔK curve and ΔK_th. Due to pores and surface roughness inherent in AM and the unique anisotropic microstructure developed from the EBM process, classical models may require modifications to accurately predict FCG of EBM Ti6Al4V. Full article

The mechanical response of 7xxx aluminum alloys is strongly influenced by both alloy chemistry and the resulting microstructure. In this study, the effect of precipitate characteristics on the fatigue behavior of three 7xxx aluminum alloys with different total amounts of main alloy elements was systematically investigated. Quantitative microstructural characterization was performed under T6 and T74 heat-treatment conditions by combining scanning electron microscopy, transmission electron microscopy, and electron backscatter diffraction. Meanwhile, hardness measurements, room-temperature tensile tests, and fatigue crack growth experiments were carried out to evaluate the mechanical behavior. The results show that, within the present alloy set, the over-aged condition and the alloys with higher overall alloying levels exhibited lower fatigue crack growth rates, which correlated with the coarsening of intragranular precipitates. Such microstructural evolution is suggested to facilitate dislocation motion and thereby reduce fatigue damage associated with dislocation pile-up in the present alloy set. In this work, three typical 7xxx aluminum alloys with different alloying levels were systematically investigated under T6 and T74 conditions. A statistical criterion was established to distinguish GPII zones from η′ precipitates, and a model linking precipitate characteristics to fatigue crack growth behavior was further developed. The present study aims to provide a quantitative framework for understanding and predicting the fatigue behavior of 7xxx aluminum alloys with different total amounts of main alloy elements. Full article

Resilient wheels are widely employed in metro vehicles to mitigate vibration and noise, in which rubber damping components play a critical role in load transmission and fatigue resistance. However, stress concentration and cyclic loading can significantly compromise their durability and service life. In this study, the structural optimization and fatigue life of rubber damping components in resilient wheels are systematically investigated based on finite element analysis and in-service metro operational data. A three-dimensional finite element model incorporating hyperelastic material behavior is developed to evaluate stress distributions under three representative conditions: press-fit assembly, straight-line operation, and curved-track operation. Based on the resulting stress fields, critical high-stress regions within the rubber component are identified and selected as targets for structural optimization. The Design of Experiments (DOE) methodology, integrated with the Isight 2022 optimization platform, is employed to determine the optimal geometric parameters that minimize the von Mises equivalent stress. Furthermore, a fatigue life prediction framework is established using actual metro service mileage data. Fatigue performance is assessed using Fe-safe 2022 software in conjunction with rubber fatigue crack propagation theory, and the results before and after optimization are systematically compared. This study demonstrates that stress concentrations in resilient wheel rubber damping components predominantly occur at fillet transition regions, governed by load transfer characteristics under press-fitting and service conditions. Through DOE-based structural optimization, the critical geometric parameters are effectively refined, leading to a significant reduction in stress levels in key regions. As a result, the proposed approach markedly improves fatigue performance, extending the minimum fatigue life from 1300 days to 24,322 days, thereby substantially enhancing the durability and reliability of the resilient wheel system. Full article

This study presents a unified mechanics-based framework for evaluating bonded composite patch repairs. Discrete fracture, fatigue, and adhesive responses are transformed into continuous master equations over the design space. Low-order polynomial surfaces model stress intensity and concentration responses, enabling continuous prediction of repair performance without repeated finite-element analyses. A fracture-based repair efficiency index is derived from the analytical master surface. This index quantifies the average reduction in crack-driving force across the domain. Combined with adhesive stiffness and strength, it defines an adhesive-based repair efficiency index (A-REI), providing a direct link between structural response and material properties. The results show that repair effectiveness is strongly influenced by both geometric severity and adhesive properties. Fatigue performance decreases significantly with increasing notch ratio in single-sided repairs. Double-sided configurations maintain consistently higher efficiency. Symmetric reinforcement more effectively reduces stress concentration, with improvements exceeding 40% at intermediate notch ratios. Adhesive selection is governed by stiffness and strength. Structural adhesives achieve significantly higher A-REI values, whereas compliant adhesives contribute negligibly. Overall, repair symmetry controls the magnitude of improvement, while adhesive properties determine performance ranking. This framework provides a clear, practical basis for design and material selection. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article

The paper investigates the applicability of time series models for processing data obtained from a customized crack-propagation sensor. Because the sensor records a variable and noise-affected waveform, the study focuses on models capable of forecasting signals composed of both trend and stochastic components. Adaptive, analytical, and autoregressive approaches were examined, with particular attention to their suitability for short, non-stationary sequences typical of fatigue-related measurements. Based on the statistical characteristics of the sensor output during crack growth, the ARIMA model was selected for further analysis and algorithm development. The forecasting performance of ARIMA was evaluated for different parameter configurations by comparing the range and variability of the base and predicted data. Initial tests using first-order parameters produced unsatisfactory results, with high variance observed in both raw and modeled signals. Therefore, model parameters were optimized using the aicbic function, and the analyses were repeated. For the selected datasets, variance reduction by 3–4 orders of magnitude was achieved, demonstrating a substantial improvement in prediction stability. The presented results confirm that the proposed methodology is effective for processing complex sensor signals and highlight the broader significance of applying statistically grounded time series models in structural health monitoring. The study introduces an innovative framework for evaluating fatigue-related sensor data and establishes a reliable baseline for future predictive methods. Full article

This review examines fatigue damage in cement-based materials across the micro-, meso-, and macroscales, with emphasis on how damage initiates, transfers, and becomes structurally observable under cyclic loading. At the microscale, capillary pores, unhydrated cement particles, and the calcium–silicate–hydrate (C-S-H) phase govern local stress concentration, bond rupture, limited healing, and microcrack development. At the mesoscale, the interfacial transition zone (ITZ), cement paste, aggregates, and fiber reinforcement effects control crack initiation, deflection, bridging, and coalescence. At the macroscale, specimen size, boundary conditions, loading regime, and environmental exposure shape stiffness degradation, residual strain accumulation, crack growth, and fatigue life. Beyond summarizing existing studies, this review synthesizes a causal damage transfer interpretation that links microscale deterioration, mesoscale crack interaction, and macroscale response. Current gaps include the limited quantitative link between microstructure-informed models and three-dimensional experimental observations, the still-incomplete validation of multiscale predictive frameworks, and the insufficient treatment of coupled fatigue–environment effects. Addressing these gaps is essential for more reliable fatigue life prediction and for developing durable, resource-efficient concrete infrastructure. Full article

In this study, the tensile properties, low-cycle fatigue behavior, and microscopic fatigue-failure mechanisms of 316H stainless steel in the temperature range of 600–800 °C were systematically investigated by means of tensile tests, high-temperature low-cycle fatigue tests, and scanning electron microscopy (SEM) analysis of fatigue fracture surfaces. Based on experimental data fitting, a life prediction model for the material in the high-temperature regime was established. The results indicate that the mechanical behavior of 316H stainless steel under both static and cyclic loading is significantly influenced by temperature and strain amplitude. Compared with its room-temperature properties, at 800 °C, the elastic modulus of 316H stainless steel decreases by approximately 30%, the tensile strength drops by about 60%, while the elongation after fracture increases by roughly 100%. Within the temperature range of 600–800 °C, the fatigue performance deteriorates with the increasing temperature, and the cyclic hardening rate accelerates as the temperature rises. The fracture mode in the instantaneous fracture zone of the fatigue fracture surface transitions from predominantly transgranular fracture to a mixed mode of transgranular and intergranular fracture as the temperature increases to 800 °C. Under higher strain amplitudes (around 0.6%), 316H stainless steel exhibits Masing behavior and dynamic strain aging (DSA). Correspondingly, the crack-initiation mode on the fatigue fracture surface shifts from a single surface source to multiple surface sources. A three-parameter model was employed to fit the strain–amplitude versus fatigue–life relationships of 316H stainless steel in the 600–800 °C range, showing good agreement with the experimental data, with most data points falling within a factor-of-two error band. Full article

Current studies on fatigue crack initiation in pipelines remain relatively limited. Existing frameworks are confronted with issues including difficulties in crack monitoring and limited consideration of intragranular short-crack propagation. To address these issues, a framework was proposed for predicting fatigue crack initiation life in pipelines with girth welds. The proposed framework incorporates full-scale testing, temperature field simulation and microstructural evolution analysis to overcome limitations in crack measurement and microstructural characterization. In addition, intragranular short-crack propagation has been taken into account in the proposed framework. The proposed framework predicts the fatigue crack initiation life through multiscale coupling. Agreement between the prediction and experimental results supports the validity of the proposed framework. The framework provides reliable predictions of fatigue crack initiation life for pipelines with girth welds under high-cycle fatigue (HCF) conditions. Full article

Liquid metal embrittlement (LME) during hot stamping of Zn-coated high-strength steels poses significant challenges to the long-term durability of automotive components. This study investigates how ~30 μm deep LME cracks affect the mechanical behavior of Zn-coated high-strength boron steel. LME-free flat specimens were compared with hat-shaped specimens containing LME cracks. While tensile strength and ductility exhibited minimal changes, the high-cycle fatigue limit (R = −1, 10⁷ cycles) decreased by 10.9% from 550 MPa to 490 MPa in hat-shaped specimens. Fractographic examination revealed distinct stress-dependent crack initiation mechanisms: at high stress amplitudes (≥690 MPa), LME cracks competed with intrinsic substrate defects but did not dominate fatigue failure. In contrast, at moderate-to-low stress amplitudes (≤630 MPa), LME cracks dominated fatigue degradation through a multi-site crack initiation tendency. El Haddad analysis positioned these cracks at the short-to-long crack transition boundary (l ≈ l₀). Preliminary fracture mechanics analysis reveals that conventional single-crack LEFM models systematically overestimate the fatigue threshold stress for LME-affected specimens, a discrepancy qualitatively attributed to the high surface density and morphological complexity of LME crack networks and to chemically assisted grain boundary weakening induced by liquid Zn infiltration—effects not captured by standard fracture mechanics frameworks. These results establish the stress-dependent mechanisms governing LME crack-induced fatigue degradation and provide a mechanistic basis for the development of more accurate fatigue life prediction methods for Zn-coated hot-stamped high-strength steels. Full article

High-modulus asphalt binders are increasingly used to improve rutting resistance and enable pavement thickness reduction. Conventional binder indices do not always capture the stress-dependent response of high-modulus systems under heavy loading, and quantitative rules for selecting a high-modulus additive dosage are still limited. This study develops a full-temperature-range evaluation and dosage determination framework for high-modulus additive-modified asphalt binders (HMABs) produced on an SBS-modified base binder. Four binders were prepared with high-modulus additive dosages of 0%, 17%, 22% and 28% with a binder mass basis. High-temperature performance was evaluated by PG grading and an enhanced MSCR protocol that included 0.1, 3.2, 6.4 and 12.8 kPa. MSCR temperatures were selected based on PG results. Intermediate-temperature performance was evaluated using LAS at 25 °C with VECD-based fatigue analysis on RTFO + PAV-aged binders. Low-temperature cracking was evaluated using ABCD on PAV-aged binders at −36 °C. The results show that the high-temperature PG increased with dosage, but the 22% and 28% binders fell into the same grade, indicating limited dosage discrimination by the PG test. The enhanced MSCR test captured clearer dosage differences under higher stresses. Non-recoverable compliance decreased markedly with dosage, and stress sensitivity showed an overall decreasing trend; 6.4 kPa provided higher dosage sensitivity and lower variability than 3.2 kPa. LAS test shows a non-monotonic fatigue response in which peak shear stress and predicted fatigue life increased up to 22% and then declined at 28%. At 2.5% and 5% strain, the 22% binder increased predicted fatigue life by about 273% and 83% relative to the base binder, while at 10% strain, it was about 11% lower. ABCD results show an upward shift in critical cracking temperature and a clear reduction in fracture stress at high dosages, indicating increasing low-temperature fracture risk. Therefore, high-modulus additives markedly improve high-temperature stability but introduce full-temperature trade-offs. The proposed full-temperature-range examined framework improves performance discrimination and supports dosage selection. A target dosage of 22% is recommended, and 17~22% is suggested as an engineering-controllable range for a balanced full-temperature performance, while 28% should be treated as an upper-bound option, primarily for warm regions where rutting dominates. Full article