Search Results (63)

As high-speed electric multiple units (EMUs) advance in speed and complexity, quasi-static design methods may underestimate the fatigue risks associated with high-frequency dynamic excitations. This study quantifies the contribution of gear meshing-induced vibrations (2512 Hz) to fatigue damage in EMU gearbox housings, revealing resonance amplification of local stresses up to 1.8 MPa at 300 km/h operation. Through integrated field monitoring and bench testing, we demonstrated that gear meshing excites structural modes, generating sustained, very high-cycle stresses (>10⁸ cycles). Crucially, fatigue specimens were directly extracted from in-service gearbox housings—overcoming the limitations of standardized coupons—passing the very high-cycle fatigue (VHCF) test to derive S-N characteristics beyond 10⁸ cycles. Results show a continuous decline in fatigue strength (with no traditional fatigue limit) from 10⁸ to 10⁹ cycles. This work bridges the gap between static design standards (e.g., FKM) and actual dynamic environments, proving that accumulated damage from low-amplitude gear-meshing stresses (3.62 × 10¹¹ cycles over a 12 million km lifespan) contributes to a 16% material utilization ratio. The findings emphasize that even low-magnitude gear-meshing stresses can significantly influence gearbox fatigue life due to their ultra-high frequency, warranting design consideration beyond current standards. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

This study established a machine learning framework to enhance the accuracy of very-high-cycle fatigue (VHCF) life prediction in selective laser melted Inconel 718 alloy by systematically comparing the use of generative adversarial networks (GANs) and variational auto-encoders (VAEs) for data augmentation. We quantified the influence of critical defect parameters (dimensions and stress amplitudes) extracted from fracture analyses on fatigue life and compared the performance of GANs versus VAEs in generating synthetic training data for three regression models (ANN, Random Forest, and SVR). The experimental fatigue data were augmented using both generative models, followed by hyperparameter optimization and rigorous validation against independent test sets. The results demonstrated that the GAN-generated data significantly improved the prediction metrics, with GAN-enhanced models achieving superior R² scores (0.91–0.97 vs. 0.86 ± 0.87) and lower MAEs (1.13–1.62% vs. 2.00–2.64%) compared to the VAE-based approaches. This work not only establishes GANs as a breakthrough tool for AM fatigue prediction but also provides a transferable methodology for data-driven modeling of defect-dominated failure mechanisms in advanced materials. Full article

A single batch of AlSi10Mg powder was used to fabricate two groups of round bars via horizontal printing, employing an identical strategy except for one parameter in the process of powder bed fusion-laser beam. The parameter is layer thickness, set at 50 and 80 μm for Group-1 and Group-2, respectively, resulting in laser energy densities of 29.95 and 18.72 J/mm³. Both materials exhibit similar microstructures; Group-1 has fewer and smaller defects than Group-2, leading to higher strength and ductility. Fatigue performance of low-cycle and long-life up to 10⁸ cycles under rotating bending was assessed, and the fracture surfaces were carefully examined under scanning electron microscopy. The S-N data converge to a single slope followed by a horizontal asymptote, indicating the occurrence of very-high-cycle fatigue (VHCF) in both cases. Group-1 shows higher fatigue strength in the range of 10⁴ to 10⁸ cycles, and a greater failure probability in VHCF regime than Group-2. This is attributed to the larger defect size in Group-2, where the smaller control volume in rotating bending greatly increases the likelihood of encountering large defects compared to Group-1. At the defect edge, the microstructure shows distinct resistance to crack propagation under high and low loads. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

The longevity of railway vehicles is an important factor in their mechanical and structural design. Fatigue is a major issue that affects the durability of railway components, and, therefore, knowledge of the fatigue resistance characteristics of critical components, such as leaf springs, must be extensively investigated. This research covers the fatigue resistance of 51CrV4 steel under bending and axial tension, for distinct stress ratios, in the low-cycle fatigue regime (LCF), high-cycle fatigue regime (HCF), and very high-cycle fatigue regime (VHCF) using experimental data collected in this work and from previous experiments. Two fatigue models were analyzed: the Walker model (WSN) and the Castillo–Fernández–Cantelli model, CFC, adapted for the presence of mean stress (ACFC). According to the analysis carried out, both fatigue resistance prediction models provided good results for the experimental data, with the ACFC model showing good fitting when considering all the failure data and outliers. Additionally, fracture surfaces showed a higher trend for crack initiation on the surface for positive stress ratios despite internal defects also possibly being responsible for some fatigue failures. This investigation aimed to provide a probabilistic fatigue model encompassing the LCF, HCF, and VHCF fatigue regimes for distinct stress ratios for the fatigue design analysis of 51CrV4 steel parabolic leaf springs manufactured by hot-forming processes with subsequent heat treatments. Full article

The longevity of railway vehicles is an important factor in their mechanical and structural design. Fatigue is a major issue that affects the durability of railway components, and therefore, knowledge of the fatigue resistance characteristics of critical components, such as the leaf springs, must be extensively investigated. This research covers the fatigue resistance of chromium–vanadium alloy steel, usually designated as 51CrV4, from the high-cycle regime (HCF) (${10}^3$–${10}^4$) up to very high-cycle fatigue (VHCF) (${10}^9$) under the bending loading conditions typical of leaf springs and under uniaxial tension/compression loading, respectively, for a stress ratio, $R_{\sigma }$, of −1. Different test frequencies were considered (23, 150, and 20,000 Hz) despite the material not exhibiting a relatively significant frequency effect. In order to create a new fatigue prediction model, two prediction models, the Basquin SN linear regression model and the Castillo–Fernandez–Cantelli (CFC) model, were evaluated. According to the analysis carried out, the CFC model provided a better prediction of the fatigue failures than the SN model, especially when outlier failure data were considered. The investigation also examined the failure modes, observing multiple cracks for higher loads and single cracks that initiated on the surface or from internal inclusions at lower loading. The present investigation aims to provide a fatigue resistance prediction model encompassing the HCF and VHCF regions for the fatigue design of railway wagon leaf springs, or even for other components made of 51CrV4 with a tempered martensitic microstructure. Full article

It has been reported that the multiphase structure with martensite and a mixed structure obtained by the quenching and tempering of bearing steel shows high strength and ductility. However, there appears to be no study on the effects of very-high-cycle fatigue (VHCF) and ultrasonic nanocrystal surface modification (UNSM) of a new bearing steel required for the durability design of next-generation bearing steel. This study analyzed the characteristics of microstructure transformation associated with heat treatment cycles and studied and evaluated the fatigue strength characteristics by the UNSM. Fisheye cracks occur from small inclusions inside the specimen only in martensitic microstructure materials in the long-life range. And the characteristic of a double S-N curve in which the fatigue limit decreased in two stages appeared. However, fisheye cracks and double S-N curves did not appear in pearlite and mixed-structure materials. Full article

The very high cycle fatigue (VHCF) strength of welded joints made of high-strength structural materials is generally poor, which poses a serious threat to the long life and reliability of the structural components. This work employs an ultrasonic vibration fatigue testing system to investigate the biaxial fatigue failure mechanism of the welded joints. The results revealed that under uniaxial loading conditions, the propensity for fatigue failure in plate specimens was predominantly observed at the specimen surface. Regardless of whether under uniaxial or biaxial loading, the initiation of fatigue cracks in cruciform joints was consistently traced back to unfused flaws, which were primarily located at the interface between the solder and the base material. Concurrently, it was noted that the fatigue strength of cruciform joints under biaxial loading was merely 44.4% of that under uniaxial loading. The geometric peculiarities of the unfused defects led to severe stress concentrations, which significantly reduced the fatigue life of the material under biaxial loading conditions. Full article

Free-spanning subsea pipelines subjected to vortex-induced vibrations (VIVs) are particularly prone to fatigue failure. Existing flume observations indicated that the VIVs of a near-bed cylinder may be triggered effectively in moderate shear flows. This may imply that the vibration cycles of a spanned pipeline could be up to tens of millions. As such, very high cycle fatigue (VHCF) can occur during engineering service. The free span length is a key parameter for determining the structural natural frequency and the corresponding reduced velocity (Vr). On the basis of the dimensionless vibration amplitude A/D–Vr curve and the recommended S-N curves for high-strength steel pipelines with cathodic protection under seawater environments, a prediction method is proposed for the fatigue life of a free span undergoing VIVs. A parametric study is then performed to evaluate the fatigue life of the spanned pipelines with a focus on VHCF. It is indicated that the minimum fatigue life emerges at certain flow with a moderate velocity for a given span length. With a further decrease or increase in the flow velocity, the fatigue life would be enhanced correspondingly, which could be within the VHCF regime. Such nonlinear variation of the fatigue life with the span length and the flow velocity is attributed to being involved in various VIV branches of the A/D–Vr curve. Full article

Additive manufacturing (AM) or 3D printing is a promising industrial technology that enables rapid prototyping of complex configurations. Powder Bed Fusion (PBF) is one of the most popular AM techniques for metallic materials. Until today, only a few metals and alloys are available for AM, e.g., titanium alloys, the most common of which is Ti-6Al-4V. After optimization of PBF parameters, with or without post processing such as heat treatment or hot isostatic pressing, the printed titanium alloy can easily reach tensile strengths of over 1100 MPa due to the quick cooling of the AM process. However, attributed to the unique features of metallurgical defects and microstructure introduced by this AM process, their fatigue strength has been low, often less than 30% of the tensile strength, especially in very-high-cycle regimes, i.e., failure life beyond 10⁷ cycles. Here, based on our group’s research on the very-high-cycle fatigue (VHCF) of additively manufactured (AMed) Ti-6Al-4V alloys, we have refined the basic quantities of porosity, metallurgical defects, and the AMed microstructure, summarized the main factors limiting their VHCF strengths, and suggested possible ways to improve VHCF performance. Full article

As the reliability and lifespan requirements of modern equipment continues to escalate, the problems with very high cycle fatigue (VHCF) has obtained increasingly widespread attention, becoming a hot topic in fatigue research. Titanium alloys, which are the most extensively used metal materials in the modern aerospace industry, are particularly prone to VHCF issues. The present study systematically reviewed and summarized the latest (since 2010) developments in VHCF research on titanium alloy, with special focus on the (i) experimental methods, (ii) macroscopic and microscopic characteristics of the fatigue fractures, and (iii) construction of fatigue fracture models. More specifically, the review addresses the technological approaches that were used, mechanisms of fatigue crack initiation, features of the S–N curves and Goodman diagrams, and impact of various factors (such as processing, temperature, and corrosion). In addition, it elucidates the damage mechanisms, evolution, and modeling of VHCF in titanium alloys, thereby improving the understanding of VHCF patterns in titanium alloys and highlighting the current challenges in VHCF research. Full article

With ultrasonic fatigue testing (UFT), it is possible to investigate the damage initiation and accumulation from the weakest link of the composite material in the very high cycle fatigue (VHCF) regime in a shorter time frame than conventional fatigue testing. However, the thermal influence on the mechanical fatigue of composites and the scatter in fatigue data for composites under ultrasonic cyclic three-point bending loading still need to be investigated. In this study, we conducted interrupted constant-amplitude fatigue experiments on a carbon-fiber satin-fabric reinforced in poly-ether-ketone-ketone (CF-PEKK) composite material. These experiments were carried out using a UFT system, which operates at a cyclic frequency of 20 kHz with a pulse–pause sequence. Various parameters, such as the CF-PEKK specimen’s surface temperature, acoustic activity, and the ultrasonic generator’s input resonance parameters, were measured during cyclic loading. During experiment interruption, stiffness measurement and volumetric damage characterization in the CF-PEKK specimens using 3D X-ray microscopy (XRM) were performed. The locations of damage initiation and accumulation and their influence on the changes in in situ parameters were characterized. Under fixed loading conditions, damage accumulation occurred at different locations, leading to scattering in fatigue life data. Further, the damage population decreased from the surface to the bulk of the composite material. Full article

The engineering application of additively manufactured (AM) metallic materials is quite limited by their fatigue behaviors, which are very inconsistent with that of conventionally wrought or cast ones. Here, based on advanced material characterization techniques, such as focused ion beam (FIB), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), the microstructures underneath fracture surfaces were thoroughly investigated in an AM aluminum (AlSi10Mg) alloy with horizontal and vertical building orientation enduring very high cycle fatigue (VHCF) loading under the stress ratios R = −1, 0, and 0.5. Two VHCF failure specimens A and B were representatively selected to further examine SEM and TEM sample preparation via FIB milling. Specimen A was horizontally printed and failed at R = −1; specimen B was vertically printed and failed at R = 0. TEM samples A1 and B1 were lifted from locations near the crack initiation sites on the fracture surfaces of specimens A and B; The locations of TEM samples A2 and B2 kept away from the crack origin sites but still within the “fish-eye” region of crack steady growth. TEM observations show that there was no characteristic microstructure induced by VHCF in different oriented specimens and under various R values. Full article

Crack initiation plays a major role in very high cycle fatigue (VHCF) life, and the initiation of cracks is related to slip behavior. There is a need for improvement in the understanding of the influence of Ti-6Al-4V microstructures on VHCF performance and crack initiation modes. In this study, through an investigation of Ti-6Al-4V VHCF in equiaxed and bimodal microstructures, two different crack initiation modes were identified. The change in crack initiation mode is related to the variation in microtexture, for which a corresponding model is proposed. The VHCF performance of the bimodal microstructure is significantly improved compared to that of the equiaxed microstructure. Full article