Search Results (159)

B is considered a valuable additive for TiAl alloys, because it is believed to improve their properties by refining their microstructures. However, the effects of B on the practical properties of TiAl alloys for jet engine blades and the optimal addition amount for achieving balanced properties remain unclear. Specifically, there have been very few studies to date in which the practical properties of alloys have been evaluated across a wide range of B addition levels. Therefore, we evaluated various reliability, cost, and performance properties of jet engine blade materials using cast Ti-45,47Al-2Nb-2Mn (the same as XD alloys), with varying B addition levels. The results showed that, in some cases, low B addition levels (0.1–0.2 at.%) could enhance the impact resistance and high-cycle fatigue performance. However, even low B addition levels negatively impacted the machinability, castability, and creep strength. Further, adding 0.4 B or more significantly reduced most practical properties. Compared to XD alloys, TiAl4822 exhibited a superior balance, which is attributed to the higher B content (1 at.%) in XD alloys and the greater effectiveness of Cr relative to Mn in improving the alloy’s high-temperature impact resistance. Full article

Understanding the fatigue behavior of materials is essential for designing components capable of enduring prolonged use under varying stress conditions. This study investigates the high-cycle fatigue and fatigue crack growth characteristics of X17CrNi15-2 stainless steel. Very high-cycle fatigue (VHCF) and fatigue crack growth tests were conducted on conventional fatigue and compact tension (CT) specimens fabricated from X17CrNi15-2 stainless steel. The fatigue crack growth behavior of the CT specimens was analyzed using Paris’ law. A revised version of Paris’ law was suggested based on the fatigue crack growth rate plotted against the stress intensity factor range, expanding on prior research utilizing three-point single-edge notch bend specimens. Scanning electron microscopy (SEM) was employed to examine the fracture mechanisms of both fatigue specimen types. The results indicated that the fatigue specimens failed in the VHCF regime under stress amplitudes ranging from 100 to 450 MPa. A power law correlation between stress amplitude and fatigue life was established, with material constants of 7670.3954 and −0.1663. These findings offer valuable insights into the material’s performance and are crucial for enhancing its suitability in engineering applications where high-cycle fatigue is a critical factor. Full article

The mechanical and structural design of railway vehicles is heavily influenced by their lifetime. Because fatigue is a significant factor that impacts the longevity of railway components, it is imperative that the fatigue resistance properties of crucial components, like leaf springs, be thoroughly investigated. This research investigates the fatigue resistance of 51CrV4 steel under bending and axial tension, considering different stress ratios across low-cycle fatigue (LCF), high-cycle fatigue (HCF), and very-high-cycle fatigue (VHCF) regimes, using experimental data collected from this work and prior research. Data included fractographic analyses aiming to help in understanding some of failures for different loads. The presence of geometric discontinuities, such as notches, amplifies stress concentrations, requiring a probabilistic approach to fatigue assessment. To address notch effects, the theory of critical distances (TCD) was employed to evaluate fatigue strength. TCD model was integrated in fatigue statistical models, such as the Walker model (WSN) and the Castillo–Fernández-Cantelli model adapted for mean stress effects (ACFC). Extending the application of the TCD theory, this research provides an improved probabilistic fatigue model that integrates notch sensitivity, mean stress effects, and fatigue regimes, contributing to more reliable design approaches of railway leaf springs or other components produced in 51CrV4 steel. Full article

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

The Ti-6Al-4V alloy is a typical α + β type titanium alloy and is widely used in the manufacture of aero-engine fans, compressor discs and blades. The working life of modern aero-engine components is usually required to reach more than 10⁸ cycles, which makes the infinite life design based on the traditional fatigue limit unsafe. In this study, through symmetrical loading high-cycle fatigue tests on Ti-6Al-4V titanium alloy, a nonlinear cumulative damage life prediction model was established. Further very-high-cycle fatigue tests of titanium alloys were carried out. The variation law of plastic strain energy in the evolution process of very-high-cycle fatigue damage of titanium alloy materials was described by introducing the internal stress parameter. A prediction model for the very-high-cycle fatigue life of titanium alloys was established, and the sensitivity analysis of model parameters was carried out. The results show that the established high-cycle/very-high-cycle fatigue models can fit the test data well. Moreover, based on the optimized model parameters through sensitivity analysis, the average error of the prediction results has decreased from 59% to 38%. The research aims to provide a model or method for predicting the engineering life of titanium alloys in the high-cycle/very-high-cycle range. 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

Cost reduction has always been a high priority target in modern management. Concentrating on material strength, the huge potential is recognized for cost reduction in finding the material fatigue coefficients by reduction the number and time required for testing specimens. The aim of this study is to evaluate the accuracy of several fatigue parameter estimation methods by comparing them with reference test data obtained for six different steel materials. In the literature, several estimation methods can be found. Those methods rely on tension or hardness tests. The concern is about the accuracy of those methods; therefore, a basic case was investigated involving estimation methods and comparing them to reference data from a physical test. The case was selected in a manner that allowed the verification of combined low and high cycle fatigue. As a result, the estimation methods produced a very wide range of fatigue life predictions, but some of them were quite accurate. This leads to the conclusion that estimation methods can be a step forward for finding the fatigue material properties; however, a study should be undertaken on which methods are the most suitable for the material family used. 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

Cement-emulsified asphalt mortar (CA mortar) is an organic–inorganic composite material composed of cement, emulsified asphalt, fine sand, water, and various admixtures. It is mainly used as the cushion layer for high-speed railway ballastless tracks. CA mortar cushion layers in North China often have to withstand the coupling effects of humidity and freeze–thaw, which has a very important impact on the fatigue performance of CA mortar. Based on the big data statistical results, the temperature conditions and cycle times of the CA mortar layer Freeze–Thaw cycle in North China were determined. Also, a fatigue performance test under humidity–freeze–thaw coupling conditions was designed and carried out. The fitting curve equations of fatigue stress and fatigue life under different humidity conditions and freeze–thaw coupling were established. The relationship between fatigue performance parameters K and n and humidity conditions was analyzed. This study shows that with the increase in humidity, the fatigue life of CA mortar under different humidity conditions shows an overall downward trend. The fatigue performance and fatigue life stress level sensitivity of CA mortar decrease with increasing humidity. The proportion of water damage and freeze–thaw damage to total damage increases with increasing humidity, which means that the humidity and freeze–thaw have a more significant impact on the fatigue properties of CA mortar. When the humidity is low, the fatigue cracks of CA mortar are mostly generated across the cement paste, and the macroscopic damage presents as longitudinal cracking. When the humidity is high, the fatigue cracks of CA mortar are mostly generated at the interface between aggregate and paste, and the macroscopic damage presents as oblique cracking. Based on the analysis of the damage mechanism, it is suggested that the humidity of CA mortar should be controlled below 25% in the actual project to ensure its durability. 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

An experimental study was conducted to analyze temperature evolution during very high cycle fatigue tests. The temperature–number of cycles (T–N) curve is typically divided into three phases: Phase I—a rapid temperature increases at the start of the test, Phase II—temperature stabilization, and Phase III—a sharp temperature rise at the test’s end, coinciding with specimen fracture. The high frequencies used in ultrasonic fatigue testing can induce self-heating in specimens, but the thermal effects are not yet fully understood. Temperature is known to influence the fatigue performance of materials. To explore this, specimens were subjected to varying stress levels and intermittent loading conditions while monitoring temperature evolution using infrared thermography. The T–N curves were obtained, and S–N curves were constructed for specimens tested at room temperature. All tests were performed under fully reversed loading conditions. The experimental data were used to evaluate models commonly applied in conventional fatigue testing. Additionally, the temperature gradient at the beginning of the ultrasonic fatigue test and the heat dissipation per cycle were estimated and analyzed as potential fatigue damage parameters. These findings indicate that parameters derived from the T–N curve have significant potential for predicting very high cycle fatigue life. Full article

Infrared thermography techniques have proven to be very effective for assessing the fatigue limits of metallic materials with obvious temperature variations. But for some materials, it has been shown that the temperature variation is very limited, and the accuracy of infrared thermographic techniques is not verified. In this study, the fatigue properties of a high-strength steel (SAE52100) were evaluated with traditional fatigue-loading techniques and infrared thermographic methods. The traditional fatigue experiments were loaded at a frequency of 80 Hz with a stress ratio of R = −1, and the fatigue limit at the fatigue lifetime of N = 10⁷ cycles was about 800 MPa. Besides, three additional specimens were loaded with step-by-step increasing stress-loading amplitude, where the maximum temperature increments and temperature distribution were recorded via infrared thermographic techniques. The infrared detections revealed that the maximum value of the temperature increase was only about 1 °C. The fatigue limit was first evaluated based on the maximum temperature variation, then the prediction was refined based on fatigue intrinsic dissipation. The fatigue limits predicted with maximum temperature variation were shown to be 841 MPa, 772 MPa, and 787 MPa, respectively, while the fatigue limits predicted based on fatigue intrinsic dissipation were 793 MPa, 791 MPa, and 789 MPa. Finally, an FEM simulation of temperature variation during fatigue loading was implemented to verify the experimental results. This study provides a solid foundation for the applications of infrared thermography techniques for materials with lower energy dissipations. Full article