Classical Fatigue Research Articles

Residual fatigue life prediction based on a novel improved Manson-Halford model considering loading interaction effect

The classical nonlinear fatigue cumulative damage model Manson-Halford is widely used in fatigue life analysis and prediction, but this model lacks consideration for the effects of load interaction. At present, some studies have proposed new correction models to overcome the shortcomings of classical models. However, the problem with these models is that the parameters are difficult to determine, resulting in a cumbersome application process or the correction process is based only on a single parameter. Insufficient correction leads to large fluctuations in life prediction errors. To overcome the above problems, this article proposes a new correction method and establishes a new improved model. Unlike existing models, the newly improved model is based solely on S-N curve parameters and does not require additional material parameters. It fully considers the relationship between adjacent loads and the difference in fatigue life under different stress states, and dynamically modifies the classical model based on larger influence weights. By conducting multi-level fatigue tests on 300M steel, a new improved model was used to accurately predict its remaining fatigue life. Compared with the classical model, the prediction error was reduced by 11.75 %. Utilized fatigue test data from various other materials to predict the fatigue life of the improved model under different levels of stress loading. The results indicate that in the vast majority of cases, the improved model has the highest prediction accuracy among all compared models, with a minimum relative prediction error of only 3.79 % and small fluctuations in prediction error. Compared with the classical model, the maximum reduction in relative prediction error after model improvement reached 28.73 %, indicating the effectiveness and accuracy of the new improved model.

Sustained improvements in patient-reported outcomes after long-term sutimlimab in patients with cold agglutinin disease: results from the CADENZA study open-label extension

Cold agglutinin disease (CAD) is a rare subtype of autoimmune haemolytic anaemia characterised by classical complement pathway-mediated haemolysis, fatigue, and poor quality of life (QoL). Sutimlimab, a C1s inhibitor, rapidly halted haemolysis, and improved patient-reported outcomes (PROs) in patients with CAD in two phase 3 trials (CARDINAL and CADENZA). Here we report PROs from the CADENZA open-label extension (Part B). The first patient was enrolled in CADENZA (NCT03347422) in March 2018 (Part A) and the last patient completed the study in December 2021 (Part B). All patients who completed the 26-week Part A were eligible to receive biweekly doses of sutimlimab in Part B for up to 1 year after the last patient completed Part A. PROs were assessed throughout Part B, until the last on-treatment visit with available assessment (LV), and after a 9-week washout. In total, 32/39 patients completed Part B; median Part B treatment duration: 99 weeks. Patients switching from placebo to sutimlimab in Part B experienced rapid improvement in Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue score and other PROs. Sustained, clinically important improvements in FACIT-Fatigue were observed throughout Part B in patients who switched to sutimlimab and those continuing sutimlimab treatment (combined-group mean [SE] change from baseline at LV: 8.8 [2.1]). Similarly, the combined-group mean [SE] change for 12-Item Short Form Health Survey physical (4.9 [1.7]) and mental (4.0 [1.8]) component scores exceeded clinically important changes from baseline at LV. EuroQol visual analogue scale showed consistent and sustained increases from baseline with sutimlimab treatment. Following a 9-week washout, all PROs approached baseline values. Continued inhibition of the classical complement pathway with sutimlimab results in meaningful long-term improvements in PROs (fatigue and QoL) in patients with CAD. Sanofi.

Interface engineering at the nanoscale: Synthesis of low‐energy boundaries

The low toughness and structural stability of nanostructured materials is strongly related to the numerous grain boundaries and interfaces. This necessitates new design strategies to inherently improve the grain boundaries. Amongst other concepts the use of low‐energy boundaries has turned out to provide the most comprehensive improvement of the property spectrum targeting on ductility, toughness, as well as thermal and microstructural stability upon mechanical loading. Cyclic high‐pressure torsion (CHPT) is one prosperous technique to synthesis low‐angle boundaries (LAGB) at the nanoscale, enabling the production of high‐strength materials. We present here an in‐depth analysis of the structural evolution focusing on the effect of different strain amplitudes and accumulated strains as well as crystal structure to understand how these parameters need to be adjusted to optimize the fraction of LAGBs. Different than expected from classical fatigue testing, the crystal structure seems to play a minor role for the cell structure evolution at comparably large strain amplitudes. It is therefore a strong asset that CHPT is feasible to produce nanostructures LAGB boundaries in both FCC and BCC structures. Furthermore, by optimizing the geometry of the anvils, it enables homogenous structural sizes in the entire sample as in contrast to other techniques the strain gradient impact on LAGB formation can be overcome.This article is protected by copyright. All rights reserved.

Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features

A data-driven approach for predicting the low-cycle fatigue (LCF) life of austenitic stainless steel (AusSS) is proposed to address the limitations of classical fatigue life prediction models. This approach integrates four key characteristics of the AusSS alloys, namely chemical compositions, microstructure, material processing parameters, and experimental parameters, to compile a comprehensive fatigue life prediction dataset. By employing the classification and regression technique, the relationship between various input features and LCF life is analyzed in depth, leading to the eventual identification of eight key features. Subsequently, various machine learning (decision tree, random forest, XGBoost, and support vector regression) and artificial neural network (ANN) are utilized to predict the LCF life using the compiled dataset. Comparative analysis of the results reveals higher prediction accuracy of ANN, with respective R2 and mean squared error values of 0.941 and 0.024, showcasing superior performance over the classical Basquin-Coffin-Manson model. This proposed methodology effectively captures the intricate feature-life relationships in AusSS, potentially reducing experimental time and costs of fatigue life estimation and offering promising applications in alloy design and manufacturing optimization.

Fatigue life analysis of deep rolled bearing inner rings

The deep rolling process can influence the surface and subsurface of hardened steels, such as the bearing steel AISI 52100, due to mechanical loading and elastoplastic deformation. By intentionally adjusting the surface and subsurface properties, the fatigue life of bearing inner rings can be increased. This is among others attributed to the strengthening of surface and subsurface. Residual stresses induced by deep rolling and modifications of the material microstructure also contribute to this effect. In the investigations presented the deep rolling process parameters of rolling pressure, overlap ratio, and ball diameter were specifically selected based on previous works. Fatigue life investigations were conducted on honed and deep rolled bearing inner rings to enhance the understanding of failure mechanisms and to quantify the influence of the deep rolling process on fatigue life. It was found that the deep rolled bearing inner rings exhibit higher compressive residual stresses in the subsurface than honed rings and also showed longer fatigue life under rolling loads. Optical analyses of bearing rings that failed due to fatigue were performed to detect the failure mechanisms. The tested bearings showed classical fatigue, where cracking is initiated below the surface and propagates to the surface under further stress. Residual stresses can influence both the crack initiation and propagation.

Integration of fatigue analysis in FEM solver for faster more reliable process

Fatigue analysis is established in industrial applications. The findings are used for the design of components in lightweight construction.The classic fatigue analysis leads to additional requirements for the simulation process. The exchange of large amounts of data between software/compute servers with different interface formats is necessary. The implementation and error-free use of this process is the responsibility of the user. In addition to data handling, the different release periods of the software involved must also be considered.The full integration of the fatigue analysis into a general FEM solver solves the process issue (Fig. 1). Both parts of the analysis, the classic stress analysis, and the fatigue analysis, are based on a common data model and use the same resources. This ensures data efficiency (large data stays internal, double data handling is avoided) and the import/export of intermediate results is no longer necessary.Stress gradients, which were previously additionally calculated by the fatigue software based on its own model data, are now available in better quality directly within the FEM software based on the original FEM model. This can be used to increase the quality of the results. Due to the elimination of data exchange and the HPC orientation of the FEM software, the integrated fatigue analysis benefits significantly in terms of performance.New analysis classes are made possible by reduced amounts of data. Thanks to the integration, the complete stress results for all calculation steps no longer must to be saved, but are used directly ("on-the-fly") by the fatigue analysis in each calculation step and then immediately deleted again. This significant process improvement means that new classes of model sizes and significantly more result steps can be considered. The results are then much more accurate.The focus is on process simplification and reducing computing time for industrial applications.

On the generalization capability of artificial neural networks used to estimate fretting fatigue life

This study assesses the generalization capacity of artificial neural networks (ANNs) for predicting fretting fatigue of mechanical contacts using different materials and geometries. These ANN utilize as inputs, material properties and stress quantities that have been physically related to the fatigue crack initiation mechanism under multiaxial loading. Initially trained and validated on aeronautical aluminum alloys data, one tests their generalization performance by applying them to fretting fatigue data for Ti‐6Al‐4V, ASTM A743 CA6NM steel, and Al 7050-T745, employing both cylindrical and spherical pads under various loading conditions, including out-of-phase loading. The ANNs adeptly predict fatigue lives across this extensive dataset, surpassing classical multiaxial fatigue criteria in accuracy. This underscores the effectiveness of ANN-based methodologies in diverse fretting fatigue scenarios.

A Novel Method for Fatigue Damage Assessment in Bimodal Processes Considering High- and Low-Frequency Reduction Effects

Due to inherent nonlinearities within floating systems and the second-order wave forces affecting them, the dynamic responses of floating systems manifest as bimodal Gaussian processes. Consequently, the classical spectral fatigue assessment method grounded in the Rayleigh distribution cannot be applied. This paper introduces the double frequency coupled (DFC) method as a spectral fatigue assessment approach, providing an accurate estimation of fatigue damage originating from bimodal Gaussian processes. Within the DFC method, the bimodal Gaussian process is partitioned into two components: low-frequency (LF) and high-frequency (HF) processes. A Gaussian distribution is employed to describe the probability distribution function (PDF) of the amplitude reduction induced by the interaction between LF and HF processes. The PDF of small-cycle fatigue can be computed by convoluting the PDF of HF amplitudes and the reduction amplitude between LF and HF. Similarly, the PDF of large-cycle fatigue can be determined through convolution, which involves the PDF of LF amplitudes and small-cycle fatigue. The overall fatigue damage arising from the bimodal Gaussian process is obtained by directly summing the contributions of small-cycle and large-cycle fatigue. Numerical investigations of the DFC method’s effectiveness are presented through a series of parametric studies, demonstrating its robustness, efficiency, and accuracy within engineering expectations. Furthermore, the DFC method is found to be applicable to both single-slope and two-slope S-N curves.

Very High Cycle Fatigue and Ultrasonic Testing in Multiaxial Loading

Abstract Fatigue damage has special relevance on the lifespan of mechanical components and structures, as it takes responsibility for the majority of registered structural failures. Most fatigue testing today still uses uniaxial loads, nonetheless it is generally recognized that multiaxial stresses occur in many full-scale structures. Nowadays, the growing need for greater lifespans forced the study of material behavior under what is nowadays very high cycle fatigue (VHCF) regime, fatigue research beyond 10E07 up to 10E10 cycles. However, most classic fatigue machines are often very costly, both in terms of time and energy, for such an elevated number of cycles. The high costs of equipment and time to conduct such experiments have seen a massive improvement with the introduction of high frequency ultrasonic machines. Presently, it is already possible to characterize materials VHCF response under axial and multiaxial loading conditions in a fraction of the time. Ultrasonic testing machines were first introduced for simpler uniaxial loading and more recently adapted for multiaxial loading conditions. The present work reviews the current background of ultrasonic fatigue testing machines working at 20-kHz frequency, with emphasis on multiaxial fatigue and VHCF. A review of a series of fatigue tests are presented, respectively, axial, shear, axial/shear, and in-plane bi-axial fatigue tests with different combining dimension specimens allowing different shear/axial stress ratios. Special attention will be put into the performance of multiaxial classical cylindrical specimens under tension/torsion and flat cruciform specimens under in-plane bi-axial testing using low-cost piezoelectric transducers. By comparing all so far developed and successfully reached multiaxial specimens and respective methodologies, two base approaches were possible to be established. When designing new specimens or ultrasonic machines, or both, one of the two methods must be followed. To have a well-comprehended methodology and successful design approach will undoubtedly guide future multiaxial ultrasonic method ideas.

Redesign of a Failed Hoisting Shaft of a Vertical Transfer Device

The redesign of a failed hoisting shaft belonging to a 10 m stroke vertical transfer device (VTD) is presented. Firstly, the operation of the VTD is thoroughly analysed, the variation of loads and moments along the operating cycle is characterised, and transients such as emergency stop loads are calculated. The selection of safety factors and duty cycle factors was followed by the shaft sizing. After an initial rough sizing, the high-cycle fatigue (HCF) design for cyclic bending moments was performed, first considering constant torque and then considering cyclic torque. The number of bending and torsion cycles performed by the hoisting shaft over 10 years was shown to exceed 106, and an infinite life design is mandatory. The analyses showed that the initial shaft diameter was insufficient, thus justifying the failures observed before the present redesign. A classical fatigue model combining torsional shear stresses with bending stresses was used to take into account reversed torsional loading and ensure infinite fatigue life. This work highlights the need to thoroughly understand a machine’s operating cycle so that the wrong premises for fatigue design calculations are not assumed.

Durability and damage model of polyacrylonitrile fiber reinforced concrete under freeze–thaw and erosion

In an alpine saline soil area, the combined effect of freeze–thaw and erosion ions causes the imbalance of concrete durability. In studying the macro–meso deterioration law and failure mechanism of polyacrylonitrile fiber-reinforced concrete (PANFC) under this action, PANFC specimens formed by open-air curing were prepared, and a composite salt solution containing 5% MgSO4, 5% Na2SO4, and 3.5% NaCl was used as the freeze–thaw medium for salt-freezing test. Results demonstrate that the degradation of PANFC under salt-frost conditions can be divided into the micro-damage stage and damage acceleration stage, with 50 cycles as the limit. When the content of PAN fiber is 1.5–1.8 kg/m3, the salt frost resistance of concrete is advantageous. The macro-scale results show that the relative dynamic elastic modulus and ultrasonic wave velocity of PANFC decrease with the increase of salt-freezing cycles, whereas the weight of PANFC increases before 25 salt-freezing cycles. The meso-scale results show that after 50 cycles, the evolution of micro-pores to macro-pores and micro-cracks in PANFC is accelerated. The pore size ratio of micro-pores (0–0.1 μm) decreases with the increase of cycles, and the pore size ratio of micro-cracks (greater than10 μm) increases with the increase of cycles. Failure mechanism analysis shows that the failure of PANFC results from the combined effect of surface damage and internal damage. Finally, the salt-frost damage model of PANFC was established on the basis of the damage mechanics and classical Aas-Jakobsen fatigue theory, and the damage determination value Dn was between 0.2527 and 0.2781. Using Dn, the failure criteria of concrete damaged under salt-frost conditions can be re-evaluated.

Fatigue failure criterion of materials with static constitutive curve as the limit value

The fatigue failure problem actually can be explained by fatigue stress–strain envelope, and the fatigue evaluation based on fatigue stress–strain can avoid the uncertainty of selecting fatigue criteria in engineering applications. However, the application of experimental fatigue envelope criterion based on stress–strain curve is limited to concrete and rocks, and it also can not predict the fatigue failure time. To further develop the availability of fatigue stress–strain envelope criterion, the fatigue failure criterion of materials with static constitutive curve as the limit value is proposed to describe high-cycle and low-cycle fatigue failure through theoretical deduction of classic fatigue criteria and analysis of experimental results. The proposed criterion reveals the constraints mechanism that the fatigue life is dominated by fatigue failure stress–strain in the fatigue envelope surface. Finally, the comparison between calculated and experimental fatigue life indicates that the proposed criterion is suitable to.describe the fatigue failure well and has the comprehensive application prospect.

Domain Wall Movement in Undoped Ferroelectric HfO2: A Rayleigh Analysis

The discovery of ferroelectricity in doped HfO2 in 2011 gave rise to quite a stir in the scientific world that persists up to this day. The complementary metal oxide semiconductor compatibility, as well as good scalability, enables versatile applications ranging from ferroelectric field effect transistors to ferroelectric tunnel junctions and neuromorphic devices. Stabilizing the metastable polar orthorhombic phase with space group Pca21-phase, which is responsible for the ferroelectricity in HfO2, is still challenging. We demonstrate for the first time a sputter deposition of undoped ferroelectric HfO2 on superconducting NbN electrodes, with a remanent polarization of 6.4 μC/cm2. Grazing incident X-ray diffraction on the layer structure, dynamic hysteresis measurements, and electron energy loss spectroscopy on fabricated devices indicate a HfO2 layer with low oxygen deficiency. Furthermore, no evidence of interdiffusion of oxygen or nitrogen at the interfaces is found. A sudden “wake-up” for the transition from the dielectric state to the ferroelectric state as well as no classical fatigue effect for the degradation of the ferroelectric performance are observed. These analyses are extended by an investigation of Rayleigh behavior using impedance spectroscopy. In that way, the domain wall flexibility is quantified and classified within different regimes of the various domain wall motions.

StressLife: A Short-Time Approach for the Determination of a Trend S-N Curve in and beyond the HCF Regime for the Steels 20MnMoNi5-5 and SAE 1045.

Within the scope of this research, a new short-time procedure designated as StressLifeHCF was developed. Through a combination of classic fatigue testing and non-destructive monitoring of the material response due to cyclic loading, a process-oriented fatigue life determination can be carried out. A total of two load increases and two constant amplitude tests are required for this procedure. By using data from non-destructive measurements, the parameters of the elastic approach according to Basquin and the plastic approach according to Manson-Coffin were determined and combined within the StressLifeHCF calculation. Furthermore, two additional variations of the StressLifeHCF method were developed in order to be able to accurately describe the S-N curve over a wider range. The main focus of this research was 20MnMoNi5-5 steel, which is a ferritic-bainitic steel (1.6310). This steel is widely used for spraylines in German nuclear power plants. In order to validate the findings, tests were also performed on an SAE 1045 steel (1.1191).

Creep–fatigue mechanical characteristics of salt rocks under triaxial loading: An experimental study

Salt rock is a sedimentary rock that is widely distributed in the crust. Since salt rock has a low permeability, rheological behavior, and self-healing characteristics, it has been extensively exploited for subsurface energy storage and waste disposal. The construction of compressed air energy storage (CAES) facilities using salt caverns for solution mining is a practical solution to achieve carbon peaking and carbon neutrality goals worldwide. The stability of the salt cavern surrounding rock is critical to the function of a CAES plant. Considering the triaxial stress condition of the rock surrounding such underground salt caverns, a number of triaxial creep–fatigue mechanics tests were performed on salt rock using an MTS815 mechanical testing device. The creep–fatigue damage to the salt rock was examined under various confining pressures and stress levels. The main research results and conclusions are as follows. First, the triaxial creep–fatigue stress–strain curves show a similar tendency to classical triaxial fatigue results: the strength and ultimate deformation of salt rocks increase as the confining pressure increases. Second, in contrast to typical fatigue test results, the residual strain before the high-stress plateaus is larger than that after the plateaus during a creep–fatigue test. However, as the confining pressure increases, this disparity decreases. Third, fatigue loading increases creep deformation, and this effect weakens when the confining pressure increases. Fourth, the residual strain differential before and after the high-stress plateau increases together with the stress level, and the contribution of creep strain increases correspondingly. Last, as the confining pressure increases, the salt rock behavior transitions from a brittle response to a fully ductile response. A high confining pressure prevents relative sliding between the salt rock particles. Thus, the effect of confining pressure on the creep–fatigue mechanical behavior of the salt rock reaches a threshold.

Loading capacity prediction of the auxetic tubular lattice structures by multiscale shakedown analysis

Auxetic tubular structures have raised increasing research interest due to their unique and tailored properties. However, the complexity of multiaxial loads eliminates the availability of the classic fatigue evaluation method and the difficulties of connecting lattice unit design with structural capacities have postponed the study on lattices’ fatigue limit loads. To fill this research gap, we present a direct shakedown method using tetrahedron elements with the reduced Gaussian integration point to predict the biaxial bearing capacities for time-varied loadings. The rationality and precision of the approach are validated using the residual stress comparison with the incremental method. The nodal-coupling-based periodic boundary conditions that are proposed and verified facilitate the parameterization study of the tubular lattice structure design. The results reveal that the edge width of the lattice unit positively impacts the elastic shakedown loads while the curvature influences reversely. The circumferential loading capacities of the auxetic tubular lattice have a significant susceptibility to curvature. This work provides an efficient tool for quantitatively evaluating the shakedown loading capacities of auxetic tubular lattice structures and the impacts of design parameters.

Linear viscoelasticity and fatigue life: Relationships between properties of asphalt binders and corresponding mixtures

Asphalt mixture performance is affected by binder’s and aggregate’s properties. Mixtures and binders share some Linear ViscoElastic (LVE) properties when small number of loading cycles and strains are considered. It is still unclear how fatigue properties relate among different scales. Three binders associated to three mixtures were characterised by stiffness (ASTM D 7175 and AASTHO T342) and fatigue (AASHTO TP107) standard tests. For stiffness modelling, complex modulus was represented by the 2S2P1D model, while the SHStS (Shift-Homothety-Shift in time-Shift) transformation was used to relate the material scales. S-VECD (Simplified Viscoelastic Continuum Damage) model was used to describe fatigue. Six out of the nine 2S2P1D model constants fitted to the binder’s complex modulus data could be kept the same for their corresponding mixtures. SHStS proved suitable for material upscaling, showing that mixtures inherit most of their LVE behaviour from binders. Although it was difficult to relate S-VECD model constants among the different scales, classical fatigue parameters (γ6 and ε6, strain levels for fatigue failure after a million cycles) suggested good correlation between binders’ and mixtures’ fatigue properties.

Variability in the Clinical Course of COVID-19 in a Retrospective Analysis of a Large Real-World Database.

The COVID-19 pandemic proceeds in waves, with variable characteristics of the clinical picture resulting from the evolution of the SARS-CoV-2 virus. This study aimed to compare the epidemiological characteristics, symptomatology, and outcomes of the disease in patients hospitalized for COVID-19 during periods of different variants dominance. Comparing the periods of dominance of variants preceding the Delta variant, the Delta period was characterized by a higher share of hospitalized females, less frequent comorbidities among patients, and a different age distribution. The lowest need for oxygen therapy and mechanical ventilation was observed under Omicron dominance. The triad of classic COVID-19 symptoms, cough, fever, dyspnoea, and fatigue, were most prevalent during the Delta period, and significantly less common under the Omicron dominance. During the Omicron period, nearly twice as many patients as in the previous periods could be discharged from the hospital within 7 days; the overall 28-day mortality was significantly lower compared to that of the Delta period. It also did not differ between periods that were dominated by the BA.1 and BA.2 subvariants. The study indicates that the Omicron SARS-CoV-2 variant that dominated between January and June 2022 caused a disease which resembled the common cold, and was caused by seasonal alpha and beta-coronaviruses with a low pathogenicity for humans. However, one should note that this effect may not only have been related to biological features of the Omicron lineage, but may additionally have been driven by the increased levels of immunization through natural infections and vaccinations, for which we could not account for due to a lack of sufficient data.

A simple and convenient fatigue analysis method considering the effect of plasticity on fatigue

Continued plastic accumulation at the notch location changes the stress distribution of the notch, causing the position of the maximum stress gradient to change. In this study,a method for analyzing notch fatigue combining maximum notch stress and the relative stress gradient at the location of yield stress was developed. Experimental data on various materials are applied for model validation and comparison. The results show that the prediction accuracy of the proposed model is significantly better than that of the classical fatigue analysis method, and has the same prediction accuracy as the line method of the critical distance theory.

Application of machine learning methods in multiaxial fatigue life prediction

AbstractThis paper compares the results of multiaxial fatigue life estimation using machine learning methods and classical fatigue models. The fatigue life of PA38‐T6 aluminum alloy under uniaxial, proportional, and non‐proportional loading, including asynchronous loading, is studied. Machine learning methods are trained only on basic loadings, namely, axial, torsional, and 90° out‐of‐phase. The results obtained with the machine learning algorithms, dense neural networks, support vector regression (with linear and radial basis functions), decision tree, random forest, and XGBoost algorithms, are comparable to the results of classical models like Ellyin–Gołoś, Fatemi–Socie, and Ince–Glinka. The best results are achieved for dense neural networks. In that case, they are often slightly more accurate than those obtained using classical methods.