Fatigue Life Prediction Research Articles

Multiphysics modeling and optimization of an innovative shape memory alloy-polymer active composite

Shape memory alloys (SMAs) are a unique class of smart materials capable of recovering significant deformations through temperature variations, making them attractive for adaptive structures and morphing applications. However, integrating SMAs into polymer composites poses significant challenges, such as interfacial delamination and matrix overheating during thermal activation, in addition predicting the stress acting on the SMA during the actuation is pivotal. Addressing these issues is crucial for realizing the full potential of SMA-based active composites in aerospace, automotive, and renewable energy sectors. This study presents a novel multi-material design strategy for SMA-polymer active composites, featuring a rigid PC/ABS layer for mechanical integrity, a soft high-temperature silicone coating for encapsulating SMA wires, and aluminum terminals for wire crimping. A comprehensive multiphysics FEM model was developed to accurately capture the coupled thermo-mechanical response. Extensive experimental characterization and validation were conducted, followed by systematic parametric studies to investigate the effects of critical design parameters on key performance metrics. The developed prototype exhibited remarkable shape morphing capabilities, with a maximum tip deflection of 68 mm (deflection-to-length ratio of 0.4). Excellent agreement between experiments and simulations was achieved, with a maximum error of 1.7 % in tip deflection, validating the accuracy of the multiphysics model. Parametric analyses quantified the trade-offs between geometric parameters, revealing their influence on activation temperature, deflection, SMA wire stress, and fatigue life. Optimal configurations enabling low activation temperatures (<150 °C), low stress levels (<400 MPa), and high predicted fatigue life (>50,000 cycles) were identified. The multi-material design approach effectively addressed common issues in SMA-polymer composites, enabling reliable actuation cycles without damage accumulation. The validated multiphysics model and parametric insights provide a comprehensive framework for optimizing the design and performance of SMA-polymer active composites, paving the way for their widespread adoption in shape morphing applications. Potential implications include the development of adaptive aerodynamic surfaces, deployable structures, and energy-efficient systems across various industries.

Data-driven online prediction of remaining fatigue life of a steel plate based on nonlinear ultrasonic monitoring

Online monitoring fatigue damage and remaining fatigue life (RFL) prediction of engineering structures are essential to ensure safety and reliability. A data-driven online prediction method based on nonlinear ultrasonic monitoring was developed to predict the RFL of the structures in real-time. Nonlinear ultrasonic parameters were obtained to monitoring the fatigue degradation. A Bayesian framework was employed to continuously compute and update the RFL distributions of the structures. Nonlinear ultrasonic experiments were performed on the fatigue damaged Q460 steel to validate the developed prediction methodology. The result indicates that the developed method has high prediction accuracy and can provide effective information for subsequent decision-making.

Microstructure based fatigue life prediction of polycrystalline materials using SFEM and CDM

Microstructure based fatigue life prediction of polycrystalline materials using SFEM and CDM

Minimum life point determination and fatigue life prediction of thermal barrier coatings based on extended stress and strain method

The study develops the extended stress and strain method (ESSM) capable of extracting the stress-strain perpendicular to the 3D thermal barrier coatings (TBCs) interface and shear stress-strain along the interface. Then, the stresses perpendicular to the interface and the shear stresses along the interface are analyzed in comparison with the conventional stresses. Finally, fatigue life prediction of TBCs is carried out based on the ESSM. The results show that, the cycle maximum value locations of shear stress along the interface are located at 5.1 μm along the circumferential direction to the peak. The previous experimental study of TBCs proves that it is reasonable to use the shear stress to determine the location of the minimum life point, and the minimum life point location is the cycle maximum value location of the shear stress. The maximum error in fatigue life prediction of TBCs using the ESSM 45.6 %, which is 20.5 % less than that using equivalent strain. The ESSM can directly determine the location of minimum life point and improve the fatigue life prediction accuracy of TBCs, which has great potential in damage analysis and fatigue life prediction.

Contact force and pressure analysis of the three-row roller pitch bearing in a large-scale wind turbine

Pitch bearings are the key components of wind turbines (WTs). The reliability of pitch bearings affects the electric energy output efficiency of WTs. Due to its large loading ability, constant contact angle and high stiffness, the three-row roller slewing bearing (TRSB) is becoming a more attractive choice as a pitch bearing, especially in large-scale WTs. The contact force and pressure distributions significantly affect the fatigue life of a three-row roller pitch bearing (TRPB). Therefore, it is essential to propose a precise calculation method for the contact force and pressure. The present work establishes a quasi-static five degree-of-freedom (DOF) mathematical model for TRPBs, in which the bearing under general loading conditions is considered. The influences of combined external forces, overturning moments and inner ring misalignment angle on the contact force and pressure distributions of TRPBs are comprehensively investigated. According to the analysis results, the overturning moment and axial force can significantly affect the number of load-carrying rollers in each row. Furthermore, a slight variation of the inner ring angular misalignment has a significant effect on the contact force and pressure of TRPBs. Finally, it is more prone to pressure concentration in the non-crowned roller than that in the crowned roller, especially in the case of the inner ring with a misalignment angle. Since the high calculation efficiency and accuracy, the proposed method has remarkable potential applications in the fatigue life prediction and design of TRPBs.

Optimizing dimension selection for rain flow counting in fatigue assessment of large-scale lattice wind turbine support structures: A comprehensive study and design guidance

The selection of dimension (d) for the rain flow counting matrix significantly influences the reliable prediction of fatigue life in wind turbine support structures. However, there are limited public reports on the impact of dimensions on the fatigue assessment of wind turbine structures. This study explores the influence of dimensions on the fatigue assessment process and outcomes, focusing on a large-scale ultra-high lattice wind turbine support structure from an engineering project. Through integrated load simulation, fatigue load time series for different design load cases (DLCs) are obtained for the overall structure. The rain flow counting method is utilized to derive the overall rain flow counting matrix (Markov_n) with varying dimensions. Subsequently, employing a multi-scale fatigue assessment method, the damage matrix (Markov_D), cumulative fatigue damage (D), and fatigue life are computed. A detailed exploration examines the impact of rain flow counting matrix dimensions on Markov_n, Markov_D, D, fatigue life, and calculation time (t). Furthermore, guidance is provided for selecting the optimal dimension for engineering design. The findings indicate that selecting dimensions that are too small results in the loss and merging of smaller mean and amplitude load values, thereby failing to accurately represent the load history. Moreover, a small dimension yields an excessively cautious estimation of cumulative fatigue damage (D) and underestimates the fatigue life, consequently inflating construction expenses.

Multiaxial fatigue life prediction based on modular neural network pretrained with uniaxial fatigue data

PurposeData-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.Design/methodology/approachDrawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.FindingsBased on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.Originality/valueThe proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

Investigating the relationship between fracture entropy and stress amplitude in CFRP laminates under low-cycle fatigue loading

Fracture fatigue entropy (FFE) is considered to be independent of the loading amplitude in fatigue experiments, and this conclusion has been applied to fatigue life prediction of composites and metals with satisfactory prediction accuracy. However, the existing research work mainly focuses on high-cycle fatigue, and it is not clear whether the FFE is independent of the loading amplitude in low-cycle fatigue, which requires more in-depth research. Fatigue experiments were conducted on three CFRP laminates with different ply orientations, and the FFE values corresponding to low-cycle fatigue were obtained by thermographic analysis. The results show that the FFE value is no longer independent of the loading amplitude under low-cycle fatigue conditions. On the contrary, it decreases with the increase of fatigue loading amplitude and also varies with different ply orientations. The relationship between low-cycle fracture fatigue entropy and loading amplitude of composites with different ply orientations was analyzed and modeled, and a novel low-cycle fatigue life prediction method based on FFE was developed.

An artificial neural network method for probabilistic life prediction of corroded reinforced concrete

Corroded reinforced concrete is affected by many factors. Therefore, the effects of these uncertainties should be considered when predicting fatigue life. However, artificial neural networks cannot determine the uncertainty of fatigue life. This paper establishes a new artificial neural network method to solve this problem. First, survival rates are obtained using the minimum number of specimens method. Second, the samples are divided. Then, a decision tree is used to determine the uncertainty of the test sample using corrosion and loading conditions. Finally, an artificial neural network is used to predict the fatigue life of the test sample using the survival rate, corrosion conditions, and loading conditions. If the training samples entered the artificial neural network are small sample data, the sample data are expanded using a beta-variational autoencoder with sample division. The established method is evaluated using fatigue test data from corroded reinforced concrete. Moreover, the evaluation results show that the established method can effectively predict the survival rate and fatigue life.

Fatigue failure behaviour of bolted joining of carbon fibre reinforced polymers to titanium alloy

Carbon fibre reinforced polymers are widely used in industry, the problem of joining them with metal materials becomes significant. This is because of the loosening of bolted joints under applied loads, leading to a rapid reduction in fatigue life and even structural failure accidents. This study focuses on fatigue failure mechanisms of bolted joints of carbon fibre reinforced polymers to titanium alloy and looking for a novel solution to improve the fatigue life of bolted joints. Firstly, fatigue life prediction of the components is conducted based on FE-SAFE. Then, experiments are conducted to verify simulation results. Finally, the thread damage and the fracture morphology are analysed after tests. It is found that the fatigue life of bolted joints can be improved by increasing the stiffness of connected parts and choosing appropriate geometric parameters.

Fatigue life prediction method for reinforced concrete deck slabs subjected to repeated moving wheel loads and failing in shear

AbstractAs bridge deck slabs experience the direct impact of repeated concentrated wheel loads, they are more susceptible to fatigue‐related damage than other bridge components. In this context, a fatigue life prediction method for reinforced concrete (RC) bridge decks has been developed based on failure mechanisms of RC slabs tested under moving wheel loads. Initially, the formulas encompassed shear strength and S–N equations, accommodating variations in environmental and support conditions, are proposed to facilitate the fatigue life prediction of slabs exposed to constant moving wheel load. Subsequently, a model for shear strength degradation formulated using experimental findings on beam‐like elements is utilized in predicting the fatigue life of slabs subjected to stepwise moving wheel loads. The validity of the proposed method was established by comparing its results with previous experimental data. Notably, compared with the existing prediction equation, the proposed method exhibited more accurate fatigue life prediction results.

Fatigue prediction through quantification of critical defects and crack growth behaviour in additively manufactured Ti-6Al-4V alloy

The study presents a methodology for predicting the fatigue life and identifying critical defects in additively manufactured (AM) Ti-6Al-4V alloy processed via laser powder bed fusion (L-PBF). Predictions were made on L-PBF Ti-6Al-4V alloy in two conditions: as-built and heat-treated by using X-ray μ-Computed Tomography (μ-CT) for the quantification of the defects and fatigue crack growth (FCG) data. For validation, fatigue life predictions were made on the same specimens on which μ-CT was conducted prior to fatigue testing. FCG and fatigue tests (S-N) highlighted differences in the performance between the as-built and heat-treated conditions: the as-built condition had a lower threshold stress intensity factor range (ΔKth) of 2.16 MPa m1/2 than that of the heat-treated condition, 4.96 MPa m1/2. Differences in fatigue limit were attributed to differences in ΔKth in as-built and heat-treated specimens. To gain mechanistic understanding of these differences near ΔKth cracks were examined using Electron Backscatter Diffraction - Kernel Average Misorientation (EBSD-KAM). It was found that upon heat treatment, the dislocation density increased around near threshold fatigue cracks. Increases in ΔKth are attributed to the increases in β-phase content and α-lath thickness caused by heat treatment.

Probabilistic LCF life prediction framework for turbine discs considering random load history

Probabilistic LCF life prediction framework for turbine discs considering random load history

Critical distance-based fatigue evaluation of welded joints in steel bridge under bending loading

Welded joints are prone to fatigue cracking under bending loading in steel bridges. However, certain design curves for these joints primarily focus on tensile or bending-tension loads, potentially leading to inaccurate assessments. This study conducted bending fatigue tests on fillet welded joints (FWJs), evaluating the suitability of specification methods for assessing their fatigue performance and attempting to determine the strength curves required for fatigue assessment. Furthermore, it validated the feasibility of the traditional theory of critical distance (TCD) in predicting the fatigue life of FWJs under bending loading. On this basis, a TCD assessment method incorporating the welding residual stress (WRS) was proposed. The results indicate that the fatigue design curve of the specifications provides conservative predictions for the fatigue performance of FWJs under bending loading, and FAT112 from Eurocode3 may be suitable for fatigue assessment using hot-spot stress and nominal stress methods. Furthermore, predicting fatigue life for FWJs under bending loading with traditional TCD is feasible. Compared to traditional TCD, the proposed method significantly improves prediction accuracy, with a minimum scatter factor of just 1.5. The proposed TCD not only considers the WRS effect but also reduce the need for numerous fatigue tests to establish the design curve for a specific welded joint.

Wind-Induced Response Analysis and Fatigue Life Prediction of a Hybrid Wind Turbine Tower Combining an Upper Steel Tube with a Lower Steel Truss

Based on the WindPACT-3MW wind turbine tower commonly used in wind power engineering, a finite element model (FEM) of a hybrid wind turbine tower combining an upper steel tube with a lower steel truss is designed and established. On this basis, a static optimization analysis, wind-induced vibration analysis, and fatigue life analysis of the hybrid tower structure are performed. The results show that under the same design parameters, the overall stiffness and static bearing capacity of the tower structure can be significantly improved by using subdivided truss webs, increasing the truss height as much as possible and increasing the width of the truss base appropriately. Under normal operation conditions, the response of the tower structure in the along-wind direction is significantly greater than the response in the crosswind direction, indicating that the aerodynamic thrust generated by the rotation of the blades is the main factor causing the wind-induced vibration of the tower structure. For the tower structure analyzed in this study, when considering the entire range of wind speeds from the cut-in wind speed to the cut-out wind speed, the fatigue life of the structure is 38.5 years.

P-S-N surfaces of lifting lug structure based on extremely small samples

Given the susceptibility of lifting lug structures to fatigue failure under heavy loads and the high cost associated with fatigue testing time, determining the P-S-N model with a limited sample size holds significance in engineering. Assuming that the logarithmic fatigue life of the lifting lug structure followed a normal distribution, this study utilized the gray model and Gaussian process regression optimized by a genetic algorithm to predict fatigue lives at equidistant stress amplitudes. In addition, the improved bootstrap method was used to expand the experimental extremely small samples. The findings demonstrate that incorporating the mean stress into the S-N curve of the exponential function and utilizing a polynomial to account for the segmented behavior proved highly advantageous in attaining a more accurate S-N model. The mean and standard deviation of logarithmic life at each stress level were assessed by the expanded samples, resulting in P-S-N surface fitting outcomes. This study contributes to the fatigue reliability analysis in situations with limited sample sizes.

Fatigue Life Prediction and Verification of Railway Fastener Clip Based on Critical Plane Method

Accurately predicting the fatigue life of fastener clips is crucial for improving material processing technology, enhancing the anti-fatigue design level, and guiding the maintenance and repair of track structures. Conventional methods that solely evaluate the fatigue life of fastener clips based on the uniaxial stress index under displacement loads may lead to significant errors. In this paper, the stress field of a type-III clip under displacement loading conditions was numerically simulated based on fatigue test standards, and the fatigue life of the clip was analyzed using the Fatemi–Socie (FS) multiaxial fatigue criterion based on the critical plane method. A comparison with standard fatigue test results revealed that, under non-resonance conditions, the predicted position of the fatigue critical plane of the fastener clip coincided with the fracture surface observed at the middle of the measured small arc, with a life prediction error of 7.3%. To further investigate the predictive capability of the FS multiaxial fatigue criterion for the fatigue life of the fastener clip under resonance conditions, the stress levels of the clip under non-resonance and resonance conditions were compared through on-site testing, and the effects of inertial loads caused by vertical and lateral vibration acceleration on the stress field of the clip were analyzed in numerical simulation according to the results of clip acceleration tests; the fastener clip’s resonance fracture position and fatigue life were also predicted based on the aforementioned multiaxial fatigue criterion. To verify the accuracy of the predicted results, a testing method was proposed that equates the high-frequency resonant inertial load of the fastener clip to a low-frequency additional preload under the premise of consistent stress fields. A comparison with the numerical simulation results shows that considering only vertical inertial loads would result in a discrepancy between the measured fracture location and the actual one. Considering both vertical and lateral inertial loads, the fracture location (at the heel of the small arc) under resonance conditions could be accurately determined, with a life prediction error of 13.8%. Compared to the non-resonant displacement loading condition, the inertial loads caused by acceleration under resonance conditions led to a reduction in fatigue life of approximately 77.8%.

A modified Manson-Halford model based on improved WOA for fatigue life prediction under multi-level loading

The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods.

Interpretation of Frequency Effect for High-Strength Steels with Three Different Strength Levels via Crystal Plasticity Finite Element Method.

The fatigue behavior of a high-strength bearing steel tempered under three different temperatures was investigated with ultrasonic frequency and conventional frequency loading. Three kinds of specimens with various yield strengths exhibited obvious higher fatigue strengths under ultrasonic frequency loading. Then, a 2D crystal plasticity finite element method was adopted to simulate the local stress distribution under different applied loads and loading frequencies. Simulations showed that the maximum residual local stress was much smaller under ultrasonic frequency loading in contrast to that under conventional frequency at the same applied load. It was also revealed that the maximum local stress increases with the applied load under both loading frequencies. The accumulated plastic strain was adopted as a fatigue indicator parameter to characterize the frequency effect, which was several orders smaller than that obtained under conventional loading frequencies when the applied load was fixed. The increment of accumulated plastic strain and the load stress amplitude exhibited a linear relationship in the double logarithmic coordinate system, and an improved fatigue life prediction model was established.

Finite Element Analysis of a Rib Cage Model: Influence of Four Variables on Fatigue Life during Simulated Manual CPR.

Cardiopulmonary resuscitation (CPR) is a life-saving technique used in emergencies when the heart stops beating, typically involving chest compressions and ventilation. Current adult CPR guidelines do not differentiate based on age beyond infancy and childhood. This oversight increases the risk of fatigue fractures in the elderly due to decreased bone density and changes in thoracic structure. Therefore, this study aimed to investigate the correlation and impact of factors influencing rib fatigue fractures for safer out-of-hospital manual cardiopulmonary resuscitation (OHMCPR) application. Using the finite element analysis (FEA) method, we performed fatigue analysis on rib cage models incorporating chest compression conditions and age-specific trabecular bone properties. Fatigue life analyses were conducted on three age-specific rib cage models, each differentiated by trabecular bone properties, to determine the influence of four explanatory variables (the properties of the trabecular bone (a surrogate for the age of the subject), the site of application of the compression force on the breastbone, the magnitude of applied compression force, and the rate of application of the compression force) on the fatigue life of the model. Additionally, considering the complex interaction of chest compression conditions during actual CPR, we aimed to predict rib fatigue fractures under conditions simulating real-life scenarios by analyzing the sensitivity and interrelation of chest compression conditions on the model's fatigue life. Time constraints led to the selection of optimal analysis conditions through the use of design of experiments (DOE), specifically orthogonal array testing, followed by the construction of a deep learning-based metamodel. The predicted fatigue life values of the rib cage model, obtained from the metamodel, showed the influence of the four explanatory variables on fatigue life. These results may be used to devise safer CPR guidelines, particularly for the elderly at a high risk of acute cardiac arrest, safeguarding against potential complications like fatigue fractures.