Fatigue Life Prediction Model Research Articles

Cyclic deformation response of annealed low-carbon steel: Insights from ratcheting and LCF experiments

Low cycle fatigue (LCF) and ratcheting experiments were carried out on annealed low-carbon steel at room temperature within a laboratory environment, utilising stress and strain control modes. The annealed low-carbon steel consistently demonstrates a cyclic softening response over its LCF lifespan, across all tested strain amplitudes. Notably, it was observed that ratcheting strain rises while ratcheting life declines with both rising mean stress and stress amplitude. Annealed low-carbon steel, being entirely ferritic and lacking precipitation or substitutional solid solution strengthening or hard phase strengthening, exhibits a restricted ability to withstand or alleviate the accumulation of ratcheting strain, particularly under very low mean stress conditions. In both LCF and ratcheting, significant substructure formation was detected. Nevertheless, there was no discernible difference in substructure formation between LCF and ratcheting when employing electron channelling contrast imaging techniques. The existing mean stress-based fatigue life prediction model has successfully forecasted ratcheting and LCF life within the 102–105 cycles range. A novel approach utilising modulus is introduced to characterise the cyclic hardening/softening behaviour of alloys in stress and strain-controlled experiments. The cyclic hardening model based on modulus effectively captures the responses observed in cyclic hardening/softening during LCF and ratcheting experiments.

Nonlinear fatigue life prediction model based on isodamage curve considering two‐block loading

AbstractA non‐linear life prediction model considering the loading history is proposed based on isodamage curve. The stress‐controlled low‐high block loading of low cycle fatigue (LCF) at high temperature, with various previous cycles, was performed on a nickel‐based superalloy. The damage evolvement of the prior loading was revealed using scanning electron microscopy under the low‐high block loading, especially the failure mechanism of coaxing effect. In addition, load history correlation factor was introduced to describe the influence of prior cycle. Based on the Ni‐based superalloy, the life prediction model of two‐step loading, including the low‐high block (LH) and high‐low block loading (HL), was proposed, agreeing well with the experimental results for different metals. Compared with other life prediction models, the proposed model demonstrated the higher prediction accuracy and wider applicability.

Experimental Investigation of the Mechanical Behavior of the Strain Isolation Pad in Thermal Protection Systems under Tension

A strain isolation pad is a critical connection mechanism that enables deformation coordination between the rigid thermal insulation tile and the primary structure in the thermal protection system of a reusable hypersonic vehicle. An experimental investigation has been conducted to determine the static, loading–unloading, and high-cycle fatigue (HCF) responses of the SIP with 0.2 mm adhesive under through-thickness tension at room temperature. The contributions of the rigid thermal insulation tile and metallic substructure have not been considered so far. The results indicate that the tensile behavior of the SIP joint is highly nonlinear. The static and fatigue tensile failures both initiate from the corner close to the adhesive/SIP interface due to the stress concentration and the edge effect. The uniform breakage of the aramid fiber can be seen on the cross-section. A novel method is proposed to quantify the residual strain due to the short-time ratcheting effect of the SIP joint in the initial loading–unloading tensile response. As the number of fatigue cycles increases, the thickness of the SIP joint continues to increase until failure. An explicit expression associated with the growth of SIP joint thickness, fatigue cycle number, and peak cyclic stress is established. The turning point of the thickness growth rate with the fatigue cycle number is proposed as a new fatigue failure index for the SIP joint under tensile fatigue, and a fatigue life prediction model is developed.

Experimental investigation and numerical modelling of the cyclic plasticity and fatigue behavior of additively manufactured 316 L stainless steel

This study addresses the critical need for a constitutive model to analyze the cyclic plasticity of additively manufactured 316L stainless steel. The anisotropic behavior at both room temperature and 300 °C is investigated experimentally based on cyclic hysteresis loops performed in different orientations with respect to the build direction. A comprehensive constitutive model is proposed, that integrates the Armstrong-Frederick nonlinear kinematic hardening, Voce nonlinear isotropic hardening and Hill's anisotropic yield criterion within a 3D return mapping algorithm. The model was calibrated to specimens in the 0° and 90° orientations and validated with specimens in the 45° orientation. A single set of hardening parameters successfully represented the elastoplastic response for all orientations at room temperature. The algorithm effectively captured the full cyclic hysteresis loops, including historical effects observed in experimental tests. A consistent trend of reduced hardening was observed at elevated temperature, while the 45° specimen orientation consistently exhibited the highest degree of strain hardening. The applicability of the model was demonstrated by computing energy dissipation for stabilized hysteresis loops, which was combined with fatigue tests to propose an energy-based fatigue life prediction model.

Investigation of high-cycle fatigue property and fatigue crack propagation behavior of a die-forged 2014 aluminum alloy aircraft wheel

The high-cycle fatigue (HCF) property and fatigue crack propagation (FCP) behavior of aircraft wheels manufactured by die forging were investigated from two sampling directions (rims and discs). The results indicate that the HCF anisotropy of the rim and disc specimens increases with decreasing stress amplitude, and the fatigue strengths are 181 MPa and 157 MPa, respectively. Based on the quantitative analysis of the fatigue fractures, a fatigue life prediction model considering the Fe/Mn-containing particles quantity factor S has been established, and the predicted values are reasonably accurate to the test values. The crack path microstructure indicates that Fe/Mn-containing particles can cause crack deflection while acting as bridges for FCP; Crack closure induced by oxides and plastic zones significantly decelerated the FCP rate. Additionally, fatigue cracks tend to propagate toward grains with smaller twist angles and higher Schmid factors (SF).

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

AbstractThis study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

An optimized fatigue model of asphalt binder combining nonlinear viscoelastic and intrinsic healing characteristics

This paper proposed an optimized fatigue life prediction model of graphene oxide (GO) modified asphalt binder considering the coupled effects of nonlinear viscoelasticity (NLVE) and intrinsic healing compensation. The equivalent nonlinear complex modulus (|G*|NLVE) was quantified accordingly and an NLVE-based fatigue model was proposed in this study. The parameters damage healing (ΔS'), local life compensation (ΔN), damage healing index (%HS), and local lifetime compensation index (%ΔN) were employed to characterize the intrinsic healing capability and mechanism of GO-modified binders. There is a strong correlation mechanism between the healing compensation effect and the damage healing of asphalt binders during healing intervals, as evidenced by the linear relationship between ΔS' vs. ΔN and %HS vs. %ΔN. Furthermore, the analysis of the total stored pseudo-strain energy counteracting the damage healing reveals that GO-modified binder has greater stiffness and require more stored pseudo-strain energy to counteract ΔS'. The fatigue life of asphalt binder is further prolonged by the healing compensation effect on the basis of GO modification and NLVE effects.

The low-cycle fatigue behavior and an entropy-based life prediction model for Nickel-based single crystal superalloy across an extensive temperature range

Nickel-based single-crystal superalloys (Ni-SX) are prominently utilized in the fabrication of turbine blade materials for advanced aero-engines, owing to their commendable material characteristics. This paper delves into the low cycle fatigue (LCF) characteristics of a second-generation Nickel-based single-crystal superalloy across a temperature spectrum ranging from room temperature to 1000 °C. During this procedure, there is an escalation in the trend towards deformation homogenization, leading to a transition in fracture mode from shear fracture to normal fracture. A novel approach is introduced in the form of an entropy-based fatigue life prediction model, grounded in the correlation between cumulative plastic strain and entropy increase rate. Through a comparative examination with experimental data, the model demonstrates proficiency in precisely forecasting the fatigue life of Ni-SX under diverse strain amplitudes and temperature conditions.

An improved Manson–Coffin model for fatigue life prediction of large crude oil storage tanks via environment temperature and surface roughness

An improved Manson–Coffin model for fatigue life prediction of large crude oil storage tanks via environment temperature and surface roughness

Speed-based analysis and prediction of fatigue life for the crankshaft via genetic algorithm

To improve the accuracy of crankshaft fatigue life prediction, a new method for predicting the fatigue life of crankshafts was proposed, considering the influence of rotational speed on fatigue life. This study investigates the relationship between engine speed and crankshaft fatigue life, and the engine speed will obviously affect the fatigue life of the crankshaft. Considering the influence of different engine speeds, a crankshaft fatigue life prediction model considering the engine speed is proposed. The method first obtains the crankshaft force time domain data under different loads through multi-body dynamics simulation, and analyzes the relationship between different engine speeds and crankpins force. The crankshaft fatigue life under different loads of the engine is calculated based on fatigue damage accumulation theory. Genetic algorithm is used to find the optimization of the relevant parameters of the crankshaft fatigue life prediction model considering the engine speed and determine the values of the parameters. The fatigue life and remaining fatigue life of the crankshaft is calculated using the proposed crankshaft fatigue life prediction model according to the engine speed signal. The proposed model in this study demonstrates a higher level of accuracy, as evidenced by the result showing a prediction error of 3.45%.

Probabilistic fatigue life prediction of corroded PC beams considering spatial variability of pitting corrosion

Pitting corrosion intensifies local stress in materials, accelerating the fatigue crack initiation and propagation at the pit root. The randomly distributed corrosion pits can result in fatigue fractures in rebars and wires at non-critical sections, which further increases the fatigue failure probability of structures. However, previous studies have scarcely taken this into account. Here, we propose a novel model for fatigue life prediction of corroded post-tensioned prestressed concrete (PC) beams, considering the spatial variability of pitting corrosion. First, a Weibull distribution model of the pitting factor is developed based on the 3D laser scanning method. Then, the 3D non-Gaussian random field of the pitting factor in PC beams is generated using the inverse Nataf transformation. Next, the 3D pit-induced stress concentration is modeled using pit shape ratios. Following that, the initiation and propagation of fatigue cracks in pitting corroded wires and rebars are considered by the equivalent initial flaw size methodology. Finally, the fatigue analyses of the pitting corroded wires and rebars in all beam elements are conducted step by step until the load-bearing capacity of PC beams becomes lower than the applied load. The proposed prediction model is validated using the experimental data. The results reveal that neglecting the spatial variability of pitting corrosion overestimates the fatigue life of corroded PC beams, especially under high corrosion degrees.

Multiaxial failure behavior and fatigue life prediction of unidirectional composite laminates

Complex coupling mechanisms exist between longitudinal and shear stress on the multiaxial fatigue behavior of composites. A thorough reanalysis of matrix shear failure mechanism under multiaxial fatigue loading is presented. Longitudinal single splitting fracture were analyzed and validated as an independent failure mode under multiaxial loading by analogy to the failure mechanism of natural composites. A new multiaxial fatigue life prediction model was established by double projection on the critical and subcritical fracture planes. Experiments on unidirectional tubular specimens were carried out under combined tension-torsion loadings to separate and identify the multiaxial fatigue failure behavior of composites. The proposed model has been verified using unidirectional laminates subjected to cyclic tension-torsion and off-axis loading, and the results showed that the predicted life is in good agreement with the experimental ones. Applications of three-dimensional digital image correlation (3D-DIC) and microscopy regarding verification in multiaxial damage mechanism are also addressed.

Study on Fatigue Life of Aluminum Alloy 6061-T6 Based on Random Defect Characteristics.

A certain number of hole-like defects will occur in aluminum alloys under cyclic loading. The internal holes will reduce the strength of the material and cause stress concentration, which will aggravate the development of fatigue damage. A classification method of defect features based on X-ray CT damage data is proposed. The random hole distribution model is established through the linear congruence method and the region division method. The hole parameter is introduced as the intermediate variable of the 3D reconstruction model of internal defects. In the mesoscopic stage, the function relationship between the distribution of random holes and the fatigue life is established based on the coupling relationship between the number and proportion of pores and the fatigue life. In the macroscopic stage, the relationship between the random holes and the macroscopic crack growth life is established by taking the crack length as the damage variable. The crack propagation rate decreased with the increase in the number of holes. The prediction model of the whole life stage is established using the life function from microcrack initiation to macroscopic crack propagation. Finally, the validity of the whole stage fatigue life prediction model is demonstrated through the comparison and verification of experiments, which provides a certain engineering value for the life estimation of 6061-T6 aluminum alloy materials.

A rolling contact fatigue life prediction model for bearing steel considering its gradient structure due to cyclic hardening

This paper presents a rolling contact fatigue life prediction model for bearing steel. In the initial stage of rolling contact, the gradient structure appears in the subsurface region of bearing steel and the hardness shows a gradient distribution along the contact depth direction due to the roller compaction effect. This indicates that the fatigue resistance of bearing steel varies in the subsurface region. Besides, each subsurface material volume element is subject to different stress cycles. Based on Weibull theory, the survival possibility of subsurface volume elements is formulated. Then the rolling contact fatigue life is evaluated considering the stress state and anti-fatigue performance of each elementary volume of the subsurface material. According to the phenomenon and assumption, the model proposed for gradient material was applied in the rolling contact fatigue life prediction of the bearing steel GCr15 and validated with the fatigue experiment data in the open literature. Furthermore, the accuracy of the proposed model results was compared with the traditional empirical rolling contact fatigue life prediction models.

Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks

In practical engineering scenarios, unforeseen failures may result in significant costs and risks. Assessing the uncertainty related to multiaxial fatigue life prediction is crucial for engineering applications. However, establishing a reliable multiaxial fatigue life prediction model remains challenging due to uncertainties in physical models, material properties, and measurement data. This paper proposes an effective Bayesian Neural Network (BNN) model for quantifying the uncertainty in multiaxial fatigue life prediction. Fatigue parameters in the multiaxial fatigue life prediction model are used as inputs for the BNN. The BNN method utilizes No-U-turn Sampler (NUTS) and Automatic Differentiation Variational Inference (ADVI) to address the inference problem in the model and perform life predictions for unknown non-proportional loading paths and different materials. Validation using experimental data from six different materials confirms the effectiveness of the BNN model. Experiments show that the performance of BNN models is beneficial in most cases compared to classical ML models, assessed through different error standards, statistical tests, Taylor diagrams, uncertainty analysis, and scatter index analysis. The proposed method provides a clear understanding of the uncertainty in multiaxial fatigue life prediction, offering a promising approach for simulating fatigue life under multiaxial loading in engineering applications.

Evaluation Method of Fatigue Life for Asphalt Pavement on the Steel Bridge Deck Based on the Inhomogeneous Poisson Stochastic Process.

The paving layer on the steel box girder bridge deck is widely used when constructing pavements for steel bridges. Owing to the orthotropic feature of steel decks, a transverse clapboard and rib can lead to a concentration of stress. Consequently, fatigue cracks are often identified in asphalt concrete pavement layers due to re-compaction caused by heavy vehicles. This study aims to derive an evaluation method of fatigue life for asphalt pavement based on the inhomogeneous Poisson stochastic process in view of the highly random and uncertain working conditions of layered composite structures. According to the inhomogeneous Poisson stochastic process, along with Miner's fatigue damage accumulation theory and the linear elastic fracture mechanics theory, the fatigue life formula could be deduced. Meanwhile, fatigue experiments for asphalt concrete are designed to investigate the correlation between the theoretical formula and the actual fatigue damage life of the material. Compared with the test, the accuracy error is within 10%, which is better than other traditional methods. Therefore, the fatigue life prediction model could better reflect the loading order effect and the interaction between loads, providing a new path for the fatigue reliability design of steel bridge deck asphalt pavement.

Theoretical and experimental study on the fatigue property of material extrusion products and improvement by using vibration machining during manufacturing process

Nowadays, material extrusion (ME) is one of the most popular additive manufacturing technology, which offers a new way of manufacturing parts with specific complex geometry and mechanical properties. However, with the expansion of the application range of ME products, it is urgent to investigate their fatigue property so as to ensure the reliability and safety. In this paper, the damage mechanical fatigue life prediction model of ME products considering void defects was established. Fatigue test specimens built with different processing parameters were then fabricated, and tensile fatigue tests were carried out under different stress levels. In addition, a novel method, utilizing vibration during ME process, was proposed to improve the built products’ fatigue property, and corresponding theoretical and experimental investigations were performed to reveal the influencing mechanism of the vibration utilized. The results showed that the proposed model was validated by comparing the theoretical predictions with the experimental measurements. As layer height and printing speed were increased, the fatigue life of ME specimens dropped, while it rised with the increasing extrusion temperature. The built products’ fatigue life could be improved obviously by using vibration.

Study on the Stress Threshold of Preventing Interfacial Fatigue Debonding in Concrete Beams Strengthened with Externally-Bonded FRP Laminates

Externally-bonded FRP laminate is widely used in structural strengthening due to the many advantages of FRP materials. Further enhancement of the strengthening effect can be achieved by inducing prestress into the FRP laminate. However, FRP debonding is still a main issue of this strengthening method, especially the Intermediate Crack-induced debonding (IC debonding). To better understand the impact of FRP debonding on the strengthening effect, a series of parameter analyses were conducted in this study based on the fatigue life prediction model proposed by the authors. The proposed model involves the fatigue damage accumulation of components of the beam, the mutual interaction between each component, and the impact of FRP fatigue debonding. As a result, a stress threshold for preventing FRP fatigue debonding in strengthening the concrete beam was proposed, which aimed to avoid safety hazards caused by IC debonding in practical engineering.

Defect sensitivity and high-cycle fatigue resistance of arc-welded 2219 aluminum alloy at 77 K

This study investigated the tensile and high-cycle fatigue (HCF) properties of a tungsten inert gas (TIG)-welded 2219 aluminum alloy at room temperature (RT) and 77 K for the first time. Defect-sensitivity-based fatigue strength and life prediction models were proposed to link welding defects with fatigue properties at cryogenic temperatures. The findings revealed that the yield strength, ultimate tensile strength, and fatigue strength of 2219 welded joints at 77 K were significantly improved by 29.7 %, 34.1 %, and 60.4 %, respectively, compared to those at RT. The largest possible pore defects near the weld surface were predicted using a combination of laboratory X-ray computed microtomography and the extreme value statistics (EVS) method. Furthermore, the fatigue strength was predicted by considering the maximum defect size in terms of the El-Haddad-based Kitagawa–Takahashi (K–T) diagram. The predicted fatigue strength was 95.5 MPa with an error of 5.4 % at RT, whereas at 77 K, the predicted value was 144.5 MPa with an error of 10.8 %. Finally, a fatigue life prediction model at both RT and 77 K was proposed based on a modified K–T diagram and Murakami’s S-N prediction model. The combination of the fatigue life prediction model and the maximum possible defect size based on EVS can provide slightly conservative predictions of the fatigue life without conducting a large number of HCF tests.

Fatigue Life Prediction Model of FRP-Concrete Interface Based on Gene Expression Programming.

Under fatigue loading, the interfacial fatigue life of fiber-reinforced polymer(FRP)-concrete is an important index for the analysis of the fatigue performance of reinforced concrete beams strengthened with FRP materials and the evaluation of the reinforcement effect. To solve the problems of the inconsistent and limited accuracy of existing fatigue life prediction models, gene expression programming (GEP) was used to study the interfacial fatigue life of FRP-concrete. Firstly, 219 sets of interfacial fatigue test data were collected, which included two kinds of reinforcement methods, namely, externally bonded (EB) reinforcement and near-surface-mounted (NSM) reinforcement; secondly, Pearson correlation analysis was used to determine the key factors affecting the fatigue life, and then GEP was used to explore the influence of different input forms on the prediction accuracy of the model. Fatigue life calculation formulas applicable to the two kinds of reinforcement methods, i.e., EB and NSM, were established, and a specific calculation formula was established. The model was subjected to parameter sensitivity analysis and variable importance analysis and was found to reflect the intrinsic relationship between the fatigue life and various factors. Finally, the GEP model was compared with the models proposed by other researchers. Five statistical indices, such as the coefficient of determination and the average absolute error, were selected to assess the model, and the results show that the GEP model has higher prediction accuracy than other models, with a coefficient of determination of 0.819, and indicators such as the average absolute error are also lower than those of the rest of the models.