Low Cycle Fatigue Life Prediction Research Articles

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.

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

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

The strain ratio-dependent multiaxial low cycle fatigue behaviour and life prediction of 316L stainless steel based on critical plane at elevated temperature

The study mainly investigates the effect of strain ratio on the multiaxial low cycle fatigue behaviour of 316L at 550 °C. The evolution of both axial and torsional stress amplitude is observed to go through three stages: cyclic hardening, softening and fracture. What's different is that the torsional loading experiences fewer cyclic hardening cycles compared to the axial loading. Regarding the interaction between axial and torsional stresses, it is observed that as the torsional strain increases, the axial deformation response presents a lower peak stress and a higher softening rate, while the torsional peak stress and softening rate hardly changes with the increase of axial strain amplitude. The fracture pattern is obtained through three-dimensional (3D) depth-of-field observation. It is discovered that as the strain ratio increases, the fracture pattern transits from tensile failure mode to shear failure mode. Furthermore, the orientation angle of the fracture surface increases and lies between the critical plane angle of maximum shear strain and maximum normal strain, indicating a mixed failure mode. Additionally, a comparison of nine representative critical plane models reveals that the energy-based models can better predict the multiaxial fatigue life influenced by the strain ratio. Finally, based on the analysis of deformation and failure mechanisms, a normal damage parameter weakening factor (NDWF) is introduced into the KBM-R model, and an improved normal stress component (INSC) is introduced into the Wu-R model. The two modified models well capture the impact of strain ratio and accurately predict the multiaxial fatigue life of both 316L and P92 steel.

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Low cycle fatigue behavior and life prediction of a directionally solidified alloy

Multiaxial low cycle fatigue behavior and life prediction of wire arc additive manufactured 308L stainless steel

Wire arc additive manufacturing (WAAM) is a technique that has gained popularity in various industrial sectors due to its high productivity and flexibility. However, the comprehension of cyclic deformation and fatigue behaviors in WAAM austenitic stainless steel (SS), particularly under complex multiaxial loading conditions, is much less advanced compared to conventional manufacturing methods. This study aims to fill some of these gaps by performing fatigue tests of WAAM 308L SS under axial, torsional and axial–torsional multiaxial loadings. Similar tests were carried out on the hot-rolled 308L SS as a basis for comparison. WAAM 308L SS exhibits notable distinctions in cyclic deformation, fatigue life, cracking mode and fatigue failure mechanisms compared to its hot-rolled counterparts as a result of its distinct manufacturing technologies and resulting microstructures. To assess all fatigue life data, three critical plane criteria, namely FS, SWT and CXH criteria, were employed. The CXH model, which considers the effects of both tensile and shear damage on fatigue properties, has the ability to predict the fatigue life of both hot-rolled and WAAM 308L SS.

High-cycle and low-cycle fatigue life prediction under random multiaxial loadings without cycle counting

A novel fatigue life prediction method under random multiaxial loadings is proposed in this paper. One unique benefit of the proposed method is that it is based on the time-derivative fatigue crack growth formulation and does not need cycle counting under arbitrary loadings. First, a brief review of subcycle fatigue crack growth (FCG) analysis and the equivalent initial flaw size (EIFS) concept for life prediction is introduced. Next, the existing subcycle fatigue crack growth model is extended to near-threshold conditions. An intrinsic fatigue threshold of the material is introduced, and a hypothesis is proposed that the crack only grows when the local stress intensity factor is beyond the intrinsic threshold. Following this, the analogy of stress intensity factor and strain intensity factor is used to extend the fatigue life prediction model to strain-based fatigue analysis. Correction for large plastic deformation is included to handle low-cycle fatigue life prediction. The novelties of this model are that it considers the FCG at the threshold and near-threshold region and it is able to predict both HCF and LCF conditions using FCG-based life prediction. Following this, extensive in-house and literature data for AL-7075-T6 under uniaxial and multiaxial, constant, and random loadings are used for model validation. Discussions for the effect of model parameters are provided based on parametric analysis. Conclusions and future work are mentioned. Code and data are released in public cloud service for interested readers.

A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction

AbstractAero‐engine turbine blades are subject to combined high and low cycle fatigue (CCF) loadings during operations. In this paper, a modified nonlinear cumulative damage model is developed based on linear damage accumulation theory and static toughness exhaustion to predict the CCF life of turbine blades and common blade materials. An interaction factor is created to reflect the integrated effect of high cycle fatigue and low cycle fatigue (HCF‐LCF). Available test data from TC4 alloy and other two alloy materials, together with two turbine blades are utilized to validate the robustness and accuracy of the proposed approach. Comparative results demonstrate that the proposed model holds better performance in life predictions under CCF loadings.

Microstructure evolution, failure mechanism and life prediction of additively manufactured Inconel 625 superalloy with comparable low cycle fatigue performance

During hot end components service, i.e., startup and shutdown processes, the cyclic stress caused by the asymmetry loading conditions is introduced. To study the effect of mean stress on the cyclic response, the low cycle fatigue, failure mechanism and life prediction model of additively manufactured (AMed) Inconel 625 superalloy at room temperature (RT) and service temperature (600°C) with strain ratio R = −1 and 0.1 are conducted. Experimental results indicate that the different cyclic softening and hardening behavior at RT and 600 °C is the result of competition between dislocation slip, back stress hardening and second phase precipitation. According to the evaluation of the internal stress variables, the long-range back stress plays a crucial role to control the cyclic behavior of the alloy. At RT, the heterogeneous grain structure introduced by additive manufacturing causes the back stress hardening, making the material exhibit high fatigue performance. But this hetero-deformation induced (HDI) strengthening is inhibited at high temperatures due to the uniform distribution of dislocations. In addition, a low cycle fatigue life prediction model based on critical plane method is established which presents an easily assess to evaluate the low cyclic fatigue life of an AMed material under various test condition.

A generalization ability-enhanced image recognition based multiaxial fatigue life prediction method for complex loading conditions

A generalization ability-enhanced image recognition based multiaxial low-cycle fatigue life prediction is proposed for complex conditions, including multiaxial variable amplitude fatigue, fatigue-creep interaction, and anisotropic materials. Notably, a novel modified dynamic hysteresis image plotting method is introduced for multiaxial variable amplitude loading. Building on a pre-trained convolution neural network (CNN) model, fine-tuning is used across three cases. It makes the error decrease to 25%. Besides, fine-tuning improves training efficiency and generalization. Based on fine-tuning results, each layer of the model is attributed with physics significance. Furthermore, regression activation map (RAM) is used for interpretation, illustrating that the model focuses on the inner and edges of hysteresis images.

Low-cycle fatigue performance and life prediction of a P/M nickel-based superalloy with artificial surface defect at elevated temperature

P/M nickel-based superalloys are widely used in turbine disks, defects such as inclusions and surface scratches inevitably arisen from manufacturing process will exert negative influences on the low-cycle fatigue performance of superalloys and make traditional life prediction method no longer reliable. The residual fatigue life in presence of defect is an important performance index for disc alloy in damage tolerance design criteria. In this work, LCF tests of FGH96 specimens with artificial surface defect were conducted at 600℃, 650℃, 700℃ and room temperature, the size of the defect is determined in accordance with the minimum flaw size which can be detected by NDT technique. The results show that the LCF life of the specimens with artificial surface defect decreases significantly with the increase of temperature and size of defect. Microscopic characterization technique SEM and EDS were executed to investigate the failure mechanism of the specimens, different mechanisms of crack initiation from artificial surface defect at room and elevated temperature were discussed. And then life prediction based on fracture mechanics was carried out by ABAQUS-FRANC3D co-simulation. This method allows that SIF can be conveniently calculated by finite element method and it is applicable to analysis of arbitrary engineering structure. Good life prediction results which fall within double dispersion band of experimental lives were obtained at elevated temperature, but the predicted life is excessively conservative at room temperature because the crack initiation process accounts for a large proportion of fatigue life. It seems that life prediction method based on fracture mechanics is more applicable to specimens with surface defect at elevated temperature due to the shorter crack initiation process. This work has reference significance to the damage tolerance analysis of turbine disks.

Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk

Turbine blisk is one of the typical components of gas turbine engines. The fatigue life of turbine blisk directly affects the reliability and safety of both turbine blisk and aeroengine whole-body. To monitor the performance degradation of an aeroengine, an efficient deep learning-based modeling method called convolutional-deep neural network (C-DNN) method is proposed by absorbing the advantages of both convolutional neural network (CNN) and deep neural network (DNN), to perform the probabilistic low cycle fatigue (LCF) life prediction of turbine blisk regarding uncertain influencing parameters. In the C-DNN method, the CNN method is used to extract the useful features of LCF life data by adopting two convolutional layers, to ensure the precision of C-DNN modeling. The two close-connected layers in DNN are employed for the regression modeling of aeroengine turbine blisk LCF life, to keep the accuracy of LCF life prediction. Through the probabilistic analysis of turbine blisk and the comparison of methods (ANN, CNN, DNN and C-DNN), it is revealed that the proposed C-DNN method is an effective mean for turbine blisk LCF life prediction and major factors affecting the LCF life were gained, and the method holds high efficiency and accuracy in regression modeling and simulations. This study provides a promising LCF life prediction method for complex structures, which contribute to monitor health status for aeroengines operation.

Low‐cycle fatigue life prediction of powder metallurgy superalloy considering characteristic parameters of inclusions

AbstractForeign non‐metallic inclusions can significantly reduce the low‐cycle fatigue (LCF) life of powder metallurgy (PM) superalloy and greatly affect the safety and reliability of aeroengines. In this paper, LCF experiments on PM FGH96 superalloy with and without inclusions were conducted. Effects of inclusions on the fatigue life and damage mechanism of FGH96 alloy are discussed by fractography analysis. Parameters related to inclusion characteristics such as strength–inclusion coefficient are proposed. By introducing characteristic parameters of inclusions, the LCF life prediction models are established based on the Manson–Coffin relationship, which significantly improved the prediction accuracy and reduced the scattering band by a factor of 2.

The Low-Cycle Fatigue Behavior, Microstructure Evolution, and Life Prediction of SS304: Influence of Temperature.

To study the fatigue failure and microstructure evolution behavior of SS304, low-cycle fatigue tests are conducted at room temperature (RT), 300 °C, and 650 °C. The results indicate that, because of the influence of the dislocation walls, carbon-containing precipitates, and deformation twins, the cyclic hardening behavior is presented at RT. However, different from the cyclic hardening behavior at RT, the cyclic softening behavior of SS304 can be observed due to the dynamic recovery and recrystallization containing dislocation rearrangement and annihilation at 300 °C and 650 °C. In addition, two fatigue crack initiation modes are observed. At RT, the single fatigue crack initiation mode is observed. At high temperatures, multiple crack initiation modes are presented, resulting from the degradation of material properties. Furthermore, a new fatigue life prediction model considering the temperature is conducted as a reference for industrial applications.

Mechanical Characterization of Fatigue and Cyclic Plasticity of 304L Stainless Steel at Elevated Temperature

BackgroundThe mechanical characterization of the cyclic elastoplastic response of structural materials at elevated temperatures is crucial for understanding and predicting the fatigue life of components in nuclear reactors.ObjectiveIn this study, a comprehensive mechanical characterization of 304L stainless steel has been performed including metallography, tensile tests, fatigue tests, fatigue crack growth tests and cyclic stress-strain tests.MethodsIsothermal tests were conducted at both room temperature and 300 °C for both the rolling direction and the transverse direction of the hot rolled steel. Mechanical properties were extracted from the uniaxial experiments by fitting relevant material models to the data. The cyclic plasticity behavior has been modelled with a radial return-mapping algorithm that utilizes the Voce nonlinear isotropic hardening model in combination with the Armstrong-Frederick nonlinear kinematic hardening model. The plasticity models are available in commercial FE software and accurately capture the stabilized hysteresis loops, including a substantial Bauschinger effect.ResultsThe material exhibits near isotropic properties, but its mechanical performance is generally reduced at high temperatures. Specifically, in the rolling direction, the Young’s modulus is reduced by 16 % at 300 °C, the yield strength at 0.2 % plastic strain is lower by 23 %, and the ultimate tensile strength is lower by 30 % compared to room temperature. Fatigue life is also decreased, leading to an accelerated fatigue crack growth rate compared to room temperature. A von Mises radial return mapping algorithm proves to be effective in accurately modelling the cyclic plasticity of the material. The algorithm has also been used to establish a clear correlation between energy dissipation per cycle and cycles to failure, leading to the proposal of an energy-based fatigue life prediction model.ConclusionsThe material exhibits reduced mechanical performance at elevated temperatures, with decreased monotonic strength, compared to room temperature. Fatigue life is also compromised, resulting in accelerated fatigue crack growth. The material’s hardening behavior differs at room temperature and elevated temperature, with lower peak stress values observed at higher temperatures. The radial return mapping algorithm can be used to determine the dissipated energy per cycle which together with fatigue testing has been used to propose a low cycle fatigue life prediction model at both temperatures.

Asymmetrical multiaxial low-cycle fatigue behavior and life prediction of CP-Ti under non-proportional stress-controlled mode

Asymmetrical multiaxial fatigue tests were performed. A new quantification parameter is proposed to represent the difference in velocity during ratcheting accumulation. Although the stress–strain response under asymmetric loading is similar to that under symmetric loading, substantial deteriorations of life are observed. The threshold value for ratcheting strain accumulation of axial stress amplitude is closer to 287 MPa. The brand-new stress parameters-based KBM-PH model with mean stress correction is the best overall among a series of models based on strain and stress parameters with or without mean strain and stress corrections with the best accuracy and ease of use.

A data-driven low-cycle fatigue life prediction model for nickel-based superalloys

A data-driven machine learning method for the low-cycle fatigue (LCF) life prediction of Nickel-based superalloys is proposed to overcome the limitations of empirical formulas. The method integrates three characteristics of the superalloys: chemical composition, heat treatment process and experimental parameters, and constructs a unified fatigue life prediction dataset. The relationship between many different microstructural parameters and LCF life is analyzed using the Pearson Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC). The fatigue life dataset is then used to predict the LCF life with four machine-learning models: Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and a Genetic Algorithm-based Random Forest (GA-RF). The comparative study results demonstrate that the models accurately predict the LCF life, as evidenced by the respective R2 values of 0.8311, 0.8345, 0.7711, and 0.9272, with GA-RF performing the best and equally better in comparison with the Coffin-Manson model. The proposed method efficiently maps feature-life relationships for the LCF life of Nickel-based superalloys, reducing experimental time and cost and promising applications in the inverse design and manufacture of alloys.

The low cycle fatigue property, damage mechanism, and life prediction of additively manufactured Inconel 625: Influence of temperature

AbstractSymmetrical high‐temperature low‐cycle fatigue tests were performed in this study to examine the influence of temperature on the fatigue failure mechanism of the additively manufactured Inconel 625. According to the fracture analysis, cracks initiate from the crystallographic plane at room temperature. At 600°C, oxidation of carbides leads to crack initiation. The strengthening effect of γ″ precipitation prevents crack propagating within the matrix. At 750°C, the crack becomes transgranular propagating, as a consequence of γ″‐δ transformation. Geometrically necessary dislocations distribution on the fracture supports this result. Furthermore, a modified life prediction formula considering effect of temperature and strain amplitude was established. It was observed that the modified model predicted the fatigue life of the studied materials well in both test conditions.

A probabilistic fatigue life prediction method under random combined high and low cycle fatigue load history

In this paper, a probabilistic high and low cycle fatigue (H-LCF) life prediction framework is proposed to consider random load history into fatigue life prediction of structures. First, the random load history is generated and quantified by finite element analysis (FEA) and Monte Carlo simulation (MCS). Subsequently, based on the quantification approach and an improved H-LCF damage curve, the framework of probabilistic H-LCF life prediction is then established. A turbine shaft is analyzed by the proposed method, which confirms the superior accuracy of the proposed method than the existing ones. Overall, the proposed method provides an effective way for structures’ fatigue life and reliability assessment.

Low-cycle fatigue behaviour of hot-rolled titanium-clad bimetallic steel

Titanium-clad (TC) bimetallic steel is able to integrate the superior corrosion resistance of titanium alloy into traditional corrosion-susceptible structural steels, producing prominent life-cycle safety, serviceability and durability. Nevertheless, the relevant seismic performance and fatigue behaviour reported in existing literature are still limited and insufficient to support the codification for structural design of such advanced steel. In order to develop proper plastic/seismic design methodology for TC bimetallic steel structure, the cyclic behaviours and low-cycle fatigue life prediction of such metal must be thoroughly investigated. This paper aims to study the low-cycle fatigue behaviour of hot-rolled bonded TC bimetallic steel manufactured from TA2 titanium alloy and Q355B structural steel. The strain amplitude of low-cycle fatigue (LCF) test ranges from 0.5% to 2.5%, and the strain ratio applied herein includes 0 and -1. The experimental phenomenon, LCF failure mechanism, cyclic stress-strain relationship of different tests are thoroughly compared and discussed. Besides, the influence of strain rate and bond strength are simultaneously demonstrated. The LCF life are fitted and validated through Basquin-Coffin-Manson model, Kuroda model and energy method, respectively. The research outcomes demonstrate that the LCF cracks of TC bimetallic steel tend to be initiated at the cladding layer and propagated into the substrate layer due to a coupled buckling-fatigue failure mechanism. Several representative fatigue behaviour of conventional mild (CM) steels including the three-stage cyclic softening and mean stress relaxation have not been observed in this experimental study. The strain rate exhibits distinct influence on the LCF life and failure mechanism. Both Basquin-Coffin-Manson model and energy method can provide more favourable prediction accuracy for LCF life of TC bimetallic steel than the Kuroda model. Generally the LCF life of TC bimetallic steel is shorter than that of structural steels, stainless steels, titanium alloys and stainless-clad (SC) bimetallic steels. The test records and fatigue-life model parameters obtained can lay a solid foundation for seismic analysis and damage evaluation of TC bimetallic steel structure.

On the consideration of loading interaction in combined high and low cycle fatigue life prediction

AbstractBased on the analysis of the test data and Miner's rule, a combined high and low cycle fatigue (CCF) life prediction model considering the loading interaction is established by introducing coupled damage. The coupled damage is determined by multiplying the high cycle fatigue damage and the low cycle fatigue damage by a coupled damage coefficient . According to the analysis of the test data, the coupled damage coefficient is positively correlated with the high cycle stress ratio . In addition, an interaction factor, which reflects the loading interaction effect, is introduced to correct the coefficient . The CCF experimental data of four materials are employed for model verification and comparison among the proposed model, Miner's rule, and Trufyakov–Kovalchuk model (T–K model). The proposed model exhibits a relatively higher prediction accuracy and is more suitable for predicting the fatigue life of CCF.