Predict Fatigue Crack Growth Research Articles

A fatigue crack growth prediction model for cracked specimen under variable amplitude loading

The objective of this study was to propose a crack growth prediction model for cracked specimens under large and variable amplitude loading conditions. In this study, the concept of change in net-section strain energy was applied to simulate elastic–plastic fracture behavior, and three-dimensional fitting of the results with respect to changes in the net-section strain energy, load ratio, and crack growth rate was performed to create the crack growth prediction model. The validity of the generated model was examined using comparisons with experimental data as well as simulation results from existing crack growth models under several variable loading conditions.

Corrosion fatigue crack growth prediction of bridge suspender wires using Bayesian gaussian process

This paper proposes a Bayesian gaussian process-based corrosion fatigue damage assessment framework for bridge suspender wires. Prior statistical parameters in the fatigue life prediction model are obtained by corrosion fatigue crack growth test under different stress ratios. A numerical simulation method of corrosion fatigue crack growth is developed combined with ABAQUS and FRANC3D. Following this, a Bayesian network-based framework is established by integrating fatigue crack growth model, stress concentration factor and observed fatigue crack length information to reduce the uncertainties. The uncertainties exist in stress concentration factor are quantified by gaussian process regression-based surrogate model, and the pit geometry of length, width and depth are selected as three-dimensional input. Markov Chain Monte Carlo simulation is used to obtain the posterior distribution of parameters in the Bayesian network. The results show that the fatigue crack growth predictions agree well with experimental observations. The proposed method can simplify the complicated modeling process of stress concentration factor under the influence of corrosion pit, and can effectively update the model parameters to accurately predict the corrosion fatigue crack of bridge suspender wires.

Fatigue crack growth prediction method based on machine learning model correction

At present, ML has become an effective method to solve the prediction problem of fatigue crack growth. To reduce the inaccurate prediction caused by uncertain factors in crack growth, this paper proposes a fatigue crack growth prediction method based on the ML model correction. This method improves the accuracy of crack growth prediction by using real crack data to correct the ML model. In the research process, the prediction performance of the three ML methods is compared, and the CGR-ML model for crack growth is established. Subsequently, dynamic correction strategy for the CGR-ML model is proposed while selecting crack detection points by using the nonlinear crack length selection method. Finally, the effectiveness of the method is verified by the central crack growth and the crack growth experiment under mixed-mode multi-step loading. It can be seen from the comparison with the previously proposed fatigue crack growth prediction method based on the theoretical model correction that the method proposed in this paper can achieve a better prediction effect.

Fatigue crack growth on modified CT specimens using artificial neural networks

The purpose of this paper is to analyse fatigue crack growth under mixed mode conditions using artificial neural networks. Mixed mode conditions were achieved using a four-point bending specimen, with different notch positions, and a modified compact tension specimen, with one hole using different diameters and centre coordinates. Finite element analysis simulations, using the vector crack tip displacement propagation criterion, were used to train the artificial neural networks. Once fully trained, the artificial neural networks were able to predict fatigue crack growth paths and lives under mixed mode conditions, showing good agreement with both experimental and numerical examples.

Predicting Fatigue Crack Growth Behavior of Coalesced Cracks Using the Global-Local Superimposed Technique

The S-version Finite Element Method (FEM) is known as a global-local superimposed approach that consists of two separate meshes referred to as global and local cracks. The relationship between the energy release rate and the Stress Intensity Factor (SIF) is evaluated to characterize the growth behavior of the fatigue cracks. The SIF is determined using the Virtual Crack Closure Method (VCCM). The cracks propagated in the direction of the loading before coalescing into a single crack. Each crack begins with a length of 10mm and a depth of 3mm. After the crack coalesces, the diameter of the surface crack before it breaks is 28mm, whereas the depth of the crack is 5.3mm. The V-shaped surface crack forms quickly after coalescence occurs and continues to propagate into a massive semi-elliptical surface crack before finally breaking. The result was validated and compared between S-version FEM and the analytical solution. The behavior of the fatigue crack growth shows a good agreement between both methods with small errors. The result indicates that the Root Mean Square Error (RMSE) values before coalescing are 0.1496 with 0.6, and after coalescing is 0.4, the RMSE value is 0.1665. Therefore, it can be stated that the S-version FEM approach can predict the growth of fatigue cracks.

Numerical evaluation on fatigue crack growth and life predictions of an FPSO mooring system

This study aims to systematically study the fatigue crack propagation characteristics of the mooring system for a floating production storage and offloading vessel (FPSO) through the fracture mechanics-based approach. The remaining fatigue life of the mooring lines is predicted by a self-integrated program through the simulation of the crack growth behavior. Additionally, parametric studies are performed to investigate the influence of initial crack status (location/size/shape) on mooring crack propagation characteristics. Results illustrate that the remaining fatigue life at the uppermost position of mooring lines at the wave-ward side is similar, which is approximately 178 years longer than that of mooring lines at the wave-back side. Fatigue issues are the severest in the crown region, where the critical fatigue life is about 49% of that in the bend region and 19.9% of that in the straight region. With the increase of the initial crack size and aspect ratio, the remaining fatigue life experiences a remarkable decrease with amplitudes up to 47.8% and 30.1% respectively. The integrated evaluation program is a promising tool for the damage tolerance design of mooring system due to the excellent capability in predicting the remaining fatigue life and charactering the crack growth behavior.

Training Deep Neural Networks with Novel Metaheuristic Algorithms for Fatigue Crack Growth Prediction in Aluminum Aircraft Alloys.

Fatigue cracks are a major defect in metal alloys, and specifically, their study poses defect evaluation challenges in aluminum aircraft alloys. Existing inline inspection tools exhibit measurement uncertainties. The physical-based methods for crack growth prediction utilize stress analysis models and the crack growth model governed by Paris’ law. These models, when utilized for long-term crack growth prediction, yield sub-optimum solutions and pose several technical limitations to the prediction problems. The metaheuristic optimization algorithms in this study have been conducted in accordance with neural networks to accurately forecast the crack growth rates in aluminum alloys. Through experimental data, the performance of the hybrid metaheuristic optimization–neural networks has been tested. A dynamic Levy flight function has been incorporated with a chimp optimization algorithm to accurately train the deep neural network. The performance of the proposed predictive model has been tested using 7055 T7511 and 6013 T651 alloys against four competing techniques. Results show the proposed predictive model achieves lower correlation error, least relative error, mean absolute error, and root mean square error values while shortening the run time by 11.28%. It is evident through experimental study and statistical analysis that the crack length and growth rates are predicted with high fidelity and very high resolution.

A probabilistic model for fatigue crack growth prediction based on closed-form solution

The stochastic property of fatigue crack growth is well-known, and brings a challenge for accuracy fatigue life prediction. This paper proposes a probabilistic model for predicting the fatigue crack growth under the framework of linear elastic fracture mechanics. The stress intensity factor is related to the crack length and load condition through finite element model. The material parameters and model error are regarded as random variables. Based on conjugate Bayesian analysis, a closed-form solution is derived to update the posterior distribution of material parameters according to the crack growth observation. Given the posterior distribution, a modified Paris–Erdogan model is adopted to predict the crack growth in a probabilistic view. The contribution of this paper is providing a closed-form solution for updating the well-known Paris–Erdogan equation, and predicting the crack propagation more efficiently and accurately. Comprehensive experiments show the superiority of the proposed method over existing Markov Chain Monte Carlo (MCMC) approaches.

Influence of temperature on the R‐effect for the fatigue crack growth of nickel‐base superalloy GH4169

AbstractThe stress ratio (R) effect, especially at elevated temperatures, is associated with the fatigue crack growth (FCG) prediction in aero‐engine hot‐end components under complex operation conditions. The FCG experiments with three R were conducted on Ni‐based superalloy GH4169 at room temperature (RT), 550°C, and 700°C. The results indicate that the R‐effect on the FCG of GH4196 is temperature‐dependent. Therefore, efforts were made to identify the R‐effect at various temperatures to describe the FCG. The concept of the crack‐closure or the two‐driving‐force was employed to quantify the R‐effect considering the temperature influence. Fractographic analyses on the fracture surface were performed to discuss the underlying mechanism responsible for the temperature influence. The study can contribute to the R‐dependent FCG modeling at various temperatures.

Three-dimensional fatigue crack growth prediction method based on consistency retention

To reduce the influence of uncertain factors, a three-dimensional fatigue crack growth prediction method based on consistency maintenance is proposed. Firstly, the crack growth update under complex loads is realized based on XFEM. Subsequently, the life correction method based on the improved particle filter and the path correction method based on the improved maximum tangential stress criterion are proposed. Taking real crack data as input, the accuracy of prediction results is improved from two dimensions of life and path. Finally, five cases are used to verify the proposed framework, which proves that it can realize dynamic tracking of crack growth.

Equivalent crack growth model for correlation and prediction of fatigue crack growth under different stress ratios

An equivalent crack growth model that accounts for the effect of stress ratio is proposed for the correlation and prediction of crack growth under different stress ratios. A previously proposed equivalent driving force ΔKeq that yields the same growth rate (da/dN)R as combined applied stress intensity factor range ΔKR and stress ratio, R is modified, and its corresponding equivalent growth rate (da/dN)eq is derived. This yields an equivalent crack growth model of (da/dN)eq versus ΔKeq capable of collapsing data of different R-ratios unto a common curve. The model was validated with excellent agreement between the model and published data.

Use of the helium ion microscope in small crack fractography

Highly stressed aerospace structures, such as the wing attachment points and load carry-through bulkheads of fighter aircraft, often grow fatigue cracks from micro-scale discontinuities. These cracks spend much of their lives close to these discontinuities in the small crack regime. The accurate prediction of fatigue crack growth in this small crack regime leads to setting realistic inspection thresholds for the aircraft and assisting in accurate risk-based assessment of the lives of such cracks. Fractography can greatly aid the assessment of the growth of small cracks. It has traditionally been carried out using optical and Scanning Electron Microscopes to gather the necessary small crack growth rate data, however, limitations on the resolution and the surface sensitivity of these instruments restricts the gathering of crack growth rate data at small crack lengths. As an extension to the current capabilities, the newly introduced Helium Ion Microscope offers improved nano-scale resolution and topographical sensitivity. As a result, the Helium Ion Microscope offers the potential for both quantitative and qualitative analysis of small fatigue crack growth about nucleating discontinuities at resolutions previously unobtainable. This paper presents an overview of the technology and gives three different fractographic examples of its possible applications to failure analysis. These examples include extension of small crack fractography, extension of near initiation fractographic feature analysis, and extension of nano-scale feature analysis. To demonstrate these capabilities, fatigue fractures produced by experimental loading spectra that were applied to the aluminium alloy AA7050-T7451 have been investigated.

Measurement-driven, model-based estimation of residual stress and its effects on fatigue crack growth. Part 2: Fatigue crack growth testing and modeling

This paper assesses the accuracy of fatigue crack growth (FCG) predictions for high-strength aluminum samples containing residual stress (RS) and complex two-dimensional cracks subjected to constant amplitude load. FCG predictions use linear-elastic, multi-point fracture mechanics. A first prediction includes RS estimated by the model described in Part 1; a second prediction includes RS measured by the contour method. FCG test data show a significant influence of RS. Ignoring the RS results in a +60% error in predicted FCG life (non-conservative). Including RS improves predictions of crack growth significantly (errors better than +26% (estimated RS) and −14% (measured RS)).

Fatigue crack growth in CNT-reinforced polymer composite

Carbon nanotubes (CNT)-reinforced polymer composite has many engineering applications due to attractive properties, like high strength to weight ratio. These materials are subjected to cyclic fatigue loading during their service life. This work proposed a dual scale modeling approach to predict fatigue crack growth behavior of CNT-reinforced polymer composite under the thermo-mechanical loading environment. The effective orthotropic property of CNT-reinforced polymer composite is estimated using the mean-field homogenization technique in meso-scale modeling. Further, the equivalent composite property is used for macro-scale fatigue crack growth study using higher-order extended finite element method. To present the efficiency of the proposed modeling approach, various combinations of polymer composite (by volume fraction of reinforcement at 7.5%, 10%, 12.5%, 15%, 17.5%, and 20%) are taken for analysis. At macro-scale modeling, XFEM with higher-order enrichment terms is employed to improve the solution accuracy at the crack tip region. The proposed computational approach has been employed with the in-house developed MATLAB code and DIGIMAT software. Several numerical examples are considered with geometrical discontinuities like multiple holes and cracks.

Cyclic cohesive zone model damage parameter acquisition for fatigue crack growth considering crack closure effect

This paper presents a method to obtain damage parameters for cyclic cohesive zone model based on the crack closure experiments. The damage evolution is modified by introducing equivalent separation to quantify the nonuniform damage distribution along the crack path. The damage parameters acquisition is realized with digital image correlation analysis, where the effect of crack closure is accordingly elaborated. An adaptive cycle jump technique is established based on the damage rate, aiming to reduce the computational cost. Compared with the experimental data, the proposed model exhibits the capability in predicting fatigue crack growth rate.

Application of strain energy based approach for evaluation of fatigue crack growth retardation effect under random overload

Fatigue crack growth in structural components subjected to random amplitude loading is a significantly complex problem. Many models have been proposed to estimate the retardation effect due to a single overload, though for the random overload sequence, there is still a lack of effective quantitative description methods. In this research, a new time to crack initiation (TTCI) type for the calculation of equivalent initial flaw size (EIFS) is proposed based on the change in net-section strain energy density. For a certain number of fatigue cycles and current crack lengths, the value of EIFS can be obtained by solving the corresponding integral equation. The proposed EIFS calculation method was applied for the prediction of fatigue crack growth under single overload and random overload sequences. The prediction results agree well with the experimental data for different conditions. For a certain single overload or overload spectrum, the retardation effect is reflected in the decrease in EIFS, which appears to propagate from a smaller initial crack length. The proposed method shows that the new TTCI type of EIFS can be used as the quantitative characterization of the retardation effect due to the random overload sequence.

Computational modelling of poro-visco-hyperelastic effects on time-dependent fatigue crack growth of hydrogels

In newly developed hydrogel-based devices, hydrogels are commonly used in load-bearing components subjected to prolonged cyclic deformations. The anti-fatigue capability of hydrogels is crucial for extending the service life of the devices. While recent developments in the synthesis and characterisation of tough hydrogels have facilitated continuous improvements of the fatigue resistance, the underlying mechanisms that dominate the fatigue fracture of hydrogels are still inconclusive. This work aims to model the complex constitutive response and predict the fatigue crack behaviour of hydrogels. The contribution of this work is twofold. (i) A physically-based poro-visco-hyperelastic model is developed within the framework of the Theory of Porous Media (TPM) at finite strains to describe the solid–fluid-coupled material behaviour of hydrogels. The fluid transport in hydrogels is governed by Darcy’s law. The non-equilibrium mechanical response induced by polymer chain rearrangement is considered by introducing internal variables based on a multiplicative decomposition of the solid deformation gradient tensor into elastic and inelastic parts. The time-dependent breaking/reforming kinetics of physical chains is described by a Bell model-based chain evolution law. (ii) An energy-based fatigue crack growth model is proposed to predict the fatigue crack growth of hydrogels. A volume averaging method is used to calculate the elastic energy density surrounding the crack tip as the driving force. In particular, the cyclic evolution of the averaged energy density due to the breaking/reforming kinetics of physical chains is incorporated in the crack growth model for hydrogels with physical networks. The computational results demonstrate that the predicted fatigue crack growth of different hydrogels reasonably agrees with experimental data. Moreover, the effects of fluid transport, time-dependent deformations and chain kinetics on the fatigue fracture behaviour of hydrogels are systematically analysed. The prediction results indicate that these time-dependent mechanisms cannot be ignored in modelling the fatigue behaviour of anti-fatigue hydrogels.

Prediction of fatigue crack propagation behavior of AA2524 after laser shot peening

Laser shot peening is a relatively novel surface treatment technology generating deep compressive residual stress and further retarding fatigue crack growth rate (FCGR). Meanwhile, the load ratio, R, plays an important role in fatigue crack growth behavior. Evaluating the remaining life of components accurately is essential to damage tolerance design, many efforts have been applied to correlate the R-effect. In this paper, the effect of load ratio on the gain of AA2524′s fatigue life after laser shot peening have been thoroughly studied. Initially, the fatigue crack growth rate of AA2524 after laser shot peening was measured under different load ratios. Subsequently, a comparative analysis between the FCGR for AA2524 before and after laser shot peening was thoroughly conducted. Then, the crack closure model has been applied to correlate the R-effect of peened specimens, but the results are unsatisfied due to the ignore of compressive residual stress. To address this issue, a novel method of predicting crack growth life considering compressive residual stress was proposed. The artificial neural network model and support vector regression model based on particle swarm optimization algorithm were trained by the FCGR data, and the fatigue crack growth life is predicted by a cycle-by-cycle method. The result shows the artificial neural network model is more accurate than the support vector regression model, and this intelligent algorithm can be a potentially effective way to predict fatigue crack growth behavior.

Prediction of fatigue crack growth under variable amplitude loading by artificial neural network-based Lagrange interpolation

Artificial neural network has been several applications on fatigue crack, with prediction on fatigue crack growth life often serving as milestones. A typical difficulty in predicting the life curve of fatigue crack growth is the retardation of crack growth caused by the overload effect. Overload retardation, resulting in the lack of experiment data points in the retardation interval, is a long-term challenge for predicting fatigue crack growth. We improve the ability of back-propagation neural network to predict fatigue crack growth life by Lagrange interpolation, an interpolation for local data points. It combines the trend of whole data point to deal with the retardation interval, and interpolates multiple data points in the retardation interval when there is a lack of local data, and uses a neural network to construct the prediction formula of crack growth through self-optimization. In the study involving constant and variable amplitude fatigue crack growth experiments, the proposed method is proved to improve, with statistical significance, the predictive ability on the whole range of experiment data. The method is simple and accurate. Consequently, it is helpful to solve the problem of fatigue crack growth life of engineering structure.

Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data

On-line monitoring and quantification of fatigue cracks are essential for ensuring the reliability of engineering structures. The acoustic emission (AE) technique is one of the structural health monitoring (SHM) techniques and is capable of detecting the growth of defects in real time. In this study, the identification and prediction of fatigue crack growth (FCG) of 316LN stainless steel under different stress ratios were investigated by the AE technique. The purpose of this study is to extract multiple AE parameters from AE waves and relate AE signals with FCG under different load ratios for qualitatively identifying the fatigue damage and quantitatively predicting the crack size. The results show that three damage stages associated with crack initiation, stable crack growth, and rapid crack growth are accurately identified by using multiple AE parameters during FCG under various load ratios. The quantitative relationships between Fatigue crack growth rates (FCGRs) and AE parameters are linear in log–log scale and independent of the stress ratio. Moreover, AE energy is found to be the most superior parameter for qualitatively identifying the fatigue damage and quantitatively relating FCGR to AE data. Finally, a combined qualitative and quantitative approach for FCG assessment based on AE monitoring is proposed. The criterion for diagnosing the critical damage state and estimating the fatigue crack size is also clarified. Results from this study will provide a strategy for effectively assessing FCG with AE data and guide complementary studies for damage identification with AE monitoring.