Prediction of fatigue crack propagation in bulb-flat by experimental and numerical method

Prediction of fatigue crack propagation in bulb stiffeners by experimental and numerical methods

The concrete fatigue analysis can be performed with the use of fracture mechanics. The fracture mechanics defines the fatigue crack propagation as the relationship of crack growth rate and stress intensity factor. In contrast to metal, the application of fracture mechanics to concrete is more complicated and therefore many authors have introduced empirical expressions using Paris law. The topic of this paper is development of a new prediction of fatigue crack propagation for concrete using rheological-dynamical analogy (RDA) and finite element method (FEM) in the frame of linear elastic fracture mechanics (LEFM). The static and cyclic fatigue three-point bending tests on notched beams are considered. Verification of the proposed approach was performed on the test results taken from the literature. The comparison between the theoretical model and experimental results indicates that the model proposed in this paper is valid to predict the crack propagation in flexural fatigue of concrete.

Effects of crack tip plasticity on fatigue crack propagation

Seeking light and transparent bridge designs, engineers and architects have found an efficient and artistic way to fulfill their requirements: steel tubular bridges. In these modern tubular truss bridges, welded K-joints have been shown to be critically susceptible to fatigue failure induced by repeated traffic loads. Despite the research devoted to the study of fatigue during the last 35 years, especially in the offshore oil industry, estimating their fatigue resistance of tubular truss bridges is difficult and solutions are approximate. This is due to the complexity of the parameters influencing bridge fatigue strength, including: 1) complex loadings, 2) applied stress concentrations in joints, 3) welding imperfections, 4) welding residual stresses, and 5) size effects. Some design rules and studies are available to estimate these effects; however, residual stresses in tubular K-joints remain unknown as well as their influence on fatigue crack propagation. Residual stresses are well known to have detrimental influence on fatigue crack propagation in other metallic structures, and further study is a necessary for improving the understanding of K-joint fatigue behaviour. To address this problem, the main objective of this research is to assess, experimentally and numerically, welding residual stresses and their influence on the fatigue response of K-joints. In this thesis, experimental measurements of the residual stress fields were investigated through neutron-diffraction, hole-drilling and X-ray methods. Results have shown that, principal residual stresses are oriented transversely, i.e. perpendicularly to the weld direction, both at and near the surface. Residual stresses can reach the S355 steel yield strength at the weld toe. This orientation is particularly detrimental for fatigue because it is also the orientation of the principal applied stresses, and as a consequence high residual and applied stresses superimpose. Moreover, it is proved that a restraining effect occurs in the gap region in-between the braces remaining the critical residual stresses high in the gap region. Two large scale tubular truss beams were subjected to high-cycle fatigue in order to assess the effect of residual stresses on fatigue response of welded tubular K-joints. These tests revealed that tensile residual stresses do not affect the fatigue crack growth in details loaded in tension (hot-spot 1), whereas they play a significant role in details loaded in compression (hot-spot 1c). Tensile residual stresses enable crack opening, making the fatigue cycle partly or entirely effective to crack propagation. Based on these tests and previous tests from the ICOM test database, it is recommended to design all (tension or compression) K-joints using a strength curve Srhs Srhs – Nf category 100 (for T = 16 mm). A thermo-mechanical finite element model was also developed to numerically reproduce the residual stress creation (using ABAQUS and MORFEO codes). Once the numerical results were validated by comparison with experimental results, the finite element model was used to model other K-joint geometries. A geometrical parametric study was performed to identify the most influential parameters affecting the residual stress distribution (technological size effect). It is found that a raise of the wall chord thickness T (proportional scaling) or of the wall thickness τ = t/T (non-proportional scaling) strongly increases the magnitude of tensile transverse residual stresses. Based on this parametric study, residual stress distributions with depth for transverse, longitudinal and radial directions are proposed. Finally, an extended finite element method (X-FEM) model was used to propagate a fatigue crack under residual and applied stresses. An analytical model based on the effective stress intensity factor range ΔKeff (fracture mechanics) and combined with the proposed distribution for residual stresses was also established to predict fatigue crack propagation in joints loaded in tension and in compression.

Welded joints are widely used in the pipeline connection of nuclear power plants. Defects in these joints are an important factor leading to the failure of welded joints. It is critical to study the fatigue crack growth and life prediction methods for the welded joints with defects, to reduce their likelihood. In this paper, we present our study of the uncertainty of fatigue crack propagation and probabilistic life prediction for welded joints of nuclear stainless steel. The standard compact tension (CT) specimens were fabricated according to the American Society for Testing and Materials (ASTM) standard. Fatigue crack propagation tests with different stress ratios were performed on CT specimens, using the Mei Te Si (MTS) fatigue test system. A fatigue crack propagation rate model considering the uncertainty of material parameters, and based on the Paris formula and crack propagation experimental data, was established. A probabilistic life prediction method based on Monte Carlo simulation was developed. The fatigue crack propagation prediction result of a CT specimen was compared with the actual tested result, to verify the effectiveness of the proposed method. Finally, the method was applied to an embedded elliptical crack in welded joints of nuclear stainless steel, to predict the fatigue crack growth life and evaluate the reliability.

Prediction of fatigue crack propagation lives based on machine learning and data-driven approach

For many fatigue-critical parts of machines and structures, the load history under operating conditions generally involves variable amplitude loading rather than constant amplitude loading. An accurate prediction of fatigue crack propagation life under variable amplitude loading requires a thorough evaluation of the load interaction effects. In this study, fatigue tests under both constant and variable amplitude loading were carried out to investigate the overload effects on fatigue crack propagation of the notched specimens. Strain distributions around the crack tip before and after a tensile overloading were measured using the ESPI (Electronic Speckle Pattern Interferometry) system. The size of the plastic zone was determined from the measured strain distributions. The study proposes a crack propagation prediction model that incorporates the overload ratio effect. A comparative work for the overload ratio effect demonstrated that the prediction by the proposed model was in good agreement with the experimental results. The prediction of fatigue crack propagation including multiple overloads with the proposed model show also a good agreement with test results.

A local strain approach was applied to an external single and double grooved C-shaped specimen in order to evaluate and predict the fatigue crack initiation life by using low cycle fatigue properties. The low cycle fatigue properties were determined from the strain-controlled fatigue tests using smooth cylindrical axial specimens. Fatigue crack initiation life was evaluated by a life prediction software, FALIPS, based on the local strain approach. The fatigue life was significantly influenced by the mean stress, and SWT parameter represented the fatigue life effectively. The predicted fatigue crack initiation life was then compared to the experimental fatigue life evaluated from the C-shaped fatigue test specimens. A good correlation was found between the experimental and predicted fatigue lives within factors of 2 and 4 for the single and double grooved C-shaped specimens respectively. Also, experimental fatigue life of the double grooved specimen was 10-12 times longer than that of the single grooved specimen.

New load-bearing structures made of fiber-reinforced polymer (FRP) composites comprise adhesively-bonded joints, which are components vulnerable to fatigue failure. These structural components are frequently subjected to complex cyclic loading histories during their service life and the development of reliable methodologies for prediction of their fatigue life under variable amplitude loading patterns is therefore essential. Experimental investigations on FRP laminates showed significant effects of spectrum loading on the fatigue life. However, scientific efforts to study the fatigue behavior of adhesively-bonded FRP joints are mainly focused on constant amplitude fatigue loading and many loading parameters involved in the variable amplitude spectrums have not yet been investigated. The aim of this research is to understand the fatigue behavior of adhesively-bonded FRP joints under different loading patterns and establish a reliable methodology for the fatigue life prediction of these structural components. The fatigue response of a typical adhesivelybonded structural joint, a double-lap joint, was experimentally investigated under different loading patterns including constant amplitude, block and variable amplitude loading. The development of fatigue cracks during the lifetime and their correlation with the observed failure modes and applied cyclic load were analyzed. The experimental investigations revealed the loading parameters that significantly influence fatigue behavior and that therefore must be considered in the fatigue life prediction methodology. A new semi-empirical S-N formulation was developed to characterize the constant amplitude fatigue life and overcome the deficiencies of the fatigue models commonly used for composite materials. Based on the experimental investigation results, two phenomenological formulations were proposed in order to model the loading parameters that affect fatigue life. A new constant life diagram was developed to model the effect of mean stress on fatigue life and its accuracy was assessed using the experimental data. Also, a method was proposed to take into account the load interaction effects under variable amplitude loading. A fatigue life prediction methodology was established using the newly developed models and implemented in the form of a computational tool to predict the fatigue life of adhesivelybonded FRP joints. The variable amplitude fatigue life predictions obtained using this methodology correlated fairly well with the experimental results and proved its effectiveness in real applications.

Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

This study presents establishment of multiple input prediction model for automotive coil spring fatigue life estimation to shorten automotive suspension design process. Automotive suspension design is a lengthy work where any changes of the design lead to repetition of the entire process. It was hypothesised that the established model could be used to predict the spring design fatigue life without using any strain measurements. To initiate this model establishment, five sets of strain and acceleration measurement across different road conditions were collected and used for validations. To include spring stiffness as a parameter, a quarter car model was generated to obtain the force time histories from spring and vertical vibration of vehicle mass. In addition, artificial road profiles of road classes “A” to “D” were also generated for the quarter car simulation. Through adjusting the spring stiffness in the quarter car model, the spring and vehicle responses were varied. The simulated force time histories were used to predict springs’ fatigue life while acceleration time histories were used to calculate ISO 2631 ride-related vertical vibration. Subsequently, multiple linear regression approach was applied to determine the relationship between vehicle body frequency, ISO 2631 ride-related vertical vibration and spring fatigue life. The obtained regression had shown significance to the spring fatigue life with coefficient of determination of 0.8320. Reciprocally, multiple linear regression models were also used to predict the ISO 2631 ride-related vertical vibration with a coefficient of determination at 0.8810 and mean squared error values below 0.3430. To optimise the prediction results, artificial neural network was trained for the fatigue and vibration prediction purposes. The architectures of the artificial neural network were designed in terms of number of neurons and hidden layers to achieve a higher coefficient of determination of 0.9926 and lower mean squared error of 0.0824. For vibration prediction, the vehicle body frequency and spring fatigue life has shown a significant coefficient of determination to the ISO 2631 weighted vertical vibration, reaching 0.9579 with mean squared error of 0.0004. Based on the experimental strain and acceleration results, the predicted fatigue lives of multiple linear regression models were correlated well with the experimental results with coefficient of determination value of 0.9275. Meanwhile, the maximum difference of vibration prediction to experimental value using multiple linear regression models was only 18%. For artificial neural network predictions, the fatigue lives were mostly distributed within 1:2 or 2:1 life correlation and vibration prediction results were within 12%. For a good prediction, the target correlation value was above 0.80 to demonstrate a good fitted curve and the difference below 20%. The trained artificial neural network has shown outstanding capability in fatigue life or ride-related vertical vibration predictions. In this research, the main novelty was the trained artificial neural network for spring fatigue life or ride-related vertical vibration predictions which serve to reduce some procedures of automotive suspension design. The outcome of this study can be used to provide a new knowledge towards the field of fatigue research as well as vehicle ride dynamics. This research contributes to automotive industries especially in suspension spring design where the analysis of fatigue and ride-related vibration are provided.

In this thesis, the role of location-specific microstructural features in the fatigue performance of the safety-critical aerospace components made of Nickel (Ni)-base superalloys and linear friction welded (LFW) Titanium (Ti) alloys has been studied using crystal plasticity finite element (CPFE) simulations, energy dispersive X-ray diffraction (EDD), backscatter electron (BSE) images and digital image correlation (DIC).<br>In order to develop a microstructure-sensitive fatigue life prediction framework, first, it is essential to build trust in the quantitative prediction from CPFE analysis by quantifying uncertainties in the mechanical response from CPFE simulations. Second, it is necessary to construct a unified fatigue life prediction metric, applicable to multiple material systems; and a calibration strategy of the unified fatigue life model parameter accounting for uncertainties originating from CPFE simulations and inherent in the experimental calibration dataset. To achieve the first task, a genetic algorithm framework is used to obtain the statistical distributions of the crystal plasticity (CP) parameters. Subsequently, these distributions are used in a first-order, second-moment method to compute the mean and the standard deviation for the stress along the loading direction (σ_load), plastic strain accumulation (PSA), and stored plastic strain energy density (SPSED). The results suggest that an ~10% variability in σ_load and 20%-25% variability in the PSA and SPSED values may exist due to the uncertainty in the CP parameter estimation. Further, the contribution of a specific CP parameter to the overall uncertainty is path-dependent and varies based on the load step under consideration. To accomplish the second goal, in this thesis, it is postulated that a critical value of the SPSED is associated with fatigue failure in metals and independent of the applied load. Unlike the classical approach of estimating the (homogenized) SPSED as the cumulative area enclosed within the macroscopic stress-strain hysteresis loops, CPFE simulations are used to compute the (local) SPSED at each material point within polycrystalline aggregates of 718Plus, an additively manufactured Ni-base superalloy. A Bayesian inference method is utilized to calibrate the critical SPSED, which is subsequently used to predict fatigue lives at nine different strain ranges, including strain ratios of 0.05 and -1, using nine statistically equivalent microstructures. For each strain range, the predicted lives from all simulated microstructures follow a log-normal distribution; for a given strain ratio, the predicted scatter is seen to be increasing with decreasing strain amplitude and are indicative of the scatter observed in the fatigue experiments. Further, the log-normal mean lives at each strain range are in good agreement with the experimental evidence. Since the critical SPSED captures the experimental data with reasonable accuracy across various loading regimes, it is hypothesized to be a material property and sufficient to predict the fatigue life.<br>Inclusions are unavoidable in Ni-base superalloys, which lead to two competing failure modes, namely inclusion- and matrix-driven failures. Each factor related to the inclusion, which may contribute to crack initiation, is isolated and systematically investigated within RR1000, a powder metallurgy produced Ni-base superalloy, using CPFE simulations. Specifically, the role of the inclusion stiffness, loading regime, loading direction, a debonded region in the inclusion-matrix interface, microstructural variability around the inclusion, inclusion size, dissimilar coefficient of thermal expansion (CTE), temperature, residual stress, and distance of the inclusion from the free surface are studied in the emergence of two failure modes. The CPFE analysis indicates that the emergence of a failure mode is an outcome of the complex interaction between the aforementioned factors. However, the possibility of a higher probability of failure due to inclusions is observed with increasing temperature, if the CTE of the inclusion is higher than the matrix, and vice versa. Any overall correlation between the inclusion size and its propensity for damage is not found, based on inclusion that is of the order of the mean grain size. Further, the CPFE simulations indicate that the surface inclusions are more damaging than the interior inclusions for similar surrounding microstructures. These observations are utilized to instantiate twenty realistic statistically equivalent microstructures of RR1000 – ten containing inclusions and remaining ten without inclusions. Using CPFE simulations with these microstructures at four different temperatures and three strain ranges for each temperature, the critical SPSED is calibrated as a function of temperature for RR1000. The results suggest that critical SPSED decreases almost linearly with increasing temperature and is appropriate to predict the realistic emergence of the competing failure modes as a function of applied strain range and temperature.<br>LFW process leads to the development of significant residual stress in the components, and the role of residual stress in the fatigue performance of materials cannot be overstated. Hence, to ensure fatigue performance of the LFW Ti alloys, residual strains in LFW of similar (Ti-6Al-4V welded to Ti-6Al-4V or Ti64-Ti64) and dissimilar (Ti-6Al-4V welded to Ti-5Al-5V-5Mo-3Cr or Ti64-Ti5553) Ti alloys have been characterized using EDD. For each type of LFW, one sample is chosen in the as-welded (AW) condition and another sample is selected after a post-weld heat treatment (HT). Residual strains have been separately studied in the alpha and beta phases of the material, and five components (three axial and two shear) have been reported in each case. In-plane axial components of the residual strains show a smooth and symmetric behavior about the weld center for the Ti64-Ti64 LFW samples in the AW condition, whereas these components in the Ti64-Ti5553 LFW sample show a symmetric trend with jump discontinuities. Such jump discontinuities, observed in both the AW and HT conditions of the Ti64-Ti5553 samples, suggest different strain-free lattice parameters in the weld region and the parent material. In contrast, the results from the Ti64-Ti64 LFW samples in both AW and HT conditions suggest nearly uniform strain-free lattice parameters throughout the weld region. The observed trends in the in-plane axial residual strain components have been rationalized by the corresponding microstructural changes and variations across the weld region via BSE images. <br>In the literature, fatigue crack initiation in the LFW Ti-6Al-4V specimens does not usually take place in the seemingly weakest location, i.e., the weld region. From the BSE images, Ti-6Al-4V microstructure, at a distance from the weld-center, which is typically associated with crack initiation in the literature, are identified in both AW and HT samples and found to be identical, specifically, equiaxed alpha grains with beta phases present at the alpha grain boundaries and triple points. Hence, subsequent fatigue performance in LFW Ti-6Al-4V is analyzed considering the equiaxed alpha microstructure.<br>The LFW components made of Ti-6Al-4V are often designed for high cycle fatigue performance under high mean stress or high R ratios. In engineering practice, mean stress corrections are employed to assess the fatigue performance of a material or structure; albeit this is problematic for Ti-6Al-4V, which experiences anomalous behavior at high R ratios. To address this problem, high cycle fatigue analyses are performed on two Ti-6Al-4V specimens with equiaxed alpha microstructures at a high R ratio. In one specimen, two micro-textured regions (MTRs) having their c-axes near-parallel and perpendicular to the loading direction are identified. High-resolution DIC is performed in the MTRs to study grain-level strain localization. In the other specimen, DIC is performed on a larger area, and crack initiation is observed in a random-textured region. To accompany the experiments, CPFE simulations are performed to investigate the mechanistic aspects of crack initiation, and the relative activity of different families of slip systems as a function of R ratio. A critical soft-hard-soft grain combination is associated with crack initiation indicating possible dwell effect at high R ratios, which could be attributed to the high-applied mean stress and high creep sensitivity of Ti-6Al-4V at room temperature. Further, simulations indicated more heterogeneous deformation, specifically the activation of multiple families of slip systems with fewer grains being plasticized, at higher R ratios. Such behavior is exacerbated within MTRs, especially the MTR composed of grains with their c-axes near parallel to the loading direction. These features of micro-plasticity make the high R ratio regime more vulnerable to fatigue damage accumulation and justify the anomalous mean stress behavior experienced by Ti-6Al-4V at high R ratios.<br>

Fatigue crack growth and life assessment of full penetration U-rib welded joints considering residual stresses

J-integral approach for main crack propagation of RC beams strengthened with prestressed CFRP under cyclic bending load

Additive manufacturing (AM) introduces high variability in the microstructure and defect distributions, compared with conventional processing techniques, which introduces greater uncertainty in the resulting fatigue performance of manufactured parts. As a result, qualification of AM parts poses as a problem in continued adoption of these materials in safety-critical components for the aerospace industry. Hence, there is a need to develop precise and accurate, physics-based predictive models to quantify the fatigue performance, as a means to accelerate the qualification of AM parts. The fatigue performance is a critical requirement in the safe-life design philosophy used in the aerospace industry. Fatigue failure is governed by the loading conditions and the attributes of the material microstructure, namely, grain size distribution, texture, and defects. In this work, the crystal plasticity finite element (CPFE) method is employed to model the microstructure-based material response of an additively manufactured Ni-based superalloy, Inconel 718 (IN718). Using CPFE and associated experiments, methodologies were developed to assess multiple aspects of the fatigue behavior of IN718 using four studies. In the first study, a CPFE framework is developed to estimate the critical characteristics of porosity, namely the pore size and proximity that would cause a significant debit in the fatigue life. The second study is performed to evaluate multiple metrics based on plastic strain and local stress in their ability to predict both the modes of failure as seen in fractography experiments and estimate the scatter in fatigue life due to microstructural variability as obtained from fatigue testing. In the third study, a systematic analysis was performed to investigate the role of the simulation volume and the microstructural constraints on the fatigue life predictions to provide informed guidelines for simulation volume selection that is both computationally tractable and results in consistent scatter predictions. In the fourth study, validation of the CPFE results with the experiments were performed to build confidence in the model predictions. To this end, 3D realistic microstructures representative of the test specimen were created based on the multi-modal experimental data obtained from high-energy diffraction experiments and electron backscatter diffraction microscopy. Following this, the location of failure is predicted using the model, which resulted in an unambiguous one to one correlation with the experiment. In summary, the development of microstructure-sensitive predictive methods for fatigue assessment presents a tangible step towards the adoption of model-based approaches that can be used to compliment and reduce the overall number of physical tests necessary to qualify a material for use in application.