Accurate Fatigue Life Prediction Research Articles

The effect of dwell times and minimum temperature on out-of-phase thermomechanical fatigue crack propagation in a steam turbine steel—Crack closure prediction

Exploring crack growth behaviour is needed to establish accurate fatigue life predictions. Cracked specimens were tested under strain-controlled out-of-phase thermomechanical fatigue conditions. The tests included dwell times and three different minimum temperatures. Higher minimum temperature gave faster crack growth rates while the additions of dwell times showed no effects. Crack closure was observed in all the tests where the addition of dwell times and change in minimum temperature displayed little to no effect on crack closure stresses. Finite element models with a sharp stationary crack and material parameters switching provided acceptable predictions for the maximum, minimum, and crack closure stresses.

A multi-axial and high-cycle fatigue life prediction model based on critical plane criterion

Accurate prediction of fatigue failure behavior and fatigue life of structures or materials under multi-axial loading are important for engineering application. In this work, a multi-axial and high-cycle fatigue failure model based on the critical plane criterion is proposed with considering the influences of material property and loading path on the initiation and growth of fatigue cracks. A material parameter, defined as the ratio of tensile yield strength σy and torsional yield strength τy, is introduced to describe the mechanical properties of the material. For σy/τy<3, it is considered that the crack growth under the multi-axial fatigue load is mainly controlled by the maximum normal stress. While for σy/τy>3, it is considered that the crack growth under the multi-axial fatigue load is mainly controlled by the maximum shear stress. Experimental results of three kinds of material were applied for model validation. For all materials, most predictions are within the ±2 times scatter band of fatigue life and only few data are around ±3 times scatter band. Good agreements of the predicted fatigue life by the model with experimental results illustrated the model applicability. The model provides meaningful tool for predicting multi-axial fatigue life of materials under different loading parameters.

Prediction of cyclic damage in metallic alloys with crystal plasticity modeling enhanced by machine learning

A computational approach consisting of two frameworks is introduced to construct a fatigue model for the Inconel 718 superalloy. Due to the extreme nature of Inconel 718’s typical service environment, fatigue is predicted to be a primary mode of failure for the material. To this end, a crystal plasticity framework is employed to assess the typical columnar (as-built) and equiaxed (heat-treated) microstructures for fatigue life under repeated loading, using the criterion of stored energy to predict life cycle. A machine learning framework is then introduced to work as an extension to the model by learning the relationship between stored energy, maximum stress, and fatigue life. By comparison of the results of both frameworks, fatigue life prediction accuracy is validated, and both approaches are established as accurate predictors of fatigue life for this family of superalloys.

Improved accuracy of low cycle fatigue life prediction of elbow

Elbow piping is characterized by complicated stress conditions and easy wall thinning. Therefore, a highly accurate fatigue life prediction method is required to accurately evaluate the soundness of elbow piping. In previous studies, the revised universal slope method was proposed as a life prediction formula for elbow pipes, and the relationship between strain range and fatigue life was obtained. However, with the conventional analysis method, the strain range is overestimated compared to the actual behavior, resulting in a significantly conservative prediction result. In this study, we will improve the prediction accuracy of the strain range by using a more detailed stress-strain diagram than before in the finite element analysis and examine the further improvement of the fatigue life prediction.

EFFICIENT FATIGUE LIFE ASSESSMENT OF COMPOSITE MATERIALS BY USING A HYBRID SURROGATE MODELING

In this study, hybrid surrogate and nonlinear autoregressive with exogenous inputs (NARX) model is developed and presented as data-driven based predictive model for efficient fatigue life assessment of composite materials. Surrogate modeling based upon wavelet neural networks (WNN) is employed to efficiently unveil mathematical pattern in S-N data, but costly to get from experiments. Moreover, the NARX architecture is chosen in order to enable multi-step ahead prediction in fatigue life assessment of multivariable amplitude loadings. By observing fatigue data as dynamic data of stress ratio series, it is shown that the hybrid model produces reasonably accurate fatigue life prediction by using fatigue data from two stress ratio values only. The use of two stress ratio values also allows usage of more limited fatigue data in the lifetime prediction. The WNN-NARX surrogate model is tested with well-known fatigue data in literature. Several composite materials examined in this study show the efficacy and robustness of the proposed model.

Strengthening of cracked DH36 steel plates by CFRP sheets under fatigue loading at low temperatures

DH36 Shipbuilding steel plate is widely used in marine applications, which undergo fatigue loading at low temperatures. This paper suggests using carbon fiber reinforced polymer (CFRP) sheets to strengthen this steel under fatigue loading in the −60 to 20 °C temperature range. The mechanical properties of the steel and adhesive were analyzed, eight bare steel plates and eight strengthened steel plates were tested under fatigue loadings at different temperatures, and a theoretical calculation was conducted. First, the elastic modulus and strength of DH36 steel and the elastic modulus of the adhesive increased with decreasing temperature. Second, the temperature decreasing from 20 to −60 °C increased the fatigue life by 113% for the bare specimens and 190%–254% for the strengthened specimens due to the high elastic modulus of the adhesive at −60 °C. The fatigue life of the strengthened specimens was 1.62–4.74 times that of bare specimens, which proved that CFRP-strengthened DH36 steel with good efficiency in the temperature range. Finally, by considering the influences of temperature on DH36 steel, adhesive and CFRP composite, a relatively accurate prediction of fatigue life is achieved using fracture mechanics theory.

Experimental study on the interlayer friction and wear mechanism between armor wires of umbilicals

Friction and wear behavior between armor wire layers of umbilical plays an important role in its mechanical properties, in particular its fatigue life. The present paper addresses friction tests to investigate the interlayer sliding friction and wear mechanism of armor wires in an umbilical. A series of friction tests were carried out to study the evolution of coefficient of friction (COF) and wear between armor wire layers. The results show that the COF increases with increasing number of reciprocating sliding cycles in dry friction condition. A dual stable trend was found in the evolution process of interlayer friction and wear of armor wires. A friction model was proposed to describe this trend by dividing the evolution process into three stages: the initial stable stage, the transition stage, and the latter stable stage. The mechanism of the dual stable trend was revealed, which was mainly caused by wear process of nylon fiber tape, and effected by dry/wet wear condition and wear coefficient. The effect of the contact condition, sliding amplitude and number of nylon fiber tape on COF was studied. The COF of the latter stable stage increases with increasing sliding amplitude and number of layers of nylon fiber tape. The finding in this paper provides a valuable approach for a more accurate fatigue life prediction of an umbilical.

Stochastic fatigue life prediction of Fiber-Reinforced laminated composites by continuum damage Mechanics-based damage plastic model

In this paper, the evolution of elastic–plastic damage in the composite laminates under fatigue conditions is modeled. Continuum damage mechanics (CDM) has been coupled with the bridge micromechanics model to estimate the fatigue damage and life for laminated composite structures. Based on the elastic–plastic bridging model, three damage variables are defined. These variables estimate the fiber, matrix, and fiber/matrix damage response at the ply scale. To model the beginning of plastic deformation, a yield function is utilized, and evolution equations of the damage variables are obtained. Then the developed deformation plastic model is calculated. The model parameters are calibrated by experimental data. The proposed model is implemented in Abaqus by a subroutine. The capability of the developed model under multiaxial loading is investigated. The constitutive behavior of composite laminates in different conditions is compared with experimental data. The estimated fatigue life of the CDM-plastic damage approach for different layups is in accordance with the experimental data. Numerical results confirm the plastic deformation model leads to more accurate fatigue life prediction, especially in low‐cycle fatigue. Finally, a stochastic model is applied to predict the fatigue life of composite laminates. In the stochastic approach, fiber volume fraction, critical damage parameters, and mechanical properties are modeled as random variables with normal distribution. The stochastic model emphasizes the importance of variations of mechanical properties.

Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen

Accurate fatigue life prediction facilitates the fatigue maintenance of steel bridges. Since Digital Twin can simulate the lifecycle for physical objects at various scales, this study aims to provide a Digital Twin-driven framework for non-deterministic fatigue life prediction of steel bridges. A probabilistic multiscale model was developed to depict the fatigue evolution throughout the bridge lifecycle. The small crack initiation period was well described by the modified Fine and Bhat model considering microstructure uncertainties. After obtaining the critical model parameter via crystal plastic finite element simulation, the modified model was further calibrated using the assumed historical fatigue data in Digital Twin database. Based on the initiated half-penny-shaped small crack, the small crack initiation period was connected to the macrocrack extension period. Given the uncertainties of macrocrack propagation, the Paris’ law with random growth parameters was adopted. The Bayesian inference of the growth parameters realized the real-time calibration of the macrocrack growth model using Markov chain Monte Carlo simulation. The feasibility of the proposed framework was demonstrated through fatigue tests on a segmental steel deck specimen with mixed-mode deformed U-rib to diaphragm welded joints. The results show that the predicted fatigue initiation life and residual fatigue life are in good agreement with the experimentally observed life results. In summary, the proposed framework enhances our understanding of the fatigue evolution mechanism throughout the bridge lifecycle and provides an entirely new approach to accurately predict the fatigue life of steel bridges under various sources of uncertainties.

Neuro-fuzzy fatigue life assessment using the wavelet-based multifractality parameters

This study aims to establish a fatigue life predictive model based on multifractality of road excitations using neuro-fuzzy method to assess the durability of suspension spring. Traditional durability analysis in time domain is complicated and time-consuming due to the needs of large data amount. Thus, it is an idea to adopt an adaptive neuro-fuzzy inference system (ANFIS) for relating the performance of coil spring to the multifractal properties of road excitations, giving a meaningful fatigue life prediction. Different membership function numbers were tested to obtain the optimum membership function number. During the data training process, the checking data was used to test the trained model each Epoch of training for overfitting detection. As a result, the Morrow-based fatigue life prediction model was found to give the most suitable result with three membership functions. The SWT-based model needed five membership functions due to nonlinear properties in the SWT-based fatigue life data. Training process of Morrow-based-ANFIS was stopped at Epoch 8 given its lowest checking root-mean-square-error of 0.6953. SWT-based model recorded a higher error of 0.7940. The neuro-fuzzy models gave accurate fatigue life predictions with 96 % of the data distributed within the acceptance boundary, hence, contributing to an acceptable assessment of coil spring fatigue life based on load multifractality. This study had shown a nonlinear relationship between road multifractality and durability performance of coil spring. Multifractality had been proven an important feature to characterise various road excitations for durability prediction.

Influence of local differences in microstructure and hardness on the fatigue behaviour of a slewing bearing steel

For a more accurate prediction of the service life of slewing bearings under oscillating mode mechanical customized fatigue test strategies and suitable calculation methods have to be developed. In this study large-diameter slewing bearings of onshore wind turbines made from induction hardened and tempered steel 42CrMo4 are investigated. Test samples were cut directly out of the bearing ring (core condition). The orientation of the test cross-section was chosen to corresponding with the presumed most critical orientation of non-metallic inclusions. After sampling three heat treatments were carried out to mimic the different heat treatment conditions of the bearing (raceway, transition region, core). Results of static tests (fracture toughness, tensile strength) and cyclic testing (crack propagation, S-N-curve) were obtained for the three different heat-treated sample batches. The results where correlated to hardness, cleanliness and local residual stresses to develop a deep understanding of crack initiation and crack propagation mechanisms and making a first step towards a more accurate prediction of fatigue life. In order to be able to transfer the analytic results to the real parts, a simulation model will be developed later on.

Accelerated mechanical low cycle fatigue in isothermal solder interconnects

Properly assessing the underlying physics of failure is critical in predicting the long term reliability of electronic packages in their intended field applications, yet traditional reliability demonstration methods are complicated by time and cost considerations as well as deterministic inadequacies when considering thermomechanical failures. In this work, an alternative reliability testing apparatus and associated protocol were utilized to provide clarity and insight to solder fatigue mechanisms at the device scale; targeting rapid testing times with minimal cost while preserving fatigue life prediction accuracy. A test stand was developed to allow for bi-directional application of shear stress at elevated steady-state temperatures. Utilizing the mechanical force of springs to apply shear loads to solder interconnects within the devices, the reliability of a given device to withstand repeated cycling was studied using in situ resistance monitoring techniques to detect the number of cycles-to-failure (CTF) based on a 30% resistance increase criterion. A mathematical method for quantifying the plastic work density (amount of damage) sustained by the solder interconnects prior to failure was developed relying on the relationship between Hooke's Law for springs and damage deflection to accurately assess the mechanical strength of tested devices.

A short and long crack growth model based on 2-parameter driving force and crack growth thresholds

The propagation life of short cracks has a great influence on the accurate fatigue life prediction. A new crack growth model is proposed to provide a unified modeling framework for integrating short and long crack propagation in the formulation of the crack driving force to account for multiple crack growth controlling parameters. Crack propagation rates and fatigue lives predicted by the model are compared with crack growth and fatigue life data sets of 2024-T3, 7075-T561 aluminum and Ti-6Al-4V titanium alloys. Predicted results are found to be in good agreement with crack growth data and fatigue life data for these materials.

Fatigue life prediction of woven composite laminates with initial delamination

AbstractAn engineering approach for fatigue life prediction of fibre‐reinforced polymer composite materials is highly desirable for industries due to the complexity in damage mechanisms and their interactions. This paper presents a fatigue‐driven residual strength model considering the effect of initial delamination size and stress ratio. Static and constant amplitude fatigue tests of woven composite specimens with delamination diameters of 0, 4 and 6 mm were carried out to determine the model parameters. Good agreement with experimental results has been achieved when the modified residual strength model has been applied for fatigue life prediction of the woven composite laminate with an initial delamination diameter of 8 mm under constant amplitude load and block fatigue load. It has been demonstrated that the residual strength degradation‐based model can effectively reflect the load sequence effect on fatigue damage and hence provide more accurate fatigue life prediction than the traditional linear damage accumulation models.

Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model

AbstractWith the advancement of machine learning in leading technologies, it is perceived that machine learning is a new and effective alternative for the classic fatigue life prediction. This paper provides a regression tree ensemble‐based machine learning approach to predict the fatigue life of GLARE composites. In the model, mechanical, geometrical properties and fatigue loading stresses are selected as the training parameters (so‐called features), and the GLARE fatigue life is predicted as the output of the model. Experimental data of a total of 98 pieces of GLARE specimens with nine different layups are used for the training and validation of the machine learning model. Results show that the model can provide good fatigue life prediction accuracy and model stability. The most correlated, either positively or negatively, parameters to the fatigue life span are the stress developed in the aluminum layer, the maximum cyclic stress, alternating stress, and mean fatigue stress.

A cuckoo search based neural network to predict fatigue life in rotor blade composites

In modern wind turbine blades industry, fiber-reinforced composites are mostly used for their good mechanical characteristics: high stiffness, low density and long fatigue life. Wind turbine blades are constructed in different structural elements from a variety of composite laminates, often including Unidirectional (UD) material in spars and multiple forms of Multidirectional (MD) in skins and webs. The purpose of this paper is to identify materials that have appropriate fiber orientations to improve fatigue life. By using Cuckoo Search-based Neural Network (CSNN), we have developed a model to predict fatigue life under tension-tension charges for five composite materials, with different fiber stacking sequences embedded in three types of resin matrices (epoxy, polyester and vinylester), which are all appropriate for the design of wind turbine blades. In the CSNN approach used in this work, the cost function was assessed using the Mean Square Error (MSE) computed as the squared difference between the predicted values and the target values for a number of training set samples, obtained from an experimental fatigue database. The results illustrate that the CSNN can provide accurate fatigue life prediction for different MD/UD composite laminates, under different angles of fiber orientation.

Models of Nonlinear Fatigue Damage Accumulation Under Cyclic Block Loadings of Layered Composites

Considered are two types of multistage cyclic block loading rather common in the practice of fatigue tests and fatigue life predictions for layered polymer composite materials: block loading by four-stage cyclic regular zero-tension loads and block loading by multistage (8-10 levels) asymmetric cyclic loads. In order to improve the prediction accuracy for the fatigue life of carbon-fiber-reinforced plastics under such loadings, special nonlinear fatigue damage accumulation models are proposed. Elaborated are methods for predicting the fatigue life of carbon-fiber-reinforced plastics by the models proposed. The prediction methods are verified by the example of T800/5245 and AS4/3501-6 carbon-fiber-reinforced laminates. A relatively high accuracy of fatigue life predictions for the laminates was obtained as compared with that given by prediction methods using the Palmgren–Miner rule.

Effects of microstructural heterogeneity on fatigue properties of cast aluminum alloys

Cast Al alloys are widely employed for engine components, structural parts, gear box, chassis, etc. and subjected to mechanical cyclic load during operation. The accurate fatigue life prediction of these alloys is essential for normal operation as fatigue cracks initiated during operation induce the lubrication oil leak and serious safety hazard. Microstructural heterogeneity, including shrinkage/gaspores and secondary phase particles, is the most detrimental factor that affects fatigue life of cast Al alloys. The approximate fatigue life cycles could be estimated based on the size distribution and locations of shrinkage pores/defects. The relationship between crack population and stress was reported by statistical distributions and the cumulative probability for cast Al alloys fail at a certain stress could be predicted by combination of Paris law and pore size distribution. Pore depth was found to dominate the stress field around the pore on the surface and the maximum stress increases sharply when the pore intercepted with the surface at its top. The microstructure of cast Al alloys usually is composed of primary Al dendrites, eutectic silicon, Fe-rich particles and other intermetallic particles are dependent upon alloy composition and heat treatment. The coalescence of microcracks initiated from the fractured secondary phases was clearly found and can accelerate the initiation and propagation of the fatigue cracks. A link between defect features and the fatigue strength needs to be established through a good understanding of the fatigue damage mechanisms associated with the microstructural features under specific loading conditions. This paper reviews the influences of shrinkage/gaspores and secondary phase particles, formed during casting process, on the fatigue life of Al-Si-Mg cast Al alloys.

Fatigue Properties Estimation and Life Prediction for Steels under Axial, Torsional, and In‐Phase Loading

In this study, several estimation methods of fatigue properties based on different monotonic mechanical parameters were first discussed. The advantages and disadvantages of the Hardness Method proposed by Roessle and Fatemi were investigated and improved through the analysis of a total of 92 fatigue test data. A new Segment Fitting Method from Brinell hardness was then proposed for the fatigue properties estimation, and a total of 96 pieces of fatigue test data under axial, torsional, and multiaxial in‐phase loading were collected to verify the applicability of the new proposal. Finally, the prediction accuracy of the new proposal and three exciting estimation methods was compared with the predictions based on the experimental fatigue properties. Based on the results obtained, the newly proposed estimation method has a significant improvement on the relation between fatigue ductility coefficient and Brinell hardness, which consequently improves the fatigue life prediction accuracy with the scatter band of 2, particularly for the materials with low Brinell hardness. The present study can provide a simplified analysis of the preliminary fatigue design of engineering structures.

Improvement and determination of the influencing factors of coiled tubing fatigue life prediction

The fatigue life prediction model cannot accurately predict the service life of coiled tubing operations, resulting in waste and accidental fracture of coiled tubing. This article introduces the fatigue life prediction model of coiled tubing and a detection system developed using weak magnetic detection method and eddy current detection method. The detection device is used to detect accurate defect dimension, wall thickness reduction, and diameter deformation to improve the accuracy of fatigue life prediction. Furthermore, the actual defect dimension is included in the fatigue life assessment calculation to eliminate the influence of the existing model on the immobilization of defects and corrosion influence factors. By analyzing field operation cases and measured values, the effects of defects, wall thickness reduction, and diameter growth on fatigue life are studied to improve the accuracy of parameters and the integrity of basic data in the calculation process. The influence of reverse bending and weld on fatigue life is discussed. The residual life can be predicted more accurately by combining field testing technology and fatigue life evaluation method. Field measurement input of fatigue life prediction model is the main means to improve its accuracy and practicability.