Accurate Fatigue Life Prediction Research Articles

Research on fatigue life evaluation method of shaft parts based on small sample

PurposeDue to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.Design/methodology/approachFirst, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.FindingsThe TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.Originality/valueThe purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

New Fatigue Life Prediction Model for Composite Materials Considering Load Interaction Effects

Damage to composite structures can accumulate over time and lead to fatigue failure in their actual use environment. Therefore, it is critical to establish a suitable fatigue life prediction model. This work developed an improved fatigue life prediction model based on the effects of equivalent damage and load interactions. Validation and comparison of the improved fatigue life prediction model were carried out using test data of composites subjected to secondary and tertiary loading. The analysis indicates that the accuracy of fatigue life prediction for composites under variable amplitude load is improved by the damage equivalence prediction model, which accounts for the influence of load application sequence and load interaction. Furthermore, a comparison with existing fatigue life prediction models reveals that the proposed model predicts fatigue life more accurately under different amplitude loads.

Digital twin-driven fatigue life prediction framework of mechanical structures using a power density theory: Application to off-road vehicle front axle housing

Accurate fatigue life prediction is beneficial for fatigue maintenance of complex mechanical structures of off-road vehicles. For fatigue life prediction of mechanical structures subjected to non-stationary random loads during operation, this paper proposes a digital twin-driven fatigue life prediction method for mechanical structures. The core consists of improved fatigue theory, stress online measurement, and fatigue damage verification. In this study, a digital twin (DT) prediction framework containing multiple correction factors is established, in which the actual stress values in the physical world and the stress predictions of the estimated twin model can be obtained in real time. The remaining strength degradation of the structure and material can be compared to correct the prediction accuracy of fatigue analysis. In addition, based on the power density theory, combined with the short-time Fourier transform, a fatigue life analysis method that can comprehensively calculate the effect of load amplitude and frequency on fatigue is proposed. A test case has been demonstrated using a front axle housing of the off-road vehicle. The difference between the results predicted by the method in this paper and the actual fatigue results in failure time is only 2.65 h, with a relative error of only 3.95 %. In terms of failure location, the relative error is only 3.15 %.

A hybrid approach for fatigue life prediction of in-service asphalt pavement.

Fatigue cracking is one of the main pavement failures, which makes accurate fatigue life prediction forthe design and maintenance of asphalt pavements crucial. The majority of traditional prediction methods are based entirely on the laboratory fatigue test, without considering the field condition and maintenance data. This paper aims to propose a hybrid approach to fill this gap. The key idea is that the damage condition isback-calculated by anartificial intelligence-based finite-element (FE) model updating using field-monitoring information (data-driven component), which is used to update the parameters in themechanistic composition-specific fatigue life prediction equation (model-driven component). The laboratory test of field cores gives the material non-destructive properties. The simulated pavement response subjected to truck loading shows good agreement with measured values, which indicates that the verified constitutive relationship could be used in the data-driven component. Furthermore, in view that the fatigue test is time- and money-consuming, this paper proposes a non-test estimation of thefatigue characteristic curve based on FE simulation of arepeated direct tension test. Three test pavement sections were employed as case studies. Results showed that the predicted fatigue life changes with the service time. At the early age, semi-rigid pavement has alarger fatigue life than flexible and inverted pavements. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Probabilistic fatigue life prediction for CSS-42L bearing in jet strengthen modification grinding using an improved WTP network

Considering the high quality requirement of advanced aeroengine equipment, accurate prediction of probabilistic fatigue life (PFL) for CSS-42 L bearing in jet strengthen modification grinding, is an important issue for aeroengine manufacturing. Here an improved robust prediction algorithm of Wavelet Transform Package (WTP) Network is designed, therefore the decisive influence of jet strengthen modification grinding on the probabilistic fatigue life of aeroengine bearing, could be predicted and analyzed. A complete set of comparative experiments demonstrate that, when Pw is 23.5 MPa, Mc is 225 g, Mb is 342 g, Ma is 304 g, Fa is 0.26 kg/min, and Ts is 40.7 mm/min, and Bf is 69.442%, Cs is 70.336%, Nb is 69.002%, Sb is 54.334%, Os is 67.334%, Fp is 71.254%, At is 33.557%, W is 56.375%, Tr is 62.445%, an optimal data set of FLC is 60.5 × 105, PFLS is 0.337, FLGV is 83.74 × 102, CMFD is 8.42, FDP is 79.43, and NPFD is 4.445 (error tolerance = ±5%), could be obtained. This research is novel and merited in implementing novel PFL prediction for CSS-42 L bearing, proposing PFL indexes to evaluate the fatigue performance of bearing material in jet strengthen modification grinding, and designing an improved WTP network to achieve accurate PFL prediction as well. Eventually it could be summarized that this research realizes extraordinary predictive capability and facilitates the productive PFL management for aeroengine equipment.

Remaining fatigue life assessment of corroded mooring chains using crack growth modelling

Failures caused by the combined actions of fatigue, corrosion and wear are important safety concerns for mooring chains used on floating structures in the oil and gas industry. Prediction of remaining corrosion fatigue life based on surface condition could therefore be a useful tool for the continued safe operation of corroded chains. This paper investigates the use of crack growth modelling for estimating the remaining corrosion fatigue life of mooring chains that exhibit significant pitting corrosion damage. A crack growth modelling approach is used to produce remaining fatigue life estimates for a selection of severely pitted mooring chains. Using fatigue crack growth rate test results for grade R4 high strength mooring chain steel, empirical crack growth laws are presented for free corrosion and cathodic protection conditions at load ratio R = 0.1. Two different methods for establishing equivalent cracks from surface scans of corrosion damage are presented. The mooring chains are proof loaded as part of their manufacturing process. Residual stresses introduced during this process have therefore been determined by finite element analysis and accounted for in the fatigue crack growth predictions. One of the equivalent crack models, accounting for the single dominant corrosion pit, provided quite accurate fatigue life predictions when compared with full scale test results.

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

S-N curve fatigue samples of titanium alloy welded joints have such a comparatively significant scatter, that results in the issue that the fatigue life prediction accuracy is not optimal. In this work, the titanium alloy welded joints' fatigue data is used as analysis data, and the neighborhood rough set reduction with improved firefly algorithm efficient method of fitting stress-life curves is set forth. The welded joint's fatigue decision system is built with fatigue data. The continuous iteration of the firefly algorithm is used as the search strategy, the neighborhood rough set is adopted to decrease attributes, and the major deciding elements of welded joints' fatigue life is identified. The fatigue characteristic domains are divided based on the neighborhood rough set reduction with improved firefly algorithm's key factor set, and the S-N curves can then be fitted to each domain individually. According to the goodness-of-fit analysis, the proposed approach can improve fatigue life accuracy and reduce sample scattering from fatigue.

Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods

This study proposed wavelet-based approaches to characterise random vibration road excitations for durability prediction of coil springs. Conventional strain-life approaches require long computational time, while the accuracy of the vibration fatigue methods is unsatisfactory. It is therefore a necessity to establish an accurate fatigue life prediction model based on vibrational features. Wavelet-based methods were applied to determine the low-frequency energy and multifractality of road excitations. Strain-life models were applied for fatigue life evaluation from strain histories. ANFIS modelling was subsequently adopted to associate the vibration features with the fatigue life of coil springs. Results showed that the proposed wavelet-based methods were effective to determine the signal energy and multifractality of vibration signals. The established vibration-based models showed good fatigue life conservativity with a data survivability of more than 90%. The highest Pearson coefficient of 0.955 associated with the lowest RMSE of 0.660 was obtained by the Morrow-based model. It is suggested that the low-frequency energy and multifractality of the vibration signals can be used as fatigue-related features in life predictions of coil springs under random loading. Finally, the proposed model is an acceptable fatigue life prediction method based on vibration features, and it can reduce the dependency on strain data measurement.

A modified highly stressed volume (HSV) method to predict fatigue life considering the critical crack size

Fatigue life prediction is important in engineering design to ensure structural reliability. However, it is difficult to achieve an accurate fatigue life prediction from a smooth specimen to a notched specimen with the traditional Highly Stressed Volume (HSV) method, as it is based on just one empirical evaluation of the HSV. A modified HSV method is proposed in this study, which is combined with the concept of critical distance, and the critical crack size of smooth specimens is obtained by observing the fracture surface. The predicted P-S-N curves of single-notched specimens and multi-notched specimens show a good agreement with experimental data compared with the traditional HSV method and the Theory of Critical Distance method.

Effect of infill pattern on fatigue characteristics of 3D printed polymers

The effect of the structure that is printed inside an object, known as infill pattern, on fatigue life of 3D printed specimens made of ABS was investigated in this paper. There are several infill pattern geometries, each with benefits and compromises between material usage, printing time or mechanical strength of the obtained part. Standard specimens with 100 % infill rate and rectilinear 0° and 90°, grid 0°–90° and ± 45°, triangular 60°, fast honeycomb, full honeycomb and wiggle infill patterns were tested. Following the completion of tensile tests to determine the ultimate tensile strength (UTS) of specimens, axial fatigue tests for each combination of infill pattern were conducted to measure the fatigue life for 90 %, 75 %, 60 %, 45 % and 40 % UTS. Comparative evaluation of the measured values revealed the strong influence of the printing patterns upon fatigue life. A simple mathematical model of the fatigue proprieties has been proposed for each infill patterns, statistical data shows that the proposed model ensures reasonably accurate prediction of fatigue life. The examination of fracture areas using electronic microscopy indicated the microstructural interaction between filaments underlying the main failure mechanisms, crazing, delamination and disbonding of the filaments and layers and their fracture. The results indicate that pattern with inclined filaments about tensile axis (grid±45°, rectilinear 0° and triangular 60°) have the maximum fatigue life compared with those with most of the filaments perpendicular to tensile direction (fast honeycomb, full honeycomb and rectilinear 90°). The pattern like grid 0°–90° and wiggle are very close to the first group, having filaments inclined (wiggle) or along and perpendicular to the tensile direction (grid 0°–90°).

Experiments on static and fatigue behaviour of corroded Q235B and 42CrMo steels

This paper studies the mechanical properties and low-cycle fatigue behaviour of corroded Q235B and 42CrMo steels. Tensile test and low-cycle fatigue test were conducted on the corroded specimens after the accelerated corrosion process in the 25 °C salt spray, 25 °C dry/wet cycle or 50 °C dry/wet cycle conditions. Based on the accelerated corrosion test, the corrosion rate and ratio of weight loss were calculated and their relationships with the exposure time were described by a time-dependent model. The tensile test results indicate that the 50 °C dry/wet cycle condition caused the most degradation on the strength and ductility of both Q235B and 42CrMo steels. The modified three-stage constitutive model is used to describe the stress-strain curve of specimens under different conditions and the key parameters in the model were obtained by regression analysis on the experimental data. The low-cycle fatigue test results demonstrate that the coupling effect of corrosion environment, such as chloride ion and higher temperature, and applied cyclic load can lead to decrease in the fatigue life of these two metallic materials. Comparisons between the test fatigue life and the design values demonstrate that the current design codes cannot provide accurate fatigue life predictions for the Q235B and 42CrMo steels in the salt spray and dry/wet cycle conditions.

Reduced Order Modeling of Forced Response in a Multistage Compressor Under Mistuning and Aerocoupling

Abstract Vibration amplitudes and fatigue life in multistage turbomachinery are commonly estimated by an investigation of the individual stages. Research is currently extending the scope to include structural and aeroelastic interstage coupling. Both effects have been shown to significantly influence blade vibrations. For safe operation of modern blisk blading with its lower structural damping due to the elimination of frictional contacts at the blade roots, an accurate prediction of the vibration behavior with mistuning is necessary to avoid high cycle fatigue failures. In this paper, a cyclic Craig-Bampton reduction method with a priori interface reduction for multistage rotors is extended to handle aeroelastic effects. This reduced order model efficiently predicts forced response in multistage applications. Aeroelastic multistage simulations are carried out using the harmonic balance method to account for the stage interactions and yield damping and stiffness coefficients, as well as excitation forces. Small structural mistuning is projected onto the tuned system modes of the rotor. The approach is applied to a 2.5-stage compressor configuration. Monte Carlo simulations show the sensitivity of vibration amplitudes to the aeroelastic coupling for mistuning. The aeroelastic interstage coupling is found to originate mainly from acoustic mode propagation between the stages. The fatigue of rotor blades is significantly affected by multistage interactions since vibration amplitudes increase due to the superposition of the responses of multiple modes. This leads to the conclusion that aeroelastic multistage effects need to be incorporated in future design procedures to allow for an accurate prediction of fatigue life.

Probabilistic Risk Assessment for Life Extension of Turbine Engine Rotors

Maintaining the component service life beyond its historical limits requires the ability to accurately quantify component reliability and address the uncertainties in material responses. A probabilistic method for predicting the total fatigue life was developed and applied to determine the probability of failure of a Ti-6Al-4V turbine disk component. The total fatigue life incorporates a dual mechanism approach including the crack initiation life and propagation life while simultaneously determining the associated initial flaw sizes. A microstructure-based model was employed to address the uncertainties in material response and relate the crack initiation life with crack size. The propagation life was characterized using both small and large crack growth models to ensure accurate fatigue life prediction. Fatigue life predictions were found to correlate with experimental data at high stress levels. The risk assessment can be used to estimate the expected initial crack sizes from variability in material properties, which can further be used to establish an enhanced inspection planning.

Vibration Fatigue Analysis of a Simply Supported Cracked Beam Subjected to a Typical Load

In order to realize the accurate prediction of the vibration fatigue life of the beam in service, a loose coupling analysis method is proposed to carry out vibration fatigue analysis of a beam with an initial crack. In modal analysis, the initial crack segment is replaced with a torsion spring, and the damping loss factor is introduced by the complex modulus of elasticity; for the simply supported beam, the inherent vibration characteristic equation of the cracked beam is derived. In vibration fatigue analysis, the interaction between the crack’s growth and vibration analysis is considered, and a loose coupling analysis method is proposed to conduct modal dynamic response and vibration fatigue analysis simultaneously. Results indicate that the crack’s relative location and depth determine the modal of the cracked beam, and crack parameters, damping loss factor and external excitation frequency are important factors for the vibration fatigue life of the beam.

The influence of machining induced surface integrity and residual stress on the fatigue performance of Ti-6Al-4V following polycrystalline diamond and coated cemented carbide milling

Accurate fatigue life predictions of titanium alloy components requires an understanding of how the machining affected metallurgical and micro-mechanical subsurface condition influences fatigue crack nucleation and growth. This study investigates the influence of surface integrity features generated during carbide and high-speed polycrystalline diamond machining on the fatigue behaviour of coarse and fine-grained Ti-6Al-4V. Mechanically induced compressive residual stresses, promoted by higher feed rates and the larger cutting edge radii of carbide tools, have been demonstrated to provide an overriding enhancing effect on fatigue life due to crack initiation suppression and reducing the deleterious effects of microstructural deformation and surface imperfections.

An improved inverse power law model for accelerated fatigue life prediction of 6061-T6 and AZ31B-F

Accelerated vibration fatigue, using the inverse power law model (IPL), is effective to evaluate the durability of materials. However, for 6061-T6 and AZ31B-F with two-stage S-N curves, the prediction error exceeds orders of magnitude. Therefore, an improved inverse power law model (IIPL) is proposed. The results obtained from the vibration fatigue experiments show that the fatigue life prediction accuracy of IIPL is more acceptable than IPL. For two-stage S-N curves, the error of IIPL is between −61.8% – −0.8%, better than that of IPL (8.68 × 101% – 2.42 × 105%). Even without accelerated fatigue tests, the error by IIPL is also reasonable (-69.1% – 23.2%).

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.