Multiaxial Fatigue Damage Parameter and Life Prediction without Any Additional Material Constants

Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress-Strain Conditions

In this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the physics-based stress and strain invariants as input quantities. The application of the machine learning algorithm solved the problem of assigning an adequate parametric fatigue model to given material and loading conditions. The model was verified using the experimental data on the CuZn37 brass subjected to various cyclic loadings, including non-proportional multiaxial strain paths. The performance of the machine learning-based fatigue life prediction model is higher than the performance of the well-known parametric models.

Using the Smith-Watson-Topper Parameter and Its Modifications to Calculate the Fatigue Life of Metals: The State-of-the-Art

The Smith-Watson-Topper parameter (SWT) in its original form was designed to estimate the fatigue life of metal materials in a uniaxial load state (tension–compression) in the range up to fatigue crack initiation, with non-zero mean values. This parameter is based on the analysis of both stress and strain. Therefore, the stress–strain criterion is the focus, rather than the energy criterion. This paper presents the original SWT model and its numerous modifications. The first part presents different versions of this parameter defined by the normal parameters. Then, it presents versions defined through the tangent parameter and the most promising parameter defined through the tangent and normal parameters. It was noted that the final form of the equivalent value is defined either by stress or by an energy parameter. Therefore, the possible characteristics from which the fatigue life can be determined are also presented.

Experimental and Numerical Analysis of Fatigue Life of Aluminum Al 2024-T351 at Elevated Temperature

This paper presents the prediction of the fatigue life of aluminum Al 2024-T351 at room and elevated temperatures under uniaxial loading using finite element simulation. Structural parts such as fuselage, wings, aircraft turbines and heat exchangers are required to work safely at this working condition even with decreasing fatigue strength and other properties. The monotonic tensile and cyclic tests at 100 °C and 200 °C were conducted using MTS 810 servo hydraulic equipped with MTS 653 high temperature furnace at a frequency of 10 Hz and load ratio of 0.1. There was an 8% increase in the yield strength and a 2.32 MPa difference in the ultimate strength at 100 °C. However, the yield strength had a 1.61 MPa difference and 25% decrease in the ultimate strength at 200 °C compared to the room temperature. The mechanical and micro-structural behavior at elevated temperatures caused an increase in the crack initiation and crack propagation which reduced the total fatigue life. The yield strength, ultimate strength, alternating stress, mean stress and fatigue life were taken as the input in finite element commercial software, ANSYS. Comparison of results between experimental and finite element methods showed a good agreement. Hence, the suggested method using the numerical software can be used for predicting the fatigue life at elevated temperature.

Investigation of Strength and Fatigue Life of Rubber Asphalt Mixture

Strength and fatigue life are essential parameters of pavement structure design. To accurately determine the pavement structure resistance of rubber asphalt mixture, the strength tests at various temperatures, loading rate, and fatigue tests at different stress levels were conducted in this research. Based on the proposed experiments, the change law of rubber asphalt mixture strength with different temperatures and loading rates was revealed. The phenomenological fatigue equation of rubber asphalt mixture was established. The genetic algorithm optimized backpropagation neural network (GA-BPNN) is highly reliable for optimizing production processes in civil engineering, and it has a remarkable application effect. A GA-BPNN strength and fatigue life prediction model was created in this study. The reliability of the prediction model was verified through experiments. The results showed that the rubber asphalt mixture strength decreases and increases with the increase of temperature and loading rate, respectively. The goodness of fit of the rubber asphalt mixture strength and fatigue life prediction model based on the GA-BPNN could reach 0.989 and 0.998, respectively. The indicators of the fatigue life prediction model are superior to the conventional phenomenological fatigue equation model. The GA-BPNN provides an effective method for predicting the rubber asphalt mixture strength and fatigue life, which significantly improves the accuracy of the resistance design of the rubber asphalt pavement structure.

Application of the S-N Curve Mean Stress Correction Model in Terms of Fatigue Life Estimation for Random Torsional Loading for Selected Aluminum Alloys

The paper presents the experimental fatigue test results for cyclic constant amplitude loading conditions for the case of the torsion of the PA4 (AW-6082-T6), PA6 (AW-2017A-T4) and PA7 (AW-2024-T3) aluminum alloy for a drilled diabolo type test specimen. The tests have been performed for the stress asymmetry ratios R = −1, R = −0.7, R = −0.5 and R = −0.3. The experimental results have been used in the process of a fatigue life estimation performed for a random generated narrowband stress signal with a zero and a non-zero global mean stress value. The calculations have been performed within the time domain with the use of the rainflow cycle counting method and the Palmgren−Miner damage hypothesis. The mean stress compensation has been performed with the S-N curve mean stress model proposed by Niesłony and Böhm. The model has been modified in terms of torsional loading conditions. In order to obtain an appropriate R = 0 ratio S-N curve fatigue strength amplitude, the Smith−Watson−Topper model was used and compared with literature fatigue strength amplitudes. The presented solution extends the use of the correction model in terms of the torsional loading condition in order to obtain new S-N curves for other R values on the basis of the R = −1 results. The work includes the computational results for new fatigue curves with and without the mean stress effect correction. The results of the computations show that the mean stress effect plays a major role in the fatigue life assessment of the tested aluminum alloys and that the method can be used to assess the fatigue life under random conditions.

Comparative Analysis of Fatigue EnergyCharacteristics of S355J2 Steel Subjected toMulti-Axis Loads

In this study, a novel test system for estimating bending and torsion fatigue under selectable kinematic and dynamic loading modes was constructed. Using S355J2 steel specimens, a series of tests were conducted to determine material sensitivity to different load paths and loading modes. The experimental results were supplemented with the results of numerical analyses, on the basis of which the components of strain and stress tensors for subsequent analyses were determined in the entire working part of the specimen. An original method for determining specific strain energy components was used. The experimental results showed the grouping of data according to the mode of loading chosen. This could signify that the selected fatigue models are sensitive to certain loading scenarios. We performed in- depth data analysis and complex numerical simulations, formulating likely explanations for the observed effect.

Application of Life-Dependent Material Parameters to Fatigue Life Prediction under Multiaxial and Non-Zero Mean Loading

This study presents the life-dependent material parameters concept as applied to several well-known fatigue models for the purpose of life prediction under multiaxial and non-zero mean loading. The necessity of replacing the fixed material parameters with life-dependent parameters is demonstrated. The aim of the research here is verification of the life-dependent material parameters concept when applied to multiaxial fatigue loading with non-zero mean stress. The verification is performed with new experimental fatigue test results on a 7075-T651 aluminium alloy and S355 steel subjected to multiaxial cyclic bending and torsion loading under stress ratios equal to R = −0.5 and 0.0, respectively. The received results exhibit the significant effect of the non-zero mean value of shear stress on the fatigue life of S355 steel. The prediction of fatigue life was improved when using the life-dependent material parameters compared to the fixed material parameters.

A New Empirical Life Prediction Model for 9–12%Cr Steels under Low Cycle Fatigue and Creep Fatigue Interaction Loadings

Low cycle fatigue (LCF) and creep fatigue interaction (CFI) loadings are the main factors resulting in the failure of many critical components in the infrastructure of power plants and aeronautics. Accurate prediction of life spans under specified loading conditions is significant for the design and maintenance of components. In the present study, various LCF and CFI tests are conducted to investigate the effects of temperature, strain amplitude, hold time and hold direction on the fatigue life of P92 steel. To predict fatigue life under different experimental conditions, various conventional life prediction models are evaluated and discussed. Moreover, a new empirical life prediction model is proposed based on the conventional Manson-Coffin-Basquin (MCB) model. The newly proposed model is able to simultaneously consider the effects of temperature, strain amplitude, hold time and hold direction on predicted life. The main advantage is that only the known input experimental parameters are required to perform the prediction. In addition to the validation made through the experimental data of P92 steel conducted in the present paper, the model is also verified through numerous experimental data reported in the literature for various 9–12% Cr steels.

Method of Fatigue-Life Prediction for an Asphalt Mixture Based on the Plateau Value of Permanent Deformation Ratio

Laboratory predictions for the fatigue life of an asphalt mixture under cyclic loading based on the plateau value (PV) of the permanent deformation ratio (PDR) were carried out by three-point bending fatigue tests. The influence of test conditions on the recovery ratio of elastic deformation (RRED), the permanent deformation (PD) and PDR, and the trends of RRED, PD, and PDR were studied. The damage variable was defined by using PDR, and the relation of the fatigue life to PDR was determined by analyzing the damage evolution process. The fatigue equation was established based on the PV of PDR and the fatigue life was predicted by analyzing the relation of the fatigue life to the PV. The results show that the RRED decreases with the increase of the number of loading cycles, and the elastic recovery ability of the asphalt mixture gradually decreases. The two mathematical models proposed are based on the change laws of the RRED, and the PD can well describe the change laws. The RRED or the PD cannot well predict the fatigue life because they do not change monotonously with the fatigue life, and one part of the deformation causes the damage and the other part causes the viscoelastic deformation. The fatigue life decreases with the increase of the PDR. The average PDR in the second stage is taken as the PV, and the fatigue life decreases in a power law with the increase of the PV. The average relative error of the fatigue life predicted by the fatigue equation to the test fatigue life is 5.77%. The fatigue equation based on PV can well predict the fatigue life.

Multiaxial Fatigue Damage Parameter and Life Prediction without Any Additional Material Constants

Based on the critical plane approach, a simple and efficient multiaxial fatigue damage parameter with no additional material constants is proposed for life prediction under uniaxial/multiaxial proportional and/or non-proportional loadings for titanium alloy TC4 and nickel-based superalloy GH4169. Moreover, two modified Ince-Glinka fatigue damage parameters are put forward and evaluated under different load paths. Results show that the generalized strain amplitude model provides less accurate life predictions in the high cycle life regime and is better for life prediction in the low cycle life regime; however, the generalized strain energy model is relatively better for high cycle life prediction and is conservative for low cycle life prediction under multiaxial loadings. In addition, the Fatemi–Socie model is introduced for model comparison and its additional material parameter k is found to not be a constant and its usage is discussed. Finally, model comparison and prediction error analysis are used to illustrate the superiority of the proposed damage parameter in multiaxial fatigue life prediction of the two aviation alloys under various loadings.

A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting from high frequency vibrations and low cycle fatigue (LCF) from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner's rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors.