High Cycle Fatigue Life Prediction Research Articles

Crystal plasticity based modelling and high cycle fatigue life prediction for bi-lamellar Ti-6Al-4V

Microstructural defects and inhomogeneity of titanium alloys fabricated by additive manufacturing technology make their fatigue performance much more complicated, especially reflected in the dispersion of fatigue life. This work employs crystal plasticity finite element method (CPFEM) to predict high cycle fatigue (HCF) life of bi-lamellar Ti-6Al-4V alloy. We first propose a modified VT technique to build representative volume element (RVE) models highlighting lamellar microstructure and micro-defects. Subsequently, fatigue indicator parameter (FIP) is adopted to analyse fatigue deformation under cyclic loading. Finally, HCF life determined by critical fatigue indicator parameter is compared with experimental data collected from published literatures. The results demonstrate that our approach is able to reflect the dispersion of fatigue life and to predict HCF life of bi-lamellar Ti-6Al-4V in a satisfactory manner.

High-cycle and low-cycle fatigue life prediction under random multiaxial loadings without cycle counting

A novel fatigue life prediction method under random multiaxial loadings is proposed in this paper. One unique benefit of the proposed method is that it is based on the time-derivative fatigue crack growth formulation and does not need cycle counting under arbitrary loadings. First, a brief review of subcycle fatigue crack growth (FCG) analysis and the equivalent initial flaw size (EIFS) concept for life prediction is introduced. Next, the existing subcycle fatigue crack growth model is extended to near-threshold conditions. An intrinsic fatigue threshold of the material is introduced, and a hypothesis is proposed that the crack only grows when the local stress intensity factor is beyond the intrinsic threshold. Following this, the analogy of stress intensity factor and strain intensity factor is used to extend the fatigue life prediction model to strain-based fatigue analysis. Correction for large plastic deformation is included to handle low-cycle fatigue life prediction. The novelties of this model are that it considers the FCG at the threshold and near-threshold region and it is able to predict both HCF and LCF conditions using FCG-based life prediction. Following this, extensive in-house and literature data for AL-7075-T6 under uniaxial and multiaxial, constant, and random loadings are used for model validation. Discussions for the effect of model parameters are provided based on parametric analysis. Conclusions and future work are mentioned. Code and data are released in public cloud service for interested readers.

A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction

AbstractAero‐engine turbine blades are subject to combined high and low cycle fatigue (CCF) loadings during operations. In this paper, a modified nonlinear cumulative damage model is developed based on linear damage accumulation theory and static toughness exhaustion to predict the CCF life of turbine blades and common blade materials. An interaction factor is created to reflect the integrated effect of high cycle fatigue and low cycle fatigue (HCF‐LCF). Available test data from TC4 alloy and other two alloy materials, together with two turbine blades are utilized to validate the robustness and accuracy of the proposed approach. Comparative results demonstrate that the proposed model holds better performance in life predictions under CCF loadings.

High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach.

The large scatter in high-cycle fatigue (HCF) life poses significant challenges to safe and reliable in-service assessment of additively manufactured metal components. Previous investigations have indicated that inherent manufacturing defects are a critical factor affecting the fatigue performance of the components, and the HCF life is significantly influenced by the geometric parameters of the critical defects inducing crack nucleation. Therefore, it is highly important to elucidate the correlation of the HCF life with the geometric parameters of critical defects. This study proposes a new fatigue life prediction model for laser additively manufactured AlSi10Mg alloys by including the combined effects of loading stress and defect geometries (size, location and morphology) in terms of domain knowledge-guided symbolic regression (SR). Domain knowledge is extracted from the semi-empirical Murakami, Z-parameter and X-parameter fatigue life models to establish the variable subtrees. The results show that compared with these semi-empirical models, the domain knowledge integration-based SR model has higher prediction accuracy and generalization ability. Moreover, compared with traditional 'black box' machine learning models, SR excels at balancing prediction accuracy and model interpretability, which provides useful insights into the relationship between fatigue life and defect geometries. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 2)'.

Very High Cycle Fatigue Life Prediction of SLM AlSi10Mg Based on CDM and SVR Models

A novel fatigue evolution model considering the effect of defect size and additive manufacturing building direction based on the theories of continuum damage mechanics and its numerical implementation in ABAQUS is proposed in this paper. First, the constitutive model, fatigue damage evolution model and their parameter calibration methods are presented. Second, using the ABAQUS platform, the proposed model is implemented with user-defined subroutines. After that, based on the proposed model and its numerical implementation, the fatigue life of additively manufactured AlSi10Mg is predicted and its applicability is verified through experimental results. Finally, a support vector regression model is established to predict the fatigue life, and its results are compared to those of the numerical finite element method. The results show that the support vector regression model makes better predictions than the finite element method.

Data-driven approach to very high cycle fatigue life prediction

The research on life prediction for mechanical structures in very high cycle fatigue regime is pivotal to improve structure service, but it can be costly and time-consuming to collect fatigue data. In response, the data-driven approach of machine learning emerged as a solution to data insufficiency. In this work, after extracting a small dataset of GCr15 bearing steel subjected to very high cycle fatigue tests from open literature, the Z-parameter model was applied to obtain extended datasets to establish models driven by support vector machine, artificial neural network, and Z-parameter based physics-informed neural network, respectively. With training on extended datasets and the original data as test set, fatigue life prediction for GCr15 steel was carried out and evaluated between these models. Results showed that the physics-informed neural network calibrated by Z-parameter model trained on a larger dataset featured more accurate and reliable prediction than other models did, which demonstrated effectiveness of Z-parameter in data extension and model construction as priori physics knowledge for a data-driven approach. Looking into the future, Z-parameter model deserves more attention to its employment in life prediction for more engineering materials and structures serving in the very high cycle fatigue regime.

High-Cycle Fatigue Properties and Life Prediction of ZK30 Magnesium Alloy at Room and Elevated Temperatures

High-Cycle Fatigue Properties and Life Prediction of ZK30 Magnesium Alloy at Room and Elevated Temperatures

Analysis of High-Cycle Fatigue Life Prediction of 304 Stainless Steel Based on Deep Learning

Analysis of High-Cycle Fatigue Life Prediction of 304 Stainless Steel Based on Deep Learning

Fatigue life prediction of Ti60 titanium alloy under very high cycle loading at different temperatures

The very high cycle fatigue crack initiation mechanism of Ti60 titanium alloy at different temperatures was studied. The results showed that the initiation of cracks occurred on oversized facets in the subsurface. Based on this crack initiation mechanism, a full-life prediction model based on crack propagation at room temperature was first proposed, and then a very high cycle fatigue life prediction model considering temperature was proposed based on the degradation law of tensile strength at different temperatures. The experimental and predicted life results of Ti60 specimen at room temperature with 300 °C and 500 °C showed that the proposed method can effectively predict the very high cycle fatigue life, and the error is within a factor of 3.

Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement

Few machine learning (ML) methods have been applied to the prediction of the highly dispersed and low Very High Cycle Fatigue (VHCF) specimens. The present work attempts to use Monte Carlo Simulation (MCS) to expand the low VHCF dataset and thereby improve the predictive performance of ML models. Moreover, a dynamic recurrent ML model is proposed to be used with the extended dataset. VHCF experiments conducted under three different stress ratios (R = −1, 0.05 and 0.5) and two temperatures (26 °C and 500 °C) show that the use of MCS has significantly improved the predictive capabilities of the ML model, with predictions almost within the scattered band of 4.0 and closer to 2.0, thus solving the challenge of predicting the life of sparse VHCF specimens made of Ti60 alloy, the cost and time of VHCF prediction will be greatly reduced.

A probabilistic fatigue life prediction method under random combined high and low cycle fatigue load history

In this paper, a probabilistic high and low cycle fatigue (H-LCF) life prediction framework is proposed to consider random load history into fatigue life prediction of structures. First, the random load history is generated and quantified by finite element analysis (FEA) and Monte Carlo simulation (MCS). Subsequently, based on the quantification approach and an improved H-LCF damage curve, the framework of probabilistic H-LCF life prediction is then established. A turbine shaft is analyzed by the proposed method, which confirms the superior accuracy of the proposed method than the existing ones. Overall, the proposed method provides an effective way for structures’ fatigue life and reliability assessment.

On the consideration of loading interaction in combined high and low cycle fatigue life prediction

AbstractBased on the analysis of the test data and Miner's rule, a combined high and low cycle fatigue (CCF) life prediction model considering the loading interaction is established by introducing coupled damage. The coupled damage is determined by multiplying the high cycle fatigue damage and the low cycle fatigue damage by a coupled damage coefficient . According to the analysis of the test data, the coupled damage coefficient is positively correlated with the high cycle stress ratio . In addition, an interaction factor, which reflects the loading interaction effect, is introduced to correct the coefficient . The CCF experimental data of four materials are employed for model verification and comparison among the proposed model, Miner's rule, and Trufyakov–Kovalchuk model (T–K model). The proposed model exhibits a relatively higher prediction accuracy and is more suitable for predicting the fatigue life of CCF.

High Cycle Fatigue Life Prediction of Single-Crystal Specimen Based on TCD Method and Crystal Plasticity Theory

This paper performs a comprehensive investigation on the high cycle fatigue (HCF) life prediction of turbine blade with film cooling holes. The modified theory of critical distance (MTCD) method is proposed to estimate the fatigue life of the specimen considering the notch sensitivity coefficient and multiaxial stress effect. Then, two types of specimens were designed with regard to the single-hole and multihole conditions. Afterwards, the dangerous path and fatigue life of the two specimens were achieved implementing the MTCD method. Then, the experiments and failure analysis were carried out. The results show that the stress concentration and multiaxial stress resulting from the film cooling holes are the primary reasons that the cracks originated. Meanwhile, the dangerous path of the single-hole specimen is quite different from the multihole specimen due to the interhole interference. Finally, most of the calculated fatigue life is within the twice error band of the tested life.

A unified rule for high-cycle and low-cycle fatigue life prediction in multi-scale framework

This paper develops a unified rule for both low-cycle fatigue and high-cycle fatigue life prediction from macro-scale to micro-scale. In the macro-scale, the unified rule is proposed on the basis of the concept of the effective strain energy density. In the micro-scale, the unified rule represented by a new fatigue indicator parameter is developed by considering the combined effects of the energies in shear and normal directions for individual slip systems. A large number of low-cycle and high-cycle fatigue experimental data of Inconel 718 superalloy at room temperature is used to verify the prediction capacities of the newly unified rule. Compared with the prediction accuracy of the existing fatigue damage models, the unified rule shows more accurately predicted results for both low-cycle and high-cycle fatigue life. Most of the fatigue experimental data falls into a range within a scatter band of ±3 on life.

High cycle fatigue life prediction model based on crystal plasticity and continuum damage mechanics for Ni-based single crystal superalloys under a multiaxial stress state

A high cycle fatigue life prediction model based on continuum damage mechanics, crystal plasticity and critical distance theory is developed. A novel damage evolution law for anisotropic Ni-based single crystal materials built on continuum damage mechanics is proposed. The crystal plasticity framework was introduced to take into account the particular characteristics of high cycle fatigue. The critical distance theory was innovatively applied to the structure of film cooling holes to predict the lifetime for materials under a multiaxial stress state. High cycle fatigue tests considering drilling technology, stress ratio and creep time were performed using Ni-based single crystal superalloy plates with film cooling holes. The constitutive equations were implemented into a finite element framework to simulate the high cycle fatigue properties of Ni-based single crystal superalloys, and the results of numerical calculations were compared with experimental observations. It was demonstrated that the constitutive formulations proposed in the present study can be used to model the mechanical behavior of Ni-based single crystal superalloys under a multiaxial stress state. The life prediction errors are found to be within the factor-of-three scatter band. The fatigue life of the specimen with ultrafast laser drilled film cooling holes is larger than that with electrostream machined film cooling holes under the same test conditions. The fatigue strength of the specimen with film cooling holes at a stress ratio R = 0.95 is higher than that at a stress ratio R = -1. The life of the specimen with film cooling holes is found to decrease with increasing creep time before high cycle fatigue. The γ/γ'-microstructure evolution in the specimens with film cooling holes is related to the kinetics of the stress redistribution during the fatigue process. A minimum stress level is essential for rafting to occur. The damage distribution for the plate model is in line with the crack initiation location and propagation region of the specimen.

GMDH-Kalman Filter prediction of high-cycle fatigue life of drilled industrial composites: A hybrid machine learning with limited data

In industrial composites applications, drilling is one of the most common operations and complex processes during the final assembly, which can generate undesirable damage to the manufactured part. Data collection from a given composite’s fatigue life is often costly and time-consuming. To address this challenge, the current case study aims to adapt a hybrid machine learning framework to predict the fatigue life of the drilled Glass Fiber Reinforced Polymer composite laminates (with both unidirectional and woven lay-ups) under a limited and noisy data assumption. Composite specimens were drilled at various cutting speeds and feed rates. The size of the delamination around the hole was scanned by a microscopic camera. Cyclic three-point bending tests were conducted, and results indicated that the drilling-induced delamination size and the composite lay-up affect the specimens’ fatigue lives. The latter were then modeled in two steps. In the first step, an offline deterministic model was established using the group method of data handling along with a singular value decomposition. Pareto multi-objective optimization was applied to prevent overfitting. In the second step, the Kalman filter was employed to update the polynomial of the deterministic model based on minimizing the mean and variance of error between the actual and modeled data. Results showed an excellent learning reliability, with a correlation coefficient of 97.6% and 96.5% in predicting the fatigue life of unidirectional and woven composite laminates, respectively. A sensitivity analysis was performed and indicated that the fatigue life of the samples has been more affected by the drilling feed rate, compared to the cutting speed.

Combined high and low cycle fatigue life prediction model based on damage curve method considering loading interaction effect

Through analyzing the characteristics of the Miner’s rule and the Manson-Halford (M-H) model, a nonlinear fatigue damage accumulation model is established based on the damage curve method under combined high and low cycle fatigue loading. In this model, an interaction factor related to the maximum stress and high cycle stress range is proposed to consider the loading interaction effect. Furthermore, the proposed model does not require any additional material parameters except for the S-N curve parameters. The experimental data of five materials, including TC11 alloy, 2024-T3 aluminum alloy, 45 steel, LY12-CZ aluminum alloy, and GH4033 Ni-based alloy, are introduced to verify the proposed model. Compared with the Miner’s rule and the M-H model, most of the prediction results by the proposed model are within the life factor range of 2, implying the established model has higher prediction accuracy.

High-Cycle Fatigue Life and Strength Prediction for Medium-Carbon Bainitic Steels

High-cycle fatigue (HCF) behaviors of medium-carbon bainitic steels with various inclusion sizes and microstructural features were studied using the rotating–bending fatigue test. Here, the medium-carbon bainitic steels with different melting processes were treated by three heat treatment routes incorporating bainite formation, namely bainite-based quenching plus partitioning (BQ&P), bainite austempering (BAT) and “disturbed bainite austempering, DBAT”. The interior inclusion-induced crack initiation (IICI) and noninclusion-induced crack initiation (NIICI) modes were found after fatigue failure. The fracture surface of IICI is characterized by a “fish-eye” surrounding a “fine granular area, FGA” in the vicinity of an inclusion. In contrast, a microfacet, instead of an inclusion, is found at the center of FGA for the NIICI fracture surface. The predications of fatigue strength and life were performed on the two crack initiation modes based on fracture surface analysis. The results showed that a majority of fatigue life is consumed within the FGA for both the IICI and NIICI failure modes. The fatigue strength of the NIICI-fatigued samples can be conveniently predicted via the two parameters of the hardness of the sample and the size of the microfacet.

Multiaxial high-cycle fatigue failure and life prediction based on critical plane method

Multiaxial high-cycle fatigue failure criterion and life prediction is important for structural components under complex low stress. A new multiaxial high-cycle fatigue damage parameter based on the plane of maximum shear stress is proposed, which includes the maximum shear stress amplitude, normal stress amplitude and maximum equivalent normal stress. The maximum equivalent normal stress is defined in the form of SWT stress function considering the influence of mean stress. The feasibility of the proposed failure criterion and life prediction model is verified by test data of different materials. The predicted results show that the life prediction error of the proposed fatigue model is basically within three times by comparing with McDiarmid, Matake, Crossland and Papadopoulos fatigue models.

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.