Prediction Of Fatigue Strength Research Articles

Evaluation of the accuracy and efficiency of the modified maximum variance method for multiaxial fatigue analysis under constant amplitude loading

An optimized version of the Maximum Variance Method (MVM) is employed to determine maximum shear stress amplitudes in high-cycle multiaxial fatigue analysis using the critical plane approach, increasing accuracy and reducing computational burden. This refined approach posits that the MVM assumes that the critical plane is the one subjected to the maximum variance of the shear stress history. Comprehensive statistical analyses, including Analysis of Variance (ANOVA) and multiple comparison techniques such as Tukey’s HSD and Bonferroni correction, were conducted to systematically investigate the impact of critical factors on fatigue resistance, such as mean stress, phase, synchrony, failure criteria, and the strategy for calculating maximum shear stress amplitude. These analyses highlighted significant effects of mean stress and phase on the accuracy of fatigue strength predictions, underscoring the effectiveness of the enhanced MVM in managing complex loading scenarios. The results demonstrate that the proposed methodology performs equal to or better than conventional methods, with significantly lower computational costs.

Exploration of materials fatigue influence factors using interpretable machine learning

AbstractData‐driven fatigue strength predictions are gaining popularity. Nevertheless, many machine learning models lack trustworthiness due to their limited decision‐making transparency which often hinders their practical application. In this investigation, we assess the expressiveness of the model‐agnostic explainable AI method known as SHapley Additive exPlanations (SHAP) for data‐driven fatigue strength prediction. Our study demonstrates that the SHAP feature sensitivity analysis underpins known physical relations from materials processing and fatigue theory. This even applies in view of the high‐dimensional, cross‐correlated fatigue feature space and despite data heterogeneity (different steels, component designs, and loads). For instance, SHAP indicates a fatigue strength increase with higher solid solution‐strengthening element concentrations, such as chromium and nickel. SHAP identifies correlations rather than causality. Thus, data science and domain knowledge should be closely linked during the SHAP assessment. If this is satisfied, plausible causal relations can be inferred, and spurious ones arising from confounding variables can be discarded.

An analytical approach to predict fatigue strength of corroded steel plates using random field-based corrosion surface modeling

This study introduces a probabilistic method to predict the fatigue strength of corroded steel members by incorporating uncertainties related to corrosion depth and surface morphology using random field modeling. The modeling successfully captured the probability density of the target corrosion depth, accurately representing corrosion geometric characteristics. Validation against fatigue testing on corroded rolled beams showcased the method’s accuracy across a range of stress levels. The application of the proposed method for time-variant fatigue strength predictions unveiled a rapid decline in both fatigue constant A and the constant amplitude fatigue threshold (CAFT) during the initial stages of corrosion. In marine and urban environments, fatigue strength fell rapidly within the first 3 years after corrosion initiation, whereas in rural environments, fatigue strength was anticipated to start to decline gradually after 5 years. These projections underscore the necessity of regular inspections to assess the condition of protective coatings. Moreover, the degradation curves generated by this method can serve as a foundational element in establishing an effective maintenance strategy for structures.

A tensile properties-related fatigue strength predicted machine learning framework for alloys used in aerospace

A tensile properties-related fatigue strength prediction framework based on machine learning (ML) methods was proposed. Firstly, 200 data containing six materials used in aerospace were collected. Secondly, tensile properties-related ML framework, including residual neural network (ResNet) model, gradient boosting decision tree (GBDT) model and LightGBM (LGB) model, was established to predict fatigue strength under symmetric cycles (R = -1) after features correlation analysis. Then, Gaussian noise was introduced into the features to prove the robustness and feature sensitivity of proposed ML framework. Finally, material characteristics of fatigue resistance in the next generation were discussed after the analysis of the correlation between the tensile properties and fatigue strength. Compared with ± 50 % error band of classical prediction models, the prediction results of proposed ML framework located in the ± 10 % error band, especially for the special crystal orientations and stress ratios. In practical applications, the tensile properties-related ML framework could be applied efficiently to materials fatigue strength prediction used in aerospace due to its robust prediction accuracy and generalization ability without additional parameters fitting.

A new method for predicting the fatigue strength at various stress ratios based on damage of metallic materials

AbstractThe prediction of fatigue strength for metallic materials in a wide range of stress ratios is a crucial engineering challenge. To pursue a more accurate method, the dependence of fatigue strength on stress ratio for 35CrMo steel and other materials was investigated in detail. It was found that as the stress ratio increases, the damage mechanism changes, and the errors of the conventional models in predicting the fatigue strength increase. The reason is that those models fail to describe the fatigue damage evolution of metallic materials under some special conditions. Based on the variational fatigue damage, a new prediction method was proposed by introducing the damage parameter. Through the validation with fatigue strength covering a variety of metallic materials and loading conditions, the method exhibits higher universality. And then the corresponding parameter of the formula is further analyzed to elucidate the influences of various loading conditions on fatigue damage of materials.

Effect of grain size on fatigue strength of 304 stainless steel

Abstract In this study, three types of 304 stainless steel samples with different strengths were prepared by refining the grain size through rolling. The microstructure of the samples was observed by electron microscopy. The influence of grain size on the static tensile properties and fatigue strength of the material is mainly attributed to changes in the plastic deformation fracture mechanism and micro-deformation mechanism. In addition, a new fatigue strength prediction model is proposed based on the influence of tensile strength and work-hardening capacity. Compared with the staircase method and Basquin formula models, the proposed model maintains the accuracy of fatigue strength prediction while reducing the cost of fatigue experiments. This provides a new approach for predicting the fatigue strength of specific materials and improving anti-fatigue design capabilities.

Defect sensitivity and high-cycle fatigue resistance of arc-welded 2219 aluminum alloy at 77 K

This study investigated the tensile and high-cycle fatigue (HCF) properties of a tungsten inert gas (TIG)-welded 2219 aluminum alloy at room temperature (RT) and 77 K for the first time. Defect-sensitivity-based fatigue strength and life prediction models were proposed to link welding defects with fatigue properties at cryogenic temperatures. The findings revealed that the yield strength, ultimate tensile strength, and fatigue strength of 2219 welded joints at 77 K were significantly improved by 29.7 %, 34.1 %, and 60.4 %, respectively, compared to those at RT. The largest possible pore defects near the weld surface were predicted using a combination of laboratory X-ray computed microtomography and the extreme value statistics (EVS) method. Furthermore, the fatigue strength was predicted by considering the maximum defect size in terms of the El-Haddad-based Kitagawa–Takahashi (K–T) diagram. The predicted fatigue strength was 95.5 MPa with an error of 5.4 % at RT, whereas at 77 K, the predicted value was 144.5 MPa with an error of 10.8 %. Finally, a fatigue life prediction model at both RT and 77 K was proposed based on a modified K–T diagram and Murakami’s S-N prediction model. The combination of the fatigue life prediction model and the maximum possible defect size based on EVS can provide slightly conservative predictions of the fatigue life without conducting a large number of HCF tests.

Load transfer distribution in bolted-sealed joints

ABSTRACT Rubber-like materials such as sealant with recent developments in formulation are widely used in various structural components, especially in aerospace industry with many applications such as the sealing of bolted joints on aircraft. The objective of this paper is to assess the load transfer distribution in bolted-sealed joints through an approach coupling both experimental and numerical tests. Quasi-static and relaxation tests under uniaxial and pure shear loading were carried out to determine the parameters of phenomenological hyperelastic laws and of the generalized Maxwell model for the sealant PR 1782 C2. The fastener is an alloy steel bolt with a protruding head when the substrates are made from aluminum 2024-T3. Experimental and numerical quasi-static tests were then performed on double-lap bonded and bolted-sealed joints under in-plane loading. The numerical tests are done using 2D and 3D Finite Element (FE) models; they involve the visco-hyperelastic behavior previously assessed for the sealant. Accounting for visco-hyperelasticity makes it possible to better estimate the load transfer between bolt and sealant layer. Typically, the bolt load transfer rate is derived from numerical output for different sealant thicknesses. In the considered geometrical joint configuration, the sealant load contribution is between 7% and 13% according to the sealant thickness. Methodology and results provide a solid basis for the fatigue strength prediction of bolted-sealed joints.

Modelling of the surface morphology and size effects on fatigue strength of L-PBF Inconel 718 by comparing different testing specimens

The aim of this study was to investigate the impact of surface roughness and size effect on the uniaxial fatigue strength of aged specimens made of Inconel 718, obtained by laser-powder bed fusion (L-PBF) both under as-built and machined surface conditions. The surface profiles were scanned with a 3D optical profilometer, and then modelled and filtered by the fast Fourier transform. This latter allowed to obtain the relevant parameters to calculate the term area in the Murakami model to account for the effect of surface roughness on fatigue strength prediction. Two additional terms were added to the standard Murakami formula: one to account for the size effect and the other to consider the nonzero value of the average stress. A cylindrical plain and a miniaturized plain specimens, in the as-built condition, were employed to calibrate the proposed model, while a machined plain cylindrical and a miniaturized notched specimens were used as validators with low prediction errors.

Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification

AbstractMaterial informatics has emerged as a valuable research field in material science, providing solutions to previously unsolvable problems or accelerating deliverables. Fatigue failure, as a complex and non‐deterministic phenomenon, requires a probabilistic approach to assess the uncertainty of the fatigue strength prediction. This study compares various probabilistic data‐driven models for credible fatigue strength predictions for three distinct steel groups. The analysis considers data and model uncertainty, evaluating their impacts on predictive quality from engineering and data science perspectives. Results reveal that deep ensembles outperform other probabilistic models regarding negative log‐likelihood (NLL), while random forest exhibits the lowest root mean square error (RMSE). Notably, the prediction accuracy of case‐hardened steels is negatively affected by insufficient material properties definitions, while stainless steels demonstrate the best performance compared to other steel types.

Review on Fatigue of Additive Manufactured Metallic Alloys: Microstructure, Performance, Enhancement, and Assessment Methods.

Additive manufacturing (AM), which is a process of building objects in a layer-upon-layer fashion from designed models, has received unprecedented attention from research and industry because it offers outstanding merits of flexibility, customization, reduced buy-to-fly ratio, and cost-effectiveness. However, the fatigue performance of safety-critical industrial components fabricated by AM is still far below that obtained from conventional methods. This review discusses the microstructural heterogeneities, randomly dispersed defects, poor surface quality, and complex residual stress generated during the AM process that can negatively impact the fatigue performance of as-printed parts. The difference in microstructural origin of fatigue failure between conventionally manufactured and printed metals is reviewed with particular attention to the effects of the trans-scale microstructures on AM fatigue failure mechanisms. Various methods for mitigating the fatigue issue, including pre-process, inter-process, and post-process treatments, are illustrated. Empirical, semi-empirical, and microstructure-sensitive models are presented to predict fatigue strength and lifetime. Summary and outlooks for future development of the fatigue performance of AM materials are provided.

Shakedown-reliability based fatigue strength prediction of parts fabricated by directed energy deposition considering the microstructural inhomogeneities

For mechanical components made by additive manufacturing (AM) techniques such as directed energy deposition (DED), micropores and other defects prevalently exist in the microstructure and they significantly reduce the reliability of these parts. To allow the influence of these microstructural features over the load bearing capacity to be considered in the design stage of AM structures, in the present paper we developed a multiscale numerical approach. The goal of this approach is to predict the failure probability of AM structures subjected to time-varied loadings, and it is realized through combining statistical homogenization, shakedown analyses, and reliability methods such as first-order reliability method and Monte Carlo simulation. Based on this strategy, we employed statistics of material parameters obtained from micromechanical models as inputs, implemented numerical tools and applied them to two exemplary structures, the plate with a hole and an aircraft bracket. Through a series of case studies carried out using different methods and under different assumptions of material randomness, the paper confirmed the robustness of the derived results and explained the mechanism of how micropores influence the structural reliability. The method developed in this paper can be a viable means for the design and optimization of metallic and metal matrix composite structures produced by AM techniques.

A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys

Fatigue is a prevalent failure mode in mechanical structures, and the significance of fatigue strength as a process parameter for predicting the life of a material or structure cannot be emphasized enough. Given the high cost and time consumption associated with traditional experimental approaches for determining fatigue strength, which leads to prolonged analysis and design cycles, it is crucial to investigate efficient and precise methods for predicting fatigue strength. In this paper, we present a fatigue strength prediction method that utilizes Bayesian optimization for the random forest regression (RFR) model. During the training of the RFR model, a Bayesian optimization algorithm is employed to optimize the five main parameters of the model, mitigating the issue of overfitting or underfitting. The optimized RFR model demonstrates superior performance and accuracy in comparison to linear regression (LR), artificial neural network (ANN), decision tree regression (DTR), and support vector regression (SVR). This is determined by evaluating the mean absolute error (MAE), root mean square error (RMSE), R-squared (R2), and mean absolute percentage error (MAPE) scores of the feature parameter-fatigue strength dataset. The fatigue strength of unknown ferrous alloys can be predicted with the proposed model, which largely reduces the need to obtain fatigue strength data through traditional fatigue tests and saves fatigue analysis time and test costs.

Effect of natural and artificial defects on multiaxial fatigue of 4140 steel

Despite the prevalence of small defects and multiaxial stress conditions in load-bearing engineering components, their combined effect on fatigue strength is not usually considered in the literature. In this study, the high-cycle fatigue behavior of 4140 steel with its inherent non-metallic inclusions, and with an artificial surface microhole, is investigated under axial/torsional loading. The multiaxial fatigue tests were performed under in-phase and out-ot-phase loading, employing different ratios between the shear and axial stress amplitudes. Microscopy of failed specimens indicated a tensile-dominated crack formation mechanism in most of the tests. Analysis of the test data using a area-based critical plane criterion showed that predictions of fatigue strength and crack direction can be made with accuracy sufficient for engineering calculations.

Machine learning-assisted extreme value statistics of anomalies in AlSi10Mg manufactured by L-PBF for robust fatigue strength predictions

Traditional Extreme Value Statistics (EVS) applied to block maxima sampled anomalies of components produced by Laser-Powder Bed Fusion may produce important inaccuracies in the estimated characteristic defects. In fact, the typical presence of multiple defect types may significantly affect the fitted maxima distributions obtained from different sampling volumes. In this work, we show how the limitations of traditional EVS can be overcome by applying supervised machine learning (ML) algorithms to classify the defects before estimating the maxima distributions for each defect type. The ML-assisted EVS provided maxima distributions unaffected by the different sampling volumes. The obtained maxima distribution lead to robust estimates of exceedance curves and finally, by employing the Shiozawa curve, to robust fatigue strength predictions.

Fatigue life of 3D-printed porous titanium dental implants predicted by validated finite element simulations.

Introduction: Porous dental implants represent a promising strategy to reduce failure rate by favoring osseointegration or delivering drugs locally. Incorporating porous features weakens the mechanical capacity of an implant, but sufficient fatigue strength must be ensured as regulated in the ISO 14801 standard. Experimental fatigue testing is a costly and time-intensive part of the implant development process that could be accelerated with validated computer simulations. This study aimed at developing, calibrating, and validating a numerical workflow to predict fatigue strength on six porous configurations of a simplified implant geometry. Methods: Mechanical testing was performed on 3D-printed titanium samples to establish a direct link between endurance limit (i.e., infinite fatigue life) and monotonic load to failure, and a finite element model was developed and calibrated to predict the latter. The tool was then validated by predicting the fatigue life of a given porous configuration. Results: The normalized endurance limit (10% of the ultimate load) was the same for all six porous designs, indicating that monotonic testing was a good surrogate for endurance limit. The geometry input of the simulations influenced greatly their accuracy. Utilizing the as-designed model resulted in the highest prediction error (23%) and low correlation between the estimated and experimental loads to failure (R2 = 0.65). The prediction error was smaller when utilizing specimen geometry based on micro computed tomography scans (14%) or design models adjusted to match the printed porosity (8%). Discussion: The validated numerical workflow presented in this study could therefore be used to quantitatively predict the fatigue life of a porous implant, provided that the effect of manufacturing on implant geometry is accounted for.

Fatigue strength and life prediction of lower limb venous stents under three-stage loading conditions

After the implantation of lower limb artery stents, the complex loading conditions imposed on the limb can lead to fatigue failure, which may induce inflammation and restenosis. To investigate the effect of multi-axial loading conditions on the fatigue performance of stents, five stents, namely APsolute Pro (APbott Vascular, USA), Complete SE (Medtronic, USA), Protégé EverFlex (PE3, USA), Pulsar-35 (Biotronik, Germany), and E-luminexx-B (Bard, USA), were analyzed based on the finite element method (FEM). Besides, their fatigue strength was determined under three levels of loading conditions, including tension-bending-torsion and compression-bending-torsion. Based on that, the fatigue life of these stents was predicted. The results showed that based on the nominal stress method, tension-bending-torsion loading had a more significant impact on the fatigue life of stents than compression-bending-torsion loading. Besides, two different types of initial cracks were analyzed by the fracture mechanics method. The results suggested that both the initial crack and the external load were the main causes of stent fatigue fractures. Compared with the loading nature, the influence of the initial crack on stent fatigue life was more significant. Under the same loading condition, the APsolute Pro stent had the longest fatigue life, while the E-luminexx-B stent had the shortest. Moreover, the mechanism of stent fatigue failure was revealed by exploring the fatigue performance and life prediction of stents under complex loading conditions. These findings have important implications for improving the structural design of stents and their clinical selection.

High-temperature fatigue strength of grain boundaries with different misorientations in nickel-based superalloy bicrystals

Nickel-based single-crystal superalloys are widely used in the manufacture of aeroengine turbine vanes for their excellent high-temperature performance. Low-angle grain boundaries (LAGBs) will be generated inevitably during their manufacture, which are often characterized by grain boundary misorientation (GBM) and will weaken the mechanical properties of superalloys. However, the relationship between GBM and the fatigue properties of superalloys at elevated temperatures has seldom been investigated due to the difficulty in the sample preparation and experiment process. Based on six kinds of bicrystals with different tilt LAGBs made by a second-generation single-crystal superalloy, the effects of misorientation on the grain boundary microstructure and fatigue properties (980 °C) of superalloys were studied systematically in this work. It is found that, with the increase of GBM, the GB precipitates combined with the cast micropores increase monotonically, accordingly both the fatigue life and fatigue strength decrease successively. Fatigue fracture observations show that the cracks of all the bicrystals initiated from the cast micropores at GBs, and then propagated along the GBs. Therefore, the coupling effect of cast micropores and GBM on the fatigue damage mechanisms of the bicrystals are evaluated according to their hindering degrees on the piled-up dislocations. Combining with a hysteresis energy model, a quantitative fatigue strength prediction model of superalloys is established and is well verified by abundant experimental data. This study could provide guidance for fatigue performance prediction and structural design of superalloys.

Prediction of high cycle fatigue strength for additive manufactured metals by defects incorporated crystal plasticity modeling

The durability assessment of additive manufacturing (AM) components is an open issue due to the large scatter in their high cycle fatigue performance. The intrinsic defects inherited from AM processes, especially the irregular defects, pose great challenge to numerical modeling of AM metals and prediction of their fatigue behavior. In this paper, a computational method is proposed to predict the high cycle fatigue (HCF) properties of AlSi10Mg alloy fabricated by laser powder bed fusion (LPBF). A virtual laboratory framework including model generation, crystal plasticity calculation and fatigue performance evaluation is established. In the framework, a representative volume element (RVE) model integrating the polycrystal matrix and defects is developed; the defects are reconstructed based on X-ray Computed Tomography (XCT) measurement and then incorporated into the synthetic polycrystal matrix as the second phase; a crystal plasticity (CP) model using a FFT solver is employed to calculate the mechanical response under cyclic loading; finally, the fatigue strength is determined by using the staircase method. The proposed method well predicts the varied fatigue strengths of specimens containing different defects.

Fatigue properties and fatigue strength prediction of 439 ferritic stainless steel

As one of the important steel, 439 stainless steel has been widely used in automobile, construction and other fields, but the studies of its high cycle fatigue properties are not enough. In this paper, the tensile and high cycle fatigue properties of three 439 stainless steels coming from different suppliers were compared. The tensile and high cycle fatigue at room temperature, 150 °C, 350 °C and 550 °C were evaluated. The results show that the tensile properties of the studied steels are very close to each other, but their fatigue properties are different, and the fatigue strength is inversely related to the crack resistance. With the increase of temperature, carbide precipitation and solute atom diffusion affect the linear trend of fatigue strength decline of ferritic stainless steel. The weakening of ferrite grain boundary is an important reason for the decline of fatigue strength in high temperature. Based on the cleavage surface in the fatigue crack source, the fatigue strength can be predicted quantitatively.