Fatigue Life Estimation Research Articles

Experimental results for fatigue damage growth in laminated fiber reinforced cross-ply laminates

In this paper, experimental results for fatigue damage growth behavior of a single-edge notched cross-ply laminate obtained using Digital Image Correlation (DIC), thermography, and supplemented by prior X-ray computed microtomography (mCT) data, are presented. These experimental results are used to develop a novel multi-scale fatigue life estimation model, whose details are reported elsewhere [1].

Crack Propagation Tests for Load Sequences Developed Using Different Flight Parameters of a Trainer Aircraft

Abstract Military aircraft are subjected to highly variable and unpredictable loads due to diverse mission profiles, armament configurations, and individual piloting styles. This variability complicates the definition of precise load spectra, particularly in cases where data loss occurs due to Flight Data Recorder (FDR) malfunctions or data mishandling. This paper investigates the use of different flight parameters, such as load factor (nz ), barometric height (Hb ), and horizontal velocity (Vp ), to define load sequences for the PZL-130 “Orlik” TC-II military trainer aircraft. These sequences were then used to evaluate crack propagation using Compact Tension (CT) specimens. The results show that the incorporation of additional flight parameters improves the accuracy of crack propagation predictions when compared to direct strain measurements. This study highlights the potential of using available flight data to develop reliable load spectra for fatigue life estimation in military aircraft, even when direct load measurements are not financially feasible.

Stress fracture of bone under physiological multiaxial cyclic loading: Activity-based predictive models

While it is known that excessive accumulation of fatigue damage from daily activities contributes to fracture, a model of bone failure under physiologically relevant multiaxial cyclic loading needs to be developed in order to develop effective management strategies for stress or fatigue fractures. The role of strain-induced damage from repetitive loading is a strong candidate for such a model, as cycles of mechanical loading leading to failure can be measured directly. However, this approach has been limited by the restrictions of uniaxial loading models, which often overestimates the fatigue life of bone and suggests that bone will only break well beyond the realistic limits of exercise. To address this gap and develop a physiologically relevant model, our study leverages the power of four commonly used engineering failure criteria as a model for multiaxial loading using a cohort of human tibiae from cadaveric donors (age range 21–85 years old). Four failure criteria (Von Mises, Tsai-Wu, Findley critical plane, and maximum shear strain) were found to be effective in vitro models of tibial fracture when age groups of donors were combined (r2 > 0.84) and stratified (younger: 21–52-years-old versus older: 57–85-years-old) (r2 > 0.83) (p < 0.001). Each failure criterion displayed distinctly lower fatigue curves for the older age group. The maximum shear strain model was used to determine the efficacy of this approach to predict fatigue fractures in humans using published in vivo data from human volunteers. Consistent with in vivo observations in general population, the model demonstrated failure at 5000 to 200,000 loading cycles depending on activities such as jumping, sprinting, and walking, with a 3-fold reduction of fatigue life in older donors. These findings dramatically improve estimates of fatigue life under repetitive loading and demonstrate how age-related changes in bone significantly increase its propensity for fatigue-induced fractures.

A generalized machine learning framework to estimate fatigue life across materials with minimal data

In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. An extreme gradient boosting algorithm-based ML framework encompassing Synthetic Minority Over-sampling TEchnique (SMOTE), categorical data encoding, and external loop cross-validation is developed to evaluate the fatigue life across materials. The influence of different training strategies based on materials, input features, encoding method, and data standardization on the model performance is explored. Additionally, the importance of anti-data-leakage and anti-overfitting measures over the ML model performance is addressed. The result shows that the data-leakage-free, external loop cross-validated model can estimate the fatigue life of selective epoxy polymers and metal alloys with an average R2 of 0.71 ± 0.06 using a mere 12 to 27 experimental data points per material category. Whereas the model trained with data-leakage and overfitting results in high R2 of 0.9.

Method for assessing the durability of structures under stationary and non-stationary random loading using variational mode decomposition (VMD)

In the era of digital transformation, most devices are equipped with a variety of sensors, which provide information to be used in developing intelligent models for predicting the fatigue life. The development of digital models of real objects for fatigue life estimation faces the lack of algorithms for performing such an estimation. The problem arises in the adaptive and automatic analysis of signals from real sensors installed on the object and their subsequent processing to estimate the fatigue life of the object. We propose a new method for assessing the fatigue life of structures based on application of the adaptive method of variational mode decomposition to signals gained from sensors, including non-stationary ones. Variational mode decomposition involves decomposition of an initial complex random process into simpler random processes (modes) that are stationary and narrowband. An original method of summarizing damage resulted from the action of the modes obtained as a result of the decomposition is used. The developed method can be used as an algorithmic support for the generation of digital doubles of the residual lifetimes of natural products. The proposed method is compared with the «rainflow» method in the time domain. Different realizations of random stationary and non-stationary signals with different bandwidths are compared numerically. In addition, the traditional frequency methods of Dirlik and Benasciutti have been compared with the «rainflow» method. The analysis showed good results for the proposed method, i.e., the error was smaller compared to traditional frequency methods, especially for non-stationary processes.

Design of an Overhead Crane in Steel, Aluminium and Composite Material Using the Prestress Method

The present research describes a design of an overhead crane using different materials with a prestress method, which corresponds to an external compression force with the aim of reducing the displacement of the beam due to the external load. This study concerns a bridge crane with a span length of 10 m, with a payload equal to 20,000 N and an estimated fatigue life of 50,000 cycles. Three different materials are studied: steel S355JR, aluminium alloy 6061-T6 and carbon fibre-reinforced polymer (CFRP). These materials are analysed with and without the contribution of the prestress method. In reference to the prestressed steel solution (which has a weight equal to 79% of the non-prestressed configuration), this study designed an aluminium solution that is 50.7% of the weight of the steel one and a composite solution that is always 20.3% of the steel configuration. In combining the methods, i.e., the materials and prestress, compared to the non-prestressed steel solution with a weight evaluated to be 758 kg, the weight of the aluminium configuration is equal to 40% of the traditional one, and the composite value is reduced to 16%, with a weight of 121 kg.

Modeling Stress Concentration Factors for Fatigue Design of KT-Joints Subjected to In-Plane Bending Loads Using Artificial Neural Networks

Stress concentration factor (SCF) is an important parameter for the fatigue design of offshore joints. There are many empirical equations for quick estimation of SCF in tubular joints, based on experimental and numerical investigations. However, most of these equations apply at the crown and saddle points only, even though the maximum SCF may not always occur at these points, resulting in overestimated fatigue life. As the maximum SCF location varies due to multiplanar loads, damage, or reinforcement of joints, its location and magnitude are critical for a realistic fatigue life estimation. However, conventional statistical tools cannot approximate the complex behavior of SCF around the brace axis. On the other hand, artificial neural networks (ANN) can efficiently approximate complex phenomena. This study uses ANN to develop empirical models for determining SCF around the weld toe of KT-joints subjected to in-plane bending (IPB) loads. Eighteen hundred and fifty-eight (1858) designs were simulated using finite element analyses to generate data for training the ANN. Two IPB load conditions were focused on, and empirical equations were proposed for SCF around the chord side of the central brace-chord interface. These equations approximate maximum SCF with less than 5% error. This methodology applies to other joints and load configurations also.

Estimation of residual fatigue life of high-strength steel welded joints based on diffuse ultrasound

Fatigue significantly affects the durability and capacity of high-strength steel welded joints (WJs) in engineering. Despite the capability of ultrasonic-based methods to issue early warnings for fatigue crack initiation and effectively monitor crack expansion, estimating residual life before surface cracks poses a considerable challenge. This study introduces a novel method that relies on the correlation coefficient of the ultrasonic energy density, α, to estimate the residual life of WJs before surface cracks become evident. The fatigue damage of 20 butt- and cross-WJs of Q690qENH and Q960E, subjected to different fatigue stresses, was monitored using α. The results revealed a cubic polynomial relationship between α and the number of fatigue cycles when a sample experienced early fatigue damage. Consequently, we introduced an α-N early fatigue damage monitoring curve. The zero value of its second derivative functioned as an early warning indicator, and the corresponding life of the sample was recorded as ζ. Finally, a warning of 20%–50% of the fatigue life for WJs made of two high-strength steels was attainable. A linear relationship between ζ and fatigue stress, denoted as the ζ-S curve, facilitated residual life estimation before surface cracks developed. The blind test produced an approximately ±10% error with 4 samples.

Prediction of fatigue life of a bolted joint in railway steel arch bridge using multiaxial fatigue criteria

Fatigue represents a critical condition for infrastructure subjected to repeated cyclic loads, such as steel railway bridges. The literature offers several methods to estimate fatigue life, consisting of three main phases: cycle counting, fatigue damage criterion, and damage accumulation criterion. Applying S-N curves might not be conservative in the case of railway bridges subjected to traffic because the stress-time history is complex and cannot be reduced to a sinusoidal history. Additionally, the presence of a multiaxial stress state must be considered. Therefore, this work compares eight multiaxial fatigue damage criteria for evaluating fatigue life in a scenario between high and low-cycle fatigue. Specifically, the authors considered four low-cycle fatigue criteria, namely Smith–Watson–Topper (SWT), Kandil, Brown and Miller (KBM), Glinka, Fatemi and Socie (FS), and four high-cycle fatigue criteria, Crossland, Basquin and the methods recommended by Eurocode 3 and British Standard. The rainflow counting method in ASTM E1049-85 (2011) and Miner’s rule for damage accumulation were used. The Polcevera railway steel bridge was selected as a case study. A 3D numerical model was developed for this purpose using Midas Civil software, taking into account the material non-linearity of the bridge’s elements. Once the area with the highest stress concentration was identified, a detailed analysis was conducted to estimate the stress and strain time histories induced by train traffic. A sensitivity analysis was conducted after critically comparing the eight methods for predicting fatigue life to assess the impact of traffic parameters, train velocity, axle load, and convoy length on fatigue life. It has been found that the criteria considering axial stress tend to overestimate the number of fatigue cycles to failure compared to the criteria, including the effect of shear stress components.

Correction: Artificial neural networks for fatigue life estimation of glass-carbon/epoxy hybrid composites

Correction: Artificial neural networks for fatigue life estimation of glass-carbon/epoxy hybrid composites

Fatigue and corrosion‐fatigue behavior of the β‐metastable Ti‐5Al‐5Mo‐5V‐3Cr alloy processed by laser powder bed fusion

AbstractWe performed rotating bending tests and axial (tension‐compression) load‐increase and constant amplitude high‐cycle fatigue tests in air and Hanks' balanced salt solution (HBSS) on the β‐metastable titanium alloy Ti‐5Al‐5Mo‐5V‐3Cr, processed by laser powder bed fusion (LPBF‐M), solution‐treated and aged, and shot‐peened. Rotating bending loading in air revealed a strong influence of process‐induced flaws on fatigue endurance. Especially in the high‐cycle fatigue range and the transition region, the stochastic distribution of the flaws and flaw sizes led to a high scatter of the number of cycles to failure. The axial load‐increase tests yielded a good fatigue life estimation, with a negligible difference between air and HBSS. The cyclic deformation behavior in HBSS was also strongly influenced by the local microstructure and defect distribution, and, thus, by crack formation and propagation. Plastic deformation and microcrack growth interact, and their relative amount resulted in different progressions of the plastic strain amplitude over the number of cycles for different specimens. Changes in the free corrosion potential and the corrosion current were highly sensitive indicators for fatigue‐induced damage on the rough surfaces, which was correlated to the microscopic examination, fracture surface features, and the fatigue crack development.

Fatigue assessment of bridges using interacting filtering approach with sub-structured predictor model based on current health

Fatigue estimation for critical structures necessitates comprehensive monitoring, which, in turn, requires dense instrumentation and models that heavily rely on computational resource models that heavily rely on computational resources. However, the fatigue vulnerability of different segments within the infrastructure can be considered to adopt a cost-effective substructure-based monitoring approach, minimizing the need for extensive instrumentation or complex models. However, estimating or instrumenting the substructure boundary adds to the complexity. In addition, conventional fatigue estimation approaches often assume constant structural health, disregarding the unknown current health status of aging structures. To overcome this limitation, a novel substructure-based fatigue life estimation approach is developed, incorporating an interacting ensemble-particle filter that considers the current health status while remaining robust against boundary forces. Numerical experiments validate the proposed approach on a simulated reinforced concrete box girder bridge, utilizing a 3D beam model that accounts for dynamic interaction with vehicles. A parametric analysis investigates the relationships between fatigue damage and factors such as surface roughness, vehicle speed, vehicle weight, and vehicle category, aiming to identify dominant stimuli. The results demonstrate an accurate estimation of health parameters and remaining useful life (RUL). Furthermore, a novel decomposed approach for RUL estimation is developed, enabling the mapping of traffic information to fatigue damage without requiring costly simulations. A case study highlights the practical applicability of the approach, focusing on a reinforced concrete box girder bridge in Himachal Pradesh, India.

Bayesian-informed fatigue life prediction in shallow shell structures with the dual boundary element method

This study introduces an innovative approach for statistically inferring the Equivalent Initial Flaw Size Distribution (EIFSD) in shallow shell structures with the aim of predicting fatigue life. The proposed approach integrates Bayesian inference and surrogate modelling techniques, and utilizes a long crack growth model. The EIFSD is crucial for estimating fatigue life and ensuring structural durability. To showcase the proposed methodology, a numerical example featuring a fuselage window structure under multi-axial loading, employing the Dual Boundary Element Method (DBEM) for crack growth modelling, is presented. Efficiency gains in the inference of the EIFSD are achieved through the development of surrogate models, notably a Co-Kriging model that significantly reduces computational time and outperforms a Kriging model in error reduction. The study explores two assumptions for inspection data sourcing, leading to enhanced methodological effectiveness across varying uncertainty levels. It considers the separate effects of Paris Law constants and loading conditions under increased uncertainty, presenting cases that demonstrate the Co-Kriging model’s high accuracy and computational efficiency. The proposed methodology’s robustness is further validated by assessing the impact of prior distribution quality on Bayesian inference, showing the method’s adaptability to high uncertainty while maintaining a good level of accuracy. This study also demonstrates an approach for fatigue life estimation of structures using the inferred EIFSD. By evaluating the structure up to a critical crack length, the methodology allows for the accurate estimation of fatigue life, which, in turn, facilitates the determination of optimal inspection intervals. Notably, under conditions of high uncertainty, the method showcased a maximum difference of 1.51% in the estimated fatigue life when compared to the true EIFSD, underscoring the technique’s high accuracy and reliability.

Analysis of the Anisotropic Cyclic Material Behavior of EN AW-1050A H24 Derived from Strain-Controlled Testing Using a Clip-On Extensometer and an Optical System

Due to its good conductive properties, unalloyed (pure) aluminum, such as EN AW-1050A H24, finds new fields of application in electromobility. To optimize components, the cyclic material behavior must be understood and described precisely as a foundation of a proper fatigue life estimation. Various cyclic tests were performed to not only derive the cyclic parameters to describe the material but also to find the most suitable procedure to deal with the challenges faced during the experiments. The main point of interest is the comparison between a surface-mounted clip-on extensometer and an optical system both used for strain control in cyclic tests. For the analysis of the anisotropic behavior of EN AW-1050A H24, un-notched flat specimens were extracted from sheet metal lengthways and crossways in respect to the rolling direction. While the cyclic material behavior for specimens of both directions of extraction is characterized by cyclic softening in general, the specimens extracted crossways show a strain-amplitude-dependent cyclic softening with strong strain localization especially at the contact points of the knives of the clip-on extensometer leading to an increased quantity of invalid experiments as well as sudden fractures. In the study, it was possible to show the benefits of a contactless optical strain control system when dealing with very soft metallic materials such as EN AW-1050A H24.

A numerical model for total bending fatigue life estimation of carburized spur gears considering the hardness gradient and residual stress

A numerical model for total bending fatigue life estimation of carburized spur gears considering the hardness gradient and residual stress

Equations for efficient cycle-by-cycle computation of fatigue crack retardation and acceleration due to amplitude changes

The paper presents an efficient methodology for computation of the evolution of the fatigue crack retardation and acceleration effects due to crack closure during variable amplitude loading. The presented approach can lead to a very effective tool for estimation of residual fatigue life of engineering structures. Conventionally, consideration of variable-amplitude loading is either computationally too demanding, such as in finite element modelling, or challenging to unify the strip-yield model with another computational code. The simple analytical formulae available in literature are based on residual stress, which does not describe correctly the delayed retardation effect and in some cases it can be non-conservative. This work presents simple analytical equations describing the development of the crack closure based on the results of the strip-yield model for amplitude changes at the load ratio R = 0.1. The parabolic function enables simple computation of the maximum crack closure occurring after an overload without running any simulation. The equations also enable computation of crack closure behaviour following amplitude changes with the consideration of various cyclic material properties reflected in the tuneable ratio between the monotonic and cyclic plastic zone sizes.

The fatigue behavior of TC17 titanium alloy under different test environments: The effect of saltwater on fatigue failure characteristics and life estimation

The study of fatigue behavior in saltwater for TC17 titanium alloy has become a potential research issue in practical engineering and theoretical scientific research. In this study, it is found from the designed fatigue test that the corrosiveness of the test environment has a significant negative effect on the fatigue life of titanium alloy, and the distributions of fatigue life in 3.5%NaCl solution and 5%NaCl solution are similar. Typical surface failure characteristics and obvious corrosion traces are detected due to the coupling effect of corrosion and fatigue load. Then, electron back scatter diffraction (EBSD) is performed to figure out the change of micro grains from fine grains to coarse grains with the test environment changes from water to NaCl solution, and the strain concentration regions are detected at grain boundary and internal of coarse grains. Finally, the comparison of S-N curve with different models are carried out, Basquin model is proved to be suitable for the S-N curve estimation of TC17, and S-N models for different test environments are established. The micro test results provide a clear understanding of the corrosion fatigue failure behavior, and the newly established model considering the corrosiveness of test environment is novel and essential to the further fatigue study in theoretical research and engineering practice.

Empirical modeling of stress concentration factors using artificial neural networks for fatigue design of tubular T-joint under in-plane and out-of-Plane bending moments

PurposeStress concentration factors (SCFs) are commonly used to assess the fatigue life of tubular T-joints in offshore structures. SCFs are usually estimated from parametric equations derived from experimental data and finite element analysis (FEA). However, these equations provide the SCF at the crown and saddle points of tubular T-joints only, while peak SCF might occur anywhere along the brace. Using the SCF at the crown and saddle can lead to inaccurate hotspot stress and fatigue life estimates. There are no equations available for calculating the SCF along the T-joint's brace axis under in-plane and out-of-plane bending moments.Design/methodology/approachIn this work, parametric equations for estimating SCFs are developed based on the training weights and biases of an artificial neural network (ANN), as ANNs are capable of representing complex correlations. 1,250 finite element simulations for tubular T-joints with varying dimensions subjected to in-plane bending moments and out-of-plane bending moments were conducted to obtain the corresponding SCFs for training the ANN.FindingsThe ANN was subsequently used to obtain equations to calculate the SCFs based on dimensionless parameters (α, β, γ and τ). The equations can predict the SCF around the T-joint's brace axis with an error of less than 8% and a root mean square error (RMSE) of less than 0.05.Originality/valueAccurate SCF estimation for determining the fatigue life of offshore structures reduces the risks associated with fatigue failure while ensuring their durability and dependability. The current study provides a systematic approach for calculating the stress distribution at the weld toe and SCF in T-joints using FEA and ANN, as ANNs are better at approximating complex phenomena than typical data fitting techniques. Having a database of parametric equations enables fast estimation of SCFs, as opposed to costly testing and time-consuming FEA.

Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features

A data-driven approach for predicting the low-cycle fatigue (LCF) life of austenitic stainless steel (AusSS) is proposed to address the limitations of classical fatigue life prediction models. This approach integrates four key characteristics of the AusSS alloys, namely chemical compositions, microstructure, material processing parameters, and experimental parameters, to compile a comprehensive fatigue life prediction dataset. By employing the classification and regression technique, the relationship between various input features and LCF life is analyzed in depth, leading to the eventual identification of eight key features. Subsequently, various machine learning (decision tree, random forest, XGBoost, and support vector regression) and artificial neural network (ANN) are utilized to predict the LCF life using the compiled dataset. Comparative analysis of the results reveals higher prediction accuracy of ANN, with respective R2 and mean squared error values of 0.941 and 0.024, showcasing superior performance over the classical Basquin-Coffin-Manson model. This proposed methodology effectively captures the intricate feature-life relationships in AusSS, potentially reducing experimental time and costs of fatigue life estimation and offering promising applications in alloy design and manufacturing optimization.

Fatigue tests of S690 as-welded and HFMI-treated details with longitudinal and transverse attachments

The complex nature of the fatigue strength of welded steel details, which is influenced by a multitude of factors, can be improved by extending the crack initiation phase of the total fatigue life. This enhancement can be achieved through the use of the High-Frequency Mechanical Impact (HFMI) method. This study discusses the fabrication and laboratory cyclic testing of as-welded and HFMI-treated transversal and longitudinal attachments made from S690QL steel. Assessment of a two-stage model (TSM) for the evaluation of their fatigue life, previously developed by the authors, is also performed. The TSM's estimated fatigue lives of the details under consideration align well with the fatigue test results. Moreover, both the TSM model results and the corresponding fatigue test results demonstrate that the HFMI method significantly prolongs their fatigue life. The fatigue resistance of HFMI-treated details compared to as-welded details increased by 1.67 and 1.91 times for longitudinal and transversal attachments, respectively.