Fatigue Life Estimation Research Articles

Fatigue Behavior Analysis and Life Prediction of Overhead Conductors Subject to Narrowband Vibration

This paper aims to present a contribution to the study of the effect of random vibration on the useful life of overhead conductors. This study compares two approaches based on an acquired vibration signal. The first one is the time-domain approach (based on rainflow cycle counting), while the second one is the frequency-domain approach (based on power spectral density moments). Laboratory simulation of aeolian vibration was used in finding the fatigue characteristics of a ACSR 397,5MCM – IBIS conductor. The S-N diagram was generated by performing 9 fatigue tests under constant amplitude conditions and 13 fatigue tests in narrowband excitation conditions. The reported tests were performed to assess the effect of random loading on fatigue life considering the two approaches for estimating fatigue life. A good correlation was found between the estimated fatigue lives using the time-domain and frequency-domain approaches and the experimental data.

Modeling fatigue life and hydrogen embrittlement of bcc steel with unified mechanics theory

The fatigue life estimation of metals operating in hydrogen-rich environments such as hydrogen pipelines, hydrogen-burning internal combustion engines, etc. is important. Studies in the past 40 years have shown that the diffusion of hydrogen into steel and other metals causes various chemical reactions, hydrogen-material interactions, and microstructural changes. That leads to hydrogen embrittlement (HE) and other types of hydrogen damage mechanisms including hydrogen environmentally assisted cracking (HEAC). Hydrogen embrittlement mechanisms, such as hydrogen-enhanced localized plasticity (HELP) and hydrogen-enhanced decohesion (HEDE) can have synergetic effects in steel depending on the hydrogen concentration level. At concentrations above and below the critical hydrogen concentration, HEDE and HELP dominate the embrittlement process, respectively. Different HE mechanisms result in distinctly different fracture modes, both ductile and fully brittle. The ultrasonic vibration fatigue life of bcc steel with a ferrite-pearlite microstructure pre-charged with hydrogen at different concentrations is studied. Modeling is based on the unified mechanics theory (UMT), which does not need any empirical dissipation/degradation potential function or an empirical void evolution function. However, the UMT does require analytical derivation of the thermodynamic fundamental equation of the material, which is used to calculate the thermodynamic state index (TSI) of the material. The UMT is ab-initio unification of the second law of thermodynamics and the universal laws of motion of Newton [1]. Dissipation/degradation evolution is governed by Boltzmann's second law of thermodynamics entropy formulation. The original contribution of this paper is the derivation of the thermodynamic fundamental equation of pre-hydrogen embrittled bcc steel subjected to ultrasonic very high cycle fatigue and the numerical simulations of fatigue life estimation using the proposed novel model. The synergetic interaction of hydrogen embrittlement mechanisms in steel and other metallic materials, i.e., HELP and HEDE at different hydrogen concentrations (HELP + HEDE model) is also studied, reviewed, and applied. The synergetic effects between ultrasonic vibration fatigue life and synergistically active hydrogen embrittlement mechanisms in low carbon bcc steel (S355J2+N, equivalent to ASTM A656), according to the HELP + HEDE model for HE, is modeled for the first time using UMT and also thoroughly discussed.

An energy‐based approach for uniaxial fatigue life estimation under asymmetric cyclic loading

AbstractEnergy parameter formed by multiplying the maximum strain range and the peak stress has the problem that one value may correspond to multiple fatigue damage states. For this issue, a new energy parameter suitable for tension–compression is proposed, which includes an improved strain‐energy model and a strain‐energy ratio. The improved strain‐energy model deduced by judging the volume change of the micro‐element has positive and negative function which can use to distinguish tensile and compressive deformation. The strain‐energy ratio is established to characterize the alternate tensile and compressive deformation under asymmetric cyclic loading. Then, applying fatigue test data, a modeling approach for fatigue life estimation based on the strain‐energy ratio is proposed, and three example models are also exhibited. Finally, according to the example model, the case of a cantilever beam under asymmetric cycle loading is carried out. The results illustrate the feasibility of the improved energy parameter in characterizing tension–compression state and the applicability of the strain‐energy ratio‐based model in fatigue analysis.

On the mode I and mode II fatigue delamination growth of CFRPs modified by electrospun Bis-maleimide resin

In the current work, fatigue life estimation for delamination growth of modified unidirectional (UD) carbon fiber reinforced plastics (CFRPs) with Bis-maleimide (BMI) resin under Mode I and Mode II loading conditions is assessed. More precisely, BMI resin containing or not four layered (4L) graphene nanoplatelets (GNPs) at the amount of 1 wt% was integrated locally into the mid-thickness area of high performance CFRPs by solution electrospinning process (SEP) technique. After manufacturing process, reference and modified CFRPs with (a) BMI and (b) BMI containing GNPs were used for fatigue onset and propagation tests. All double cantilever beam (DCB) and end-notched flexure (ENF) samples were both tested under displacement control conditions. During the tests, a travelling microscope was utilized to measure the crack length at the end of precise loading cycles while load and delamination length were recorded at the same time. Based on experimental results, BMI modified CFRPs presented the best resistance to delamination under Mode I fatigue loading conditions, while the BMI and GNP modified CFRPs exhibited the best performance under Mode II fatigue loading conditions. Taking into consideration of the obtained results, this approach can be considered a viable solution for increasing the delamination resistance of CFRPs.

Fatigue & Fracture of Engineering Materials & Structures Virtual Special Issue: Data science and machine learning for fatigue and fracture assessment

<scp>Fatigue &amp; Fracture of Engineering Materials &amp; Structures Virtual Special Issue</scp>: Data science and machine learning for fatigue and fracture assessment

Study of the Plastic Behavior of Rough Bearing Surfaces Using a Half-Space Contact Model and the Fatigue Life Estimation According to the Fatemi–Socie Model

A multiscale approach for the fatigue life estimation of rolling bearings is presented in this paper and applied to inner rings of cylindrical roller bearings of the type NU208. The forces acting in the rolling contact are determined from system-oriented modeling at the macro level. The microscale contact simulations are carried out in a half-space contact model. The stresses on the inner ring are determined and used in the local fatigue approach, according to Fatemi–Socie, for fatigue life estimation. Four surface variants were investigated, one ideally smooth surface and three real (rough) surfaces. The three rough surface variants used different finishing processes: fine ground, hard turned, and rough ground were produced. A load case with a maximum pressure of 2.4 GPa in the roller-inner ring contact was investigated. In addition to the fatigue life estimation, the plasticity behavior (surface topography, resulting contact pressure, and residual stress) of the three manufactured surfaces stood in the focus of the work. As the comparison between experimental and simulated results confirms, good predictions can be made with the simulation model.

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem.In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren–Miner index) in composite materials by processing the signals of Lamb Waves – a non-destructive SHM technique – with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude.The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an online learning context where streams of data are fed continuously to the model, avoids repeated training—essential for reliable real-time, continuous monitoring.

Cooling rate effects on fatigue life prediction using hardness measurements for in-service steel patch-welds with and without TIG dressing treatment

In-service patch welding is commonly used to repair corroded or damaged steel infrastructures. However, the in-service conditions may unfavorably affect the long-term mechanical and structural integrity. The present research studies the fatigue behavior of fillet welded patch repairs with and without TIG-dressing (TD). Six types of panels were constructed under various in-service cooling conditions. Specimens cut from the main panels were subjected to high-cycle fatigue, hardness, and metallography tests. The fatigue life of the specimens was also predicted using a novel numerical approach. The model incorporated micro-hardness test data from the weldment section. Macrographic and microhardness data were used for the zoning of the weldment section. The harness data were then employed to define the post-weld mechanical properties of the materials in different weldment zones. The fatigue life was estimated using the microhardness data, stress and strain data obtained from the finite element analysis of the specimen, a UVARM subroutine, and several multiaxial fatigue life prediction models. The numerical fatigue life estimations showed reasonable agreement with the experimental data. Both experimental and numerical modeling results proved significant improvements in the fatigue life of the TD specimens. Regardless of the welding conditions/treatments, the best fatigue life estimates were provided by the BMM, SWT, Glinka, and KBM criteria, respectively.

Statistical Inference of Equivalent Initial Flaw Size Distribution for Fatigue Analysis of an Anisotropic Material

A novel methodology for the fatigue life uncertainty quantification of anisotropic structures is presented in this work. The concept of the equivalent initial flaw size distribution (EIFSD) is employed to overcome the difficulties in small cracks detection and fatigue prediction. This EIFSD concept is combined with the dual boundary element method (DBEM) to provide an efficient methodology for modelling the fatigue crack growth. Bayesian inference is used to infer the EIFSD based on inspection data from the routine maintenance of the structure, simulated with the DBEM. A large amount of DBEM simulations were required for the Bayesian inference. Therefore, surrogate models are used as part of the inference to further improve computational efficiency. A numerical example featuring an anisotropic plate is investigated for demonstrating the proposed methodology. When considering a low level of uncertainty in the crack propagation parameters, an error of 0.12% was found between the estimated fatigue life obtained using the proposed method compared to actual fatigue life, and only 0.35% error when considering high level of uncertainty. The application of the estimated fatigue life can be used to determine an appropriate inspection interval for aircraft maintenance.

A multiaxial probabilistic fatigue life prediction method for nickel‐based single crystal turbine blade considering mean stress correction

AbstractGas turbine blades normally operate under high‐temperatures, extreme pressure, and high‐speed conditions, which leads to complex failure mechanisms and difficulties in fatigue life assessment. Moreover, due to the randomness of manufacturing errors, working loads, and material properties, the low cycle fatigue (LCF) life normally presents unavoidable stochastic behavior. Therefore, accurate and efficient fatigue life prediction is critical for the design of gas turbine blades. Accordingly, this paper analyzes the effects of mean stress, anisotropy, and uncertainties on the LCF life of nickel‐based single‐crystal turbine blades. The modified Hill yield criterion is employed to deal with the anisotropic damage characteristics of nickel‐based single‐crystal superalloy (NSCS) under a multiaxial stress state. Meanwhile, the effects of the temperature and crystal orientation are introduced into the walker mean stress correction term. A multiaxial LCF life prediction model is developed based on the energy criterion‐based fatigue life estimation method. Moreover, multi‐source uncertainties originating from rotation speeds, material properties, and temperature are analyzed and quantified. Comparison between the predicted and tested life indicates that the proposed method produces good accuracy and robustness, which can provide a reference for the reliability design of NSCS turbine blades.

Stiffness Degradation of PolyJet Printed Nano Embedded Configuration with Digital Material

Introduction: Polymer-based additive manufacturing (AM) parts are used in a variety of engineering applications in the automotive, aerospace, and energy industries. However, AM printed parts are a new class of materials and the structural performance of these materials is not yet fully understood, and research on the exact performance of PolyJet printed parts and related digital materials under fatigue loading is still minimal. The present study provides insight into the fatigue damage state, cyclic performance, and comparison between pristine (baseline) and Carbon Nano Fibers (CNFs) modified embedded test coupons formed using tailored digital designs and printed with digital polypropylene from PolyJet printing. Methods: The investigations employed 3-dimensional (3D) test coupons formed using PolyJet printing. The nanomodified coupons were formed using digital design configurations for tailored deposition locations that allowed the integration in a 2-step process. The limitations of the conventional ASTM D638 2-dimensional (2D) test coupon for AM-treated materials caused by the process are eliminated by the homogeneous 3D test coupon used in this work. An analytical model of the accumulated damage state based on stiffness deterioration under cyclic loading is presented along with an empirical model of effective elastic modulus based on the analysis of fatigue data. Additionally, the predicted model and the actual damage accumulation due to cyclic loading are compared. Results: With a high correlation coefficient, R2 of 0.9971 for the baseline and 0.9885 for the CNFs embedded configuration, it is observed that the linear function model can estimate fatigue life with an agreement to experimental results and can be used to extrapolate for other stress levels. CNFs modified resin embedded configuration showed lower fatigue life as compared to baseline. This reduction can be attributed to the stress concentration at the interfaces. Conclusion: This work fills the gap in the literature by characterizing the fatigue life, and cyclic performance of PolyJet printed parts and nano-modified systems with digital material.

Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: A data-driven approach

The fatigue life prediction of welded joints with different specifications under different conditions was a challenging issue due to the quite complex influence. Specifically, the current fatigue life prediction methods lacked comprehensive analysis of multiple influence factors and reasonable incorporation of physical models. So, the analysis of factors influencing the fatigue life of welded joints and the fatigue life prediction were critical to the safety and reliability of engineering structures. In this study, a prediction approach for fatigue performance was proposed based on influence factor analysis using data-driven methods. The fatigue performance dataset was processed via physical models to realize the physics-based analysis of the factors affecting fatigue performance. The weights of the physics-informed influence factors on the fatigue life were analyzed using the extreme gradient boosting (XGBoost) algorithm and verified by cross-validation. The prediction results extracted from the SN curves predicted by the deep convolutional neural network (DCNN) model incorporating the weight analysis of influence factors exhibited better accuracy and stability than direct prediction and other existing prediction models. Because of the advantages of DCNN in avoiding over fitting and local optimization, the proposed approach can better describe and estimate the fatigue performance of welded structures.

Multiaxial fatigue life estimation based on weight-averaged maximum damage plane under variable amplitude loading

An innovative critical plane determination approach with weight-averaged largest fatigue damage is proposed, in which the material failure modes can be considered. If material exhibits shear cracking behavior, a strain-based critical plane model with shear failure mode is selected to evaluate the weight function. Otherwise, other one with tensile failure mode is adopted. According to the proposed critical plane, a multiaxial fatigue lifetime estimation methodology is established for evaluating fatigue life. And, six kinds of materials are employed to validate the validity of presented methodology. The validation results reveal the presented methodology can estimate the orientation angles of failure plane accurately and supply satisfactory fatigue lifetime estimations for both shear and tensile failure mode materials. Furthermore, the proposed critical plane framework can be extended to be utilized with stress-based fatigue criteria, and prediction results show a good agreement with experimental data by another two materials.

Implementation of the Peak Stress Method for the automated FEA-assisted design of aluminium welded joints subjected to constant amplitude multiaxial fatigue loads

The Peak Stress Method (PSM) is a FE-oriented local approach to the fatigue strength assessment of welded structures subjected to fatigue loading. Starting from the peak stresses calculated at the V-notch tip nodes defining weld toes or the weld roots, the PSM defines an equivalent peak stress which allows to estimate the fatigue failure location and fatigue lifetime of welded structures, in compliance with appropriate fatigue design curves. An Ansys® Mechanical extension has been developed to achieve full automated implementation of the tasks and calculations necessary to apply the PSM to welded structures. The tool allows to identify and analyse all the V-notch tip edges of the structure and perform fatigue life estimation on each analysed node. As an output, fatigue life results can be visualized through dedicated tabular data, graphs and contour results generated over the edges of the model. In this work, common-to-complex 3D geometries taken from the literature and related to aluminium alloys welded joints subjected to uniaxial as well as multiaxial fatigue loads have been analysed by comparing two design approaches: (i) manual application of the PSM, (ii) automated implementation of the PSM. The tool developed in Ansys® Mechanical allows to significantly contain the time and effort required to analyse welded structures according to the PSM.

An energy-based fatigue life estimation and optimization of an electric mining dump truck welded frame

An energy-based fatigue life estimation and optimization of an electric mining dump truck welded frame

A probability density model of stress amplitude under bimodal vibration response

In order to improve the fatigue life prediction of engineering structures under bimodal vibration response stress power spectra, a new probability density model of stress amplitude is proposed in this paper, which is composed of an exponential distribution and a two-parameter Weibull distribution. Under ideal bimodal response stress power spectra with different bandwidths, the connection between the first three order moments of rain-flow amplitude distributions and the spectral parameters is found. Then, according to the equations between the first three order moments of stress amplitude distributions of the model and those of rain-flow amplitude distributions, combined with the numerical optimization method, the relationship between model parameters and spectral parameters is established. Through comparative studies, the model in this paper leads to more accurate stress amplitude distributions and fatigue damage, which is very close to those obtained from time domain approach. Through the stochastic vibration fatigue test of aluminum alloy notched specimens, the high accuracy of the proposed model for estimating fatigue life is further verified.

A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace

Within this work, a unified physics-informed machine learning (PIML) framework is proposed for notch fatigue life prediction and key feature parameters identification of aerospace polycrystalline alloys. The unified PIML approach reaches a capable accuracy in notch fatigue life prediction of the polycrystalline alloys under a wide range of notch geometries and loading conditions compared with physics-based life models. In addition, the global sensitivity analysis method is used to accurately identify the key feature parameters that affect the notch fatigue life. The nominal maximum stress and unnotched specimen reference life are recognized as highly relevant features for notch fatigue life whereas the notch root radius is the key parameter among the notch parameters. Finally, life uncertainty induced by the notch geometry parameters is performed by using the proposed PIML model based on Latin hypercube sampling, which accomplishes the probabilistic estimation of notch fatigue life well. In practical applications, the PIML framework can be applied efficiently to notch fatigue life prediction of a new material dataset due to its powerful generalization ability without additional parameter fitting or finite element analysis. The well-trained PIML model and the uncertainty assessment method provide potential tools for notch fatigue evaluation under complex loading conditions.

Influence of PSD Estimation Parameters on Fatigue Life Prediction in Spectral Method.

The paper analyzes in detail the influence of the Welch method on the estimation of fatigue life in the frequency domain. The aim is to examine how the setting of parameters, such as window size, weighting and overlap, affects the calculation of power spectral density (PSD) and thus lifetime. The paper uses knowledge from the theory of signal processing, statistics and fatigue life estimation in the frequency domain. In order to demonstrate the general validity of the conclusions, four different reference PSD spectra are generated and examined. The spectra are converted to time histories using an inverse fast Fourier transform and then used as input (measured) data for the Welch method. The PSD thus obtained are subsequently subjected to lifetime estimation using the Dirlik and Tovo-Benasciutti models. Based on the obtained results, it was found that the best configuration of the parameters of the Welch method for fatigue calculations is the selection of the smallest possible time window (maximum 60% of the observation length) and the largest possible overlap (minimum 70% of the window size).

Frequency analysis of random fatigue: Setup for an experimental study

AbstractThe frequency‐domain approach to fatigue life estimation in random loading has been largely investigated due to its computational advantages, and several methods for the frequency translation of the most common time‐domain methods have been proposed. Between the most known frequency methods, there are Bendat's method, valid for narrow‐band signals, and Dirlik's formula, which ifis considered the best result for wide‐band signals. However, a great part of the frequency methods takes the rainflow count as a reference time‐domain method and uses the rainflow damage computation as the exact value to emulate. Therefore, very few experimental data for the fatigue life of mechanical components subject to random loads are available in the literature. This work presents the setup for a series of experimental tests for specimens subjected to random loads, aiming at achieving experimental data to compare with the results provided by frequency methods. After a brief description of the materials used for the setup, the two‐step test concept is described: first, the specimen will be subjected to random loads obtained by a certain power spectral density for an amount of time which should nominally cause a 30% of damage; then, the fatigue test will be ended on a resonance testing machine to compute the actual residual fatigue life of the specimen; this two‐step testing also allows to reduce the time requested for the tests. The test bench developed for the experimental investigation is described in the paper, together with the results of some preliminary tests, aimed at verifying the feasibility of the conceived procedure.

One-stage extraction four-point algorithm for rainflow cycle counting in fatigue life estimation

PurposeIn order to improve the computation efficiency of the four-point rainflow algorithm, a one-stage extraction four-point rainflow algorithm is proposed based on a novel data preprocessing method.Design/methodology/approachIn this new algorithm, the procedure of cycle counting is simplified by introducing the data preprocessing method. The high efficiency of new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.FindingsAccording to the data preprocessing method, in the process of cycle extraction, all equivalent cycles can be extracted at just one stage instead of two stages in the four-point rainflow algorithm, where the cycle extraction has to be performed from the doubled residue. Besides, there are no residues in the new algorithm. The extensive numerical simulation results demonstrate that the accuracy of new algorithm is the same as that of the four-point rainflow algorithm. Moreover, a comparative study based on a long input data sequence shows that the computation efficiency of the new algorithm is 42% higher than that of the four-point rainflow algorithm.Originality/valueThis merit of new algorithm makes it preferable in some application scenarios where fatigue life estimation needs to be accomplished online based on massive measured data. And it may attribute to preprocessing of input data sequence before data processing, which provides beneficial guidance to improve the efficiency of existing algorithms.