Random Load Fatigue Research Articles

An Equivalent Structural Stress-Based Frequency-Domain Fatigue Assessment Approach for Welded Structures under Random Loading

Welded structures under random loadings are usually susceptible to fatigue-induced failures that lead to significant economic and safety effects. However, accurately predicting these structures’ fatigue damage and life in the frequency domain remains challenging due to the limitations associated with using traditional weld stress extrapolation methods, such as nominal, hotspot, and notch stress methods. These methods struggle with precisely defining and characterizing the stresses at the weld toe and root as they vary depending on factors like weld stress concentration effects, joint geometry, and loading modes. This research introduces an Equilibrium Equivalent Structural Stress (EESS)-based frequency-domain fatigue analysis approach for welded structures subjected to random loading. The proposed method utilizes the EESS formulations, which are based on the decomposition and characterization of weld toe stresses with a single stress parameter, together with incorporating structural dynamic properties’ effects on the stresses acting on the weld joints and the corresponding accumulated fatigue damage of the structure. The numerical demonstration and validation of the proposed method have been performed using a welded Rectangular Hollow Section (RHS) T-joint structure subjected to stationary random fatigue loading. The proposed method’s fatigue damage and life results are compared with the fatigue test data and the equivalent hotspot stress extrapolation-based technique results.

Structural assessment of a multi-functional additively manufactured space component with bulk-lattice hybrid architecture

The paper shows the results of a research aimed to re-design a space component and to assess its structural integrity under the operating mechanical loading. To achieve multi-functional requirements, lattice structures were used in some regions of the component. An assessment procedure for static and random fatigue loads was developed. The procedure is applicable to components which have both bulk and lattice sections, and constitutes an important element for a successful diffusion of lattices. Experiments were performed on lattice samples and on breadboard samples, to validate the assumption of the assessment procedure. Good correlation was found between predictions and experiments.

Characterizing fatigue damage behaviors of concrete beam specimens in varying amplitude load

In this paper, the random and nonlinear mechanical characteristics of the composition distribution of concrete in each phase are investigated to solve the difficulty of fatigue damage test of concrete beam specimens under variable amplitude loading. A three-dimensional (3D) concrete composite random aggregate meso-model of concrete beam specimens is built with Digimat as a composite modeling tool. Subsequently, the bending-tensile strength and fatigue life of the model under four-point bending loading is analyzed and calculated using Abaqus finite element analysis model based on the concrete elastic-plastic damage criterion. The mechanical behaviors of concrete materials under static load failure, multistage fatigue loading, and random fatigue loading are studied. Finally, compared with the test results, the following conclusions are obtained. The random aggregate model and elastoplastic damage mechanics model of concrete composite constructed by concrete in Abaqus and Digimat can better describe the geometric and distribution characteristics of composite structure and aggregate in concrete beams and the mechanical behavior of fatigue and static load failure. The difference is 4.08 % with the experimental results, which verifies the validity and reliability of the numerical simulation model, and the fatigue life of concrete beams under random loading Sra and Srb is 1622 and 164 times, respectively. The research results have certain guiding significance and reference value for accurate calculation of bending and tensile strength of concrete beams under specified raw material and mix ratio and prediction of fatigue damage cracking in airport cement concrete pavement structure.

Stress intensity-dependent relation between overload plastic zone and fatigue retardation in Al-alloy

Unveiling tensile overload (OL) influence during constant amplitude fatigue is fundamental to life prediction of random fatigue load. Although the same OL results in the same plastic-zone size for a material, subsequent fatigue retardation strongly depends on applied stress intensity, e.g. stress ratio R. This study attempts to separate the contributions of OL-induced residual stress (RS) related to crack closure and applied stress, crack-tip damage zone (DZ), and R-ratio depended crack deflection. To this end, fatigue tests are comparatively analyzed based on combination of different R ratios, the same OL condition, and removing DZ and crack-tip plastic zone. The results show that the fatigue retardation induced by the same OL decreases with increasing R, and the delay retardation disappears after removing DZ at R = 0.7 case. In addition, the OL-induced crack deflection is more obvious at low R = 0.1–0.4 cases than that at R = 0.7 case.

Comparison of fatigue damage criteria for a CFRP aeronautical joint subjected to random fatigue load

The fatigue life prediction of components in CFRP subjected to random fatigue load was carried out considering different fatigue damage models. The selected component is a filled hole tension specimen, which is representative of the typical aeronautical riveted joint. The stress state of the riveted section was determined through a FEM model, created with CODE ASTER. Subsequently, the fatigue load history was introduced, calculating the stress field for the different load levels. Applying the constant life diagrams, fatigue cycles were counted and the damage was evaluated. Finally, the damage that occurs in the different ply of the specimen was calculated using different fatigue damage criteria present in literature. The creation of a parametric FEM model offers the possibility of optimizing the study and constitutes a useful tool in order to choose the most suitable damage criteria for the particular geometry and loading mode of the component. Moreover, different severity of the load history and different load sequence were introduced to show their effect in the fatigue failure.

A new time-frequency domain simulation method for damage accumulation and life prediction of composite thin-wall structures under random cyclic loadings

A time-frequency domain simulation method has been developed to predict the lifetime and simulate the damage evolution of composite thin-wall structures subjected to random fatigue loading. Simulation method contains two main parts: (1) structural response based on the power spectral density loading, and (2) damage progression according to residual stiffness model of material. Simulation is proceeded based on step-by-step update of material properties in time domain. For each step fundamental frequency is calculated to determine the degradation ratio of frequency. Time corresponding to the critical degradation ratio of frequency is defined as the lifetime. The method presented in this paper presents a possible solution for practical engineering applications of composite thin-wall structures subjected to the random fatigue loadings and can be applied to other similar thin-wall structure with more complex features.

An improved fatigue life model for mechanical components considering load strengthening characteristics

AbstractThe small load has fuzziness on the strengthening and damage of the component under the action of random fatigue load. This paper introduces the two‐dimensional membership function in fuzzy mathematics to characterize the relationship between stress amplitude, mean value, and damage below the fatigue limit. An exponential function is used to describe the strengthening effect. Under the two‐dimensional load spectrum, the influence of various levels on the component's life is comprehensively considered, and the fatigue life prediction model under random load is proposed. Through the small sample experiment of the car swing support rod, the relevant parameters of the model are obtained. The difference between the modified two‐dimensional and the two‐dimensional load spectrum is compared in fatigue life prediction results. The rules of membership function selection and parameter setting are summarized, and it makes the estimation of component load spectrum life more reasonable.

Uncertainty-Based Analysis of Random Load Signal and Fatigue Life for Mechanical Structures

In the course of driving, due to the influence of environment, signal acquisition device and the degree of unevenness of road surface, the collected signals fluctuate within certain error range and have randomness. To predict the fatigue life of mechanical structures more accurately, the processing method of random load signals for uncertainty is proposed. The collected load signals are converted into interval load signals by Bootstrap error circle method, and based on the generalized Pareto distribution, the peak-over-threshold extrapolation is performed on the interval load signals. Based on the mixed probability distribution model, the one-dimensional and two-dimensional interval load spectrum for shock absorber cylinder are established by analyzing the characteristics of load signals. In addition, based on the uncertain signal problems, the interval life spectrum and interval damage spectrum of one-dimensional and two-dimensional are constructed by the interval algorithm. This paper lays a foundation for load signals processing and fatigue life prediction of parts.

Research on Random Fatigue Load Model of Highway Bridge by Vehicle Traffic Based on GMM

Research on Random Fatigue Load Model of Highway Bridge by Vehicle Traffic Based on GMM

Random loading fatigue life of a press-formed short carbon fiber reinforced polyamide composite and development of life prediction methodology

Random loading fatigue life of a press-formed short carbon fiber reinforced polyamide composite and development of life prediction methodology

Universal cycle counting for non-proportional and random fatigue loading

The article examines a universal method to count cycles in the context of non-proportional, multiaxial fatigue. It is independent of the damage criterion used to predict the lifetime curve, and provides the same number of cycles as when using the classic counting methods for proportional loading cases. It is shown that, at any point on the loading path, an immediate estimation of the current fractional cycle count, mean and amplitude values can be computed. This enables to describe lifetime as a function of these variables, and also to apply the method to fatigue problems with variable amplitude and frequency.

Artificial neural network for Gaussian and non-Gaussian random fatigue loading analysis

There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.

Experimental validation of an ANN model for random loading fatigue analysis

The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis.

Improved density peak clustering-based adaptive Gaussian mixture model for damage monitoring in aircraft structures under time-varying conditions

Abstract For the reliable damage monitoring of aircraft structures under Time-Varying Conditions (TVCs), Gaussian Mixture Model (GMM)-based damage monitoring methods combined with Guided Wave (GW) have been studied increasingly frequently in recent years. In this paper, to enhance the performance of the GMM-based damage monitoring method, an Improved Density Peaks Clustering (IDPC)-based Expectation Maximization (EM) algorithm is proposed to improve the constructing algorithm of the GMM. In the algorithm, the unique initial value of the GMM parameters is obtained based on an adaptive searching strategy of the probability density peaks of GW Damage Features (DFs) first. Next, the GMM is constructed simply and efficiently by the EM algorithm based on the initial parameters. The IDPC-EM algorithm can stably construct the GMM with fewer experience-dependent initialization parameters while promising the probability modelling accuracy and maintaining high computational efficiency. Based on the IDPC-EM algorithm, a new adaptive GMM-based damage monitoring method combined with the GW is established. The method is simple, stable, highly computationally efficient and less experience-dependent due to the adaptive GMM. In this method, two GMMs of the DFs obtained in the healthy state and the damage monitoring state of an aircraft structure are constructed, respectively. By measuring the similarity of the two GMMs, the variation trend of the probability distributions of the DFs between the two states is obtained to detect the damage. Finally, the method is validated in a full-scale aircraft fatigue test which is close to an in-flight load test on the ground. The cracks of the right landing gear spar and the left wing panel are monitored reliably and stably under random fatigue load conditions.

Stochastic analysis of fatigue of concrete bridges

A novel method is proposed in this work for the assessment of the remaining fatigue life and fatigue reliability of concrete bridges subjected to random loads. The fatigue reliability of a bridge is a function of the fatigue damage accumulation; a stochastic fatigue damage model (SFDM) with physical mechanism is introduced for deriving the fatigue damage process. In order to implement the probabilistic analysis, based on the probability density evolution method (PDEM), the generalised density evolution equation (GDEE) for the remaining fatigue life is developed. Finally, a prestressed concrete continuous beam bridge located in China is illustrated. The random fatigue load acting on the bridge is modelled as the compound Poisson process, and the simulation of the random load uses the stochastic harmonic function (SHF) method. To simplify the reliability analysis, an equivalent constant-amplitude (ECA) load process is introduced based on energy equivalence. By employing SFDM, the finite element analysis of the bridge under the fatigue loading is performed. Then, the fatigue damage accumulation process of the bridge under the fatigue loading is obtained. Through solving the probability density evolution equation for the remaining fatigue life, the probability density functions (PDFs) of the remaining fatigue life evolving with time is obtained. The fatigue reliability is then calculated by integrating the PDF of the corresponding remaining life.

Random Loading Fatigue Life of Injection Molded Short Carbon Fiber Reinforced Composite and Its Prediction

Random Loading Fatigue Life of Injection Molded Short Carbon Fiber Reinforced Composite and Its Prediction

Fatigue Life and Strength Analysis of a Main Shaft-to-Hub Bolted Connection in a Wind Turbine

High-strength bolts are widely used in wind turbines and play a significant role in their operation. In this paper, in order to study the ultimate strength of high-strength bolts in the connection between the hub and main shaft in a 2.5 MW wind turbine with pretension effects, two kinds of finite element models are presented: a solid bolt model and a simplified bolt model. Theoretical calculation was used to compare these models with the results of finite element methods. The ultimate strength analysis results showed that the simplified bolt model was the most efficient and useful in terms of computational time and memory usage. Based on this study, the simplified bolt model was used in the fatigue calculation considering multiple random fatigue loads, and the fatigue life of high-strength bolts was determined by combining the S–N curve of the bolt, based on the Palmgren–Miner Linear damage accumulation hypothesis.

An investigation on residual strength and failure probability prediction for plain weave composite under random fatigue loading

The residual strength and the failure probability of the plain weave carbon-fiber reinforced epoxy composite subjected to random fatigue loading are investigated. According to the constant amplitude residual model and the experimental S-N curves, a new model is proposed, in which both the probability density function of stress peaks and the power spectrum density of stochastic processes are employed. The degradation of residual strength (dSr) in a time increment (dt) is calculated via an integration of the decrease of residual strength due to the stress peaks (dSrp) in the definitional domain of the peak probability density function. A linear damage accumulation of residual strength is used in this time increment. The evolutions of the residual strength with time are determined via a double integral method. The new model’s prediction results are in good agreement with the experiment results and it can give the curves of the variation of fatigue life with failure probability.

Failure detection technique under random fatigue loading by machine learning and dual sensing on symmetric structure

To avoid the sudden failure of mechanical structures under repeated loading and to supplement conventional methods of managing life time, we propose a failure detection technique under random fatigue loading using machine learning and dual sensing on a symmetric structure. The state of a shackle, which is used to connect the cargo to the hoist efficiently, under fatigue loading was collected using two strain sensors of a dual system. The strains were preprocessed and labeled as normal or abnormal. Logistic regression machine learning was employed to determine the decision boundary line. Then, we gathered the decision boundary lines of each experiment for determining the time of failure, and we verified every experiment with the most conservative decision boundary line. The results indicate that failure was detected before the crack occurred and the time to notice maintenance could be controlled.

Influence of random fatigue loading non-proportionality on damage

The aim of the present paper is to evaluate the effect of the loading non-proportionality on damage accumulation. Such an analysis is performed at material macro-level by means of numerical simulations concerning random bending-torsion loading. A wide group of random bending and torsion input signals is generated by varying (a) the spectral content and variance of the above signals, and (b) the value of the cross correlation coefficient. The spectral criterion by Carpinteri et al. is applied to compute the expected damage. A linear relationship between damage ratio and cross correlation coefficient is proposed for signals characterised by variance ratio equal to 1.0.