Prediction Of Fatigue Behavior Research Articles

Effects of mean stress on the fatigue properties of core‐in‐sheath‐type carbon/glass hybrid thermoplastic composite rods: Experimental investigation and practical predictive method

AbstractA systematic acquisition of fatigue test data was conducted under different stress ratio conditions to investigate the effect of mean stress on the fatigue properties of carbon/glass hybrid thermoplastic composite rods. Our findings revealed that as the stress ratio increased with a higher mean stress, the stress dependency of the fatigue life also increased, and the slope of the S‐N curve became gentler. Under identical stress ratio conditions, hybrid rods with a greater volume fraction of carbon fiber exhibited the highest strength and a more gradual slope of the S‐N curve. To account for mean stress effects, an equivalent stress amplitude was introduced, resulting in data collapse at around 107 cycles, rendering this approach an industrially useful method to predict fatigue behavior when working with limited experimental datasets. This study presents a practical technique for estimating fatigue strength under mean stress through a weighted average efficiency and equivalent stress amplitude.

A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle

AbstractThe fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.

Advancements in stress‐based multiaxial fatigue prediction: A data‐driven approach and a new criterion

AbstractA highly accurate iteration of the Liu & Zenner multiaxial fatigue criterion is presented and validated against the FatLim dataset. The proposed criterion modifies the mean stress parameters of the original criterion while keeping the amplitude parameters unchanged. The investigation of the best‐performing stress amplitude parameters revealed that existing multiaxial fatigue criteria that rely on dimensional weight parameters exhibit a notable prediction bias. This bias leads to inaccurate fatigue assessments. The paper sheds light on the extent of this bias and its implications for common engineering practices. By introducing the refined criterion, the study addresses these limitations and provides a more reliable framework for multiaxial fatigue analysis. The comprehensive validation against the FatLim dataset demonstrates the superiority of the proposed criterion in accurately predicting fatigue behavior under diverse loading conditions. The findings underscore the significance of considering both amplitude and mean stress parameters in multiaxial fatigue criteria, offering valuable insights for enhancing the accuracy of fatigue assessments in engineering applications.

Estimating fatigue life of carbon/epoxy composites: A rapid method coupling thermo‐mechanical analysis and residual strength

AbstractThe investigation of fatigue behavior typically involves time‐consuming tests, leading some researchers to explore methodologies based on self‐heating tests to reduce the process. For composite materials, the conventional approach involves piecewise linear approximations of the self‐heating curve and the stress transition between the first and second regimes is arbitrarily associated with a fatigue lifetime equal to 106 cycles. This paper proposes a novel methodology to address these simplifications. First, a non‐linear viscoelastic model is used to describe the self‐heating curve. Based on the mechanisms established in the literature, a link is proposed between the dissipation and the fatigue limit. The load leading to a significant contribution of non‐linear mechanisms is associated with an infinite life time. It allows the identification of an – curve and prediction of fatigue behavior through a minimal number of tests. The comparison with fatigue results is satisfactory.

Prediction of fatigue crack behaviour of carbon fibre reinforced asphalt using fracture testing and modelling of the adhesive zone CZM

ABSTRACT Mechanical behaviour and fatigue failure characteristic of asphalt mixtures is complex under cyclic loading conditions and require better recognition due to the importance of these infrastructures. Fatigue tests are usually time-consuming, expensive, and require experience. These challenges increase when the mixtures contain a variety of additives that complicate the recognition of material behaviour. In this study, carbon fibres were investigated as additives with high tensile strength. After failure tests on SCB samples(5 different percentages of fibres and 5 different lengths), 1.5% fibres and 15 mm length were used as optimum values for other experiments for 3 fracture models (I,II, and mixed I&II modes). Monotonic and cyclic failure tests of mixtures containing carbon fibres are presented by Abaqus modelling to predict fatigue behaviour of these materials. For this purpose, the Cohesion Zone Model (CZM) was selected for modelling and the failure parameters obtained from the experiments were validated by the results of the modelling. Comparison of experimental and modelling results in the study of mulmouthle outputs indicates that the predicted and measured fatigue behaviour have good conformity. Achieving such models reduces the need for expensive and time-consuming experiments and provides a useful tool for examining other effective parameters.

Development of a Graph-GPT Model for Predicting (Fretting) Fatigue Behavior, Accounting for Experimentally Observed Scatter Phenomena Influenced by Microstructure Inhomogeneity

Development of a Graph-GPT Model for Predicting (Fretting) Fatigue Behavior, Accounting for Experimentally Observed Scatter Phenomena Influenced by Microstructure Inhomogeneity

Artificial Neural Network-based fatigue behavior prediction of metals and composite materials

This article presents a study devoted to predicting the fatigue behavior of two different materials: aluminum alloy AL-2024-T6 and glass fiber composite samples. The approach used in the study involves the use of artificial neural networks (ANNs) to develop accurate models for predicting the fatigue life of these materials at various skewness ratios (R). For the first case study, the S-N curve of tensile-tested AL-2024-T6 was predicted for different values of R using a few sets of data for learning. The model was then tested on the same values of R as the learning set, as well as on a different value of R (-0.4), to demonstrate the ability of the model to predict fatigue data under varying conditions. The results showed that the model was capable of accurately predicting the fatigue life of AL-2024-T6 for different values of R. For the second case study, the stiffness degradation of bending-tested glass fiber woven composite samples was predicted for different values of R using ANN. Different layups of composite samples were considered in this study. The model was trained on a few sets of data and tested on the same and different values of R, demonstrating the ability of the model to accurately predict stiffness degradation of composite samples under varying coefficients of asymmetry. The results of both case studies showed that ANN-based models can be effective in predicting the fatigue behavior of different materials tested using different methods under varying coefficients of asymmetry. These findings have practical implications for industries involved in the design and manufacturing of materials, particularly in the aerospace and automotive sectors, where fatigue behavior is critical to the structural integrity of components.

Multi-physics Approach to Predict Fatigue Behavior of High Strength Aluminum Alloy Repaired via Additive Friction Stir Deposition

A smooth particle hydrodynamic (SPH) simulation of an additive friction stir deposition (AFSD) repair was used to inform a multi-physics approach to predict the fatigue life of a high strength aluminum alloy. The AFSD process is a solid-state layer-by-layer additive manufacturing approach in which a hollow tool containing feedstock is used to deposit material. While an understanding of the evolving microstructures is necessary to predict material performance, the elevated temperatures and strain rates associated with severe plastic deformation processes (SPDP) make accurate collection of experimental data within AFSD difficult. Without the ability to experimentally determine material history within the AFSD process, an SPH model was employed to predict the thermomechanical history. The SPH simulation of an AFSD repair was used to inform several microstructural models to predict material history during and after processing with AFSD and a post-processing heat treatment. These microstructure models are then used to inform a mechanistic microstructure and performance model to predict the fatigue life of an AFSD repair in AA7075.

Risk volume effect in very high cycle fatigue of 304L stainless steel

Risk volume can be considered as a volume where the crack will initiate with the highest probability. Thus, it is a serious issue considering the specific geometry of the samples for ultrasonic loading. Also, the distribution of the stresses within the risk volume represents important information for fatigue behaviour prediction and description. The risk volume for 304L stainless austenitic steel samples with different geometry was numerically analysed and correlated to the results of the ultrasonic fatigue experiments. Obtained fatigue data and the evolution of the microstructure related to the calculated risk volume sizes. The formation of slip marks corresponded to the calculated stress distribution within the risk volume. The differences in the calculated sizes of the risk volume for different sample geometries were reflected in changes in the microstructure of the material and in the measured fatigue lifetimes. Obtained results can partially explain observed differences in the fatigue properties when comparing ultrasonic fatigue testing with conventional methods.

Experimental and numerical study of the compressive fatigue behavior of a Ti6Al4V mapped rhombioidal dodecahedral structure fabricated by electron beam melting

Triple-periodicarrayed porous structures are regarded as the most promising materials for the fabrication of medical bone implants. The fatigue properties of cellular structures are critical for their long-term use in dynamic biological skeletal environments. In this study, the rhomboidal dodecahedral cell structure of a Ti6Al4V alloy prepared by electron beam melting with different mapping ratios in one coordinate direction was homogenized. Based on this, the compressive fatigue behavior and fatigue failure mechanism under high cyclic loading were studied. To improve the fatigue performance and reduce the influence of residual stress, the microstructure was post-treated by cryogenization and characterized to analyze the evolution mechanism. The fatigue test results of the entire cell structure showed that failure was asynergy between fatigue damage and the ratchet effect. A change in the mapping ratio significantly affected the homogenization mechanical performance, which resulted in the fatigue properties. Based on the test results and numerical simulations, the cross-sectional elastic properties and local maximum stress of struts with different structural mapping ratios were expressed analytically. The fatigue life of the strut with the maximum local stress in the range of elastic deformation and that of the homogeneous block under the preset loading limit of the system were both modeled with appropriate material constants. The high-cycle fatigue life model was established to assist the fatigue behavior prediction of lattice structures.

Experimental and numerical investigation of rotating bending fatigue of polylactic acid 3D printed parts by an extrusion-based additive manufacturing method

This study investigates the impact of four parameters, namely layer height, nozzle temperature, infill percentage, and bed temperature, on the fatigue life of polylactic acid (PLA) printed parts using the extrusion-based additive manufacturing (AM) process. The experiments were designed using the Taguchi method, considering three levels for each parameter. The extent of the impact and the optimal values of the process variables were determined by using analysis of variance (ANOVA) and signal-to-noise ratio. To predict the fatigue behavior, an empirical model was presented, which was fitted to the fatigue results of the samples made with the optimal process variable values using the least squares method. Additionally, finite element simulations were conducted, and the results were compared with the experimental study findings. The results indicated that the optimal process variable values for maximum fatigue strength are a layer height of 0.3 mm, a nozzle temperature of 220 ℃, a 100 % infill and a bed temperature of 60 ℃. The ANOVA results revealed that the infill percentage, nozzle temperature, and layer height have the greatest influence on fatigue life, with respective contributions of 60.5 %, 28.1 %, and 7.7 %. The experimental modeling and finite element simulation results indicate that the proposed models predict fatigue behavior with regression coefficients of 96.3 % and 98.7 %, respectively.

An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime

Fretting fatigue is a common type of problem in the aviation and other engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue conditions. Fretting fatigue crack initiation lifetime is a crucial part of the total lifetime, and the currently dominant method for research on fatigue behavior is the combination of the theoretical and numerical models. With the advent of the era of data science, machine learning has been widely used to predict fatigue behavior, but there are no many applications in the field of fretting fatigue. This paper proposed an improved Artificial Neural Network (ANN) using Balancing Composite Motion Optimization (BCMO) for quick prediction of fretting fatigue crack initiation lifetime. Physical-mechanical reasoning parameters, axial stress amplitude, shear stress amplitude, half contact width, and half stick zone width are considered as input parameters, and fretting fatigue crack initiation lifetime is set as the output feature. The main aim of BCMO is to improve the robustness of the ANN based on the influential parameters, namely bias and weight. The provided results using ANN-BCMO are robust compared to ANN and traditional techniques from the literature. The Matlab code of improved ANN using BCMO can be found at https://github.com/Samir-Khatir/BCMO-ANN.git

Microstructure-Sensitive Crystal Plasticity Modeling for Austenitic Steel and Nickel-Based Superalloy Under Isothermal Fatigue Loading

Intermittent mechanical loads combined with high temperatures appear during the operation of turbines in jet engines or in power plants, which can lead to high-temperature fatigue or to thermomechanical fatigue. Since the assessment of fatigue properties is a complex and time-consuming process, it is essential to develop validated material models that are capable of predicting fatigue behavior, thus allowing the extrapolation of experimental results into a broader range of thermomechanical conditions. To accomplish this, two representative volume elements (RVEs), mimicking the typical microstructure of single crystal Ni-based superalloys and polycrystalline austenitic steels, respectively, are introduced. With the help of these RVEs, the temperature and deformation-dependent internal stresses in the microstructure can be taken into account. In the next step, phenomenological crystal plasticity models are implemented and parameterized for cyclic deformation of these two materials. The RVE, constitutive model, and the material parameters for the Ni-based superalloy are taken from a former study. For the austenitic steel, however, an inverse procedure has been used to identify its material parameters based on several isothermal fatigue tests in a wide temperature range. With the identified material parameters, a valid description of the isothermal fatigue behavior at different temperatures is possible. The most important conclusion from the comparison of the isothermal fatigue behavior of the two different materials is that the kinematic hardening, which is responsible for the shape of the hysteresis loops, is entirely described by the internal stresses within the typical microstructure of the Ni-based superalloy, which is modeled in a scale-bridging approach. Hence, no additional terms for kinematic hardening need to be introduced to describe the cyclic plasticity in the superalloy. For the austenitic steel, in contrast, the Ohno–Wang model for kinematic hardening needs to be considered additionally to the internal stresses in the polycrystalline microstructure to obtain a correct description of its cyclic plasticity.

Applications of data-driven approaches in prediction of fatigue and fracture

Since various mechanical components could undergo cyclic loadings in their operations, fatigue evaluation of the parts is a crucial issue. In this context, different experimental practices have been developed to investigate fatigue strength, crack growth rate, and fracture behavior of mechanical parts. Along with conventional fatigue test methods, data-driven approaches can be used to investigate fatigue life and fracture behavior of metallic and non-metallic structural elements. A data-driven analysis is a technique which uses collected data to answer the particular demand. In this contribution, we have reviewed data-driven approaches which have been applied to predict fatigue life and fracture of components. To this aim, we discuss the documented results in applied data-driven techniques in evaluating fatigue performance of metallic, composite, and 3D-printed parts. The material, fabrication process, geometric parameters, and details of data-driven approaches have been considered in review of the reported literature. Although improvements are recorded due to using data-driven approaches in prediction of fatigue behavior of parts, there are challenges and limitations which have been highlighted in the present study. Moreover, this review explains the key parameters which play crucial roles in fatigue life of metallic and non-metallic components.

Predicting fatigue life of metal LPBF components by combining a large fatigue database for different sample conditions with novel simulation strategies

This study presents a unique methodology to systematically predict and evaluate the fatigue behaviour of additively manufactured components. A multitude of factors influence the fatigue performance of end-use application components. To analyse this effect, fatigue behaviour of samples manufactured by laser powder bed fusion (LPBF) is evaluated for different process conditions, namely, build orientation, heat treatment and surface treatment. An extensive experimental test campaign is performed to develop a database of fatigue results capturing all these influencing factors. A Gaussian process is then trained with the observed experimental results to predict the fatigue behaviour for unknown process conditions. This machine learning method is subsequently validated to confirm its capability to represent the experimental data on one hand and to generalize well to unseen data on the other hand. In addition, the method is integrated in a fatigue durability prediction solver. The predictive capability of large-scale simulations was demonstrated for low pressure steam turbine blades manufactured by LPBF and subjected to different combinations of post processes. Subsequent experimental fatigue testing of the blades indicated that the durability solver accurately predicted the crack initiation location. For a combination of stress relieved microstructure and as-fabricated surface, the experimental fatigue lives lie within 95% confidence interval of the lifetimes predicted by the durability solver. For the components with full annealed microstructure and machined surface, the durability solver predicted conservative lifetimes compared to the experimental fatigue lives, demonstrating the capability to predict fatigue behaviour of end-use components in the field. • A new methodology is developed to predict fatigue of additively manufactured metals. • Fatigue behaviour of samples is investigated for different process conditions. • Machine learning and finite element analysis are used to predict fatigue lifetimes of components. • Predicted crack initiation sites coincide with experimental tests.

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 µm were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10–15 µm had the highest effects on fatigue life improvement.

Fatigue Evaluation of Offshore Steel Structures Considering Stress Concentration Factor

The installation of offshore structures and facilities in the marine environment, usually for the production and transmission of oil, gas exploration, electricity, and other natural resources is referred to as offshore construction. Since offshore structures are subjected to changing threats to the environment year-round. Fatigue behavior prediction noticed on these structures should be considered during the design stage. Fatigue is one of the failure mechanisms of offshore steel structures, and it must be investigated properly during system design. The fatigue analysis of offshore structures under drag wave force, total wave force, total moment about the sea bed, and other variables are reviewed thoroughly. The structure's dynamic response becomes a critical aspect in the whole design process. The fatigue analysis was carried out using MATLAB software, material properties of the offshore structure, and wave spectrum characteristics in this study. This study shows the JONSWAP spectrum and stress concentration analysis prediction. The offshore support structure that is predicted during the design phase will help to keep the stress concentration factor below the fatigue threshold and anticipate safe life design, according to the results of the fatigue study. The fatigue performances of tripod and jacket steel support structures in intermediate waters depth are compared in this project (20-50 m). The North Atlantic Ocean is used as a reference site, with a sea depth of 45 meters. The tripod and jacket support structures will be designed by using current industry standards. Keywords: [Fatigue evaluation, North Atlantic Ocean and Failure].

Fatigue of engineered metallic materials using small‐crack theory under constant‐ and variable‐amplitude loading

AbstractThe current paper reviews application of an improved FASTRAN life‐prediction code to predict fatigue behavior of notched coupons made of an aluminum alloy and a titanium alloy. For each material, fatigue‐crack‐growth‐rate data were determined over a very wide range in rates from threshold (small‐crack region) to near fracture on standard crack‐growth specimens. In the low‐rate regime, small‐crack data were used on the aluminum alloy, whereas a compression pre‐cracking constant‐amplitude method was used on the titanium alloy. A characteristic microstructural flaw size was determined from fatigue‐test data on notched coupons under constant‐amplitude loading. These data were then used to predict fatigue behavior of similar notched coupons under various spectrum loadings. For variable‐amplitude loading conditions, an improved “rainflow‐on‐the‐fly” subroutine was used in the life‐prediction code. Comparisons are made between previous life predictions and those made with the current code under variable‐amplitude loading. In general, the new code produced longer fatigue lives than older versions. The new code worked well using the same initial microstructural flaw size for a given material under both constant‐ and variable‐amplitude loading.

On the Influence of Mean Shear Stress on Multiaxial High Cycle Fatigue of Metallic Materials

A study has been made of the influence of a superimposed mean shear stress on the capability of some multiaxial high cycle fatigue criteria to predicting fatigue behavior of 42CrMo4 and 34Cr4 alloy steels. Five selected critical plane-based criteria, namely Matake (M), Susmel & Lazzarin (S&L), Findley (F), Carpinteri & Spagnoli (C&S) and Liu & Mahadevan (L&M), were applied to a number of published experimental fatigue resistance limit tests, involving synchronous sinusoidal in-phase and out-of-phase bending and torsion. Applying to the same loading conditions a mesoscopic scale-based criterion proposed by Papadopoulos (P), one could verify that predictive capability of such an approach is almost invariably superior to those associated with the M, S&L, F, C&S and L&M models. As the Papadopoulos criterion is independent of mean shear stress, it seems appropriate to conclude that the inclusion of such a stress as loading parameter in the critical plane-based models does, in fact, exert a negative influence on their predictive capability. Finally, it is worth mentioning that, except for the Matake, S&L and L&M criteria, the other critical plane-based criteria exhibit a dependence of the fatigue resistance in pure torsion with respect to a superimposed mean shear stress, in disagreement with well-established experimental observations.

Influence of different loading paths on the multiaxial fatigue behavior of 2024 aluminum alloy under the same amplitude values of the second invariant of the stress deviator tensor

2024 aluminum alloy is a common aeronautic material. During operations, construction elements made of aluminum alloys undertake complex cyclic loadings. Therefore, it is important to estimate the influence of these loadings on the durability of the material. Hereby, multiaxial fatigue tests with the same amplitude values of the second invariant of the stress deviator tensor are conducted, and test data are analyzed. The modified Sines method is utilized to predict fatigue experimental data. Results show that the model is accurate enough to fatigue behavior prediction of 2024 aluminum alloy.