Fatigue Life Prediction Research Articles

Fatigue life prediction method for composite laminates based on equivalent life under time-varying loads

Fatigue life prediction method for composite laminates based on equivalent life under time-varying loads

Prediction of multiaxial fatigue life with a data-driven knowledge transfer model

A data-driven model is presented for accurate prediction of multiaxial fatigue life based upon the principle of transfer learning (TL). The Tradaboost framework is explored to adjust the weights of training data from different sources, actuating information transfer from domain knowledge to the data-driven modeling of multiaxial fatigue life. Subsequently, extensive experimental results tested under the proportional and non-proportional circle loadings are collected for model evaluation. The results demonstrate that the proposed model is more accurate than domain knowledge-based, conventional data-driven, and comparable TL-based models, with a low data requirement, showcasing good applicability for multiaxial fatigue life assessment.

An experimental-cohesive zone model approach to predict fatigue life of adhesive joints with varying modes of loading and joint configurations for automotive applications

ABSTRACT Predictive fatigue life models of adhesive joints are necessary to enable the assessment of automotive-bonded structures while reducing costly experimental testing. However, contemporary models have typically been calibrated for specific joint configurations and modes of loading, limiting their applicability to large-scale structures. Additionally, available models are based on simulation of cumulative fatigue cycling, making them computationally prohibitive. In the current study, cross-tension (CT) (load angles of 0°, 45°, and 90°) and single-lap shear joint (SLJ) configurations were bonded using an epoxy adhesive (BetaGuard CI6125R; PPG, France) used in automotive production (one part) and tested under fatigue cyclic loading. A total of nine joint configurations, having symmetrical (same material and thickness) and asymmetrical (dissimilar material or unequal thickness) joints, were tested. Fatigue tests at load levels between 25% and 75% of the static peak load were performed until joint failure or to runout (two million load cycles). The static tests of the joints were simulated to failure using finite element (FE) models with the cohesive zone method (CZM). The maximum strain energy release rates ( G max ) were calculated within the adhesive bond line at static loads corresponding to the peak loads of the fatigue tests. The G max values, computed from single cycle, specimen-specific FE simulations, were correlated with the measured fatigue life ( N f ) of the adhesive joints with varying modes of loading and joint configurations. The fatigue life prediction model, based on G max − N f correlation and following a crack propagation approach, predicted the cycles to failure for 85% of the fatigue tests, and 81% of the independent validation tests. The proposed fatigue life prediction approach provides computational efficiency and large-scale compatibility.

Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method

Cold expansion (CE) serves as a practical surface enhancement to improve the fatigue life of hole structures by improving surface integrity in both macro-scale and micro-scale. Due to the inaccessibility and high cost of experimental measurements, the physical relation between surface integrity and fatigue life are always implicit, serving as the major challenge for accurate life prediction. To address this issue, a novel method is proposed by introducing physical information to traditional data-driven method, where surface integrity enriched by multi-scale simulation is mapped to fatigue life via machine learning (ML) mechanism. As integrated to four typical ML algorithms, the proposed physics-enhanced data-driven method exhibit outstanding capability for accuracy improvement, decreasing the scatter band by amplitude between 27.3 % and 71.4 %. The proposed method offers a promising option for fatigue life prediction on surface treated structures with limited physical information.

Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material

The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.

S–N curves established from limiting energy in the case of specimens additively manufactured from AlSi10Mg

AbstractThis study investigates the varying thermal response of additively manufactured specimens made of AlSi10Mg and subjected to cyclic loading. It is well known that the thermal response is driven primarily by the self‐heating effect. The paper explores the possibility of employing thermographic methods to establish an S–N curve for fatigue life prediction with the use of fewer specimens than are traditionally required for an S–N curve established from constant‐amplitude fatigue tests. Specific self‐heating step tests are conducted by gradually increasing the loading amplitude while monitoring the temperature of the specimen. A new approach is introduced to assess the lower limitation of the existing Fargione method, which addresses the absence of the fatigue limit threshold in previously published works. The validity of this approach is compared with our own experimental data. This new dataset concerns additively manufactured (AM) specimens made from AlSi10Mg powder, which were all printed on a single platform. To observe the sensitivity of the updated approach, four groups with different heat treatments were evaluated.

Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework

To address the limitations of traditional methods (such as the S-N curves and Paris’s law) in evaluating the fatigue life of multi-defect materials, this study developed a fracture mechanics-based physics-informed neural network (PINN) to predict the lifetime of multi-defect materials using cyclic loading (Δσ) and the equivalent damage area (AD). Influences of multiple defects were unified characterized through the equivalent damage area, which was calculated based on the M−integral fatigue model. This model reflects the energy evolution of multi-defect fatigue damage. By embedding the prior knowledge of fracture mechanics derived from the M−integral fatigue model into the loss function of PINN, crucial physical information was captured during the training progress, enhancing the interpretability of the neural network. By integrating the advantage of the M−integral fatigue model in characterizing the fatigue performance of multi-defect materials and the nonlinear fitting ability of neural networks, the proposed approach effectively improves the generalization ability and predictive accuracy of limited fatigue data. The presented PINN models accurately forecast the fatigue life of multi-defect materials, with a squared correlation coefficient (R2) exceeding 0.9. The presented methodological framework addresses the existing gap in methods for evaluating the fatigue performance of multi-defect materials and reliance on fatigue testing.

A novel fatigue life model for MCrAlY coated superalloys considering interfacial microstructure evolution

MCrAlY coatings, extensively utilized for safeguarding turbine blades against oxidation and erosion, encounter impediments due to inter-diffusion between the coating and substrate, thereby exacerbating fatigue life degradation at elevated temperatures. In this study, we introduce a novel approach involving the modification of critical depth in interfacial strain energy density to elucidate the impact of interfacial microstructure evolution on mechanical properties. Building upon this concept, we propose a fatigue life prediction model, which incorporates the dynamic evolution of interfacial structure and mechanical characteristics. Validation against empirical data underscores the commendable precision of the model. Our inquiry not only advances the comprehension of mechanical-chemical coupled behaviors but also yields significant insights for the optimization and maintenance of turbine blades.

Finite element analysis of fatigue life of commercially pure titanium clasps additively manufactured with different building orientations.

The geometrical accuracy of additively manufactured pure titanium clasps depends on the building orientation. The aim of this study is to compare the geometrical accuracy and the fatigue lives predicted by finite element analysis (FEA) among three clasps manufactured with different building orientations. Besides, this paper proposed a calculation method of the moment of inertia of area and cross-sectional area along with the arm as the geometrical parameters. One of the clasps manufactured with a cylindrical chucking part for the fatigue test had almost the same geometrical parameters with the CAD design. Also, the authors' fatigue life prediction method using the CAD based FEA was verified through comparison with micro-CT image-based FEA. The other two clasps had larger geometrical parameters than the CAD design, resulting in longer fatigue lives. The results implied the importance of calculating the moment of inertia of the area in the design of the clasp arm.

Predicting the fatigue life of T800 carbon fiber composite structural component based on fatigue experiments of unidirectional laminates

This study is based on the fatigue experiment results of unidirectional laminates and investigates the fatigue performance of T800 carbon fiber/epoxy resin composite structural components through experimental and numerical analysis. Fatigue experiments were performed on [0]16, [90]16, and [±45]8 laminates individually, obtaining the fundamental fatigue parameters necessary for modeling. The fatigue life model for T800 carbon fiber/epoxy resin unidirectional laminates was refined, and fatigue degradation rules were provided for the fatigue progressive damage model. A fatigue progressive damage analysis model for T800 laminates was established based on the 3D Hashin criterion, predicting the fatigue life and fatigue damage failure process of the laminates. A fatigue life prediction model was developed for T800 carbon fiber composite I-beam structural components, which can predict the primary types of fatigue failure, fatigue life and the location of fatigue failure occurrence. The predicted fatigue life of structural components shows good consistency with the experimental results. This method can calculate structural components including ply angles of 0°, 45°, and 90° and obtain the fatigue life contour.

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.

In-plane biaxial fatigue life prediction model for high-cycle fatigue under synchronous sinusoidal loading

The unique advantage of in-plane biaxial loading is no rotation of the maximum principal stress during cyclic loading, which has a distinct effect on fatigue life. This study aims to propose a life prediction model for in-plane biaxial fatigue encompassing both in-phase and out-of-phase conditions. For synchronous sinusoidal loadings, an equivalent stress expression is derived using the integral method, accounting for the influences of shear and normal stresses across all planes. The equivalent stress is then compared to a reversed uniaxial constant amplitude loading to predict fatigue life. To validate and compare the model, in-phase and out-of-phase in-plane biaxial fatigue experiments were conducted using 24 nickel-based superalloy cruciform specimens at temperatures of 420℃, 550℃, and 600℃. The results show that the proposed model is superior to conventional stress-invariant based models, with most results staying within the ± 2 error bands. Using the proposed model, the effects of mean tensile stress, biaxiality and phase shift are discussed. Additionally, a novel non-proportional factor is introduced to enhance multiaxial fatigue life prediction by distinctly separating the effects of principal stress and its directional variations.

Residual stress induced granular bright facets around inclusions in high-strength steels under high-cycle fatigue

Granular bright facets (GBFs) are frequently observed adjacent to inclusions and within fish-eye areas in the fatigue fractures of high-strength steels under very-high-cycle fatigue (VHCF), considered as a characteristic fracture feature of VHCF. Previous understanding on GBF formation emphasizes the occurrence of nanocystallization in microstructure. In this study, however, the same morphology can also be observed under high-cycle fatigue (HCF). GBF, although closer to fatigue crack initiator and experiencing more stress cycles than the peripheral part of fish-eye (PPFE), exhibits rougher surface morphology in the absence of accumulated plastic strain in microstructure, manifesting relieved crack surface wear. This indicates that the traditional theories on GBF formation for VHCF becomes invalid for HCF. The root cause for GBF formation under HCF is analyzed to be the presence of residual stress around inclusions, which reduces the contact pressure of fatigue crack surfaces inducing wear relief. Accordingly, an analytical model capable of predicting GBF thickness with the effects of HCF conditions and steel properties taken into consideration is established. The model yields accurate predictions of GBF thickness with various loading stresses and inclusion diameters, validated by experimental observations. This study provides theoretical guidance for HCF fracture analysis and fatigue life prediction.

Novel integration of PSO-enhanced damage mechanics and finite element method for predicting medium–low-cycle fatigue life in perforated structures

In this research, we introduce an innovative approach that combines the Continuum Damage Mechanics-Finite Element Method (CDM-FEM) with the Particle Swarm Optimization (PSO)-based technique, to predict the Medium-Low-Cycle Fatigue (MLCF) life of perforated structures. First, fatigue tests are carried out on three center-perforated structures, aiming to assess their fatigue life under various strengthening conditions. These tests reveal significant variations in fatigue life, accompanied by an examination of crack initiation through the analysis of fatigue fracture surfaces. Second, an innovative fatigue life prediction methodology is applied to perforated structures, which not only forecasts the initiation of fatigue cracks but also traces the progression of damage within these structures. It leverages an elastoplastic constitutive model integrated with damage and a damage evolution model under cyclic loads. The accuracy of this approach is validated by comparison with test results, falling within the three times error band. Finally, we explore the impact of various strengthening techniques, including cross-sectional reinforcement and cold expansion, on the fatigue life and damage evolution of these structures. This is achieved through an in-depth comparative analysis of both experimental data and computational predictions, which provides valuable insights into the behavior of perforated structures under fatigue conditions in practical applications.

Fatigue life prediction method for bolted joints based on equivalent structural stress under tensile–compressive loading

In this study, load amplitude–life (Fa–N) curves were obtained through tensile–compressive fatigue tests of bolted joints. It was observed that the correlation coefficient squared (R2) value of the Fa–N curve with the same geometric and pre-tightening parameters was high, but the R2 value of the Fa–N curve with all the parameters was low, indicating poor correlation and inability to meet the engineering requirements. Therefore, an equivalent structural stress model for the bolted joint was first developed based on a mechanical model with a strict mathematical definition to normalize these Fa–N curves, which considered the bolted joint loads as the input conditions and integrated the geometric and pre-tightening parameters. Subsequently, a classical beam–shell equivalent finite element model of the bolted joint was constructed. The nodal loads in the bolted connection zone were coupled with the forces and moments of the beam element nodes through finite element simulation, and the equivalent structural stress (σs) of the bolted joint was then obtained based on the equivalent structural stress model. Consequently, the equivalent structural stress–life (Ss–N) curve and probabilistic stress–life (P–Ss–N) curve normalized for different Fa–N curves were obtained by fitting the data of σs and N. Lastly, the accuracy of the fatigue life prediction method based on equivalent structural stress was verified by conducting the vibration fatigue test on the bolted joint structure of the subway antenna bracket.

High cycle fatigue behavior and strengthening mechanisms of laser powder bed fusion pure tantalum

The fatigue behavior and strengthening mechanisms of the laser powder bed fusion pure tantalum (LPBF-Ta), coupled with a fatigue life prediction model, were originally explored. The fatigue strength of LPBF-Ta fabricated at the optimal laser energy density (154.16 J/mm3) is 348 MPa (106 cycles), showing a remarkable improvement of about 80 % and 50 % compared with that of the annealed and cold worked commercially pure tantalum, respectively. The microstructural analysis identifies that cellular structures averaging 300 nm in size contribute 438.2 MPa to strengthening, serving as the main contributor to fatigue strength. The breaching of cell walls by dislocations, along with the activation of cross-slip, ensures plasticity and ultimately enhances fatigue strength. Besides, the analysis of the defects reveals that the internal lack of fusion pore can induce the secondary fatigue crack, resulting in a region of brittle crack propagation. Furthermore, a finite life prediction model is established by originally incorporating the size of the crack source defect, and it significantly improves the prediction accuracy of the fatigue life. These results provide guidance for the development of fatigue strengthening strategies for LPBF-Ta.

Effect of shot peening equivalent impact force on fatigue crack growth behavior and fatigue life prediction of train brake discs

Brake discs, vital rail components, are subjected to intense friction and thermal cycling during braking, leading to surface cracking and fatigue failure. To address these issues, a novel equivalent impact force model for shot peening is proposed in this paper, with the aim of measuring the efficacy of shot peening and predicting the fatigue crack propagation life of brake discs with the assistance of an enhanced SSO algorithm. A finite element model of a cast steel brake disc simulated fatigue crack propagation under sinusoidal cyclic loading. The validity of the multiple projectile random impact model was experimentally confirmed by introducing a residual stress field into the disc surface using equivalent impact forces of 0.3, 0.4 and 0.5 MPa. This stress field was incorporated into the cyclic fatigue crack propagation model and compared with the FCGR results. Shot peening produced a compressive residual stress layer which increased in thickness with increasing impact force. The fatigue crack propagation rate was highly sensitive to the equivalent impact force, decreasing between 0.3 and 0.4 MPa and increasing from 0.4 to 0.5 MPa. Optimal crack propagation inhibition was at 0.4 MPa, which increased fatigue life by 457 % compared to non-peened discs. The improved SSO algorithm also achieved a 6 % increase in prediction accuracy.

A new approach to multiaxial fatigue life prediction: A multi-dimensional multi-scale composite neural network with multi-depth

Many multiaxial fatigue life prediction methods tend to increase additional parameters and model depth as the complexity of the loading path increases. This leads to issues such as poor model robustness, limited flexibility, and single-dimensional approaches. In this study, a multi-dimensional multi-scale composite neural network with multi-depth is proposed to address these challenges and enhance prediction accuracy. Initially, physical and sensitive features serve as input data for the proposed model, to enhance the richness of input features. Subsequently, a multi-dimensional feature extraction module is deployed to extract feature information from the composite data. To process these features, an improved multi-domain query cascaded transformer network (IMQCT) is employed as the feature processing module of the proposed model. The proposed model is verified to have better prediction accuracy and extrapolation capability by using experimental data from nine materials and comparing its performance with six machine learning models.

Multiscale modelling strategy for predicting fatigue performance of welded joints

This study predicts the fatigue performance of welded joints through a multiscale modelling strategy accounting for material and structural inhomogeneities. An S-N curve and detailed fracture surfaces with distinct beach marks were first derived by uniaxial fatigue tests utilising the designed cruciform welded joints. Considering the intrinsic features of welded joints, a multiscale modelling strategy was proposed to integrate multiple factors, including microstructural variations, strength distributions within the heat-affected zone (HAZ), and the diversity of three-dimensional weld toe shapes. Significantly, a modelling strategy was presented for the first time to simulate the simultaneous initiation, growth, and coalescence of multiple cracks, and was validated against experimental evidence. The results indicate that the proposed strategy can accurately predict both the fatigue strength and the overall crack growth process. Additionally, comparative assessments of single-crack and multiple-crack modelling strategies revealed notably shorter predicted fatigue lives when considering crack coalescence. Overall, this work establishes a multiscale framework for assessing the fatigue performance of welded joints considering both microscopic and macroscopic factors, offering substantial practical implications for engineering applications.

The study on the high cycle fatigue performance and life prediction of Ti–6Al–4V alloy fabricated by laser engineered net shaping

The study on the high cycle fatigue performance and life prediction of Ti–6Al–4V alloy fabricated by laser engineered net shaping