Fatigue Life Prediction Method Research Articles

Evaluation of fatigue life prediction models and investigation of fracture mechanisms of mild steel

Fatigue is one of the dilemmas of coiled tubing (CT) in oil and gas engineering. This study investigated the fatigue behavior of CT employing mild steel, which is commonly utilized in the CT110. Two fatigue life prediction methods based on cyclic strain-life principles, the Manson–Coffin and Landgraf models, as well as the Smith and Ellyin models based on energy-life principles, were evaluated. The Ellyin model demonstrated superior accuracy in predicting the low-cycle fatigue life of CT110, displaying dispersion within the 1.5 scatter band. This research integrated mechanical property analysis and microscopic deformation assessments for CT110. Fatigue mechanics studies show cyclic softening behavior in the CT110 of mild steel. The results of the microscopic analysis show that the fatigue fracture of CT110 has typical fatigue striations and equiaxial dimples as a plastic fracture. Combined with TEM and EBSD analysis, fatigue deformation increases grain deformation and dislocation density. In addition, the increase in dislocation density results from the combination of cycles and strain amplitude. The results of in situ EBSD further support this view. At 8% strain amplitude, the dislocation density increases to 0.196B when the cycle increases from 0 number to 10 number. Furthermore, the ferrite grain slip becomes accessible, but the 〈111〉 // Z0 direction is still the main texture. These are fundamental reasons for the cyclic softening of the CT110 mild steel, supported by a decrease in stress amplitude.

Investigating the relationship between fracture entropy and stress amplitude in CFRP laminates under low-cycle fatigue loading

Fracture fatigue entropy (FFE) is considered to be independent of the loading amplitude in fatigue experiments, and this conclusion has been applied to fatigue life prediction of composites and metals with satisfactory prediction accuracy. However, the existing research work mainly focuses on high-cycle fatigue, and it is not clear whether the FFE is independent of the loading amplitude in low-cycle fatigue, which requires more in-depth research. Fatigue experiments were conducted on three CFRP laminates with different ply orientations, and the FFE values corresponding to low-cycle fatigue were obtained by thermographic analysis. The results show that the FFE value is no longer independent of the loading amplitude under low-cycle fatigue conditions. On the contrary, it decreases with the increase of fatigue loading amplitude and also varies with different ply orientations. The relationship between low-cycle fracture fatigue entropy and loading amplitude of composites with different ply orientations was analyzed and modeled, and a novel low-cycle fatigue life prediction method based on FFE was developed.

Fatigue life prediction method for reinforced concrete deck slabs subjected to repeated moving wheel loads and failing in shear

AbstractAs bridge deck slabs experience the direct impact of repeated concentrated wheel loads, they are more susceptible to fatigue‐related damage than other bridge components. In this context, a fatigue life prediction method for reinforced concrete (RC) bridge decks has been developed based on failure mechanisms of RC slabs tested under moving wheel loads. Initially, the formulas encompassed shear strength and S–N equations, accommodating variations in environmental and support conditions, are proposed to facilitate the fatigue life prediction of slabs exposed to constant moving wheel load. Subsequently, a model for shear strength degradation formulated using experimental findings on beam‐like elements is utilized in predicting the fatigue life of slabs subjected to stepwise moving wheel loads. The validity of the proposed method was established by comparing its results with previous experimental data. Notably, compared with the existing prediction equation, the proposed method exhibited more accurate fatigue life prediction results.

Probabilistic LCF life prediction framework for turbine discs considering random load history

Probabilistic LCF life prediction framework for turbine discs considering random load history

Simulation of fatigue crack initiation life under compressive loading based on material meso-characteristics

The fatigue problem under compressive loading is common in the key components of aeroengine. Most of the fatigue life prediction methods studied in the past are macroscopic phenomenological methods based on a large amount of experimental data. Due to the difficulty in carrying out the fatigue test under compressive loading, the S-N curve or Manson-Coffin formula commonly used on the fatigue life prediction of material under compressive stress were obtained through fatigue tests under conditions where the average stress was greater than or equal to zero. Although the average stress can be corrected by some formulas, whether the life estimation formulas fitted by those fatigue test data are applicable to the fatigue life analysis with average stress less than zero has not been solved in engineering. And there were few studies on the influence of compressive stress on fatigue life at home and abroad. On the basis of Tanaka-Mura fatigue crack initiation theory, a mesoscopic model suitable for fatigue characteristic analysis under compressive loading was established in this article. The model verified the feasibility in predicting the fatigue initiation life using test data of 7075-T651 aluminum alloy under the compressive fatigue load.

Master S-N Curve Fatigue Life Prediction of a Railroad Car Bogie Based on Mesh Insensitive Structural Stress Method

Fatigue life prediction of a welded structure is a complex phenomenon due to the nature of fatigue and the welding process. Additionally, Finite Element Method (FEM) results are extremely sensitive to the size of elements. Therefore, it is difficult to adopt a method to estimate the fatigue life, especially for welded structures. Besides, mesh size independence is a critical issue to perform fatigue life prediction methods that eliminates the need for excessive element numbers in the mesh. This paper investigates the Master S-N Curve Approach (MCA) using the output parameters of the mesh insensitive Structural Stress Method (SSM). MCA based on SSM employs structural stresses recovered from nodal forces and nodal moments. To recover these inputs, FEM model should be established properly. Thus, boundary conditions and applied loads were prepared for the model according to the BS EN 13749:2021. The submodeling technique in ANSYS software was used to analyze the bogie structure. To justify the mesh independence for the model, different mesh sizes were tested. In a specific range for shell bodies, SSM was shown to provide sufficient mesh independence feature. Furthermore, MCA was compared with Hot Spot Stress Method and Nominal Stress Method based on their fatigue life estimations.

Probabilistic gear fatigue life prediction based on physics-informed transformer

Gear fatigue testing is a complex process that is both time-consuming and labor-intensive, and often produces dispersed results. Traditional statistical analysis methods for probability-stress-life (P-S-N) and fatigue limit require large sample sizes. This study proposes a novel probabilistic gear fatigue life prediction method based on physics-informed Transformer. This method seeks to overcome the limitations of traditional statistical analysis methods and data-driven models by integrating stress-life physical knowledge with the data-driven model. Specifically, based on the obtained gear fatigue limit, this method predicts the probabilistic fatigue life at each stress level by using only a set of probability distribution characteristics of fatigue life under constant amplitude load. The proposed method was validated through multiple cases of gear bending fatigue tests with varying materials, treatments, and geometries, and prediction errors were within an error factor of 2. Compared with other traditional machine learning methods, the accuracy of life prediction was improved by 19.74% to 29.44%. Additionally, feature analysis was conducted to identify key parameters influencing the accuracy of probabilistic fatigue life prediction and provide a range of optimal parameter selection, which is expected to significantly promote the efficiency of fatigue basic data construction in the gear industry.

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

AbstractThis study proposes a fatigue life prediction method for composite bolted joints, which combines algorithm optimization‐based hybrid neural networks with finite element modeling. First, based on the Hashin failure criterion of physical mechanism, a finite element model for fatigue life prediction of composite bolted joints is established, and the simulation calculations have been conducted using various initial conditions. Then, by integrating the simulation and experiment data, we have established a fatigue life database that serves machine learning training and prediction. Finally, the data undergo a comprehensive process of deep feature extraction through the utilization of a convolutional neural network (CNN). The resulting deep features are utilized as inputs for training the backpropagation neural network (BPNN) to predict fatigue life. The results indicate this synergistic combination of CNN and BPNN results in a substantial improvement in prediction accuracy and has remarkable superiority in predicting the fatigue life of composite bolted joints.

Fatigue performance analysis of precast segmental assembled concrete beams

In recent years, assembled bridges have become widely utilized in bridge construction, raising concerns about durability-related bridge diseases over time. These issues significantly impact the fatigue life of assembled bridges, necessitating an in-depth exploration of their fatigue performance. While existing research primarily concentrates on the transverse connection of multiple longitudinal beams, there is a notable dearth of studies on longitudinal precast segmental assembled bridges. This paper addresses this gap by establishing a fatigue benchmark finite element model for segmental assembled concrete beams, building upon existing experiments. The study employs numerical simulation to analyze the entire fatigue process, examining stress distribution, damage development, and considering the influence of reinforcement corrosion. Furthermore, a fatigue life prediction method, based on fatigue residual strength (R), is proposed for predicting the fatigue life (N) of concrete in precast segmental assembled beams. Results reveal that prestressed and ordinary reinforcements experience increasing stress with loading cycles, peaking around 100,000 cycles. Throughout fatigue loading, compressive stress in concrete remains low, preventing fatigue compression failure. However, tensile stress near joints gradually rises, initiating cracks at the mid-span beam’s bottom. With continued cyclic loading, these cracks propagate towards the loading point. The upper and lower limits of fatigue life predicted by the fatigue life prediction method closely align with the compressive fatigue test values of concrete, proposed fatigue life prediction method is efficient and accurate.

Multi-Stage Hybrid Loading Fatigue Life Prediction Study

Abstract Fatigue is a damage accumulation process in which material properties are continuously deteriorated and degraded under cyclic loading. Damage accumulation analysis plays a key role in preventing the occurrence of fatigue failure, and the damage evolution mechanism is an important parameter of interest for fatigue behavior. The fatigue cumulative damage life prediction model is established by focusing on the damage evolution mechanism, and an improved Manson–Harford cumulative damage nonlinear model is developed to calculate fatigue damage life by considering the effect of load ratio of multi-stage loading on fatigue life. A fatigue life prediction method considering high-low cycles and multi-stage mixed loads is proposed. The fatigue life prediction accuracy of the model is verified by fatigue tests on structural samples. The accuracy of the fatigue life prediction error is the highest among similar models under the same test, the model proposed in this paper.

An improved J-integral based adhesive joint fatigue life estimation method for automotive structural durability analysis

ABSTRACT To estimate the fatigue life of lightweight automotive structures in compliance with top-down design principles, there is a need for an accurate adhesive joint fatigue life prediction method that is compatible with industrial finite element modeling practices. This paper proposes a method to split the analytical J-integral solution based on the fracture mode of joints. The split mode I and II J-integrals correspond to the opening mode and sliding mode of the bonded joint, respectively. By splitting the J-integral and introducing the concept of the mixed mode ratio, an improved approach for estimating adhesive joint fatigue life is presented, considering the influence of loading modes. The proposed fatigue life prediction method is validated through fatigue tests on a sub-component level bonded structure. The results of the validation demonstrate that the split J-integral analytical solution method, considering the mixed mode ratio, provides better predictions compared to its predecessor approach.

High-cycle and low-cycle fatigue life prediction under random multiaxial loadings without cycle counting

A novel fatigue life prediction method under random multiaxial loadings is proposed in this paper. One unique benefit of the proposed method is that it is based on the time-derivative fatigue crack growth formulation and does not need cycle counting under arbitrary loadings. First, a brief review of subcycle fatigue crack growth (FCG) analysis and the equivalent initial flaw size (EIFS) concept for life prediction is introduced. Next, the existing subcycle fatigue crack growth model is extended to near-threshold conditions. An intrinsic fatigue threshold of the material is introduced, and a hypothesis is proposed that the crack only grows when the local stress intensity factor is beyond the intrinsic threshold. Following this, the analogy of stress intensity factor and strain intensity factor is used to extend the fatigue life prediction model to strain-based fatigue analysis. Correction for large plastic deformation is included to handle low-cycle fatigue life prediction. The novelties of this model are that it considers the FCG at the threshold and near-threshold region and it is able to predict both HCF and LCF conditions using FCG-based life prediction. Following this, extensive in-house and literature data for AL-7075-T6 under uniaxial and multiaxial, constant, and random loadings are used for model validation. Discussions for the effect of model parameters are provided based on parametric analysis. Conclusions and future work are mentioned. Code and data are released in public cloud service for interested readers.

Pore-induced fatigue failure: A prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning

Pores are major cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloy. For the safe application of L-DED titanium alloys, it is essential to establish a fatigue life prediction method based on pore-induced fatigue. This paper proposes a prior progressive fatigue life prediction framework based on ridge classification and kernel ridge regression algorithms. The fatigue life prediction was carried out on L-DED Ti-6Al-4V alloy in three steps: critical pore identification, fine granular area existence prediction and final fatigue life prediction. The fatigue life prediction method adopted in the current study outperform the others with a correlation coefficient as high as 0.951, followed by a comparison with the results derived from different machine learning algorithms. The results show that the proposed fatigue life prediction framework can predict the fatigue life of L-DED Ti-6Al-4V alloy based on computed tomography tests and microstructure features. Due to its strong generalization ability and effectiveness, the proposed prediction method is expected to be valuable for fatigue-resistant design of L-DED Ti-6Al-4V alloy.

Damage-Accumulation-Induced Crack Propagation and Fatigue Life Analysis of a Porous LY12 Aluminum Alloy Plate.

Rivets are usually used to connect the skin of an aircraft with joints such as frames and stringers, so the skin of the connection part is a porous structure. During the service of the aircraft, cracks appear in some difficult-to-detect parts of the skin porous structure, which causes great difficulties in the service life prediction and health monitoring of the aircraft. In this paper, a secondary development subroutine in PYTHON based on ABAQUS-XFEM is compiled to analyze the cracks that are difficult to monitor in the porous structure of aircraft skin joints. The program can automatically analyze the stress intensity factor of the crack tip with different lengths in the porous structure, and then the residual fatigue life can be deduced. For the sake of safety, the program adopts a more conservative algorithm. In comparison with the physical fatigue test results, the fatigue life of the simulation results is 16% smaller. This project provides a feasible simulation method for fatigue life prediction of porous structures. It lays a foundation for the subsequent establishment of digital twins for damage monitoring of aircraft porous structures.

Fatigue life prediction considering individual modulus for unidirectional needled C/SiC composites under variable amplitude loading

Needled C/SiC composites are widely used in the aerospace field, making the evaluation of fatigue damage critically important. A fatigue damage model associated with the individual modulus is proposed. Compared to the fatigue damage model by S-N curve, the proposed fatigue damage model can better characterize the fatigue damage behavior of UN-C/SiC composites. By combining the rainflow counting method, the Goodman equation, and the Palmgren-Miner damage accumulation rule, a fatigue life prediction method considering the individual modulus is developed for UN-C/SiC composites under variable amplitude loading. Compared with the experimental life, the predicted fatigue life shows a good result.

Fatigue life prediction method of Q235 steel single lap thin-walled bolted connections under shear loading

Fatigue life prediction method of Q235 steel single lap thin-walled bolted connections under shear loading

A generalization ability-enhanced image recognition based multiaxial fatigue life prediction method for complex loading conditions

A generalization ability-enhanced image recognition based multiaxial low-cycle fatigue life prediction is proposed for complex conditions, including multiaxial variable amplitude fatigue, fatigue-creep interaction, and anisotropic materials. Notably, a novel modified dynamic hysteresis image plotting method is introduced for multiaxial variable amplitude loading. Building on a pre-trained convolution neural network (CNN) model, fine-tuning is used across three cases. It makes the error decrease to 25%. Besides, fine-tuning improves training efficiency and generalization. Based on fine-tuning results, each layer of the model is attributed with physics significance. Furthermore, regression activation map (RAM) is used for interpretation, illustrating that the model focuses on the inner and edges of hysteresis images.

Thermal fatigue life prediction method for die casting mold steel based on the cooling cycle

Thermal fatigue life prediction method for die casting mold steel based on the cooling cycle

Notch effect identification and fatigue life prediction of undercut weld

Undercut, as a typical surface imperfection at the weld toe, significantly impacts the weld fatigue life. Neither the ISO5817 standard for imperfection level definition nor the traditional fatigue life prediction methods for welds can accurately identify the undercut effect on weld fatigue life. To solve this problem, a new stress concept and life prediction method are proposed, which can predict the fatigue life of undercut welds in both linear elastic and elastic–plastic conditions. Its prediction accuracy is verified by test data and engineering examples.

Predicting asphalt mixture fatigue life via four-point bending tests based on viscoelastic continuum damage mechanics

A more technically accurate approach to predict fatigue life of asphalt mixtures can enhance the precision of performance evaluation for asphalt pavements. This study applied the viscoelastic continuum damage mechanics (VECD) to investigate the fatigue behavior of asphalt mixtures. The fatigue life of typical asphalt mixtures was evaluated through four-point bending tests under various conditions. The strain-modulus empirical equation was firstly employed to fit the experimental data. However, it was noted that this equation was not entirely suitable for all conditions, as factors like temperature and loading frequency can influence fatigue life not only through the dynamic modulus but also via the damage properties of asphalt mixtures. To address this limitation and achieve a comprehensive fatigue life prediction equation, the Time-Strain Superstition Principle (TSSP) and Frequency-Strain Superstition Principle (FSSP) were introduced to characterize the impact of temperature and loading frequency on fatigue life, which can reduce fitting errors. Additionally, a mechanical prediction equation was also derived by the VECD approach, and its applicability under different conditions was explored. The results revealed that the damage characteristic curve (C-S curve) remained unaffected by loading frequency and strain level but was highly dependent on temperature. Significant fitting errors were observed in different temperatures when employing the VECD method for fatigue life prediction, as the VECD method did not account for the influence of temperature on the viscoelastic properties of asphalt mixtures, which in fact plays a critical role in fatigue life. In response, temperature adjustment coefficients were introduced into the derived equation. The predicted results demonstrated that the newly modified method significantly improved prediction precision, yielding an error of less than 20%.