Fatigue Life Prediction Method Research Articles

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%.

Tensile efficiency and fatigue life of similar and dissimilar carbon steel joints subjected to rotary friction welding

Rotary friction welding (RFW) is a solid-state welding method that can address the melting point limitation of other welding types; however, the welded area remains at risk of failure due to fatigue during operation. Fatigue failure is difficult to detect and predict, making it a critical failure mode in engineering. Although many fatigue life prediction methods are available, they do not consider the impact of RFW parameters (such as pressure, speed, and friction time) on fatigue strength. Owing to their good mechanical properties and commercial viability, carbon steel of grades C35 and C45 is commonly applied to manufacture main parts in the automotive industry. This study centers on employing RFW to join C35 and C45, representing the first-ever instance of using this technique to weld them together. The primary goal of RFW for C35 and C45 is to construct optimum parts that meet specific design requirements and have a long fatigue life. Findings showed C45–C45 joints (77 %) were more tensile-efficient than C35–C35 joints (73 %). Dissimilar C35–C45 joints reached 60 % of C45 base metal and 76 % of C35 base metal in terms of tensile efficiency. RFW welds had lower tensile strength than their base metals. Optimal results came at 35 MPa friction pressure and 8–12 s friction time for both similar and dissimilar RFW joints using the Coffin–Manson method, dissimilar C33–C45 joint fatigue life improved by over 85 %, aligning C35 and C45 engineering aspects. SEM microstructure analysis showed two regions: non-deformed (NDZ) and plastically deformed (PDZ), with broader PDZ indicating better tensile strength. These findings enhance RFW's efficiency, promoting automotive part longevity and safety.

Multiaxial fatigue life prediction of notched specimens based on multidimensional grey Markov theory

It is always a challenge to accurately predict the fatigue life of notched specimens under multiaxial loading, which is a guarantee of structural integrity. Therefore, in this paper, multiple factors affecting fatigue life are considered and a multidimensional grey Markov model is used for fatigue life prediction. Firstly, the plane of maximum shear strain amplitude is determined as the critical plane. Meanwhile, the stress–strain components on the critical plane are extracted, and the relevant parameters affecting the fatigue life are calculated. Secondly, due to the defects of the grey theory itself, the prediction error is larger. Therefore, the grey model is modified with Markov theory to get the grey Markov model. The correlation degree analysis between relevant factors and fatigue life is carried out with grey theory to analyze the degree of influence of different factors on fatigue life. Finally, the grey Markov model is analyzed and verified using experimental data from Q355(D), GH4169 and Al7050 notched specimens and the results show that the grey Markov model can provide an economical and effective method for multiaxial fatigue life prediction.

A novel machine learning method for multiaxial fatigue life prediction: Improved adaptive neuro-fuzzy inference system

In this study, a neuro-fuzzy-based machine learning method is developed to predict the multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and neural network are combined to identify and capture the nonlinear mapping relationship between multiaxial fatigue damage parameters and fatigue life. Non-proportionality and phase differences are introduced to characterize different loading paths. Next, the Adam algorithm is employed to update the premise parameters of the original model to achieve fast and accurate convergence. Then, subtractive clustering is applied to extract fuzzy rules between input variables and output for more efficient prediction. Moreover, the hyperparameters in the proposed model are automatically optimized by the adaptive opposition slime mould algorithm to obtain the optimal model. The predictive performance of the proposed model is verified by fatigue experimental data for six materials in published literature, which indicates that the proposed model can effectively predict the fatigue life of various materials under different loading paths. Meanwhile, compared with six classical machine learning models, it is found that the proposed model has better predictive performance and extrapolation capability.

BPNN Based Multi-factor Synthesis Prediction Method for Coal Rock Fatigue Life

BPNN Based Multi-factor Synthesis Prediction Method for Coal Rock Fatigue Life

Two-Phase fatigue life prediction method based on scSE U-net algorithm for abrasive belt grinding of titanium alloy

Two-Phase fatigue life prediction method based on scSE U-net algorithm for abrasive belt grinding of titanium alloy

Total stress field method for fatigue life prediction of finite residual thickness/width notched specimen under tensile load

The notch stress fields in current studies are mainly not affected by the residual thickness/width at notch root. The Theory of Critical Distances (TCD) has been widely applied in notch fatigue analysis. However, fatigue tensile experiment results show that TCD has limitation in surface-notched specimens with finite residual thickness and side-notched specimens with finite residual width. Under the assumption that the total stress field should be considered in determining the effective stress σeff, we proposed the total stress field method (TSFM). It has advantages in prediction accuracy and convenience of finite residual thickness/width notched specimen (FRT/WNS).

Comparison of Various Intrinsic Defect Criteria to Plot Kitagawa-Takahashi Diagrams in Additively Manufactured AlSi10Mg.

Selective laser melting is a form of additive manufacturing in which a high-power density laser is used to melt and fuse metallic powders to form the final specimen. By performing fatigue and tensile tests under various loading conditions, the study sought to establish the impact of internal defects on the specimens' fatigue life. Scanning electron microscopy and finite element simulation were conducted to determine the defect characteristics and the stress intensity factor of the specimens. Four different methods were used to determine the intrinsic defect length of the specimen, using data such as grain size, yield strength, and hardness value, among others. Kitagawa-Takahashi and El-Haddad diagrams were developed using the results. A correction factor hypothesis was established based on the deviation of measured data. Using Paris law, fatigue life was determined and compared to the experimental results later. The study aims to select one or more approaches that resemble experimental values and comprehend how internal defects and loading situations affect fatigue life. This study's findings shed light on how internal defects affect the fatigue life of selective laser-melted AlSi10Mg specimens and can aid in improving the fatigue life prediction method of additively manufactured components, provided an appropriate intrinsic crack criterion is selected.

A fast fatigue life estimation method for concrete based on the energy dissipation approach

This study proposed a concrete fatigue life prediction method based on energy dissipation analysis. The fatigue life is estimated by dividing the total critical energy dissipation by the average energy dissipation per cycle. Based on a unified concrete damage model that can be applied to both monotonic and fatigue loading conditions, the total critical energy dissipation value under fatigue loading can be estimated by the stress-strain curve under monotonic loading. The average energy dissipation per cycle is obtained by calculating numerical samples with different strengths. Several numerical examples show that the proposed prediction model can rapidly estimate fatigue life under uniaxial loading with high efficiency and excellent precision.

Response Prediction of Asphalt Pavement in Cold Region with Thermo-Hydro-Mechanical Coupling Simulation

Although the theoretical pavement structure design method (TPSDM) is widely used for designing asphalt pavements in Japan, it still exhibits certain limitations, such as not considering the variation in moduli of the base and subgrade layers due to water contents, freeze–thaw action, and stress states. This study aims to enhance Japanese TPSDM’s accuracy by considering variations in the resilient modulus of environmental impacts, pavement materials, pavement structure, and traffic load actions to accurately calculate the mechanical responses and predict pavement fatigue life. Firstly, the study develops a 3D Thermo-Hydro-Mechanical (THM) model using the finite element method (FEM) to investigate temperature and moisture distributions of the pavement with time. Then, based on the numerical results of the moisture, temperature, and stress state obtained from the THM analysis, the constant resilient modulus of the base and subgrade layers in the Japanese TPSDM is replaced with a resilient modulus that considers the stress state and the combined effects of water content fluctuations and freeze–thaw action. Finally, the fatigue life of the pavement is calculated based on the obtained mechanical response in THM analysis. The reliability and validity of the proposed fatigue life prediction method are well verified by comparing the calculated with the actual pavement fatigue life. Results indicate that the modifications improve the Japanese TPSDM by considering the environmental impacts, traffic load actions, pavement materials, and pavement structure, thereby improving the accuracy of predicting the fatigue life of asphalt pavements, particularly in cold regions.

Fatigue life prediction and structural optimization design of the bionic petal non-pneumatic tire with spokes

Fatigue durability of the non-pneumatic tire (NPT) has a decisive influence on vehicle safety and transport economy. This paper aims at the research on the fatigue life prediction method suitable for the flexible spokes of the NPT, and explores the influence of structural parameters on fatigue performance. By simultaneously taking consideration of fatigue performance, bearing capacity, and weight of the NPT, the optimal model parameters are obtained by the proposed structural optimization method. Firstly, based on the Thomas crack model, the fatigue life of the bionic petal non-pneumatic (BP-NPT) is predicted. Then, the influential mechanism of key structural parameters including the curvature, array period, and spoke thickness on tire fatigue durability is investigated by numerical simulation methods. Finally, an optimization method based on the Kriging model and modified NSGA-II is proposed to optimize the structure characteristics of the BP-NPT for better fatigue life and bearing capacity while ensuring lightweight. The results show that the predicted fatigue failure mileage of the BP-NPT is 2.54 × 104 km. By properly increasing the curvature and thickness of the spoke, the maximum strain energy density of the BP-NPT will be reduced, thus improving its fatigue durability. The performance of the optimized structure is significantly improved by the proposed optimization method. The total mass is reduced by 7.44%. The fatigue life and bearing capacity are improved by 9.75% and 4.16%, respectively. It is indicated that a more durable and safer tire configuration is provided for tire safety applications.

Novel fatigue life prediction method of a Ni-based superalloy welded joint considering defect and temperature

Defects are the primary cause of fatigue failure of welded structures, resulting in high dispersion in fatigue test results. In this study, a rapid fatigue life evaluation model is established, which considered both the effects of initial welding defects and service temperature. Firstly, the mathematical relationship between temperature and Vickers hardness is obtained by fitting a series results of high temperature Vickers hardness tests. Then, based on the SEM fracture morphology analysis of fatigue failure samples, the concept and formula of modified stress considering the information of defect size and position are proposed. Finally, a new life prediction model is established by combining the temperature-hardness relationship model with Murakami's model. The fatigue life of GH4169 electron beam welding joints can be evaluated by this model at any temperature, and most of the predicted results are within a scatter band of ±2.

Experimental study of the strengthening effect: A novel fatigue life prediction method for variable amplitude loads

AbstractThe strengthening effect of low‐amplitude loading at elevated temperatures was investigated experimentally. A powder metallurgy nickel‐based superalloy FGH96 was preloaded with different cycle numbers and stress levels, and the residual fatigue life was tested under subsequent high‐amplitude loads. The results show that when the number of preloading cycles is in a specific range, the residual fatigue life is higher than that of the virgin specimen under the same load. A novel damage accumulation model was proposed to incorporate the strengthening effect into the life prediction framework. The proposed model was validated using experimental results for various materials in the literature. The mechanism of the strengthening effect of FGH96 was discussed based on electron backscatter diffraction (EBSD) analysis.

A data-driven low-cycle fatigue life prediction model for nickel-based superalloys

A data-driven machine learning method for the low-cycle fatigue (LCF) life prediction of Nickel-based superalloys is proposed to overcome the limitations of empirical formulas. The method integrates three characteristics of the superalloys: chemical composition, heat treatment process and experimental parameters, and constructs a unified fatigue life prediction dataset. The relationship between many different microstructural parameters and LCF life is analyzed using the Pearson Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC). The fatigue life dataset is then used to predict the LCF life with four machine-learning models: Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and a Genetic Algorithm-based Random Forest (GA-RF). The comparative study results demonstrate that the models accurately predict the LCF life, as evidenced by the respective R2 values of 0.8311, 0.8345, 0.7711, and 0.9272, with GA-RF performing the best and equally better in comparison with the Coffin-Manson model. The proposed method efficiently maps feature-life relationships for the LCF life of Nickel-based superalloys, reducing experimental time and cost and promising applications in the inverse design and manufacture of alloys.

Prediction of remaining fatigue life of metal specimens using data-driven method based on acoustic emission signal

Aiming at the difficulty of predicting the remaining fatigue life of mechanical parts, a similar fatigue life prediction method based on acoustic emission signals is proposed considering the failure of vibration signals. Moreover, this paper innovatively introduces the temperature signal as a degradation feature to assist the acoustic emission signal feature for fatigue life prediction. The proposed fatigue life prediction method involves multiple processes such as feature extraction, feature smoothing, feature selection, feature compression and health index construction. A variational autoencoder structure combined with a long short-term memory neural network is used to achieve feature compression and retain feature trend. A tensile fatigue test bench was developed to collect the degradation signal from health to fatigue fracture to validate the proposed method. The validation results show that the proposed method can accurately predict the remaining fatigue life. In addition, the role of various data processing methods and the applicability of the proposed method in different working conditions are also discussed.