Fatigue & Fracture of Engineering Materials & Structures最新文献

The austenitic stainless steels are widely used in several engineering fields due to their high ductility, corrosion and high temperature performance. Despite its noble properties, components manufactured in austenitic stainless steel are subject to fatigue failure. Studies indicate that loading frequency can impact the austenitic stainless steel fatigue performance. In this scenario, the present study aims to evaluate the effect of frequencies of 3 and 30 Hz on the fatigue behavior of SS 304 alloy under load control in order to identify in which fatigue stage the effect is outstanding. Therefore, fatigue and fracture mechanics tests were evaluated on the alloy annealed at 1000°C. Furthermore, fatigue tests were also applied to the alloy after previous tensile plastic strain of 0.5. The analyses denoted a significant reduction in fatigue strength with increasing frequency, especially for the strained alloy. Fatigue crack nucleation is encouraged with greater load frequency. This behavior may be attributed to strain-induced martensite and other strain mechanisms such as twinning and slip bands that are encouraged by lower strain rates but are relieved by auto-heating achieved in higher frequencies, as mentioned in the literature, which decrease the strength to fatigue nucleation.

The principles of the mechanisms for fatigue crack propagation in metals have been established several decades ago. By definition, fatigue might be summarized as a damage process imparted to a material under the action of cyclic loads. The relation between fatigue crack growth rates (FCGRs) and formation of striation marks has been a controversial topic. Some authors stated that, in certain fatigue regimes, several fatigue cycles are required to form a single striation. The disagreement found was the motivation for the present work. This study's main goal is to provide a comparison between the striation spacing and the respective FCGR. These analyses will be performed in a Nickel superalloy, Inconel 625, obtained via directed energy deposition. Investigation of the striations spacing are compared with data obtained from FCGR tests. A good agreement between striation spacing and FCGRs was found to intermediate and large values of stress intensity factor.

Fretting fatigue is a common engineering failure phenomenon, often resulting in a shorter lifespan compared to plain fatigue. To consider the interaction between different scales, this study proposes a fully coupled two-scale model based on continuum damage mechanics (CDM) and the crystal plastic finite element method (CPFEM) for the fretting fatigue crack initiation. Furthermore, the life data series are generated by employing feature engineering and long short-term memory (LSTM) networks optimized with a genetic algorithm, ensuring the minimization of redundancy. Additionally, the genetic algorithm, enhanced by the Markov chain Monte Carlo method, was used to optimize the hyperparameters of the LSTM network. Simulation results indicate that the two-scale model offers improved accuracy in predicting crack initiation life and provides physical information of crack initiation from different scales simultaneously.

This study aimed to investigate the influence of semicircular bend (SCB) specimen size (R), loading mode (M^e), and loading rate (Lr) on fracture resistance indicators, namely, fracture work (W_f), fracture energy (G_f), and fracture strength (K_f), of asphalt concrete at three different temperatures (−30°C, −20°C, and 10°C). Using Minitab software, response surface methodology (RSM) under central composite design (CCD) was employed to design experiments and develop predictive models for W_f, G_f, and K_f in terms of R, M^e, and Lr at each temperature. The results demonstrated that the RSM models accurately predicted the fracture test data for all temperatures. The analysis of variance (ANOVA) revealed that R, M^e, and Lr significantly influenced W_f, G_f, and K_f at each temperature, whereas the square terms R², M^e2, and Lr² were not significant. The significance of two-way interaction terms varied across different responses and temperatures. Overall, the experiments conducted at −30°C, −20°C, and 10°C indicated that varying R, Lr, and M^e had notable effects on W_f, G_f, and K_f. Increasing R and M^e while decreasing Lr resulted in an increase in W_f and G_f. Furthermore, K_f exhibited a direct relationship with R and Lr but an inverse relationship with M^e.

Aiming at the issue of fatigue test data for large-scale mechanical components of building steel are very limited, a method for fitting P-S-N curves under small sample data of notched specimens is proposed to predict fatigue life. First, a fatigue life subsample augmented and its reliability assessment method are established, based on Bayesian hierarchical modeling and modified Monte Carlo method. Second, a clustering combination weighting method is proposed, to define weights of hidden variables of the binomial mixture Weibull distribution, and the expectation–maximization algorithm is used to determine probability density function of the distribution. Finally, the P-S-N curves under various failure probabilities are fitted with Weibull distributed life models, and the convergence and prediction accuracy of the different models are compared. The results show that the fatigue data of small samples can be predicted better by using mixed Weibull distribution, and the fitting P-S-N curve is more reliable and accurate.

This study presents a procedure based on constant amplitude (CA) fatigue data to predict the statistical fatigue lifetime of glass/orthopolyester composites subjected to repeated ordered and random two, three, and six sequences of block loadings. A numerical routine was developed to detect cycle-by-cycle the statistical strength degradation progression until failure, assuming that the strength at the end of a block cycle equals the strength at the start of the successive one and that the individual samples' static strength, the amount of degraded strength, and fatigue life share the same rank in their respective cumulative distribution function. Predictions conform to the statistically undetectable loading sequence effects and lightly overestimate the lifetimes of random and ordered high-to-low (1/100 cycles) repeated two-block loadings. The vanishing effect of the loading sequence when the block extents remain fixed, the block extent effects for a given three-block sequence, and the lifetimes of three-block loadings were fully predicted. The six-block sequence's experimental lifetimes with different block loading orders and block extent fell within the predicted lifetimes' cumulative distribution function. A reliable damage rule based on residual strength was proposed and compared to the Miner's rule.

The milled surface of TC4 titanium alloy was treated by laser shock peening (LSP) and laser shock wave planishing (LSWP) to investigate the effect of compressive residual stress (CRS) and surface roughness (SR) on the vibration fatigue performance. The results demonstrate that although the amplitude of CRS induced by LSWP is lower than that of LSPed specimens, the vibration fatigue life of LSWPed specimens increased by 63.78% due to a significant reduction in SR from Sa 14.1 μm to Sa 4.21 μm. When the SR is low, increasing the amplitude of CRS is more advantageous to enhance fatigue life. The fractographic analysis further confirmed that compared with LSPed and T0.2-LSWPed specimens, T0.1-LSWPed specimens have considerably less initial fatigue crack initiation, and the crack initiation location is deeper. The fatigue striation spacing of T0.1-LSWPed specimens is the smallest (0.25 μm), greatly lowering the fatigue crack growth rate.

Accurately and efficiently predicting the fatigue life of rubber materials has been a long-standing challenge due to limited understanding of the fatigue mechanism. In this study, a variational assimilation-based machine learning method assisted with incremental crack propagation model is proposed to predict the fatigue life of rubber materials. Firstly, according to the fracture mechanics theory, a new rubber fatigue life prediction model based on incremental crack propagation and sparse experimental data is established, which owns higher accuracy than the classical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method is introduced to generate a dense fatigue life dataset of rubber materials with high accuracy. Finally, the artificial neural network model is trained, cross-validated, and tested using the dense dataset, and the three-dimensional variational assimilation model is employed to merge the predicted values of artificial neural network with experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified; thereby, we offer an accurate and efficient approach to predict the rubber fatigue life.

This research delves into the fracture analysis of cellular thermosetting polymers using Mode I fracture tests with a compact tension geometry subjected to both monotonic and cyclic loadings. The equivalent linear elastic fracture mechanics concept effectively described crack initiation and propagation. Numerical simulations estimated the equivalent linear elastic crack length aligning with experimental measurements. Resistance curves revealed a transient regime followed by a self-similar regime with relatively constant fracture energy, typical of quasi-brittle materials. Finally, the fracture energy evolution correlated with macroscopic density evolution exhibiting a linear relationship relaying the influence of microstructure to a second order.

A combined high and low cycle fatigue (CCF) life prediction model, considering creep, low-cycle fatigue (LCF), high-cycle fatigue (HCF) at the maximum LCF nominal stress (HLCF) damages, and the interaction damage between LCF and HLCF, is proposed. The proportions for these four types of damage in total CCF damage are quantified. Compared with the existing CCF models, the CCF model proposed in this paper considers creep damage in CCF and has higher accuracy. The escalation in HCF stress amplitudes leads to reduced CCF life owing to the augmentation of HLCF and interaction damages. The increase in the cycle ratio of HCF to LCF, causing a reduction in CCF life is ascribed to elevated HLCF and creep damages. The calculated damage proportion results from the proposed model are consistent with the observations of the fracture characteristics using a scanning electron microscope (SEM), indicating the necessity of considering creep damage in CCF life prediction at high temperatures.