Explore the vast range of titles available.

The disadvantages of widely used austenitic stainless steels are their low hardness and relatively low fatigue strength. Conventional chemical-thermal surface treatments are unsuitable for these steels since they create conditions for inter-granular corrosion. An effective alternative is a low-temperature surface treatment, creating an S-phase within the surface layer, but it has a high cost/quality ratio. Austenitic steels can increase their surface micro-hardness and fatigue strength via surface cold working. When the goal is to increase the rotating bending fatigue strength of austenitic chromium-nickel steels, and the requirements for significant wear resistance are not paramount, diamond burnishing (DB) has significant potential to increase the fatigue strength and, based on the cost/quality ratio, can successfully compete with low-temperature chemical-thermal treatments. The main objective of this study is to establish the effect of DB on the rotating fatigue strength of AISI 304 L chromium-nickel austenitic steel. The influence of DB parameters on the surface integrity (SI) characteristics was studied. Optimal DB parameters under minimum roughness and maximum micro-hardness criteria were obtained. Rotating bending fatigue tests of the diamond burnished (in a different manner) and untreated specimens were performed. DB implemented via parameters providing maximum micro-hardness increased fatigue limit by 38% compared to untreated specimens.

Overall fatigue strengths and hardness distributions of the aluminum alloy similar and dissimilar friction stir welding (FSW) joints were determined. The local fatigue strengths as well as local tensile strengths were also obtained by using small round bar specimens extracted from specific locations, such as the stir zone, heat affected zone, and base metal. It was found from the results that fatigue fracture of the FSW joint plate specimen occurred at the location of the lowest local fatigue strength as well as the lowest hardness, regardless of microstructural evolution. To estimate the fatigue strengths of aluminum alloy FSW joints from the hardness measurements, the relationship between fatigue strength and hardness for aluminum alloys was investigated based on the present experimental results and the available wide range of data from the references. It was found as: σa (R = −1) = 1.68 HV (σa is in MPa and HV has no unit). It was also confirmed that the estimated fatigue strengths were in good agreement with the experimental results for aluminum alloy FSW joints.

We examined the fatigue properties in very high cycle regime of large FV520B-I specimens in an ultrasonic fatigue test. The fatigue mechanism in very high cycle regime did not change and the fatigue properties obviously degraded as the specimen size enlarged. The fatigue life decreased and the S – N curve moved downward due to the increase in inclusion size in large specimens. The maximum inclusion sizes in specimens were predicted by the method of statistics of extreme value. The characteristic area sizes were obtained by experiment, including the distance from the crack origin to the specimen surface, the fish-eye diameter, the GBF diameter, and the inclusion diameter. According to these data, the relationships of characteristic area sizes with VHCF life were analyzed. The prediction of fatigue strength using the modified Murakami model was closer to the test result, and the fitting of fatigue life using the corrosion fatigue crack initiation life model was less effective compared with the fitting of small specimen test results.

The accurate prediction of fatigue performance is of great engineering significance for the safe and reliable service of components. However, due to the complexity of influencing factors on fatigue behavior and the incomplete understanding of the fatigue failure mechanism, it is difficult to correlate well the influence of various factors on fatigue performance. Machine learning could be used to deal with the association or influence of complex factors due to its good nonlinear approximation and multi-variable learning ability. In this paper, the gradient boosting regression tree model, the long short-term memory model and the polynomial regression model with ridge regularization in machine learning are used to predict the fatigue strength of a nickel-based superalloy GH4169 under different temperatures, stress ratios and fatigue life in the literature. By dividing different training and testing sets, the influence of the composition of data in the training set on the predictive ability of the machine learning method is investigated. The results indicate that the machine learning method shows great potential in the fatigue strength prediction through learning and training limited data, which could provide a new means for the prediction of fatigue performance incorporating complex influencing factors. However, the predicted results are closely related to the data in the training set. More abundant data in the training set is necessary to achieve a better predictive capability of the machine learning model. For example, it is hard to give good predictions for the anomalous data if the anomalous data are absent in the training set.

In order to obtain the optimum fatigue performance, 35CrMo steel was processed by different heat treatment procedures. The microstructure, tensile properties, fatigue properties, and fatigue cracking mechanisms were compared and analyzed. The results show that fatigue strength and yield strength slowly increase at first and then rapidly decrease with the increase of tempering temperature, and both reach the maximum values at a tempering temperature of 200 °C. The yield strength affects the ratio of crack initiation site, fatigue strength coefficient, and fatigue strength exponent to a certain extent. Based on Basquin equation and fatigue crack initiation mechanism, a fatigue strength prediction method for 35CrMo steel was established.

A reliable local-fatigue assessment approach for rotary friction-welded components does not yet exist. The scope of this paper is to present test results for the fatigue behaviour of rotary friction-welded solid shafts made of structural steel S355J2G3 (1.0570) and an approach to fatigue assessment considering residual stress. In contrast to fusion-welded joints, components made by rotary friction welding usually contain compressive residual stress near the weld, which can significantly affect the fatigue strength. For this purpose, specimens were welded and characterised, including metallographic micrographs, hardness measurements, and residual stress measurements. The fatigue tests were performed with a constant amplitude loading in tension/compression or torsion with R = −1. All specimens were investigated without machining of the weld flash, either in the as-welded state or after a post-weld stress-relief heat treatment. In addition, the friction welding process and the residual stress formation were analysed using numerical simulation. The characterisation results are integrated into a fatigue assessment approach. Overall, the specimens perform comparatively well in the fatigue tests and the experimentally observed fatigue behaviour is well described using the proposed local approaches.

The synergistic effect of applying hybrid nanoparticles in improving the fatigue property of fiber reinforced polymer composites has rarely been explored before. Hence the monotonic and fatigue flexure properties of the carbon fiber reinforced epoxy laminates with matrix modified by multiwalled carbon nanotubes and graphene nanoplatelets were experimentally studied herein. The nanofiller ratio applied in the matrix modification was considered as a variable in the experimental program to investigate the effect of nanofiller ratio on the studied mechanical properties. A synergistic index has been employed to evaluate the synergistic effect of hybrid nanoparticles on the studied properties successfully. Experimental results show that the laminates with matrix modified under a nanofiller ratio (multiwalled carbon nanotube: graphene nanoplatelet) of 9:1 have the higher monotonic and fatigue strengths than those modified under other nanofiller ratios. The monotonic flexural strength and fatigue limit of the specimens modified under a nanofiller ratio of 9:1 are higher than the neat laminate specimens by 9.3 and 11.0%, respectively. The fatigue limits of the studied nano-modified laminates increase with the static strengths. Adding hybrid nanoparticles under proper nanofiller ratios in the matrix can suppress the degradation of the stiffness, further increase the resistance to fatigue damage. Examining the fracture surfaces of fatigued specimens reveals that the pullout/bridging effects of carbon nanotubes and the crack deflection effect of graphene nanoplatelets are the main reinforcement mechanisms in enhancing the fatigue strength of the composites.

All major rules and guidelines include fatigue design (FAT) classes for flush ground butt-welded joints. These FAT classes vary between FAT110 and FAT155; however, in the majority of cases, the underlying database and specimen-related details are unclear or unknown. This study evaluates 1003 fatigue test results gathered from various literature sources and tries to relate the fatigue strength improvement to typical specimen types and test conditions. To this goal, statistical methods based on correlation analysis are employed. Next, proposals for updates of rules and guidelines for flush ground butt-welded joints made of steel are established by determining new FAT classes and a suitable slope exponent. In addition, an overview of design standards and recommendations is given and main influencing factors are discussed.

Since hot-dip galvanizing causes a heat effect on cold-worked steel substrate and produces a coating layer comprised of distinct phases with varying mechanical properties, the fatigue mechanism of hot-dip galvanized steel is very complex and hard to clarify. In this study, AISI 1020 steel that has been normalized to minimize susceptibility to the heat effect was used to clarify the effect of the galvanizing layer on the tensile and fatigue properties. The galvanizing layer causes a reduction in the yield point, tensile strength, and fatigue strength. The reduction in the fatigue strength was more significant in the high cycle fatigue at R = 0.5 and 0.01 and in the low cycle fatigue at R = 0.5. The galvanizing layer seems to have very little effect on the fatigue strength at R = −1.0 in the low and high cycle fatigue. Since the fatigue strengths at R = 0.01 and −1.0 in the low cycle fatigue were strongly related to the tensile strength of the substrate, the cracking of galvanized steel was different than that of non-galvanized steel. The fatigue strength of galvanized steel at R = 0.5 dropped remarkably in the low cycle fatigue in comparison to the non-galvanized steel, and many cracks clearly occurred in the galvanizing layer. The galvanizing layer reduced the fatigue strength only under tension–tension loading. We believe that the findings in this study will be useful in the fatigue design of hot-dip galvanized steel.

The high-cycle fatigue (HCF) strength of titanium alloy blades, particularly Ti-6Al-4V, is critical for the reliability and performance of aviation engine components. Foreign object damage (FOD), which introduces notches, microstructural alterations, and residual stresses, significantly degrades the fatigue performance of these blades. Of particular concern are tensile residual stresses, which, caused by factors such as FOD, notches, or non-uniform plastic deformation, lead to increased local tensile loads, promote crack initiation, and accelerate crack growth. This study investigates the effects of external damage, including the impact angle and the resultant residual stresses, on the high-cycle fatigue strength of Ti-6Al-4V titanium alloy blades. Blade simulation specimens with impact-induced damage were tested under high-cycle fatigue conditions to assess the influence of various impact angles and the resulting tensile residual stresses. A modified fatigue strength prediction model was developed, incorporating shear factors and tensile residual stresses, to improve the accuracy of fatigue life predictions. Compared with the Neuber model and Peterson model, the modified model increases the proportion of predictions falling within the 10% scatter band by 30%, resulting in a significant improvement in prediction accuracy. The experimental results demonstrated that the modified model more accurately predicted the high-cycle fatigue strength, particularly in the presence of external damage and tensile residual stresses.