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• Fatigue life prediction method of 25CrMo4 steel is investigated using fracture mechanics and machine learning methods. • Residual stress relaxation process is incorporated into fatigue crack growth in combination with NASGRO equation and equivalent initial flaw size (EIFS) methodology. • Different machine learning methods areemployed to investigate theireffectiveness in fatigue life prediction. In this work, fracture mechanics and machine learning methods are separately employed to predict the high cycle fatigue (HCF) life of micro-shot peened 25CrMo4 alloy steel containing artificial notches. The influences of notch size, stress level, and surface condition on fatigue life are incorporated into the prediction model. The results shows that the fatigue life of shot-peened specimens is enhanced by the compressive residual stress (CRS) compared with that of the un-peened specimens. The prediction result using fracture mechanics method is within the error band of ±2, while that using machine learning method almost lies in the error band of ±1.5. The stress level and notch size are the most critical factors affecting the HCF fatigue life. The applicability and limitation of the two methods are finally discussed. [ABSTRACT FROM AUTHOR]

• Effects of the corrosive environment on the fatigue strength of EA4T axle steel are explored. • The evolution of corrosion process during fatigue loading is fully investigated. • Crack aspect ratio in the corrosive environment is obtained by using synchrotron radiation X-ray computed microtomography (SR-μCT). • Fatigue life prediction model is developed based on the pit growth, short crack growth, and long crack growth stages. High-speed railway axle is the critical safety component of modern ground vehicles, which experiences extremely complex environments. In this paper, the effect of corrosion on the fatigue behavior of EA4T axle steel was investigated in an artificial rainwater environment. Combining scanning electron microscopy (SEM) and synchrotron radiation X-ray micro computed microtomography (SR-μCT) with theoretical analysis, damage measurement enabled a full evolution characterization of the corrosion process for exploring the corrosion mechanism and its influence on the fatigue life. It is found that the corrosive environment significantly reduced fatigue strength and facilitated multiple crack initiation, while showed relatively little effect on the long fatigue crack growth. The fitness of the 3-P Weibull distribution was slightly higher than that of the Gumbel distribution in terms of crack population. The life prediction model incorporating the pit growth, short crack, and long crack stages showed a good agreement within the 95% and 5% probabilistic S-N curve. [ABSTRACT FROM AUTHOR]

• The preferentially dissolved ferrite in pearlite and shedding of cementite lamellae contribute to the formation of corrosion pits. • Corrosion fatigue crack initiation and growth mechanism of U75V rail steel is clarified. • Corrosion fatigue crack initiation behavior is related to morphology ratio and local shape of corrosion pits. Understanding the corrosion fatigue mechanism of rail materials is of great importance to guide the assessment of the structural integrity of modern railway components. To this purpose, the corrosion fatigue crack (CFC) initiation and growth behaviors of heat-treated U75V rail steel are investigated under 3.5 wt% NaCl solution in terms of high-cycle fatigue, fatigue crack growth (FCG) rate, together with surface morphology and microstructure. Experimental results reveal that the U75V rail steel is susceptible to corrosion fatigue failure. Examinations on fractured samples clearly show that the electric potential difference between ferrite and cementite triggers the preferential anodic dissolution of ferrite, leading to corrosion fatigue crack initiation. The crack initiation is not only solely determined by the size of the corrosion pit, but also strongly influenced by its local shape. In comparison with the fatigue crack growth in air, the corrosion FCG tends to grow in a relatively straight path, because of hydrogen embrittlement. Additionally, the CFC behaviors indicate a tendency to transverse through soft-oriented grains and deflect upon encountering hard-oriented grains. [ABSTRACT FROM AUTHOR]

• Effect of artificial notches is studied in terms of fatigue crack non-propagation behaviors. • Non-propagation crack rate firstly increases and then decreases with the increased notch depth. • Gradient residual stress and microstructure are responsible for the crack non-propagation. • Crack traces of post-mortem specimen in the hardened layer and the matrix are compared. Abnormal damages in railway axles can lead to a significant hazard to running safety and reliability. To this end, a surface treatment was selected to effectively inhibit fatigue crack initiation and growth. In this study, a single-edge notch bending fatigue test campaign with artificial notches was conducted to elucidate the fatigue crack non-propagation behavior in railway S38C axles subjected to an induction hardening process. The fatigue cracking behavior in the gradient structure was revealed by optical microscopy, electron backscatter diffraction, and fractography. The microhardness distribution was measured using a Vickers tester. The obtained results show that the microhardness of the strengthening layer is nearly triple that of the matrix. Owing to the gradient microstructures and hardness, as well as compressive residual stress, the fatigue long crack propagates faster once it passes through the hardened zone (approximately 2.0 mm in the radial depth). Thereafter, local retarding (including deflection, branching, and blunting) of the long crack occurs because of the relatively coarse ferrite and pearlite in the transition region and matrix. Totally, this fatigue cracking resistance is reasonably believed to be due to the gradient microstructure and residual stress. These findings help to tailor a suitable detection strategy for maximum defects or cracks in railway axles. [ABSTRACT FROM AUTHOR]