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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]

The effects of micro-shot peening (MSP) and artificial surface defects were investigated in relation to the defect tolerance of Al-Si-0.6Mg alloy in the framework of fracture mechanics. The results indicated that coral-shaped casting defects inside the alloy could be effectively quantified using the X-ray synchrotron radiation technique. An affected layer with a surface roughness (Ra) of 1.25 and residual stress of − 125 MPa was introduced on the MSP specimen compared to the un-peened (UP) specimen. The fatigue strengths of the UP and MSP specimens containing the artificial defects decreased as the defect size increased under rotating bending fatigue tests, except in the case of defects with the equivalent size of 395 and 490 µm, respectively. MSP could shift the crack initiation site toward the subsurface, while all the cracks of the specimens with the artificial defects originated from the surface and maintained an aspect ratio of 0.66 during the crack growth process. The relationship between the defect sizes and the fatigue strengths could be reasonably assessed by the modified Kitagawa-Takahashi (K-T) diagrams. The critical defect size of the MSP specimens was increased from 450 to 542 µm compared with that of the UP specimens, owing to the beneficial effect of the compressive residual stress. The defect tolerance of the Al-Si-0.6Mg alloy was improved by the MSP process. • Effects of micro-shot peening (MSP) on the fatigue properties of Al-Si-0.6Mg alloy are explored. • Casting defect inside the alloy was identified using synchrotron radiation X-ray computed microtomography (SR-μCT) technique. • The influence of MSP on the fatigue tolerance was characterized by Kitagawa-Takahashi (K-T) diagrams. • The critical defect size harmless to fatigue strength was evaluated using fracture mechanics. [ABSTRACT FROM AUTHOR]

• The extended finite element method is used in the fracture mechanics simulation of EA4T axle steel specimen and full-scale axle. • Numerical model is built considering residual stress and wheelset press-fitting. • The effectiveness of the extended finite element method (XFEM) was verified by comparing the simulation and experimental results. • The measured load spectrum was applied, and the NASGRO equation was used to calculate the residual life of the axle. High-speed railway axles may be damaged during service or maintenance. Therefore, to ensure axle service safety, the residual life of damaged axles is necessary for an accurate assessment. In this study, a small-sized specimen was analyzed and the effectiveness of the extended finite element method (XFEM) was verified by comparing the simulation and test results. Subsequently, the residual life of the EA4T axle with an initial crack was evaluated using the XFEM, which is based on fracture mechanics, and the actual axle service load spectrum. The results show that the critical failure sizes for the residual life of the power axle and non-power axle were 7 and 4 mm, respectively, for a crack located in the middle of the axle body; in contrast, for a crack located in the arc transition of the wheel seat, the critical failure dimensions were 9 and 5 mm, respectively. Based on the NASGRO equation and considering the damage tolerance safety factor, the residual lives of the power axle for the two crack locations were estimated to be 1.3953 × 106 and 9.636 × 105 km, respectively, while those of the non-power axle were 2.095 × 106 and 1.2583 × 106 km, respectively. The results of this study can provide a reference for the axle non-destructive testing (NDT) period. [ABSTRACT FROM AUTHOR]

In this study, EA4T axle steel specimens were subjected to three shot peening treatments: conventional shot peening (CSP), micro-shot peening (MSP) and their combination, i.e. dual shot peening (DSP). The residual stress, surface roughness, morphology, microhardness, were used to characterize the affected surface layer. The results indicated that MSP achieved maximum surface compressive residual stress (SCRS), minimum surface roughness, high surface microhardness. Moreover, compared to CSP, DSP improved the SCRS and microhardness while reducing surface roughness. However, scanning electron microscopy (SEM) observations indicated the presence of intensive defects of the DSP-treated specimens, which hindered the fatigue strength of the specimens. Fatigue tests were conducted to evaluate the effect of different treatments on fatigue life, the median S - N curve demonstrated that the fatigue limit at 107 cycles was improved by 24, 32, and 27% for the CSP, MSP, and DSP specimens, respectively. Furthermore, SEM observation was conducted to analyze the fracture mechanisms, the results indicated that the failure of all specimens originated at the surface and the fracture mechanisms were not changed by the shot peening treatments. The SCRS during the cyclic loading have also been analyzed. Moreover, the factors influencing fatigue limit improvement were discussed. [ABSTRACT FROM AUTHOR]