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FATIGUE STRENGTH PREDICTION OF RAILWAY AXLE SPECIMEN WITH SURFACE NOTCH BY TCD
This paper, based on theory of critical distance(TCD), carried out a research on the fatigue strength prediction of axle specimens with surface notch. The fatigue limit of smooth specimens, specimens with V-shaped surface notch, and specimens with hole-shaped surface notch were tested by axial loading. Meanwhile, the crack growth threshold value when stress ratio R=-1 was tested. The fatigue limit of railway axle specimens with surface notch were predicted by the point method and line method of TCD with the aid of finite element calculation. Calculation results show that, when TCD is used to predict fatigue limit of axle specimens with surface notch, the relative error of point method is larger, and line method has higher prediction accuracy. Line method is able to meet engineering needs and has important guiding significance in predicting the fatigue strength of axles with surface notches.
FATIGUE STRENGTH PREDICTION OF RAILWAY AXLE SPECIMEN WITH SURFACE NOTCH BY TCD
This paper, based on theory of critical distance(TCD), carried out a research on the fatigue strength prediction of axle specimens with surface notch. The fatigue limit of smooth specimens, specimens with V-shaped surface notch, and specimens with hole-shaped surface notch were tested by axial loading. Meanwhile, the crack growth threshold value when stress ratio R=-1 was tested. The fatigue limit of railway axle specimens with surface notch were predicted by the point method and line method of TCD with the aid of finite element calculation. Calculation results show that, when TCD is used to predict fatigue limit of axle specimens with surface notch, the relative error of point method is larger, and line method has higher prediction accuracy. Line method is able to meet engineering needs and has important guiding significance in predicting the fatigue strength of axles with surface notches.
FATIGUE STRENGTH PREDICTION OF RAILWAY AXLE SPECIMEN WITH SURFACE NOTCH BY TCD
XU YouKun (Autor:in) / LU LianTao (Autor:in) / ZENG DongFang (Autor:in) / XU Tian (Autor:in) / LI Wei (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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