Abstract：Cast magnesium alloy ZM6 is a typical material used in the manufacturing of helicopter reducer casing. However, the internal defects produced during the casting process have a significant effect on the fatigue properties of the material. In this paper, the fatigue damage model and life prediction method of ZM6 material with internal pore defects are studied. First, X-ray tomography scan of specimens made of three batches of blank material is conducted, and the distribution characteristics of internal pores is obtained. It can be observed that the pore distributions are much different for three batches of specimens. In addition, the randomly distributed small pores and few large dimension pores are both observed for each tested specimen, which will provide the basic data for the following influencing analysis of pores. Afterwards, high cycle fatigue tests of 48 specimens are conducted under two stress ratios and several stress levels, and the fatigue life results of each batch of specimens are obtained. After that, the correlation between life result and the distribution of internal pores is further studied. It can be concluded that the porosity and the large near-surface pores are the two key factors affecting the fatigue lives of specimens. Accordingly, based on the theory of damage mechanics, a fatigue damage model is then proposed to reflect the influences of porosity and large near-surface pores by introducing the initial elastic modulus of representative volume element (RVE) and the equivalent local stress and strain fields around the key pore. The corresponding parameters calibration method is presented as well. This model is then combined with the ABAQUS software platform to implement the numerical calculation of fatigue damage evolution of specimen with internal pores. Finally, the proposed theoretical model and calculation method are used to predict the fatigue lives of specimens, and the prediction results agree well with the experimental data, which validates the applicability of the proposed method.
2022, Vol. 43 
