Integrated predictions of the influence of mesh size, casting defects and SDAS on the fatigue life of aluminum alloy wheels

In this study, a finite element-based fatigue prediction method that incorporates mesh size, casting defects, and secondary dendrite arm spacing (SDAS) is proposed. First, the grid convergence index (GCI) theory is employed to assess the mesh independence of fatigue life simulations for wheel radial fatigue, leading to the determination of an optimal mesh size of 5 mm. The aluminum alloy wheel casting process, including the assessment of casting defects and cooling rate distribution, is then simulated using a finite element model. An algorithm for transferring data from the process simulation model to the structural simulation model is developed. As a result, a radial fatigue model that accounts for the effects of porosity and SDAS in the wheel is established. The integrated fatigue model is used to predict the influence of mesh size, casting defects, and SDAS on wheel fatigue performance, enabling accurate prediction of radial fatigue life as a function of casting conditions. This work lays the foundation for process optimization aimed at improving the service life of aluminum wheel castings.