Multi-scale fatigue life prediction method of the A356 wheel considering the effects of casting microstructure

A356 alloys produced by low pressure die casting (LPDC) typically contain casting defects and non-uniform microstructure. In this study, a multi-scale fatigue prediction method for the automotive wheel considering both hydrogen and shrinkage microporosity and secondary dendrite arm spacing (SDAS) has been developed. A three-dimensional cellular automata (CA) model is used to simulate both dendritic growth and hydrogen microporosity. The database of equivalent diameter of microporosity and casting conditions has been established, and the data is mapped to a structural mesh by an efficient mesh mapping algorithm in order to perform both static and dynamic simulations. The wheel's cornering fatigue is successfully simulated by taking into account the microstructure features. By combining the high-cycle fatigue test and finite element analysis (FEA), the specific effects of stress concentration due to far-field stress, pore size, and location on fatigue life have been quantified. Based on this method, S-N curves have been derived for different conditions. Finally, a multi-scale fatigue life prediction model for the wheel is developed and the corresponding S-N curves are imported for each node of the model, enabling accurate prediction of the cornering fatigue life of the wheel. This method innovatively proposes integrative optimization of automotive wheel manufacturing process.