TY - JOUR
T1 - Integrated predictions of the influence of mesh size, casting defects and SDAS on the fatigue life of aluminum alloy wheels
AU - Wang, Shihao
AU - Li, Zhongyao
AU - Ma, Xiaoying
AU - Wu, Xuelong
AU - Kong, Decai
AU - Qiao, Haibo
AU - Ci, Xiang
AU - Wang, Wenbo
AU - Lang, Yuling
AU - Xu, Shiwen
AU - Hou, Qinghuai
AU - Miao, Yisheng
AU - Chen, Yuanlu
AU - Wang, Junsheng
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/3/1
Y1 - 2025/3/1
N2 - 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.
AB - 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.
KW - Al casting
KW - Fatigue life
KW - Grid convergence index (GCI)
KW - Mesh mapping
KW - Microstructure
KW - Porosity
KW - Secondary dendrite arm spacing (SDAS)
UR - https://www.scopus.com/pages/publications/85217896546
U2 - 10.1016/j.jmrt.2025.02.091
DO - 10.1016/j.jmrt.2025.02.091
M3 - Article
AN - SCOPUS:85217896546
SN - 2238-7854
VL - 35
SP - 3956
EP - 3967
JO - Journal of Materials Research and Technology
JF - Journal of Materials Research and Technology
ER -