Numerical prediction of fatigue life of an A356-T6 alloy wheel considering the influence of casting defect and mean stress

• Published in: Engineering failure analysis Vol. 118; p. 104903
• Main Authors: Duan, Yong-chuan, Zhang, Fang-fang, Yao, Dan, Tian, Le, Yang, Liu, Guan, Ying-ping, Hu, Jin-hua
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2020
• Subjects: Data mapping algorithm; Mean stress; Radial fatigue life prediction of wheels; SDAS; Shrinkage cavity; Mean stress; Data mapping algorithm; Radial fatigue life prediction of wheels; Shrinkage cavity; SDAS
• ISSN: 1350-6307; 1873-1961
• DOI: 10.1016/j.engfailanal.2020.104903

•A fatigue prediction method is proposed, predicated life is more accurate.•The integrated coupling of the three software prediction models is realized based on the self-developed Transfer Couple Date (TCD) software.•The material is sensitive to macroscopic shrinkage defects exceeding 400 μm.•Compared with the results of traditional fatigue life prediction method, the new results show that the influence of microscopic effect on the fatigue position prediction is small, and the influence on the fatigue life is larger. A fatigue prediction method considering shrinkage cavity, secondary dendrite arm spacing (SDAS) and mean stress level is presented in the paper. Firstly, the casting process of an aluminum alloy wheel is simulated based on ProCAST software. And the data of SDAS and porosity of different parts are predicted based on the solidification process. Then the data mapping algorithm between tetrahedral mesh elements is developed to realize the unidirectional transformation of microcosmic data from a cast model to a static mechanical model. And the radial loading mechanical analysis model of a wheel containing microcosmic information is further established. According to the specific mechanical and fatigue parameters of each node, the fatigue life prediction model is established by Fesafe software. Based on the self-developed Transfer Couple Data (TCD) software, the integrated coupling method of the three software prediction models is realized, and the method is further used to realize a precise prediction of the radial fatigue life of a wheel considering effects of shrinkage cavity, SDAS and mean stress. Compared with the experimental results, after considering the microcosmic influence, the predicted position of the minimum life is unchanged, and the predicted life value is more accurate after considering the microcosmic influence. The proposed method lays a solid foundation of the optimization design and lightweight design of aluminum alloy wheels.