Fatigue life prediction of aluminum alloy via knowledge-based machine learning

Author(s):  
Zhengheng Lian ◽  
Minjie Li ◽  
Wencong Lu
2021 ◽  
Vol 143 ◽  
pp. 105993
Author(s):  
Bingfeng Zhao ◽  
Liyang Xie ◽  
Lei Wang ◽  
Zhiyong Hu ◽  
Song Zhou ◽  
...  

2012 ◽  
Vol 578 ◽  
pp. 191-196
Author(s):  
Yu Tong He

In this paper, based on the Manson-Coffin fatigue theory, a thermal fatigue life prediction method for aluminum alloy engine piston is established, by introducing the nonlinear functions between the fatigue strength coefficient, fatigue continuation coefficient and elastic modulus with temperature. Compared with the results from the linear elastic model and the linear plastic model application, this nonlinear thermal fatigue life prediction method’s results fit much better to the experimental results, which means that this method is more accurate and credible than the other models.


2020 ◽  
Author(s):  
Yongchuan Duan ◽  
Fangfang Zhang ◽  
Le Tian ◽  
Yingping Guan ◽  
Jinhua Hu

Abstract In order to solve the problem of isolated design in multi-process using multi-assistant software, a through-software radial fatigue life prediction model was established, the effects of shrinkage cavity, SDAS and mean stress on fatigue life were considered. The casting process of the aluminum alloy wheel was simulated based on ProCast, and the data of SDAS and porosity of different parts were predicted based on the solidification process; The data mapping algorithm between tetrahedral mesh elements was developed to realize the unidirectional transformation of microcosmic data from a cast model to a static mechanical model, the radial loading mechanical analysis model of a wheel containing microcosmic information was established; The fatigue life prediction model was established by Fesafe based on the specific mechanical and fatigue parameters of each node. Based on the self-developed TCD software, the integrated coupling method of the three software prediction models was realized. The application of this method on the virtual fatigue prediction experiment of unidirectional tensile specimen reduce the result dispersion between virtual and physical experiment, and the predicted life result error is reduced from 51% to 16%. The proposed method lays a solid foundation of the optimization design and lightweight design of aluminum alloy wheels.


Author(s):  
A. Salas-Zamarripa ◽  
C. Pinna ◽  
M.W. Brown ◽  
M. P. Guerrero-Mata ◽  
M. Castillo Morales ◽  
...  

2014 ◽  
pp. 239-250
Author(s):  
A. Salas-Zamarripa ◽  
C. Pinna ◽  
M. W. Brown ◽  
M. P. Guerrero-Mata ◽  
M. Castillo Morales ◽  
...  

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