Application of Artificial Neural Network for Fatigue Life Prediction under Interspersed Mode-I Spike Overload

2010 ◽  
Vol 38 (2) ◽  
pp. 101907 ◽  
Author(s):  
M. R. Mitchell ◽  
R. E. Link ◽  
J. R. Mohanty ◽  
B. B. Verma ◽  
P. K. Ray ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Mode I ◽  
Artificial Neural

Application of Artificial Neural Network for Fatigue Life Prediction

2005 ◽  
pp. 96-101
Author(s):  
Ishak Abdul Azid ◽  
Lee Kor Oon ◽  
Ong Kang Eu ◽  
K.N. Seetharamu ◽  
Ghulam Abdul Quadir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Artificial Neural

A Hybrid Finite Element Modeling: Artificial Neural Network Approach for Predicting Solder Joint Fatigue Life in Wafer-Level Chip Scale Packages

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Zhiwen Chen ◽  
Zhao Zhang ◽  
Fang Dong ◽  
Sheng Liu ◽  
Li Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Fatigue Life ◽  
Solder Joint ◽  
Finite Element Modeling ◽  
Life Prediction ◽  
Electronic Devices ◽  
Fatigue Life Prediction ◽  
Wafer Level

Abstract Fatigue life prediction of electronic devices is of great importance in both research and industry. Traditionally, fatigue tests and finite element modeling (FEM) are the two main methods. This paper presents a new hybrid approach (FEM combined with artificial neural network, (ANN)) for fatigue life prediction. Finite element models on wafer-level chip scale packages (WLCSP) with different chip thickness, PCB thickness, and solder joint pitches were created to evaluate the effect of structure parameters on the increase in maximum creep strain under thermal fatigue load. Modified Coffin–Manson equation was then employed to estimate the corresponding fatigue life. ANNs were built, and then trained, tested, and optimized with the datasets from modeling to predict increase in maximum creep strain and fatigue life. For the ANN built for strain prediction, prediction accuracy of the optimal network was 97% in accuracy tests and 93% in generalization tests. Accuracy of the other ANN for predicting fatigue life was 94.2% in accuracy tests and 88% in generalization tests. This hybrid method shows very promising application in fatigue life estimation of electronic devices which requires much less time and lower cost.

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