吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 1710-1715.doi: 10.13229/j.cnki.jdxbgxb201406027

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Fatigue life prediction of materials based on BP neural networks optimized by genetic algorithm

YAN Chu-liang, HAO Yun-xiao, LIU Ke-ge   

  1. Beijing Aircraft Strength Institute, Beijing 100083, China
  • Received:2014-07-01 Online:2014-11-01 Published:2014-11-01

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Abstract:

The error of traditional fatigue life algorithms of materials is big. To overcome this problem, a model of the relationship among stress concentration factor (Kt), stress average (Sm), stress amplitude (Sa) and median life (N50) was built using BP neural network combined with genetic algorithm. This model is based on the classification of fatigue life data of materials, and it can be used to predict fatigue lift of materials with finite life data. Experiment results show that, using this model the relative prediction error is below 5%, in another word, the prediction accuracy of this model is higher than that of the traditional prediction algorithms. So this model can be applied to prediction the fatigue life of materials.

Key words: fatigue life of materials, BP neural networks, genetic algorithm, fatigue life prediction

CLC Number: 

  • O346.2
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