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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2013, Vol. 34 ›› Issue (5): 1114-1121.doi: 10.7527/S1000-6893.2013.0200

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Fatigue Life Prediction Method for Aircraft Metal Material Under Alternative Corrosion/Fatigue Process

ZHANG Haiwei1, HE Yuting1, FAN Chaohua2, LIU Zhaotong3, WU Liming1   

  1. 1. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038;
    2. The Military Representative Office of PLA Residing in Factory 122, Harbin 150060;
    3. Xi'an Branch, Chinese Academy of Space Technology, Xi'an 710000
  • Received:2012-11-01 Revised:2012-12-25 Online:2013-05-25 Published:2013-01-09
  • Supported by:

    National Natural Science Foundation of China (50975284)

Abstract:

Residual life evaluation for aircraft metal components is critical in the consideration of the relationship of fatigue life and calendar life under in-service environments. Therefore, the process of "corrosion on the ground+fatigue in the air" is simulated to establish a fatigue life prediction model under an alternative corrosion-fatigue process. Firstly, 2A12-T4 aluminum alloy specimens are implemented with a pre-corrosion fatigue test. Compared with the actual data, the theoretical life obtained by simulating the alternative corrosion-fatigue process based on the pre-corrosion test results is rather conservative. Afterwards, an alternative corrosion-fatigue prediction model based on actual alternative test results is established by regression arithmetic with coupling damage uniform distribution. Furthermore, the BP and Elman artificial neural networks are used to verify the model. The result shows that the predicted life by the coupling damage uniform distribution model is in good agreement with the actual life. Further calculation and test results show that the model can be used to predict the fatigue life with different combinations of loading cycles and corrosion times, and it exhibits good perspective for application.

Key words: corrosion, fatigue, 2A12-T4 aluminum alloy, uniform distribution, neural network

CLC Number: