Fatigue Crack Growth Predictions in Aging Aircraft Panels Using Optimization Neural Network

AIAA Journal ◽  
10.2514/2.514 ◽  
1998 ◽  
Vol 36 (7) ◽  
pp. 1300-1304 ◽  
Author(s):  
R. M. V. Pidaparti ◽  
M. J. Palakal
Keyword(s):  
Neural Network ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Aging Aircraft ◽  
Aircraft Panels

scholarly journals PREDICTION OF FATIGUE CRACK GROWTH PROCESS VIA ARTIFICIAL NEURAL NETWORK TECHNIQUE

2014 ◽  
pp. 21-32
Author(s):  
Konstantin N. Nechval ◽  
Nicholas A. Nechval ◽  
Irina Bausova ◽  
Daina Skiltere ◽  
Vladimir F. Strelchonok
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Data Processing ◽  
Crack Growth ◽  
Growth Monitoring ◽  
Growth Data ◽  
On Line ◽  
Artificial Neural

Failure analysis and prevention are important to all of the engineering disciplines, especially for the aerospace industry. Aircraft accidents are remembered by the public because of the unusually high loss of life and broad extent of damage. In this paper, the artificial neural network (ANN) technique for the data processing of on-line fatigue crack growth monitoring is proposed after analyzing the general technique for fatigue crack growth data. A model for predicting the fatigue crack growth by ANN is presented, which does not need all kinds of materials and environment parameters, and only needs to measure the relation between a (length of crack) and N (cyclic times of loading) in-service. The feasibility of this model was verified by some examples. It makes up the inadequacy of data processing for current technique and on-line monitoring. Hence it has definite realistic meaning for engineering application.

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