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.

Artificial Neural Network – An Application to Predict the Fatigue Crack Growth Rate of HSLA Steel and Copper

2012 ◽  
Vol 630 ◽  
pp. 8-13
Author(s):  
Archana Mishra ◽  
Antaryami Mishra
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Crack Growth Rate ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Hsla Steel ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present work , a prediction method has been used to describe the life of High Speed Low Alloy steel (HSLA Steel ) and Copper under constant load ratio by using Artificial Neural Network (ANN). Therefore a methodology has been developed to determine the fatigue crack growth rate (da/dN) of HSLA steel and Copper under constant amplitude loading at different load ratios i.e. R = 0, 0.2, 0.4, 0.5, 0.6 and 0.8 by adopting an exponential model to raw experimental a – N data. A soft-computing technique, i.e. Artificial Neural Network (ANN) has been formulated and implemented to estimate the fatigue life at R = 0.5. A comparison has been made with experimental data obtained by earlier researchers and found to be within limits and in good agreement. It is observed that percentage deviations from the experimental values for HSLA steel and Copper are 4.14 and 4.574 respectively. The error values are well within limits of -0.06% and -0.09% for both the materials.

Sign in / Sign up

Export Citation Format

Share Document