Study of effect of R-ratio and overload on fatigue crack growth using artificial neural network

2017 ◽  
Author(s):  
K. N. Pandey ◽  
Saurabh Kumar Gupta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
R Ratio ◽  
Artificial Neural

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.

Modeling of Fatigue Crack Propagation

2004 ◽  
Vol 126 (1) ◽  
pp. 77-86 ◽  
Author(s):  
Yanyao Jiang ◽  
Miaolin Feng
Keyword(s):  
Fatigue Crack ◽  
Fatigue Crack Growth ◽  
Crack Initiation ◽  
Crack Propagation ◽  
Crack Growth ◽  
Fatigue Damage ◽  
Fatigue Crack Propagation ◽  
Fatigue Crack Initiation ◽  
Cyclic Plasticity ◽  
R Ratio

Fatigue crack propagation was modeled by using the cyclic plasticity material properties and fatigue constants for crack initiation. The cyclic elastic-plastic stress-strain field near the crack tip was analyzed using the finite element method with the implementation of a robust cyclic plasticity theory. An incremental multiaxial fatigue criterion was employed to determine the fatigue damage. A straightforward method was developed to determine the fatigue crack growth rate. Crack propagation behavior of a material was obtained without any additional assumptions or fitting. Benchmark Mode I fatigue crack growth experiments were conducted using 1070 steel at room temperature. The approach developed was able to quantitatively capture all the important fatigue crack propagation behaviors including the overload and the R-ratio effects on crack propagation and threshold. The models provide a new perspective for the R-ratio effects. The results support the notion that the fatigue crack initiation and propagation behaviors are governed by the same fatigue damage mechanisms. Crack growth can be treated as a process of continuous crack nucleation.

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