Bayesian neural network analysis of fatigue crack growth rate in nickel base superalloysFujii, H., Mackay, D.J.C. and Bhadeshia, H.K.D.H. ISIJ International (1996) 36(11), 1373–1382

1998 ◽  
Vol 20 (1) ◽  
pp. 73-73
Keyword(s):  
Neural Network ◽  
Growth Rate ◽  
Fatigue Crack ◽  
Network Analysis ◽  
Crack Growth Rate ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Neural Network Analysis ◽  
Bayesian Neural Network ◽  
Nickel Base

scholarly journals Proposal of Fatigue Crack Growth Rate Curve in Air for Nickel-Base Alloys Used in BWR

2013 ◽  
Vol 79 (806) ◽  
pp. 1550-1554
Author(s):  
Takuya OGAWA ◽  
Masao ITATANI ◽  
Hiroshi NAGASE ◽  
Satoru AOIKE ◽  
Hideki YONEDA
Keyword(s):  
Growth Rate ◽  
Fatigue Crack ◽  
Crack Growth Rate ◽  
Fatigue Crack Growth ◽  
Crack Growth ◽  
Fatigue Crack Growth Rate ◽  
Rate Curve ◽  
Nickel Base

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.

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