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Abstract
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Article Information:
Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network
F. Djavanroodi
Corresponding Author: F. Djavanroodi
Submitted: November 24, 2012
Accepted: January 01, 2013
Published: July 25, 2013 |
Abstract:
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Prediction of crack growth under creep condition is prime requirement in order to avoid costly and time-consuming creep crack growth tests. To predict, in a reliable way, the growth of a major crack in a structural components operating at high temperatures, requires a fracture mechanics based approach. In this Study a novel technique, which uses Finite Element Method (FEM) together with Artificial Neural Networks (ANN) has been developed to predict the fracture mechanics parameter (C*) in a 1%Cr1%MoV low alloy rotor steel under wide range of loading and temperatures. After confirming the validity of the FEM model with experimental data, a collection of numerical and experimental data has been used for training the various neural networks models. Three networks have been used to simulate the process, the perceptron multilayer network with tangent transfer function that uses 9 neurons in the hidden layer, gives the best results. Finally, for validation three case studies at 538°C, 550°C and 594°C temperatures are employed. The proposed model has proved that a combinations of ANN and FEM simulation performs well in estimation of C* and it is a powerful designing tool for creep crack growth characterization.
Key words: ANN, creep, failure analysis, FEM, fracture mechanics parameter (C*), ,
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Cite this Reference:
F. Djavanroodi, . Creep Crack Growth Modeling of Low Alloy Steel using Artificial Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (11): 1984-1992.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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