Methodologies for Predicting the Thermomechanical Fatigue Life of Unidirectional Metal Matrix Composites

2009 ◽  
pp. 1-1-23 ◽  
Author(s):  
RW Neu ◽  
T Nicholas
Keyword(s):  
Fatigue Life ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Thermomechanical Fatigue ◽  
Matrix Composites

scholarly journals Fatigue Life Predictions of Metal Matrix Composites Using Artificial Neural Networks

2014 ◽  
Vol 59 (1) ◽  
pp. 97-103 ◽  
Author(s):  
I. Uygur ◽  
A. Cicek ◽  
E. Toklu ◽  
R. Kara ◽  
S. Saridemir
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Volume Fraction ◽  
Matrix Composites ◽  
R Ratio ◽  
Artificial Neural ◽  
Life Predictions

Abstract In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.

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