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PREDICTION OF YIELD STRENGTH OF LOW/MEDIUM CRMO FERRITIC STEELS USING ARTIFICIAL NEURAL NETWORKS | ||
| Iraqi journal of mechanical and material engineering | ||
| Article 1, Volume 11, Issue 4, 2011, Pages 649-662 | ||
| Authors | ||
| R. M. Hussein; N. J. Hammadi; Q. A. Rishaq | ||
| Abstract | ||
| Abstract The yield strength of Low/Medium Cr- Mo ferritic steels has been analyzed by a well selected artificial neural networks (ANN) model using data sets obtained from ASTM publications. The qualitative and quantitative effects of chemical composition, heat treatment and test temperature have been studied. The proposed ANN model was obtained by applying averaging process to the first best three models. The first one consists of 24 input nodes (the input variables), 23 hidden nodes and the output node which is the target for the required yield strength. Among the previous variables, it was found that the heat treatment ones have the greatest contribution to the yield strength especially the tempering one i.e. the average contribution of about 15% was obtained | ||
| Keywords | ||
| yield strength; ferritic steels; artificial neural networks; Mo steels; averaging process | ||
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