Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels
In this investigation, an artificial neural network model with feed forward topology and back propagation algorithm was developed to predict the toughness (area underneath of stress-strain curve) of high strength low alloy steels. The inputs of the neural network included the weight perc...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Belgrade, Technical Faculty, Bor
2015-01-01
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| Series: | Journal of Mining and Metallurgy. Section B: Metallurgy |
| Subjects: | |
| Online Access: | http://www.doiserbia.nb.rs/img/doi/1450-5339/2015/1450-53391500025P.pdf |
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| Summary: | In this investigation, an artificial neural network model with feed forward
topology and back propagation algorithm was developed to predict the
toughness (area underneath of stress-strain curve) of high strength low
alloy steels. The inputs of the neural network included the weight
percentage of 15 alloying elements and the tensile test results such as
yield strength, ultimate tensile strength and elongation. Developing the
model, 118 different steels from API X52 to X70 grades were used. The
developed model was validated with 26 other steels from the data set that
were not used for the model development. Additionally, the model was also
employed to predict the toughness of 26 newly tested steels. The predicted
values were in very good agreement with the measured ones indicating that
the developed model was very accurate and had the great ability for
predicting the toughness of pipeline steels. |
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| ISSN: | 1450-5339 2217-7175 |