Study on the correlation of toughness with chemical composition and tensile test results in microalloyed API pipeline steels - Pouraliakbar H.; Khalaj G.; Jandaghi M.R.; Khalaj M.J. (Journal of Mining and Metallurgy. Section B: Metallurgy)
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.