investigation of chemical composition influence on mechanical properties of api x65 microalloyed steel

a neural network with feed forward topology and back propagation algorithm was used to investigate the effect of composition on mechanical properties in api x65 microalloyed steel (used in manufacturing of large diameter pipes). experimental data was obtained by cutting 100 specimens from pipes manufactured in industrial scale (with similar heats and manufacturing processes). the chemical analysis and tensile tests were conducted according to the requirements specified by api 5l standard. scatter diagrams and two statistical criteria: correlation coefficient and mean squared relative error were used to evaluate the prediction performance of developed model. with regard to the satisfactory performance of the developed neural network, it was used then to investigate the effect of ni, cu and microalloying elements (nb + ti + v + al) on mechanical properties of test steel.