(untitled) (Journal of Mining and Metallurgy, Section B: Metallurgy, 2015)
Artificial neural networks with feed forward topology and back propagation algorithm were implemented to predict the effects of chemical composition and tensile characteristics on both hardness and impact toughness of microalloyed API X70 pipeline steels. The chemical composition measurements comprising of “carbon equivalent based upon the International Institute of Welding equation (CEIIW)”, “carbon equivalent based upon the Ito-Bessyo equation (CEPcm)”, “the sum of niobium, vanadium and titanium concentrations (VTiNb)”, “the sum of niobium and vanadium concentrations (NbV)” and “the sum of chromium, molybdenum, nickel and copper concentrations (CrMoNiCu)”, as well as, mechanical properties of “yield strength (YS)”, “ultimate tensile strength (UTS)” and “elongation (El)” were considered together as input parameters of networks while Vickers microhardness with 10 kgf applied load (HV10) and Charpy impact energy at -10 oC (CVN) were considered as outputs. For purpose of constructing models, 104 different measurements were gathered from examinations which were performed on different test specimens and randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of “absolute fraction of variance (R2)”, and “mean relative error (MRE)” were used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models could be further used in practical applications and thermo-mechanical manufacturing processes of microalloyed steels.