Predictions of toughness and hardness by using chemical composition and tensile properties in microalloyed line pipe steels

Abstract

Artificial neural networks with multilayer feed forward topology and back propagation algorithm containing two hidden layers are implemented to predict the effect of chemical composition and tensile properties on the both impact toughness and hardness of microalloyed API X70 line pipe steels. The chemical compositions in the forms of "carbon equivalent based on the International Institute of Welding equation (CEIIW)", "carbon equivalent based on 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, tensile properties of "yield strength (YS)", "ultimate tensile strength (UTS)" and "elongation (El)" are considered together as input parameters of networks while Vickers microhardness with 10 kgf applied load (HV10) and Charpy impact energy at ?10 °C (CVN ?10 °C) are assumed as the outputs of constructed models. For the purpose of constructing the models, 104 different measurements are performed and gathered data from examinations are randomly divided into training, testing and validating sets. Scatter plots and statistical criteria of "absolute fraction of variance (R2)", and "mean relative error (MRE)" are used to evaluate the prediction performance and universality of the developed models. Based on analyses, the proposed models can be further used in practical applications and thermo-mechanical manufacturing processes of microalloyed steels.