Artificial Neural Networks for Hardness Prediction of HAZ with Chemical Composition and Tensile Test of X70 Pipeline Steels

Abstract

A neural network with feed-forward topology and back propagation algorithm was used to predict the effects of chemical composition and tensile test parameters on hardness of heat affected zone (HAZ) in X70 pipeline steels. The mass percent of chemical compositions (i. e. carbon equivalent based upon the International Institute of Welding equation (CEIIW), the carbon equivalent based upon the chemical portion of the Ito-Bessyo carbon equivalent equation (CEPcm), the sum of the niobium, vanadium and titanium concentrations (CVTiNb), the sum of the niobium and vanadium concentrations (CNbV), the sum of the chromium, molybdenum, nickel and copper concentrations (CCrMoNiCu), yield strength (YS) at 0.005 offset, ultimate tensile strength (UTS) and percent elongation (El) were considered as input parameters to the network, while Vickers microhardness with 10 N load was considered as its output. For the purpose of constructing this model, 104 different data were gathered from the experimental results. Scatter diagrams and two statistical criteria, i. e. absolute fraction of variance (R2) and mean relative error (MRE), were used to evaluate the prediction performance of the developed model. The developed model can be further used in practical applications of alloy and thermo-mechanical schedule design in manufacturing process of pipeline steels.