Artificial Neural Networks for Modelling of the Impact Toughness of Steel

Abstract

The application of artificial neural networks (ANNs) to the prediction of the Charpy impact toughness of quenched and tempered (QT) steels and ferrous weld metals is examined in detail. It is demonstrated that the Charpy impact toughness can be accurately predicted using the selected input variables and their ranges of values. The capacity of ANNs to handle problems involving large sets of input variables is illustrated by a model developed to predict the impact energy of weld metal (WM) produced by flux cored arc welding (FCAW). The usefulness of ANNs for alloy design and process control is demonstrated through another model developed to predict the toughness of a QT structural steel as a function of composition and postweld heat treatment. Although comparison of the two models indicates that the trends in toughness with changes in Mn and B concentrations are in opposite directions for weld metal and QT steel, it is shown that these trends can be reconciled with reported experimental results and theoretical interpretations.