The Application of Constitutive and Artificial Neural Network Models to Predict the Hot Strength of Steels.

Abstract

Many constitutive models have been successfully used to interpolatively and extrapolatively predict the hot strength of metal materials and artificial neural network (ANN) models have recently appeared to be an alternative for constitutive modelling due to the strong capability of the ANN in predicting and correlating nonlinear relationship between inputs and outputs. In this work, the constitutive and ANN models will initially be used to predict the complex stress strain behaviours of an austenitic steel with carbon content ranging from 0.0037 to 0.79 wt%. Due to the limitations of the models and the complexity of the material properties, both the constitutive and ANN models cannot accurately predict the effect of chemical composition. As both models have their advantages, the integration of constitutive and ANN models significantly improves the prediction accuracy and the complex influence of the chemical composition is more accurately predicted.