Prediction of creep curve of HP40Nb steel using artificial neural network

Abstract

Simulation of creep curves using data obtained from a limited number of short-time creep tests is helpful for predicting the long-time creep life of materials by extrapolation techniques. The present paper demonstrates the application of artificial neural network (ANN) for the prediction of creep curves for HP40Nb micro-alloyed steel. The network consists of stress, temperature and time as the input parameters and the creep strain as the output parameter. The data used are taken from accelerated creep tests carried out at constant temperatures in the range 650–1050 °C and constant stresses 47–120 MPa. The network was trained using a three-layer feed-forward back-propagation network, having a 15-neuron hidden layer, using the Levenberg–Marquardt optimization algorithm. After successful network training, the model was subjected to several tests to demonstrate consistent prediction capability. 98% of the creep strain data could be predicted within an error of ±10% deviation from the experimental values. An additional experiment carried out to check the prediction capability of the model confirms very good prediction capability, with a correlation coefficient of 0.994, by ANN modeling.