Development of a P91 uniaxial creep model for a wide stress range with an artificial neural network

Abstract

ABSTRACT A uniaxial creep model that describes creep over a wide stress range was developed for P91 steel using an artificial neural network (ANN). The training dataset was based on measurements from uniaxial creep tests and information derived from a combination of the logistic creep strain prediction and the Wilshire models. The ANN model reproduces the training dataset with high accuracy (R 2 = 0.975; RMSE (Root Mean Square Error) = 0.19). The model can be easily implemented in finite element analysis (FEA) codes since it provides an analytical expression of the true creep rate as a function of temperature, true stress and true creep strain. In FEA simulations under the same conditions as the training dataset, the model provides times to rupture and minimum creep rates very close to those in the training dataset. The model can be adapted for heats with different properties from the average behaviour of the training dataset by means of a stress-scaling factor.