Abstract
An neural network-based modelling approach is employed to predict the grain growth behaviour during continuous reheating. Using a significant data set containing critical information on the grain growth, a neural network based model has been trained. A compact set of process variables has been selected as the model inputs, based on expert knowledge as well as data analysis techniques. Ensemble modelling techniques have been used to improve model performance as well as to provide error bounds for prediction confidence. The resulting neural network model gives an impressive prediction performance, with the prediction error very close to the maximal measurement standard deviation. The neural network model has been tested on new grain growth data with more divergence in the reheating patterns, and gives a satisfactory prediction on these data as well. It is concluded that the developed grain growth model is capable of providing the initial microstructures for an integrated thermomechanical model, with a very fast computing speed.
