Comparison and error evaluation of Arrhenius model and typical machine learning algorithms for high-temperature flow stress prediction of GH3536 superalloy

Abstract

In this work, the Arrhenius model and four typical machine learning (ML) algorithms including random forest (RF), support vector machine (SVM), back propagation artificial neural network (BP-ANN) and radial basis function artificial neural network (RBF-ANN) were used to forecast the high-temperature flow stress of GH3536 superalloy. The prediction accuracy order is RBF＞ BP＞SVM＞Arrhenius model＞RF. The accuracy of SVM, BP and RBF algorithms is significantly higher than that of Arrhenius model, and the error distribution range is much smaller than that of Arrhenius model and RF. For all ML algorithms, the error distributions of test and training set are basically similar. The RBF-ANN model presents extremely excellent prediction performance, the correlation coefficient R2/ root mean square error (RMSE)/ average absolute relative error (AARE) of reaches 0.9998/0.63 MPa/0.3%. Except RF, the prediction performance of test set is basically equivalent to that of training set.