Prediction of mechanical properties for typical pressure vessel steels by small punch test combined with machine learning

Abstract

To understand the fitness of different machine learning methods to small punch test (SPT), three machine learning methods, namely, back propagation (BP) neural network, radial basis function (RBF) neural network and random forest (RF) regression model were used to establish machine learning-based correlation models of SPT load–displacement and uniaxial tensile test true stress–strain curves. Five typical pressure vessel steels were tested to reveal the prediction accuracies of different machine learning methods. Statistical analysis, unveiled the following order of prediction accuracy for pressure vessel steels: BP > RBF > RF. However, the differences in the mean absolute percentage erros between BP and RF decreased from 5.64% to 1.59%, with the training data volume increasing from 50 data sets to 629 data sets. Therefore, if a large training data volume is used, then the three machine learning methods can provide acceptable prediction results. However, if the training data volume is insufficient, then the BP, and RBF neural networks are better choices.