Prediction for mechanical properties of deposited metal containing rare earth elements based on artificial neural networks
GUO Yong-huan
MENG Xiang-li
GUO Yan
FAN Xi-ying
ZHANG Liang
Abstract：In order to enhance the mechanical properties of electrode and shorten the development cycle of electrode, the CeO2 and rare earth element (REE) La were added into the coating formula of E4301 electrode, and the mechanical properties of electrode were tested.Through analyzing the test data, it is found that the appropriate addition of REE can improve the mechanical properties of electrode.The prediction models for mechanical properties were established with BP and RBF neural networks, respectively.The contents of CeO2, La, Si and Mn in the electrode and the welding speed were taken as the input variables of prediction models.In addition, the tensile strength, lower yield strength, elongation and average hardness in the heat affected zone (HAZ) of deposited metal were taken as the output variables.The results show that it is feasible to use BP and RBF neural networks in predicting the mechanical properties of electrode containing REE.The prediction accuracy and efficiency of RBF neural network model are higher than those of BP neural network model.
Keywords：La elementwelding speedBP neural networkRBF neural networkprediction modeldeposited metalmechanical propertyelectrode
Publication Date：2017-05-02
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 269-274 )