沈阳工业大学学报2017，Vol.39Issue(3)：269-274,6.DOI:10.7688/j.issn.1000-1646.2017.03.06
基于人工神经网络含稀土元素熔敷金属力学性能预测
Prediction for mechanical properties of deposited metal containing rare earth elements based on artificial neural networks
摘要
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.关键词
La元素/焊接速度/BP神经网络/RBF神经网络/预测模型/熔敷金属/力学性能/焊条Key words
La element/welding speed/BP neural network/RBF neural network/prediction model/deposited metal/mechanical property/electrode分类
矿业与冶金引用本文复制引用
郭永环,孟祥里,郭妍,范希营,张亮..基于人工神经网络含稀土元素熔敷金属力学性能预测[J].沈阳工业大学学报,2017,39(3):269-274,6.基金项目
国家自然科学基金资助项目(51475220) （51475220）
江苏省产学研前瞻性联合研究项目(BY2016028-02) （BY2016028-02）
徐州市科技计划资助项目(KC15SM031). （KC15SM031）
