Forecasting of mechanical properties of covered electrode containing La/CeO2 based on fuzzy neutral network

In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standa...

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Bibliographic Details
Published in:Rare metals 2018-02, Vol.37 (2), p.161-166
Main Authors: Xu, Yi-Jun, Guo, Yong-Huan, Fan, Hui
Format: Article
Language:English
Subjects:
Artificial neural networks
Biomaterials
Cerium oxides
Chemistry and Materials Science
Electrodes
Elongation
Energy
Fuzzy logic
Ilmenite
Manganese
Materials Engineering
Materials Science
Mathematical models
Mechanical properties
Metallic Materials
Nanoscale Science and Technology
Neural networks
Physical Chemistry
Prediction models
Rare earth elements
Tensile strength
Welding parameters
Yield strength
Yield stress
模糊神经网络模型
机械性质测试
焊接电极
预报
稀土元素
生产过程
测试盘
国家标准
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Summary:In order to improve the mechanical properties of deposited metal of ilmenite type welding electrode, CeO2/La rare earth elements were added into electrodes based on E4301 electrode, then electrodes were produced, test plates were welded, and mechanical properties were tested based on National Standards of China. For the sake of solving the problems of large amount of mechanical properties tests, long test cycle and high test cost during the conventional production process of electrode, a prediction model of the mechanical properties of deposited metal based on Takagi-Sugeno （T-S） fuzzy neural network was established. Mn, Si and C contents of medium manganese in electrode, CeO2, and La contents of electrode and welding speed were selected as input variables of the prediction model, and the tensile strength, lower yield strength, elongation, impact energy and hardness of de- posited metal were selected as output variables. Finally, predicting experiment was done under test samples, and results show that average relative prediction error of the tensile strength, lower yield strength, elongation and hardness are 0.91%, 2.57 %, 4.94 % and 1.94 %, respec- tively, which reach the need of actual production. The re- sults of prediction show that the mechanical properties of deposited metal of electrode containing rare earth can be forecasted accurately through material composition of electrode and welding parameters based on T-S fuzzy neural network model.
ISSN:1001-0521
1867-7185
DOI:10.1007/s12598-015-0474-9