摘要
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.
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.
基金
financially supported by the National Natural Science Foundation of China (No.51305178)
Xuzhou City Science and Technology Plan Project (No. XC12A013)
the Research and Innovation Key Project of Graduate of Jiangsu Normal University (No. 2013YZD016)
