Nonlinear combination neural network model of intelligent prediction on electrode properties

The elongation after fracture and impact energy of the deposited metal by electrode were tested by experiments.A nonlinear combination neural network model to predict the deposited metal mechanical properties by electrode was built by taking predicted data acquired by such single models as BP,RBF and adaptive fuzzy neural network as input parameters.42 groups of experimental samples were used to train and verify the model.The results show that the average relative prediction errors of both elongation after fracture and impact energy are less than 5% and it satisfies the demands of practical production.An intelligent prediction system of electrode properties was developed by Matlab and visual C++and it can correctly predict the elongation after fracture and impact energy of deposited metal by electrode according to the raw material components.It provides a new,simple and effective way to predict and control the electrode quality.