Transactions of Materials and Heat Treatment

Title：Study on forming quality prediction of connecting rod bushing by power spinning forming based on RBF neural network

Authors： Gao Li Fan Wenxin Ma Xuejun Li Gui

Unit： North University of China

KeyWords： power spinning process parameters forming quality RBF neural network

ClassificationCode：TH161；TG376

year,vol(issue):pagenumber：2015,40(9):134-138

Abstract：

In order to predict the forming quality（wall-thickness-difference and inner diameter expending quantity） of connecting rod bushing by power spinning forming and optimize the process parameters, a RBF neural network model showing the relationship between process parameters and forming quality was established by neural network toolbox in MATLAB. Based on the improved K-means algorithm, the RBF neural network was trained by the experiment data and forming quality was predicted. Comparing with the measured data, it is found that the model shows high accuracy. Therefore, RBF neural network can be used in the power spinning field. Through comparing BP neural network and with RBF neural network trained by original K-means algorithm, it is indicated that RBF neural network has better accuracy and adaptability. This method can not only provide reference for optimization of process，but also shorten the period of process parameter optimization and save the cost on experiments.

Funds：

高立（1991-），男，硕士研究生

Reference：

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