Transactions of Materials and Heat Treatment

Title：Study on mechanical property prediction of power spinning connecting rod bushing based on RBF neural network

Authors： She Yong Fan Wenxin Chen Dongbao Cao Cuncun

Unit： North University of China

KeyWords： power spinning spinning process parameter mechanical properties RBF neural network

ClassificationCode：TG376

year,vol(issue):pagenumber：2016,41(6):128-132

Abstract：

For the difficulty of expressing the complex relationships between process parameters and mechanical property of connecting rod bushing formed in power spinning using a formula, it was established the model of RBF neural network between the spinning process parameters (thinning ratio, heat treatment temperature and feed ratio) and mechanical property (brinell hardness, elongation, yield strength and tensile strength). Then, the RBF neural network was trained by experimental data, and the mechanical property of formed part was predicted by the above trained RBF neural network. Comparing the experimental data with the predicted results of BP neural network, it is found that the RBF neural network has better prediction performance than BP neural network. Therefore, the RBF neural network model is of high prediction performance and short modeling time. Thus, it can effectively improve the design efficiency of connecting rod bushing process and reduce the cost in actual experiment.

Funds：

基金项目:山西省自然科学基金资助项目(2012011023-2)；山西省高校高新技术产业化项目(20120021)

作者简介：佘勇（1990-），男，硕士研究生 E-mail：sy09020641@163.com 通讯作者:樊文欣（1964-），男，博士，教授 E-mail：fanwx@nuc.edu.cn

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