STUDY ON SPRINGBACK OF HIGH STRENGTH STEEL MPV FRONT SIDE RAIL BASED ON BP NEURAL NETWORK MODEL

被引：0
|
作者：
Li, Wenping [1 ]
Ma, Yunwang [1 ]
Jin, Miao [1 ]
Nie, Baofeng Guo Shaomin [1 ]
Wang, Zhong [2 ]
Liu, Jiyan [2 ]
机构：
[1] Yanshan Univ, Coll Vehicle & Energy, 438 Hebei St, Qinhuangdao 066004, Hebei, Peoples R China
[2] Jilin Prov Yuanlongda Mould Manufacture Co Ltd, Liaoyuan 136600, Jilin, Peoples R China
来源：
ENGINEERING PLASTICITY AND ITS APPLICATIONS | 2010年
基金：
中国国家自然科学基金;
关键词：
Front side rail; Springback; Neural network; High strength steel;
D O I：
暂无
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
This paper deals with the optimization of process parameters in high strength sheet metal forming in order to reduce the springback effects after forming. The optimum parameters including holder force, friction coefficient and die radius which influence springback are studied through numerical simulation and orthogonal experiment. For predicting the springback accurately, the uniform experiment was used and BP neural network model was built up. The results show that springback of the front side rail is seriously influenced by these parameters, and the springback under the case of giving a group of process parameters can be predicted by the BP neural network model.
引用
收藏
页码：270 / 274
页数：5
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