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无模拉拔过程中金属线材直径的BP神经网络预测模型
引用本文:杨勇强,何勇,刘雪峰,谢建新.无模拉拔过程中金属线材直径的BP神经网络预测模型[J].塑性工程学报,2008,15(1):118-122.
作者姓名:杨勇强  何勇  刘雪峰  谢建新
作者单位:北京科技大学材料科学与工程学院,北京,100083
基金项目:国家重点基础研究发展计划(973计划) , 国家自然科学基金 , 教育部长江学者和创新团队发展计划
摘    要:利用Matlab软件建立了无模拉拔成形过程中金属线材直径的BP神经网络预测模型,通过实验验证了网络模型的可靠性。网络预测值与实测值之间的平均相对误差为0.52%,最大相对误差为2.42%,具有较高的预测精度。将所建模型用于无模拉拔成形过程中线材尺寸的在线控制,具有明显效果,在较大程度上减小了线材直径沿长度方向的波动。

关 键 词:无模拉拔  神经网络  线材直径  预测模型  在线控制
文章编号:1007-2012(2008)01-0118-05
收稿时间:2007-02-27
修稿时间:2007-03-13

Prediction model of wire diameter in dieless drawing process based on BP neural network
YANG Yong-qiang,HE Yong,LIU Xue-feng,XIE Jian-xin.Prediction model of wire diameter in dieless drawing process based on BP neural network[J].Journal of Plasticity Engineering,2008,15(1):118-122.
Authors:YANG Yong-qiang  HE Yong  LIU Xue-feng  XIE Jian-xin
Abstract:Using Matlab software, the prediction model of wire diameter in dieless drawing process is established based on BP neural network method. The experimental results show that the prediction model is effective and reliable. The average relative error between the predictions and the experimental results is 0. 52%, and the maximal relative error is 2. 42%. Adopting the developed model to the on-line controlling of wire diameter in dieless drawing process, obvious control effect is obtained, and the fluctuation of wire diameter along the length is reduced to a great extent,
Keywords:dieless drawing  neural network  wire diameter  prediction model  on-line controlling
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