锻压技术

基于BP神经网络的强力旋压成形本构关系模型

英文标题：Research on constitutive relation of tube power spinning forming based on BP neural network

作者：李涛樊文欣赵俊生梁玉秀王连宏

分类号：TG376.3

出版年,卷(期):页码：2014,39(2):150-153

摘要：

利用TLS-W50000A微机控制弹簧试验机，对不同壁厚减薄率下的锡青铜QSn7-0.2强力旋压件进行等温恒应变速率下的单向准静态拉伸试验。基于获得的试验数据，建立基于BP神经网络技术、不同壁厚减薄率下的常温本构模型。结果表明：BP神经网络本构关系模型具有很高的预测精度，可以较好地描述不同壁厚减薄率下锡青铜QSn7-0.2在拉伸变形时的应力-应变关系，为强力旋压工艺本构关系模型的建立提供了一种准确有效的方法。

Using TLS-W50000A microcomputer control spring testing machine, the uniaxial tensile quasistatic experiment under the isothermal constant strain rate was conducted for QSn7-0.2 copper alloy power spinning spieces with different wall thickness reduction ratios. Based on the obtained experimental data, the BP neural network technology was adopted to establish the normal temperature constitutive relationship model under different wall thickness reduction ratios. The results show that the BP neural network constitutive relationship model has high prediction accuracy and can accurately describe the relationship between stress and strain of tin bronze QSn7-0.2 with different wall thickness reduction ratios during tensile deformation，and it provides an accurate and effective method for the constitutive modeling of power spinning.

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）

李涛（1989- ），男，硕士研究生

参考文献：

［1］王煜，孙志超，李志颖，等. 挤压态7075铝合金高温流变行为及神经网络本构模型[J]. 中国有色金属学报，2011，21(11)：2880-2886.Wang Yu, Sun Zhichao, Li Zhiying, et al. High temperature flow stress behavior of as-extruded 7050 aluminum alloy and neural network constitutive model[J]. The Chinese Journal of Nonferrous Metals, 2011, 21(11):2880-2886.
［2］鲁世强，周细林，王克鲁，等. 基于BP神经网络的2D70铝合金本构关系模型[J]. 锻压技术，2008，33(1)：148-151.Lu Shiqiang, Zhou Xilin, Wang Kelu, et al. Model of constitutive relationship for 2D70 aluminum alloy based on BP neural network[J]. Forging & Stamping Technology, 2008, 33(1):148-151.
［3］孙宇，曾卫东，王卲丽，等. 应用人工神经网络建立Ti-22Al-25Nb合金高温本构关系模型[J]. 塑性工程学报，2009，16(3)：126-129.Sun Yu, Zeng Weidong, Wang Shaoli, et al. Modeling the constitutive relationship of Ti-22Al-25Nb alloy using artificial neural network[J]. Journal of Plasticity Engineering, 2009，16(3):126-129.
［4］黄志斌，万敏，伍惠，等. TC4钛合金神经网络本构模型及在有限元模拟中的应用[J]. 塑性工程学报，2013,20(1)：89-94.Huang Zhibin, Wan Min, Wu Hui, et al. Constitutive model of Ti-6Al-4V alloy based on artificial neural network and its application on FEM simulation[J]. Journal of Plasticity Engineering, 2013,20(1):89-94.
［5］霍肖菲，陈威，汤秀山，等. 基于BP神经网络的金属陶瓷TiC-Ni触变成形本构关系模型[J]. 热加工工艺，2010,39(8)：79-81.Huo Xiaofei, Chen Wei, Tang Xiushan, et al. Model of constitutive relationship of TiC-Ni based on BP neural network[J]. Hot Working Technology, 2010,39(8)：79-81.
［6］孙宇，曾卫东，赵永庆，等. 基于BP神经网络Ti600合金本构关系模型的建立[J]. 稀有金属材料与工程，2011，40(2)：221-224.Sun Yu, Zeng Weidong, Zhao Yongqing, et al. Modeling of constitutive relationship of Ti600 alloy using BP artificial neural network[J]. Rare Metal Materials and Engineering, 2011，40(2):221-224.
［7］闫盖，郑燕萍，何镇罡，等. 板料可成形区域图的研究与应用[J]. 锻压技术，2013，38(4)：40-44.Yan Gai, Zheng Yanping, He Zhengang, et al. Design and application about partition of feasible formability diagram in sheet stamping[J]. Forging & Stamping Technology, 2013, 38(4):40-44.

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