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

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