锻压技术

基于BP神经网络的条带刚凸特征回弹预测

英文标题：Springback prediction on rigidity and convexity charateristices of strip based on BP neural network

作者：冯斌毛建中胡晖

单位：湖南大学

分类号：TG386.1

出版年,卷(期):页码：2020,45(3):20-26

摘要：

为了研究核燃料组件格架的条带刚凸特征的回弹量与压边力、冲压速度、凸凹模间隙、摩擦系数等冲压工艺参数之间的关系，首先，获取包含50个GA拉丁超立方抽样的数据点以及10个随机抽样的数据点的数据集，前者作为训练集、后者作为测试集。将前者输入到BP神经网络进行训练，后者验证训练模型的精度。最后，通过响应面图研究各因素之间的交互作用以及各因素的敏感程度。结果表明：BP神经网络能够有效预测刚凸回弹量与冲压工艺参数之间的关系，相对于其他因素，压边力对回弹量的影响特别明显，冲压速度对回弹量的影响不明显，但与凸凹模间隙和摩擦系数有明显的交互作用。

In order to study the relationship between the springback amount of the rigidity and convexity charateristices of strip for the nuclear fuel assembly grid and the stamping process parameters, such as blank holder force, stamping speed, clearance between punch and die and friction coefficient. Firstly, a data set containing fifty GA Latin hypercube sampled data points and ten randomly sampled data points were obtained with the former as the training set and the latter as the test set. Then, the former was inputted to the BP neural network for training, and the latter verified the accuracy of the training model. Finally, the interaction among various factors and the sensitivity of various factors were studied by response surface graphs. The results show that BP neural network effectively predicts the relationship between the springback amount of rigid and convexity and the stamping process parameters, and compared with other factors, the influence of the blank holder force on the springback amount is particularly obvious. However, the impact of the stamping speed on the springback amount is not significant, and it has obvious interaction with the clearance between punch and die and the friction coefficient.

基金项目：

国家科技重大专项子课题（761215007）

冯斌（1995-），男，硕士研究生 E-mail：fengb34567@163.com 通讯作者：胡晖（1969-），男，硕士，高级工程师 E-mail：huhui@hnu.edu.cn

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