Transactions of Materials and Heat Treatment

Title：Springback prediction on rigidity and convexity charateristices of strip based on BP neural network

Authors： Feng Bin Mao Jianzhong Hu Hui

Unit： Hunan University

KeyWords： zirconium alloy stamping process parameters Latin hypercube sampling BP neural network springback rigidity and convexity characteristics of strip

ClassificationCode：TG386.1

year,vol(issue):pagenumber：2020,45(3):20-26

Abstract：

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.

Funds：

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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