中国舰船研究2023，Vol.18Issue(6)：197-207,11.DOI:10.19693/j.issn.1673-3185.02964
基于全卷积神经网络的板条多压头成形回弹预测及模具设计
Springback prediction and mould design for multi-square punch forming of the strip based on FCN
摘要
Abstract
[Objectives]The springback is the main factor affecting the forming quality of hull plates in the cold forming process.To improve the forming quality,it is necessary to investigate springback prediction,ob-tain the appropriate springback control method and further guide the die design.[Methods]A fully convolu-tional network(FCN)is used to perform pixel-level calculations and regression calculation on the springback image so as to achieve springback prediction for each forming position on the sheet.In this study,a finite ele-ment(FE)model is established using ABAQUS 2019,and the numerical results are validated by the experi-mental results.The verified model is then applied to obtain the training sample set.The workpiece geometric information is used as the input of the neural network to retain all the information of the image,and the TensorFlow Core V2.2.0 platform is used to build the FCN based on different convolutional neural network models.Finally,the pros and cons of different neural networks are compared,and the optimal network is ap-plied to the die design.[Results]The results show that the maximum error of the predicted springback is 8.49%,where the constructed FCN32 has the highest accuracy.The proposed model can also realize one-time mould design with a calculation time of only 0.5 seconds and a maximum error of only 1.00%,significantly improving calculation efficiency.[Conclusions]The FCN-based algorithm proposed herein provides a springback prediction method for strips with high accuracy and efficiency,as well as offering a new approach to quick mould design.关键词
多压头成形/全卷积神经网络/回弹预测/板条成形Key words
multi-square punch forming/fully convolutional networks(FCN)/springback prediction/sheet metal forming分类
交通运输引用本文复制引用
朱凌,董金辉,梁棋钰..基于全卷积神经网络的板条多压头成形回弹预测及模具设计[J].中国舰船研究,2023,18(6):197-207,11.基金项目
国家自然科学基金青年科学基金资助项目(52201378) （52201378）
中央高校基本科研业务费专项资金资助项目(2022IVA019) （2022IVA019）
