Zhou Huifang, Zhang Shuyou, Qiu Lemiao, Wang Zili
The State Key Laboratory of Fluid Power and Mechatronic System, Zhejiang University, Hangzhou, China.
Sci Prog. 2021 Jan-Mar;104(1):36850420984303. doi: 10.1177/0036850420984303.
The springback directly affects the forming accuracy and quality of metal bent-tube, and accurate springback prediction is the key to the springback compensation and control. This paper investigates the springback of mandrel-less rotary draw bending (MLRDB) of circular metal tubes, and an innovative method, springback angle prediction considering the interference of cross-sectional distortion (IoCSD-SAP), is proposed. The digit decomposition condition variational auto-encoder generative adversarial network (D2CVAE-GAN) is developed to augment the data samples. Considering the nonlinear interference of the cross-sectional distortion on springback, auxiliary extended radial basis function (AE-RBF) is proposed. It establishes the mapping relationship between the characteristic parameters and cross-sectional distortion. By extracting the information encode of cross-sectional distortion as the condition input, this model realizes the condition prediction of springback angle. Taking MLRDB of 6060-T6 Al-alloy circular tube as a case study, the proposed method, IoCSD-SAP, is verified. According to the experimental results, the mean absolute percentage error (MAPE) for the springback angle of our proposed method is 4.73%, and three different analytical models are 38.92%, 14.39%, and 14.22%, respectively. It can be seen that our proposed method significantly improves the prediction accuracy of springback angle. For the springback angle prediction of circular metal tube in MLRDB, the data augmentation can effectively reduce the generalization error and improve the prediction accuracy. The nonlinear interference of the cross-sectional distortion on springback should be taken into account to improve the accuracy and robustness of the springback prediction model.
回弹直接影响金属弯管的成形精度和质量，准确的回弹预测是回弹补偿与控制的关键。本文研究了圆形金属管无芯旋压拉弯（MLRDB）的回弹问题，并提出了一种创新方法，即考虑横截面畸变干涉的回弹角预测方法（IoCSD-SAP）。开发了数字分解条件变分自编码器生成对抗网络（D2CVAE-GAN）来扩充数据样本。考虑到横截面畸变对回弹的非线性干涉，提出了辅助扩展径向基函数（AE-RBF）。它建立了特征参数与横截面畸变之间的映射关系。通过提取横截面畸变的信息编码作为条件输入，该模型实现了回弹角的条件预测。以6060-T6铝合金圆管的MLRDB为例，验证了所提出的IoCSD-SAP方法。根据实验结果，所提方法回弹角的平均绝对百分比误差（MAPE）为4.73%，三种不同的解析模型分别为38.92%、14.39%和14.22%。可以看出，所提方法显著提高了回弹角的预测精度。对于MLRDB中圆形金属管的回弹角预测，数据扩充可以有效降低泛化误差，提高预测精度。考虑横截面畸变对回弹的非线性干涉，有助于提高回弹预测模型的准确性和鲁棒性。
