Yu Yongsen, Guan Zhiping, Ren Mingwen, Song Jiawang, Ma Pinkui, Jia Hongjie
Key Laboratory of Automobile Materials of Ministry of Education & School of Materials Science and Engineering, Jilin University, 5988 Renmin Street, Changchun 130022, China.
International Center of Future Science, Jilin University, Changchun 130012, China.
Materials (Basel). 2021 Aug 24;14(17):4790. doi: 10.3390/ma14174790.
During air bending of sheet metals, the correction of punch stroke for springback control is always implemented through repeated trial bending until achieving the forming accuracy of bending parts. In this study, a modelling method for correction of punch stroke is presented for guiding trial bending based on a data-driven technique. Firstly, the big data for the model are mainly generated from a large number of finite element simulations, considering many variables, e.g., material parameters, dimensions of V-dies and blanks, and processing parameters. Based on the big data, two punch stroke correction models are developed via neural network and dimensional analysis, respectively. The analytic comparison shows that the neural network model is more suitable for guiding trial bending of sheet metals than the dimensional analysis model, which has mechanical significance. The actual trial bending tests prove that the neural-network-based punch stroke correction model presents great versatility and accuracy in the guidance of trial bending, leading to a reduction in the number of trial bends and an improvement in the production efficiency of air bending.
在金属板材的空气弯曲过程中，用于回弹控制的冲头行程校正总是通过反复的试弯来实现，直到达到弯曲零件的成形精度。在本研究中，基于数据驱动技术，提出了一种用于指导试弯的冲头行程校正建模方法。首先，模型的大数据主要来自大量的有限元模拟，考虑了许多变量，如材料参数、V型模具和坯料的尺寸以及加工参数。基于这些大数据，分别通过神经网络和量纲分析开发了两个冲头行程校正模型。分析比较表明，神经网络模型比具有力学意义的量纲分析模型更适合指导金属板材的试弯。实际的试弯试验证明，基于神经网络的冲头行程校正模型在指导试弯方面具有很大的通用性和准确性，从而减少了试弯次数，提高了空气弯曲的生产效率。
