基于仿真的拼焊板方盒件冲压成形因素研究及变压边力预测

【作者】 罗云；

【导师】 张卫；

【作者基本信息】 南京理工大学 ， 机械制造及其自动化， 2008， 硕士

【摘要】 拼焊板技术作为汽车轻量化的重要措施之一,在汽车工业中得到了广泛的应用,但拼焊板冲压成形存在焊缝移动、成形性能降低等问题。为提高设计的效率、减少试模时间、降低生产成本,拼焊板冲压成形性能及工艺参数的过程控制已经成为当今研究热点。本课题以简单的非轴对称零件——方盒形零件为研究对象,首先通过理论分析介绍了影响拼焊板冲压成形性能的主要因素:压边力、焊缝位置、板厚比及凹模圆角半径。然后运用DYNAFORM进行了单因素影响的仿真分析,找出了有助于提高拼焊板的成形性能的变压边力曲线,并得到了成形性能随着焊缝往薄侧偏移和板厚比减少而增加,以及拼焊板的成形深度和焊缝移动量同时随着凹模圆角半径的增大而增大的结论。为寻找方盒形件拼焊板冲压成形过程中这些因素的较优水平组合,进而提出采用正交试验方法进行了多因素多目标的参数优化,为现实中零件的设计和生产提供方法和理论指导。最后,针对下一步智能数控冲压机床的研究设计,提出运用双隐含层BP神经网络的方法对冲压过程中可控变量——压边力建立了预测模型,采用仿真样本对模型进行了训练和检验,确定了该方法的可行性,为复杂形状的拼焊板零件冲压成形的压边力智能预测的研究奠定了基础。更多还原

【Abstract】 As an important approach of the light-weighting technology, tailor-welded blank (TWB) has been broadly applied in the auto-industry. But there exists some problems in TWB forming, such as the movement of the weld-line, the reduction of the formability and so on. In order to improve the designing efficiency, reduce the die testing time and cost, the research of TWB’s formability and the control of its process parameters have became the research focus at present.A simple non-axial symmetry part, square-box is chosen as the beginning of the research of TWB forming. Firstly, the TWB forming affecting factors, such as BHF (blank holder force), weld-line location, different thickness ratio and the die fillet radius, are analyzed in theory. Then, these factors are simulated respectively via DYNAFORM. As a result, the variable BHF curve is found, which can improve the formability of the TWB square-box. It is concluded that the formability can be improved when the weld-line location is biased to the thinner blank and the thickness ratio is reduced. Besides, the depth of forming and the movement of weld-line are increased simultaneously, when the die fillet radius is increased. Furthermore, an orthogonal experiment method for the muti-factor muti-target optimization is proposed to get a superior combination of the affecting factors during the forming process. Thus, the instruction of method and theory for the TWB part’s design and production in reality is provided.Finally, aimed at the design of the intelligent numerical control stamping tools, a forecasting model is built. It is based on a two hidden layers BP neural network for the BHF. which is a control variable factor during the stamping process. It is verified as feasible by trained and checked in the simulation experiment. Therefore, the foundation research of the intelligent BHF forecast for complex TWB forming parts has been built.更多还原