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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Optimum design of preform geometry and forming pressure in tube hydroforming using the equi-potential lines method
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Optimum design of preform geometry and forming pressure in tube hydroforming using the equi-potential lines method

机译:等电位线法优化管件液压成形中瓶坯的几何形状和成形压力

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摘要

The quality of a tube-hydroformed component as well as the tooling cost and processing time involved in the process are notably affected by the component's preform. Hence, it is necessary to produce a proper preform shape for the successful hydroforming from the initial tube to final shape. In this paper, the notion of equi-potential lines (EPLs) is used to find an appropriate preform shape in the tube hydroforming process for the first time. The EPLs generated between two conductors of different voltages show minimum work paths between the initial and final shapes. Based on this similarity, the EPLs method is utilized for preform shape design. Next, the forming pressure of the preform is determined using finite element analysis. Finally, the computationally expensive procedure introduced above is significantly facilitated by employing a multi-layer perceptron neural network which is trained using results from application of the procedure to a set of uniformly distributed random input vectors. Real-world examples are presented to demonstrate the applicability and efficiency of the proposed approach.
机译:管材加氢成型组件的质量以及过程中涉及的模具成本和处理时间受组件的预成型件显着影响。因此,为了成功地进行从初始管到最终形状的液压成形,必须产生适当的预成型件形状。在本文中,等势线(EPL)的概念首次用于在管液压成型过程中找到合适的预成型坯形状。在两个不同电压的导体之间产生的EPL显示出初始形状和最终形状之间的最小工作路径。基于这种相似性,EPLs方法用于瓶坯形状设计。接下来,使用有限元分析确定预成型件的成型压力。最后,通过采用多层感知器神经网络,大大简化了上面介绍的计算上昂贵的过程,该神经网络使用对该过程应用到一组均匀分布的随机输入向量的结果进行训练。给出了实际示例,以证明所提出方法的适用性和效率。

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