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Prediction of metal sheet forming based on a geometrical model approach
International Journal of Material Forming ( IF 2.4 ) Pub Date : 2019-12-20 , DOI: 10.1007/s12289-019-01529-9
Pascal Froitzheim , Michael Stoltmann , Normen Fuchs , Christoph Woernle , Wilko Flügge

The panel production of small batch sizes for the hull of large ships requires a stable and flexible forming process, which is momentarily manually controlled by a system operator. The manual forming press control includes the metal sheet handling above the forming tool for defining the contact point and engagement depth of the sword and subjective monitoring of the forming degree by using the light gap check method. For objectifying the process monitoring and reducing the dependency on the experience of the system operator an automated solution is needed. Within the automated process control the metal sheet deformation behavior has to be predicted in real-time during the forming process. To achieve this, the deformation prognosis for the ship panel’s production is handled inside the described work. Based on a state of art analysis a geometrical approach to describe the metal sheet deformation behavior is developed for the multi-step forming process by three-point-bending. The related geometrical parameters are predicted using a new type of prediction method by means of an artificial neural network. This prediction method requires the network definition and extensive experimental investigations for training the artificial neural network.



中文翻译:

基于几何模型方法的金属板成形预测

为大型船的船体生产小批量的面板需要稳定且灵活的成型过程,该过程由系统操作员暂时手动控制。手动成型压力控制包括在成型工具上方进行金属板处理,以定义剑的接触点和接合深度,并通过使用光间隙检查方法对成型程度进行主观监控。为了使过程监控客观化并减少对系统操作员经验的依赖,需要一种自动化的解决方案。在自动化过程控制中,必须在成型过程中实时预测金属板的变形行为。为此,在上述工作中处理了船面板生产的变形预测。基于最新技术分析,开发了一种用于描述金属板变形行为的几何方法,用于通过三点弯曲的多步成型过程。借助人工神经网络,使用一种新型的预测方法来预测相关的几何参数。这种预测方法需要网络定义和广泛的实验研究,以训练人工神经网络。

更新日期:2019-12-20
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