Designing extrusion dies remains a tricky issue when considering polymers. In fact, polymers exhibit strong non-Newtonian rheology that manifest in noticeable viscoelastic behaviors as well as significant normal stress differences. As a consequence, when they are pushed through a die, an important die-swelling is observed, and consequently the final geometry of the extruded profile differs significantly from the one of the die. This behavior turns the die’s design into a difficult task, and its geometry must be defined in such a way that the extruded profile results in the targeted one. Numerical simulation was identified as a natural way for building and solving the inverse problem of defining the die, leading to the targeted extruded geometry. However, state-of-the-art rheological models reveal inaccuracies for the desired level of precision. In this paper, we propose a data-driven approach that, based on the accumulated experience on the extruded profiles for different dies, learns the relation enabling efficient die design.

在考虑聚合物时，设计挤出模具仍然是一个棘手的问题。事实上，聚合物表现出很强的非牛顿流变性，表现为明显的粘弹性行为以及显着的法向应力差异。因此，当它们被推过模具时，会观察到重要的模具膨胀，因此挤出型材的最终几何形状与模具的几何形状显着不同。这种行为使模具的设计变成一项艰巨的任务，并且必须以这样的方式定义其几何形状，以便挤出轮廓产生目标轮廓。数值模拟被认为是构建和解决定义模具逆问题的自然方法，从而产生目标挤压几何形状。然而，最先进的流变模型揭示了所需精度水平的不准确性。在本文中，我们提出了一种数据驱动的方法，该方法基于不同模具的挤压型材积累的经验，学习可实现高效模具设计的关系。