This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite Element Method (FEM) simulation to better analyse the effects of varying process parameters. As however traditional FEM simulations, while accurate, are time-consuming and computationally intensive, which renders them inapplicable for real-time application, we present a novel modeling framework based on specifically designed graph neural networks as surrogate models. To this end, we extend the neural network architecture by directly incorporating information about the nosing process by adding different types of edges and their corresponding encoders to model object interactions. This augmentation enhances model accuracy and opens the possibility for employing precise surrogate models within closed-loop production processes. The proposed approach is evaluated using a new evaluation metric termed area between thickness curves (ABTC). The results demonstrate promising performance and highlight the potential of neural networks as surrogate models in predicting wall thickness changes during nosing forging processes.

翻译：本研究提出了一种新颖的方法，用于预测收口过程中管材壁厚的变化。具体而言，我们首先对收口工艺及其影响参数进行了全面分析。我们进一步建立了有限元法（FEM）仿真，以更好地分析不同工艺参数的影响。然而，传统的FEM仿真虽然精确，但耗时且计算量大，这使其不适用于实时应用。因此，我们提出了一种基于专门设计的图神经网络作为代理模型的新型建模框架。为此，我们通过添加不同类型的边及其相应的编码器来直接纳入收口过程的信息，从而扩展了神经网络架构，以建模对象间的相互作用。这种增强提高了模型的准确性，并为在闭环生产过程中采用精确的代理模型提供了可能。所提出的方法使用一种新的评估指标——壁厚曲线间面积（ABTC）——进行评估。结果展示了良好的性能，并凸显了神经网络作为代理模型在预测收口锻造过程中壁厚变化方面的潜力。