Metal forging is used to manufacture dies. We require the best set of input parameters for the process to be efficient. Currently, we predict the best parameters using the finite element method by generating simulations for the different initial conditions, which is a time-consuming process. In this paper, introduce a hybrid approach that helps in processing and generating new data simulations using a surrogate graph neural network model based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach that helps in processing and generating new data simulations using the model. Given a dataset representing meshes, our focus is on the conversion of the available information into a graph or point cloud structure. This new representation enables deep learning. The predicted result is similar, with a low error when compared to that produced using the finite element method. The new models have outperformed existing PointNet and simple graph neural network models when applied to produce the simulations.

翻译：金属锻造用于制造模具。为了确保工艺高效，我们需要确定最佳输入参数集。目前，我们通过有限元法对不同初始条件进行仿真来预测最优参数，但这一过程耗时较长。本文提出了一种基于图卷积的代理图神经网络混合方法，能够以更低的时间成本处理并生成新的数据仿真。我们还引入了一种利用该模型处理与生成新数据仿真的混合方法。针对表示网格的数据集，我们重点关注将现有信息转换为图或点云结构。这种新表示形式使得深度学习成为可能。与有限元法生成的结果相比，预测结果误差较低且具有相似性。在应用于仿真生成任务时，新模型的表现优于现有的PointNet和简单图神经网络模型。