This paper presents an innovative method for predicting shape errors in 5-axis machining using graph neural networks. The graph structure is defined with nodes representing workpiece surface points and edges denoting the neighboring relationships. The dataset encompasses data from a material removal simulation, process data, and post-machining quality information. Experimental results show that the presented approach can generalize the shape error prediction for the investigated workpiece geometry. Moreover, by modelling spatial and temporal connections within the workpiece, the approach handles a low number of labels compared to non-graphical methods such as Support Vector Machines.

翻译：本文提出了一种利用图神经网络预测五轴加工形状误差的创新方法。该图结构以节点表示工件表面点，以边表示相邻关系。数据集包含材料去除仿真数据、工艺数据以及加工后质量信息。实验结果表明，所提出的方法能够对所研究的工件几何形状实现形状误差预测的泛化。此外，通过建模工件内部的空间与时间关联，该方法在标签数量较少的情况下（相较于支持向量机等非图方法）仍能有效处理预测任务。