用图神经网络预测五轴加工形状误差，提升少标签情况下的泛化能力。
Shape error prediction in 5-axis machining using graph neural networks
- 构建工件表面点为节点的图结构，捕捉空间与时间关联
- 在少标签条件下实现对工件几何形状误差的有效预测
- 适合加工质量预测、智能制造领域研究者参考
本文提出一种基于图神经网络的五轴加工形状误差预测新方法。图结构以工件表面点为节点，邻接关系为边。数据集包含材料去除仿真、工艺参数及加工后质量信息。实验表明，该方法能有效泛化于所研究工件几何形状的误差预测。通过建模工件内部的空间与时间关联，相比支持向量机等非图方法，在标签数量较少时仍具优异表现。
原文摘要 · Abstract (English)
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.
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