Shape error prediction in 5-axis machining using graph neural networks
This addresses machining quality control for manufacturing, but it is incremental as it applies a known AI method to a specific domain.
The paper tackles shape error prediction in 5-axis machining by using graph neural networks, achieving generalization for the workpiece geometry and handling low label counts compared to methods like Support Vector Machines.
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.