Shape error prediction in 5-axis machining using graph neural networks

Autor:	Huuk, Julia, Dhingra, Abheek, Ntoutsi, Eirini, Denkena, Bernd
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.10341 Zobrazit plný text záznamu View this record from Arxiv