Current simulation of metal forging processes use advanced finite element methods. Such methods consist of solving mathematical equations, which takes a significant amount of time for the simulation to complete. Computational time can be prohibitive for parametric response surface exploration tasks. In this paper, we propose as an alternative, a Graph Neural Network-based graph prediction model to act as a surrogate model for parameters search space exploration and which exhibits a time cost reduced by an order of magnitude. Numerical experiments show that this new model outperforms the Point-Net model and the Dynamic Graph Convolutional Neural Net model.
Cite
@article{arxiv.2211.09373,
title = {Graph Neural Network-based Surrogate Models for Finite Element Analysis},
author = {Meduri Venkata Shivaditya and José Alves and Francesca Bugiotti and Frederic Magoules},
journal= {arXiv preprint arXiv:2211.09373},
year = {2023}
}