A Hybrid GNN approach for predicting node data for 3D meshes
Shwetha Salimath Francesca Bugiotti Frederic Magoules
Metal forging is used to manufacture dies. We require the best set of input
parameters for the process to be efficient. Currently, we predict the best
parameters using the finite element method by generating simulations for the
different initial conditions, which is a time-consuming process. In this paper,
introduce a hybrid approach that helps in processing and generating new data
simulations using a surrogate graph neural network model based on graph
convolutions, having a cheaper time cost. We also introduce a hybrid approach
that helps in processing and generating new data simulations using the model.
Given a dataset representing meshes, our focus is on the conversion of the
available information into a graph or point cloud structure. This new
representation enables deep learning. The predicted result is similar, with a
low error when compared to that produced using the finite element method. The
new models have outperformed existing PointNet and simple graph neural network
models when applied to produce the simulations.