A Hybrid GNN approach for predicting node data for 3D meshes

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

Hybrid approach for simulating finite element methods using Graph Neural Networks for metal forging process

Finite element methods is used in simulation software to calculate the variables in metal forging process. For manufacturing die's using forging, we requires the best set of input parameters. Finding the best set requires a lot of time as generating the simulation through finite element method is time consuming as they need to solve a system of linear equation derived after differentiation. In this paper, we propose a surrogate graph neural network models based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach which includes the model in the whole process of parameter search space exploration. The die simulated using our models is similar with low error when compared with that using finite element method. The new models have outperformed existing Point-net and simple graph neural network model, when applied to produce die simulations

Hybrid approach for simulating finite element methods using Graph Neural Networks for metal forging process

Finite element methods is used in simulation software to calculate the variables in metal forging process. For manufacturing die's using forging, we requires the best set of input parameters. Finding the best set requires a lot of time as generating the simulation through finite element method is time consuming as they need to solve a system of linear equation derived after differentiation. In this paper, we propose a surrogate graph neural network models based on graph convolutions, having a cheaper time cost. We also introduce a hybrid approach which includes the model in the whole process of parameter search space exploration. The die simulated using our models is similar with low error when compared with that using finite element method. The new models have outperformed existing Point-net and simple graph neural network model, when applied to produce die simulations