2021
DOI: 10.1115/1.4052195
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SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Physical Fields in Metal Forming Numerical Simulation

Abstract: In stress field analysis, the finite element method is a crucial approach, in which the mesh-density has a significant impact on the results. High mesh density usually contributes authentic to simulation results but costs more computing resources. To eliminate this drawback, we propose a data-driven mesh-density boost model named SuperMeshingNet that uses low mesh-density as inputs, to acquire high-density stress field instantaneously, shortening computing time and cost automatically. Moreover, the Res-UNet ar… Show more

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Cited by 14 publications
(3 citation statements)
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References 39 publications
(57 reference statements)
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“…This model is used to produce meshes automatically during the finite element method (FEM) computation process. Although this does not save time, it increases computing productivity [ 105 ]. Figure 13 shows the network structure of Residual MeshNet.…”
Section: Object Reconstructionmentioning
confidence: 99%
“…This model is used to produce meshes automatically during the finite element method (FEM) computation process. Although this does not save time, it increases computing productivity [ 105 ]. Figure 13 shows the network structure of Residual MeshNet.…”
Section: Object Reconstructionmentioning
confidence: 99%
“…Sepasdar et al [22] constructed a two-stacked generator CNN framework to predict complete field damage and failure pattern prediction in composite materials. To enhance deep learning models' precision and training cost, Xu et al [23] increased the computational efficiency of low-mesh density FEM models by developing a data-driven mesh density boosting model that uses the low mesh-density physical field as inputs to predict high-density physical field as outputs. A Fourier neural operator (FNO) was used by Rashid et al [24] to accurately predict and design the mechanical responses of complex 2D composite microstructures, demonstrating high-fidelity stress and strain predictions with few training data and showing zero-shot generalization and super-resolution abilities, even for unseen geometries and low-resolution inputs.…”
Section: Introductionmentioning
confidence: 99%
“…U-Net has been extended to several other fields beyond image segmentation, such as solving for the temperature field given a map of sources [28]. U-Net has also been used as a mesh super-resolution method for stress fields [25], where the output of a low resolution FE simulation is refined by a U-Net to match the output of a high resolution FE simulation without the required computation time and memory requirements.…”
Section: Introductionmentioning
confidence: 99%