  In stress field analysis, the finite element analysis 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, leading to curtailing efficiency during the design
process. To eliminate this drawback, we propose a new data-driven mesh-density
boost model named SuperMeshingNet that strengthens the advantages of finite
element analysis (FEA) with low mesh-density as inputs to the deep learning
model, which consisting of Res-UNet architecture, to acquire high-density
stress field instantaneously, shortening computing time and cost automatically.
Moreover, the attention mechanism and the perceptual features are utilized,
enhancing the performance of SuperMeshingNet. Compared to the baseline that
applied the linear interpolation method, SuperMeshingNet achieves a prominent
reduction in the mean squared error (MSE) and mean absolute error (MAE) on test
data, which contains prior unseen cases. Based on the data set of metal
forming, the comparable experiments are proceeded to demonstrate the high
quality and superior precision of the reconstructed results generated by our
model. The well-trained model can successfully show more excellent performance
than the baseline and other methods on the multiple scaled mesh-density,
including $2\times$, $4\times$, and $8\times$. With the refined result owning
broaden scaling of mesh density and high precision, the FEA process can be
accelerated with seldom cost on computation resources. We publicly share our
work with full detail of implementation at
https://github.com/zhenguonie/2021_SuperMeshing_2D_Metal_Forming
