Article3D printing and additive manufacturing2025
Deep Learning-Based Super Resolution Applied to Finite Element Analysis of Fused Deposition Modeling 3D Printing.
Article in 3D printing and additive manufacturing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Finite element analysis (FEA) of fused deposition modeling (FDM) has recently been recognized in additive manufacturing (AM) for predictions in temperature and displacement. These predictions can be invaluable for making corrections to the printing process to improve quality of printed components. However, FEA has limitations that discourage manufacturers from using it. For example, the fine mesh model takes considerably long computational times (although it yields more accurate results than those of a coarse mesh model). In this work, an innovative deep learning (DL)-based super-resolution (SR) approach is proposed to improve the result accuracy of a coarse mesh model to the higher accuracy level of a fine mesh model and reduce the computational time. The element in the FEA is treated as the physical pixel in an image, so the grid from fine mesh model and coarse mesh model in the FEA is analogous to high resolution (HR) images and low resolution (LR) images, respectively. The results show that the difference value
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