Version 1
: Received: 10 October 2019 / Approved: 12 October 2019 / Online: 12 October 2019 (03:56:19 CEST)
How to cite:
Xue, L.; Liu, J.; Wen, G.; Wang, H. An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks. Preprints2019, 2019100137. https://doi.org/10.20944/preprints201910.0137.v1
Xue, L.; Liu, J.; Wen, G.; Wang, H. An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks. Preprints 2019, 2019100137. https://doi.org/10.20944/preprints201910.0137.v1
Xue, L.; Liu, J.; Wen, G.; Wang, H. An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks. Preprints2019, 2019100137. https://doi.org/10.20944/preprints201910.0137.v1
APA Style
Xue, L., Liu, J., Wen, G., & Wang, H. (2019). An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks. Preprints. https://doi.org/10.20944/preprints201910.0137.v1
Chicago/Turabian Style
Xue, L., Guilin Wen and Hongxin Wang. 2019 "An Efficient and High-Resolution Topology Optimization Method Based on Convolutional Neural Networks" Preprints. https://doi.org/10.20944/preprints201910.0137.v1
Abstract
Topology optimization is a pioneering design method that can provide various candidates with high mechanical properties. However, the high-resolution for the optimum structures is highly desired, normally in turn leading to computationally intractable puzzle, especially for the famous Solid Isotropic Material with Penalization (SIMP) method. In this paper, an efficient and high-resolution topology optimization method is proposed based on the Super-Resolution Convolutional Neural Network (SRCNN) technique in the framework of SIMP. The SRCNN includes four processes, i.e. refining, path extraction & representation, non-linear mapping, and reconstruction. The high computational efficiency is achieved by a pooling strategy, which can balance the number of finite element analysis (FEA) and the output mesh in optimization process. To further reduce the high computational cost of 3D topology optimization problems, a combined treatment method using 2D SRCNN is built as another speeding-up strategy. A number of typical examples justify that the high-resolution topology optimization method adopting SRCNN has excellent applicability and high efficiency for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
