Efficient, high-resolution topology optimization method based on convolutional neural networks

Abstract

Topology optimization is a pioneer design method that can provide various candidates with high mechanical properties. However, high resolution is desired for optimum structures, but it normally leads to a computationally intractable puzzle, especially for the solid isotropic material with penalization (SIMP) method. In this study, an efficient, high-resolution topology optimization method is developed based on the super-resolution convolutional neural network (SRCNN) technique in the framework of SIMP. SRCNN involves four processes, namely, refinement, path extraction and representation, nonlinear mapping, and image reconstruction. High computational efficiency is achieved with a pooling strategy that can balance the number of finite element analyses and the output mesh in the optimization process. A combined treatment method that uses 2D SRCNN is built as another speed-up strategy to reduce the high computational cost and memory requirements for 3D topology optimization problems. Typical examples show that the high-resolution topology optimization method using SRCNN demonstrates excellent applicability and high efficiency when used for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

Keywords

topology optimization / convolutional neural network / high resolution / density-based

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 11672104 and 11902085), the Key Program of National Natural Science Foundation of China (Grant No. 11832009), and the Chair Professor of Lotus Scholars Program in Hunan Province, China (Grant No. XJT2015408).

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