Data-driven geometry-based topology optimization

In this paper, a simple deep learning network (DLN) based on the geometry parameters of moving morphable bars (MMBs) was proposed for the data-driven optimal topology prediction. The MMBs-based topology optimization approach is adopted to generate datasets that contain optimized topologies described by the geometry parameters. The DLN is simply built based on linear regression using a rectified linear unit (ReLU) activation function to minimize the loss function, which can be measured by the mean square error of the geometry parameters. The proposed approach could instantaneously provide an appropriate topology optimization design once the DLN has been trained. This approach does not require finite element analysis, design variable update, and other computations (e.g., sensitivity analysis) as often seen in the existing topology optimization approaches. Compared to the DLN based on element densities, the number of design variables and training time can be reduced significantly; the gray elements in void zones can also be also discarded.

Automated summary

A simple deep learning network based on the geometry parameters of moving morphable bars has been proposed in this paper for data-driven optimal topology prediction. This approach eliminates the need for finite element analysis, design variable update, and other computations, significantly reducing the number of design variables and training time compared to existing topology optimization methods.