Topology Optimization of Multi-material Structures Based on Deep Learning

Abstract:

A topology optimization method of multi-material structure based on deep convolution neural network （CNN） is proposed， which can predict the optimized structure of multi-material in a very short time without any iteration. The popular U-Net network structure is adopted to improve the edge extraction ability of neural network. To train the network， the ordered multi-material SIMP （isotropic real material penalty density method） interpolation method （Ordered SIMP） is used to generate multi-material optimal structure data sets under random loading conditions， mass fraction and cost fraction. The efficiency and accuracy of the proposed method are compared with traditional algorithms， and the performance of the proposed method is evaluated. The results show that the proposed method can significantly reduce the computational cost with little sacrifice on the performance of the design scheme. The proposed method has great potential and broad application prospects for topology optimization in the practice of multi-material structure design in the future.