Real-Time structure topology optimization using CNN driven Moving Morphable component method

Abstract

Classical optimization methods require finite element analysis in iterations, which increase the computing time and decrease the algorithmic efficiency. The deep learning model can potentially realize real-time topology optimization design, but it normally requires large training set. This paper presents a real-time topology optimization algorithm based on the Moving Morphable Component (MMC) method using a Convolutional Neural Network (CNN). The optimization algorithm uses a new data pre-processing method, which can preserve the numerical characteristics and smoothness of the structure boundary, hence it can help CNN to capture data features with a limited sample set. The topology optimization boundary information of the optimized result is used as the sample set label to avoid the components dislocation phenomenon. The new algorithm effectiveness has been verified with several examples. The trained model can significantly improve the optimization efficiency of the MMC method and offer accurate results with a clear structure boundary.