Development of Deep Convolutional Neural Network for Structural Topology Optimization

Abstract

This research develops a highly effective deep-learning-based surrogate model that can provide the optimum topologies of 2D and 3D structures. In general, structural topology optimization requires plenty of computations because of a large number of finite element analyses to obtain optimal structural layouts by reducing the weight and satisfying the constraints. Therefore, many researchers have developed a deep-learning-based model using the initial static analysis results to predict the optimum designs. However, these studies still considered relatively simple example problems, such as a cantilever plate and MBB beam, even though they required a large number of data to achieve an accurate surrogate model for the simple application. To overcome these limitations, we propose a new framework, which 1) efficiently uses limited data by normalizing it and 2) employs a surrogate model capable of handling more practical cases, such as a 2D panel and 3D stiffened panel subjected to a distributed load, and can be used for local–global structural optimization. In all cases, the developed surrogate models can predict the optimum layouts with structural performance levels equal to those of the structures considered to be ground truths. Also, when the optimal structures were obtained using the proposed method, the total calculation time was reduced by 98% as compared to conventional topology optimization once the convolutional neural network had been trained.