A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties

Abstract

The increasing demand for load-carrying multiscale structures with ultimate lightness requires corresponding development in topology optimization methods. However, current multiscale topology optimization methods are hindered by the contradiction between the freedom of design space and the dimensionality of the design variables. Moreover, the unstable additive manufacturing process and working conditions would result in possible structural failure of the multiscale structure optimized under deterministic conditions. To address these problems, we propose a deep generative multiscale topology optimization framework considering both manufacturing defects and parametrical uncertainties. A database consisting of minimum volume unit cell topologies is obtained via the inverse homogenization method. Then the variational autoencoder network is introduced to capture the patterns in the database and to reconstruct unit cells with a low-dimensional latent vector, which effectively compresses the number of design variables for a microstructure. Then, a self-adaptive clustering strategy is proposed to efficiently quantify the influence of random manufacturing defects in microstructures. A reliability-based optimization framework is constructed with a reliability index to evaluate the complex effect of multisource uncertainties. The effectiveness of the proposed framework is validated through a series of numerical examples, and conclusions are presented at the end of the article.