A Structural-Similarity Conditional GAN Method to Generate Real-Time Topology for Shell–Infill Structures

Abstract

Topology optimization (TO) can generate innovative conceptual configurations with shell–infill geometric features by distributing materials optimally within the design domain. However, physics-based topology optimization methods require repeated finite element analysis and variable updating, in which expensive computational cost limits their applications in wider industrial fields, especially for topology optimization for shell–infill structures. Fortunately, the arising of the data-based topology optimization method using deep learning has paved the way to realize real-time topology prediction for shell–infill structures. In this work, a novel and differentiable structural similarity (SSIM) loss function is introduced into the conditional generative adversarial network (cGAN) to construct the SSIM-cGAN model, and the single-channel coding strategy of initial condition is proposed to simplify the inputs of the deep learning model. SSIM-cGAN can generate shell–infill structures in real time after training with a small-scale dataset. The results generated by SSIM-cGAN and cGAN were put together for comparison, demonstrating that the shell–infill structure generated by SSIM-cGAN has lower error than cGAN, and the shell layer and porous infill structures are more integrated.