Intelligent lightweight design of bolted spherical joints in spatial grid structures based on topology optimization with 3D-LSGAN predictor

Abstract

Bolted spherical joints (BSJs) are critical components in spatial grid structures. Traditionally, they consist of solid steel spheres and bolt holes, offering good manufacturability and versatility but limited potential for weight reduction and performance enhancement. This study proposes an intelligent lightweight design method that integrates topology optimization (TO) and deep learning. A design database was first established using the SIMP method. A three-dimensional Least Squares Generative Adversarial Network (3D-LSGAN) was then trained to generate innovative structural designs. After post-processing, the performance of these designs was evaluated and optimized using a TOPSIS-based multi-attribute decision-making approach. Validation on a double-layer grid structure shows that the optimal intelligent design maintains acceptable maximum von Mises stress levels across multiple loading conditions, significantly improves stress distribution uniformity, and achieves mass reductions of 83.86 % and 5.46 % compared with the initial and single-TO designs, respectively. These results demonstrate that the proposed framework provides an effective approach for the intelligent and lightweight design of BSJs.