Designing a high-strength node is significant for space structures. Topological optimization can optimally allocate the material distribution of components to meet performance requirements. Although the material distribution after topology optimization is optimum, the structure becomes complicated to manufacture. By using additive manufacturing technology, this problem can be well solved. At present, both topology optimization technology and additive manufacturing technology are quite mature, but their application in the design of spatial nodes is very recent and less researched. This paper involves the study and improvement of the node optimization design–manufacturing integrated method. This study used the BESO optimization algorithm as the research algorithm. Through a reasonable improvement of the material interpolation method, the algorithm’s dependence on the experience of selecting the material penalty index P was reduced. On this basis, the secondary development was carried out, and a multisoftware integration was carried out for optimization and manufacturing. The spatial node was taken as the research object, and the calculation results of the commercial finite element software were compared. The comparison showed that the algorithm used in this paper was better. Not only was it not trapped in a local optimum, but the maximum stress was also lower. In addition, this paper proposed a practical finite element geometric model extraction method and smoothing of the optimized nodes, completing the experiment of the additive manufacturing forming of the nodes. It provides ideas for processing jagged edges brought by the BESO algorithm. This paper verified the feasibility of the multisoftware integration method of optimized manufacturing.

Optimization methods tailored for practical engineering applications continue to evolve in order to realize lightweight single-layer reticulated shell structures and maximize node stiffness. This paper takes the minimum amount of steel as the objective function, and divides the rod types into three groups and three corresponding one-to-one optimization schemes. Considering the stress and stiffness of the rod and the displacement and stability constraints of the whole structure, the equal step search method combined with the criterion method is used to optimize the rod size. Then the multi-scale calculation model based on the multi-point constraint method is established. Through calculation and analysis, the boundary load condition of the target node is obtained as the boundary condition of node optimization. Finally, the variable density method is used to optimize the topology of the node domain, and the minimum member size is included in the constraint conditions to obtain the optimized node form that is conducive to additive manufacturing. The research shows that reasonable cross-section value and grouping of members can effectively reduce the steel consumption without compromising the overall stability performance. The amount of steel used in the three optimization plans was reduced by 12%, 23%, and 28%, respectively, compared to before the optimization. The multi-scale model not only takes into account the calculation accuracy, but also can effectively simulate the stress conditions in the node domain. The development of topology optimization and additive manufacturing technology broadens the space for optimization design, and provides new ideas for advanced design to integrate intelligent manufacturing.

At present, a large number of scholars have conducted related research on topology optimization for additive manufacturing (AM). However, there are few relevant research reports on the impact of different directions of additive manufacturing on the optimal design and manufacturing results. In this paper, using the bidirectional evolutionary optimization (BESO) method, anisotropic optimization analysis was carried out on space nodes that are currently popular in the field of additive manufacturing and topology optimization. The elastic constants in different directions were used as anisotropic material properties for optimization research in this paper through tensile testing, which was carried out on 316L stainless-steel specimens fabricated using Selective Laser Melting (SLM) technology. In addition, SEM analyses were performed to explore the microscopic appearance of the material. The study found that additive manufacturing is affected by the printing direction in terms of both macroscopic mechanical properties and microscopic material structure; the deformation obtained by anisotropic optimization was about 1.1–2.3% smaller than that obtained by isotropic optimization.

To address the poor vertical vibration reduction in laminated rubber bearings, the high cost and low practicality of combined three-dimensional isolation bearings, and the low load-bearing capacity of thick-layer rubber bearings, this paper proposes a stiffness-reduced rubber isolation bearing. Based on the deformation coordination principle and the incompressibility of thick-layer rubber, theoretical formulas for the horizontal and vertical stiffness of the proposed bearing are established. Compression–shear tests and finite element simulations are then conducted to investigate its mechanical properties under vertical compressive stress. The results show that the theoretical predictions agree well with the simulation and experimental results. The maximum error of horizontal stiffness is no more than 5.6% relative to the finite element simulation and no more than 3.3% relative to the experimental results, while the maximum error of vertical stiffness is no more than 7.9% and 2.3%, respectively. Compared with the traditional laminated rubber bearing, the stiffness-reduced rubber isolation bearing reduces the average vertical stiffness by 35.8% while maintaining stable horizontal mechanical performance and overall integrity within the tested range. Furthermore, parametric analysis indicates that the stiffness can be effectively adjusted by changing the inner-diameter/outer-diameter ratio. A case study based on measured metro-induced vibration time-history curves further shows that the proposed bearing has potential for achieving the dual objective of horizontal isolation and vertical vibration reduction.

The vulnerability curve serves as a precise evaluation metric for structural seismic performance and a critical component in earthquake loss assessment. In this study, the orthogonal expansion method for random ground motion generation is integrated with the probability density evolution method (PDEM) to address the dynamic reliability and vulnerability of general Multi-Degree of Freedom (MDOF) nonlinear structures. By employing dynamic reliability as a constraint and vulnerability as an evaluation index, the guided firework algorithm (GFWA) is introduced to optimize the design of viscoelastic damper (VED) structure systems. To validate the proposed methods, several examples are presented, including the generation of artificial waves, the vulnerability analysis of a five-story reinforced concrete (RC) structure, and a comparative study of GFWA and genetic algorithm (GA) optimization for VED parameters to assess the optimization efficiency. The results demonstrate that the proposed vulnerability method achieves satisfactory accuracy and is well suited for evaluating damper structure optimization designs. Furthermore, GFWA outperforms GA significantly in terms of efficiency and feasibility, offering a promising approach for optimization design in architectural structures.