Based on the lightweight characteristics of automotive energy absorption boxes and the requirement of good energy absorption effect, this article first applies the node-enhanced body centered cubic (NBCC) lattice structure to the inner core design of automotive energy absorption boxes. The gradient study of the NBCC lattice structure was carried out using a drop hammer impact and split Hopkinson pressure bar (SHPB). The results indicate that gradient lattice structures have advantages in energy absorption, but there are differences under different gradient strategies. When the impact is not sufficient to compact the structure, the vertical rod diameter gradient node-enhanced lattice structure (RGNBCC) can absorb more energy and improve energy absorption performance by 25%. The vertical height gradient node-enhanced lattice structure (HGNBCC) is more suitable for high-speed impact conditions. Based on the advantages of the RGNBCC in resisting low-speed impacts, it is applied to the inner core design of automotive energy absorption boxes and optimized using multi-objective optimization methods. The optimization results show that the maximum peak impact force is reduced by 45.6% and the specific energy absorption is increased by 30.4%.

Lattice materials are widely used in industries due to their designable capabilities of specific stiffness and energy absorption. However, evaluating the mechanical response of macroscopic lattice structures can be computationally expensive. Homogenization-based multi-scale analysis offers an efficient approach to address this issue. To achieve a simpler, while precise, homogenization, the authors proposed an equidistant segmentation (ES) method for the measurement of the effective shear modulus. In this method, the periodic boundary conditions (PBCs) are approximated by constraining the lateral displacement of nodes between parallel layers of periodic cells. The validations were applied to three typical lattice topologies: body-centered cubic (BCC) lattices, gyroid-, and primitive-triply periodic minimal surface (TPMS) lattices, to predict and compare their anti-vibration capacities. The results demonstrated the rationality and the promising precision of the multi-scale-based equivalent modal analysis through the proposed method and that it eliminated the geometric limitation of lattices with diverse frameworks. Overall, a higher anti-vibration capacity of TPMS was observed. In the study, the authors examined the influence of the relative densities on the balance between the anti-vibration capacity and loading capacity (per unit mass) of the TPMS topologies. Specifically, the unit mass of the TPMS with lower relative densities was able to resist higher frequencies, and the structures were dominated by the anti-vibration capacity. In contrast, a higher relative density is better when emphasizing the loading capacity. These findings may provide notable references to the designers and inform the selection of lattice materials for various industrial applications.

Metal-coated lattice structures hold significant promise for customizing mechanical properties in diverse industrial applications, including the mechanical arms of unmanned aerial vehicles. However, their intricate geometries pose computational challenges, resulting in time-intensive and costly numerical evaluations. This study introduces a parameterization-based multiscale method to analyze body-centered cubic lattice structures with metal coatings. We establish the validity and precision of our proposed method with a comparative analysis of numerical results at the Representative Volume Element (RVE) scale and experimental findings, specifically addressing both elastic tensile and bending stiffness. Furthermore, we showcase the method’s accuracy in interpreting the bending stiffness of coated lattice structures using a homogenized material-based solid model, underscoring its effectiveness in predicting the elastic properties of such structures. In exploring the mechanical characterization of coated lattice structures, we unveil positive correlations between elastic tensile stiffness and both coating thickness and strut diameter. Additionally, the metal coating significantly enhances the structural elastic bending stiffness multiple times over. The diverse failure patterns observed in coated lattices under tensile and bending loads primarily stem from varied loading-induced stress states rather than external factors. This work not only mitigates computational challenges but also successfully bridges the gap between mesoscale RVE mechanical properties and those at the global structural scale.

Customizing and optimizing lattice materials poses a challenge to designers. This study proposed a data-driven generative method to customize and optimize lattice material. The method utilizes subdivision modeling to parametrically describe lattice morphologies and skeletons. Next, the homogenization method is employed to analyze elastic moduli for collecting a dataset. Then, a two-tiered machine learning (ML) framework is proposed to predict the elastic modulus for a forward design. The first-tier model employs polynomial regression to estimate relative density, which serves as an additional input feature for the second-tier model. The prediction accuracy of the second-tier model is improved through the additional inputs. The forward and reverse design strategies offer a flexible and accurate means of tailoring lattice properties to meet specific performance requirements. Two case studies demonstrate the practical value of the framework: customizing a lattice material to achieve a desired elastic modulus and optimizing the mechanical performance of lattice materials under relative density constraints. The results show that the prediction accuracy of the elastic modulus using the two-tiered ML model achieved an error of less than 10% compared to finite element analysis, demonstrating the reliability of the proposed approach. Furthermore, the optimization design achieved up to a 25% improvement in mechanical performance compared to conventional lattice configurations under the same relative density constraints. These findings underscore the advantages of combining generative design, machine learning, and genetic algorithms to navigate complex design spaces and achieve enhanced material performance.