Herein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as lightweight core. While stretching dominated and bending dominated cells such as octahedron, tetrahedron and octet have been designed for lightweight structures, it is plausible that other cells exist which might perform better than the existing counterparts. Machine learning technique was used to discover new optimal unit cells. An 8-node cube containing a maximum of 27 elements, which extended into an eightfold unit cell, was taken as representative volume element (RVE). Numerous possible unit cells within the RVE were generated using permutations and combinations through MATLAB coding. Uniaxial compression tests using ANSYS were performed to form a dataset, which was used to train machine learning algorithms and form predictive model. The model was then used to further optimize the unit cells. A total of 20 optimal symmetric unit cells were predicted which showed 51–57% higher capacity than octet cell. Particularly, if the solid rods were replaced by porous biomimetic rods, an additional 130–160% increase in buckling resistance was achieved. Sandwich structures made of these 3D printed optimal symmetric unit cells showed 13–35% higher flexural strength than octet cell cored counterpart. This study opens up new opportunities to design high-performance sandwich structures.

本文报道了新型晶格晶胞，其屈曲载荷比经典八位晶胞高 261-308%。格子结构作为轻质芯材已广泛应用于夹层结构中。虽然八面体、四面体和八面体等拉伸主导和弯曲主导的单元是为轻质结构而设计的，但似乎存在其他单元，其性能可能比现有的对应单元更好。使用机器学习技术来发现新的最佳晶胞。最多包含 27 个单元的 8 节点立方体，扩展为八倍晶胞，被视为代表性体积单元（RVE）。 RVE 内的许多可能的单位单元是通过 MATLAB 编码进行排列和组合生成的。使用ANSYS进行单轴压缩测试以形成数据集，用于训练机器学习算法并形成预测模型。然后使用该模型进一步优化晶胞。总共预测了 20 个最佳对称单元电池，其容量比八位单元电池高 51-57%。特别是，如果将实心棒替换为多孔仿生棒，抗弯强度可额外增加 130-160%。由这些 3D 打印的最佳对称单元制成的三明治结构的弯曲强度比八角单元核心的同类结构高 13-35%。这项研究为设计高性能夹层结构开辟了新的机会。