A novel three-dimensional(3D) orthogonal star honeycomb structure with increased strength and negative Poisson’s ratio(NPR) performance that is not subject to angle constraints was proposed, and the calculation model of Young’s modulus and Poisson’s ratio of the cell structure under axial load was derived in this study. Through the investigation of the deformation mode and the finite element (FE) research results of the structure, it is found that the longitudinal length parameter l 2 and angle parameter θ 2 are important concave axes that affect the mechanical properties of materials. The quasi-static compression experimental results shows that the constraints of adjacent elements can enhance the strength of the structure to a certain extent, and the theoretical analysis results and the FE simulation results were in good agreement with the experimental results. In addition, the results are also concluded that the prediction of Young’s modulus and Poisson’s ratio and the designability of mechanical properties of 3D orthogonal star honeycomb structures could be achieved by the theoretical calculation formula derived in this study.

The mechanical metamaterials have promising applications in aerospace, construction, biomedical and many other fields due to their excellent properties, but the expensive manufacturing cost greatly affects the practical application. Although many existing mechanical metamaterials enable significant tuning of mechanical properties to suit different application scenarios without requiring structural redesign, the adjustment of these properties often depends on the variation of parameters. This results in the need to manufacture different mechanical property structural components separately, making it difficult to achieve large-scale production and reduce manufacturing costs. In this study, a novel mechanical metamaterial was proposed that could adjust the positive and negative Poisson’s ratio, elastic modulus, and compression-torsion properties only by simply changing the unit cell number and the combination method. This asymmetric structural design provides a new perspective for the design of metamaterials with tunable mechanical properties.

The areal Gaussian filtering is the current standardized approach for the separation of roughness and waviness components of a surface topography for areal surface measurements and analysis. Although the technical specifications (ISO 25178) recommend а certain nesting index for areal Gaussian filters, the use of standard values is not possible in some cases, particularly when the recommended value exceeds the range of the measurement software. Therefore, it is necessary to find an appropriate nesting index value other than that recommended by the standard. The aim of this study is to investigate the influence of Gaussian filter selection on areal surface texture parameter determination for structured surfaces. The results suggest that the nesting index of a Gaussian filter for areal surface parameter determination and analysis should be at least three times the scale of the coarsest surface structure.

Isotropy is an important feature of an area filter in the three-dimensional surface roughness evaluation. First, the transmission characteristic deviation between the approximating spline filter and the Gaussian filter is reduced by cascading approximating. Second, the approximating spline filter is superimposed on the orthogonal direction to obtain an isotropic areal filter. Then, four direct methods for the solving approximating spline matrix are applied. Based on the profile filtering and areal filtering, the computational efficiency and accuracy are compared. The experimental results show that the improved square root method (LDLT decomposition) combines both computational efficiency and filtering precision, and is a good choice for solving the approximating spline matrix. Finally, six kinds of robust approximating spline filters are constructed. Taking the output value of robust Gaussian regression filter (RGRF) as reference, and the honing profile and step surface with deep valley characteristics were used as test surfaces to compare their robustness and iteration time. The experimental results show that the approximating spline filter based on the ADRF function has the shortest iteration times, while its roughness is close to the robust Gaussian regression filter.