Slider-based performance-driven metamaterial connectivity enhancement method

Abstract

This invention discloses a performance-driven metamaterial connectivity enhancement method based on a slider, relating to the field of metamaterial design optimization technology. The method includes: screening adjacent design units in the metamaterial whose connectivity does not meet a preset target; combining each pair of adjacent design units to form a composite unit; constructing a slider on the composite unit and setting the slider's movement step size; calculating the target performance corresponding to the area covered by the current slider, using the target performance as an optimization constraint; and optimizing the topology of the slider while keeping the material information of the partially overlapping areas covered by the current slider and the previous slider unchanged, obtaining the optimization result; moving the slider along the composite unit by one step and continuing optimization until the slider sweeps through the entire area of ​​the composite unit, obtaining the optimized result of the composite unit. This invention can improve the physical connectivity between adjacent microstructure units and enhance metamaterial performance.

Description

Technical Field

[0001] This invention relates to the field of metamaterial design optimization technology, and in particular to a slider-based performance-driven method for enhancing metamaterial connectivity. Background Technology

[0002] Metamaterials are a class of special materials that achieve extraordinary physical properties through artificially designed microstructures rather than relying on the chemical composition of the material. They have broad application prospects in fields such as thermal, electromagnetic, and acoustics. To obtain excellent macroscopic properties, the microstructure of metamaterials needs to be carefully designed, and topology optimization methods, due to their extremely high degree of design freedom, have become one of the most promising microstructure design tools.

[0003] Currently, unit cell-by-unit topology optimization based on inverse homogenization theory is a commonly used method for designing metamaterials. The core of this method lies in utilizing homogenization theory to ensure that the equivalent performance of each micro-unit precisely matches the theoretical target value at the corresponding point in the macroscopic design domain. This method can fully explore the design space and is easily parallelized. However, in practical applications, because the optimization process of each micro-unit is independent and only aims at matching its own performance, adjacent units often cannot form effective physical connections at the boundaries, resulting in a "lack of connectivity" problem. This geometric discontinuity severely blocks the transmission paths of physical fields such as force and heat, leading to a significant decrease in the overall structural performance of the metamaterial.

[0004] To address these issues, existing technologies have proposed several improvements, such as applying geometric constraints on cell boundaries, sharing boundary design variables, or directly incorporating connectivity indices into the optimization objective. However, these methods often suffer from problems in practical applications, such as compressed design space, excessive computational complexity, or poor performance in heterogeneous multi-scale structures. Summary of the Invention

[0005] To address some or all of the technical problems existing in the prior art, this invention provides a slider-based performance-driven metamaterial connectivity enhancement method, which can significantly improve the physical connectivity between adjacent microstructure units and improve metamaterial performance without reducing design space.

[0006] The technical solution of the present invention is as follows: A slider-based performance-driven metamaterial connectivity enhancement method is provided, including: Step 1: Obtain the metamaterial to be processed; Step 2: Evaluate the connectivity between adjacent design units in the current metamaterial, screen out adjacent design units whose connectivity does not meet the preset target, and independently combine each pair of lateral adjacent design units and each pair of longitudinal adjacent design units to form a composite unit. Step 3: Construct a slider with the same size as the design unit on each of the composite units, and set the step size of the slider movement, wherein the step size is less than the length of the slider; Step 4: For the area currently covered by the slider, calculate the target performance corresponding to the area covered by the slider using the target physics theory solution method. Use the calculated target performance as an optimization constraint. While keeping the material information of the partially overlapping area between the area currently covered by the slider and the area covered by the slider in the previous step unchanged, construct the topology optimization formula of the slider for optimization and obtain the optimization result. Step 5: Move the slider along the long side of the composite unit it is located by one step length and continue to execute Step 4 until the slider sweeps through the entire area of ​​the composite unit, obtain the optimization result of the composite unit, and execute Step 6. Step 6: Determine whether the preset optimization stopping condition has been met. If yes, output the optimized hypermaterial. If no, return to step 2 to continue iterative optimization.

[0007] Furthermore, in some embodiments, the metamaterial to be processed is a metamaterial designed by an inverse homogenization method.

[0008] Furthermore, in some implementations, connectivity is assessed by quantitative calculation based on the material distribution density or topological connection path at the interface of adjacent design units.

[0009] Furthermore, in some embodiments, if the metamaterial is a thermal metamaterial, the calculation of the target performance corresponding to the region covered by the slider using the target physics field theory solution method includes: calculating the target thermal conductivity tensor corresponding to the region covered by the slider using a transformation thermal method; If the metamaterial is a mechanical metamaterial, the calculation of the target performance corresponding to the region covered by the slider through the target physics field theory solution method includes: calculating the target elastic tensor corresponding to the region covered by the slider using a mechanical transformation method or a machine learning method.

[0010] Furthermore, in some embodiments, if the metamaterial is a thermal metamaterial, the topology optimization formula for the slider is expressed as: ; in, Indicates the first Material volume fraction of each slider Indicates the first The area covered by each slider Indicates the first The total number of finite units covered by each slider. Indicates the first The density of a finite number of cells Represents the overall heat conduction tensor matrix. Represents the overall temperature vector. Represents the thermal load vector. Indicates the first The equivalent thermal conductivity tensor of the area covered by each slider. Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first Thermal conductivity of a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first The target thermal conductivity tensor of the area covered by each slider. This represents the preset mapping function. Indicates the preset tolerance, subscript and The superscript T represents the different macroscopic directional components of the tensor, and the superscript T indicates the transpose operation of the matrix.

[0011] Furthermore, in some embodiments, the preset optimization stopping condition includes one of the following: the connectivity between adjacent design units in the optimized metamaterial all meet a preset target, or the number of iterations has reached a preset iteration threshold.

[0012] Furthermore, in some embodiments, the optimization process of each of the composite units is independent of each other and is performed simultaneously.

[0013] Furthermore, in some embodiments, the method is applicable to enhancing the connectivity of thermal metamaterials, mechanical metamaterials, acoustic metamaterials, or electromagnetic metamaterials.

[0014] The main advantages of the technical solution of this invention are as follows: The slider-based performance-driven metamaterial connectivity enhancement method of this invention constructs sliders of the same size as the design units and moves them incrementally in a stepwise manner within composite units with insufficient connectivity. In each optimization step, the target performance of the area covered by the current slider is used as a constraint, while locking the material information of the partially overlapping area covered by the previous slider. This achieves a smooth transition and information transfer of material distribution across units, thereby accurately repairing the geometric discontinuities between adjacent units without compressing the original design space, and significantly improving the physical field transfer efficiency of the metamaterial macrostructure. At the same time, through performance-driven local optimization, the material volume fraction can be further reduced while enhancing connectivity, achieving structural lightweighting. In addition, since the optimization processes of multiple composite units are independent and can be calculated in parallel, it has good computational efficiency and scalability, and can be widely applied to the connectivity enhancement design of various types of metamaterials such as thermal and mechanical materials. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and constitute a part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A schematic flowchart of a performance-driven metamaterial connectivity enhancement method based on sliders provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a composite unit before and after optimization, provided in an embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0017] The technical solutions provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] See Figure 1 This invention provides a slider-based performance-driven metamaterial connectivity enhancement method, which includes the following steps: Step 1: Obtain the metamaterial to be processed; Step 2: Evaluate the connectivity between adjacent design units in the current metamaterial, screen out adjacent design units whose connectivity does not meet the preset target, and independently combine each pair of lateral adjacent design units and each pair of longitudinal adjacent design units to form a composite unit. Step 3: Construct a slider with the same size as the design unit on each composite unit, and set the step size of the slider movement, where the step size is less than the length of the slider; Step 4: For the area currently covered by the slider, calculate the target performance corresponding to the area covered by the slider using the target physics theory solution method. Use the calculated target performance as an optimization constraint. While keeping the material information of the partially overlapping area between the area currently covered by the slider and the area covered by the slider in the previous step unchanged, construct the topology optimization formula of the slider for optimization and obtain the optimization result. Step 5: Move the slider along the long side of the composite unit it is located by one step and continue to execute Step 4 until the slider sweeps through the entire area of ​​the composite unit, obtain the optimization result of the composite unit, and execute Step 6. Step 6: Determine whether the preset optimization stopping condition has been met. If yes, output the optimized hypermaterial. If no, return to step 2 to continue iterative optimization.

[0019] In this embodiment of the invention, the preset target corresponding to connectivity is specifically set according to actual needs. For example, the preset target is set to connectivity greater than a set connectivity threshold. The connectivity threshold is set according to actual needs.

[0020] In this embodiment of the invention, a pair of adjacent design units whose connectivity does not meet a preset target are combined to form a composite unit.

[0021] In this embodiment of the invention, the step size of the slider movement is specifically set according to actual needs. For example, the step size is set to 1 / n of the design unit length, where n is a positive integer, such as n=10.

[0022] In this embodiment of the invention, the area covered by the slider is used as the design area to be optimized.

[0023] In this embodiment of the invention, the size of the partially overlapping region where the material information remains unchanged is set according to actual needs. Preferably, in this embodiment of the invention, the material information of the first half of the overlapping region between the area currently covered by the slider and the area covered by the previous slider remains unchanged. The first half of the overlapping region refers to the half of the overlapping region closest to the area covered by the previous slider.

[0024] In this embodiment of the invention, steps 4 and 5 are performed for each composite unit. The optimization process for each composite unit is independent and can be executed simultaneously.

[0025] In this embodiment of the invention, the preset optimization stopping condition is specifically set according to actual needs. For example, the preset optimization stopping condition includes one of the following: the connectivity between adjacent design units in the optimized metamaterial all meet the preset target, or the number of iterations has reached a preset iteration threshold.

[0026] The performance-driven metamaterial connectivity enhancement method based on sliders provided in this invention constructs sliders of the same size as the design units and moves them incrementally in steps within composite units with insufficient connectivity. In each optimization step, the target performance of the area covered by the current slider is used as a constraint, while locking the material information of the partially overlapping area covered by the previous slider. This achieves a smooth transition and information transfer of material distribution across units, thereby accurately repairing the geometric discontinuities between adjacent units without compressing the original design space, significantly improving the physical field transfer efficiency of the metamaterial macrostructure. At the same time, through performance-driven local optimization, the material volume fraction can be further reduced while enhancing connectivity, achieving structural lightweighting. Furthermore, since the optimization processes of multiple composite units are independent and can be computed in parallel, it has good computational efficiency and scalability, and can be widely applied to the connectivity enhancement design of various types of metamaterials, such as thermal and mechanical materials.

[0027] Furthermore, in this embodiment of the invention, the metamaterial to be processed is a metamaterial designed by an inverse homogenization method.

[0028] In this embodiment of the invention, the design of metamaterials using the inverse homogenization method includes the following steps: Discrete macroscopic design domain: The design domain of the macroscopic structure is divided into grids, and the target performance tensor at the center of each grid is calculated and determined. Define the micro-unit cell design domain: For each macro-grid point, establish a micro-unit cell design domain and perform finite element meshing on it, setting the material density of each finite element as the design variable; Establish a homogenization analysis model: Based on the density distribution of the current microscopic unit cell, solve the physical field under periodic boundary conditions, and calculate the equivalent performance tensor of the unit cell using the homogenization formula; Introducing SIMP (Solid Isotropic Material with Penalization) material interpolation: A penalized model of solid isotropic materials is adopted to establish a power-law relationship between element material properties and density variables, allowing continuous density variation, and using a penalty factor to force intermediate densities to converge toward 0 / 1; Construct an inverse homogenization optimization model: set the optimization objective as minimizing the material volume fraction within the unit cell, and constrain the error between the equivalent performance tensor and the target performance tensor to be less than the tolerance; Iterative solution of the optimization problem: The design variables are repeatedly updated using the topology optimization algorithm, and the homogenization analysis is performed again until the objective function is stable and the constraints are satisfied, resulting in a clear 0 / 1 distribution of the unit cell configuration; Output microstructure: The final density distribution is mapped to a defined material layout as the microstructure design result of the macroscopic grid points. The above steps are performed in parallel or serially on all macroscopic grid points to assemble a complete metamaterial.

[0029] Specifically, taking thermal metamaterials as an example, in order to restore the thermal conductivity tensor distribution field of the spatial transformation of thermal metamaterials, the design domain of the macroscopic structure is discretized into a grid. For the microstructure within the grid, the equivalent thermal conductivity tensor calculated by the homogenization method is required to approximate the target thermal conductivity tensor at the center of the grid.

[0030] Homogenization theory is a method that calculates the macroscopic equivalent properties of a microstructure by using material distribution information within the microstructure. This method is applicable to the design domain of macrostructures. and micro-unit cell design domain Define macroscopic coordinates and micro coordinates Meet the conditions , This is a scale separation parameter representing the proportional relationship between macroscopic and microscopic scales. Since homogenization calculations must satisfy the scale separation assumption, it must therefore satisfy... .

[0031] In homogenization calculations, all variables depend on both macroscopic and microscopic scales, exhibiting Y-periodicity, which can be specifically expressed as: ; in, The original depends on macroscopic coordinates. physical quantity, Represented in macroscopic coordinates Location, micro coordinates Physical quantities on Represented in macroscopic coordinates Location, micro coordinates Physical quantities on The periodic domain represents a microscopic unit cell.

[0032] In steady-state heat conduction, the temperature field and heat flux density of the macroscopic structure can be expressed as: The nth-order asymptotic approximation is specifically expressed as: ; ; in, The original depends on macroscopic coordinates. Temperature field, Represents the macroscopic average temperature field. This represents the first-order correction term for the temperature field, used to reflect the perturbation of the temperature field by microscopic periodic structures. This represents the second-order correction term for the temperature field, used to reflect the perturbation of the temperature field by the microscopic periodic structure. The original depends on macroscopic coordinates. Heat flux density field, Represents the macroscopic average heat flux density field. This represents the first-order correction term for the heat flux density field, used to reflect the perturbation of the heat flux density field by the micro-periodic structure. This represents the second-order correction term for the heat flux density field, used to reflect the perturbation of the heat flux density field by micro-periodic structures.

[0033] Considering only parameters First-order variational term, equivalent heat conduction tensor of macroscopic structure The amount It can be represented as: ; in, The components of the equivalent heat conduction tensor of the macroscopic structure are represented by the subscript. and Represents the different macroscopic directional components of a tensor. , , Represents the design domain of a micro-unit cell. Indicates the local thermal conductivity of a micromaterial, subscript and Represents the microscopic directional components. Represents the microscopic test heat flow field The first direction component corresponding to The unit test heat flux applied in a macroscopic direction, Represents the microscopic test heat flow field The first direction component corresponding to The unit test heat flux applied in a macroscopic direction, This represents the actual heat flow field within the microstructure after applying a unit test heat flux. The first direction component corresponding to The actual heat flow applied in a macroscopic direction, This represents the actual heat flow field within the microstructure after applying a unit test heat flux. The first direction component corresponding to The actual heat flow applied in a macroscopic direction.

[0034] To facilitate calculation, the microstructure is discretized using the finite element method. For a finite element, the homogenized equivalent heat conduction tensor can be transformed into the energy average form of the finite element within the microstructure, specifically expressed as: ; in, This represents the equivalent heat conduction tensor of the microscopic unit cell design domain. This represents the total number of finite elements in the microscopic unit cell design domain. Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first Thermal conductivity of a finite element Indicates the first The density of a finite number of cells Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element, subscript and The superscript T represents the different macroscopic directional components of the tensor, and the superscript T indicates the transpose operation of the matrix.

[0035] Numerical homogenization methods can be used to calculate the equivalent thermal conductivity tensor of a microstructure. Conversely, this allows for the construction of a microstructure topology optimization model that searches for a specific thermal conductivity tensor, i.e., inverse homogenization design of microstructure configurations. Specifically, the SIMP scheme is used to construct the interpolation relationship between the thermal conductivity of finite elements and the thermal conductivity of two given materials, as expressed below: ; in, Indicates the first Thermal conductivity of a finite element This indicates the thermal conductivity of the first material. This indicates the thermal conductivity of the second material, which is a higher thermal conductivity material compared to the first material. This represents the penalty factor of the interpolation function.

[0036] Based on the above analysis, to ensure that the microstructure with a specific thermal conductivity tensor can be optimized, to minimize the total volume of the second material within the design region, and to constrain the equivalent thermal conductivity tensor to be equal to the target thermal conductivity tensor, the optimization formula is constructed as follows: ; in, The material volume fraction representing the microscopic unit cell design domain. Represents the design domain of a micro-unit cell. This represents the total number of finite elements in the microscopic unit cell design domain. Indicates the first The density of a finite number of cells Represents the overall heat conduction tensor matrix. Represents the overall temperature vector. Represents the thermal load vector. This represents the equivalent heat conduction tensor of the microscopic unit cell design domain. Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first Thermal conductivity of a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element This represents the target heat conduction tensor of the microscopic unit cell design domain. This represents the preset mapping function. Indicates the preset tolerance, subscript and The superscript T represents the different macroscopic directional components of the tensor, and the superscript T indicates the transpose operation of the matrix.

[0037] The mapping function can be set as a linear weighted function, and the tolerance can be set according to the actual needs.

[0038] Furthermore, in this embodiment of the invention, the connectivity assessment is based on a quantitative calculation of the material distribution density or topological connection path at the interface of adjacent design units.

[0039] Furthermore, in this embodiment of the invention, for different metamaterials, the target performance corresponding to the region covered by the slider is calculated using the corresponding target physics theory solution method. For example, if the metamaterial is a thermal metamaterial, calculating the target performance corresponding to the region covered by the slider using the target physics theory solution method includes: calculating the target thermal conductivity tensor corresponding to the region covered by the slider using a transformation thermal method; if the metamaterial is a mechanical metamaterial, calculating the target performance corresponding to the region covered by the slider using the target physics theory solution method includes: calculating the target elasticity tensor corresponding to the region covered by the slider using a mechanical transformation method or a machine learning method.

[0040] Furthermore, in this embodiment of the invention, a topology optimization formula for the corresponding slider is constructed for different metamaterials. For example, if the metamaterial is a thermal metamaterial, the topology optimization formula for the slider is expressed as: ; in, Indicates the first Material volume fraction of each slider Indicates the first The area covered by each slider Indicates the first The total number of finite units covered by each slider. Indicates the first The density of a finite number of cells Represents the overall heat conduction tensor matrix. Represents the overall temperature vector. Represents the thermal load vector. Indicates the first The equivalent thermal conductivity tensor of the area covered by each slider. Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first Thermal conductivity of a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first The target thermal conductivity tensor of the area covered by each slider. This represents the preset mapping function. Indicates the preset tolerance, subscript and The superscript T represents the different macroscopic directional components of the tensor, and the superscript T indicates the transpose operation of the matrix.

[0041] In this embodiment of the invention, the method is applicable to enhancing the connectivity of thermal metamaterials, mechanical metamaterials, acoustic metamaterials, or electromagnetic metamaterials.

[0042] In this embodiment of the invention, for thermal metamaterials, the method provided by this embodiment of the invention was experimentally verified at different resolutions (50×50 to 100×100) and different initial material distribution types (random distribution type, central hole type, central distribution type) according to the steps defined above, and the connectivity improvement results are shown in Table 1.

[0043] Table 1. Connectivity Improvement ; As can be seen from Table 1, the connectivity of all examples was significantly improved, demonstrating that the method provided by the embodiments of the present invention can significantly improve connectivity.

[0044] Furthermore, for thermal metamaterials, following the steps defined above, the method provided in the embodiments of the present invention was experimentally verified at different resolutions (50×50 to 100×100) and different initial material distribution types (random distribution type, central pore type, central distribution type), and the volume fraction improvement results are shown in Table 2.

[0045] Table 2 Improvement in volume fraction ; As can be seen from Table 2, further optimization of the composite unit can yield additional benefits. While improving connectivity, the volume fraction of the composite unit is significantly reduced, indicating that the method provided by the embodiments of the present invention can further eliminate unnecessary materials and achieve lightweight design.

[0046] Furthermore, for thermal metamaterials, following the steps defined above, the method provided in the embodiments of the present invention was experimentally verified at different resolutions (50×50 to 100×100) and different initial material distribution types (random distribution type, central hole type, central distribution type), and the changes in the performance of the composite unit were obtained as shown in Table 3. In Table 3, the values ​​represent the F-norm distance between the equivalent thermal conductivity tensor of the composite unit and the target thermal conductivity tensor.

[0047] Table 3. Performance Changes of Composite Units ; As can be seen from Table 3, under performance-driven conditions, while improving connectivity, the heat transfer performance of the central region is closer to the target performance, indicating that the method provided by the embodiments of the present invention can further improve material performance.

[0048] It should be noted that, in this document, relational terms such as “first” and “second” are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A slider-based performance-driven metamaterial connectivity enhancement method, characterized in that, include: Step 1: Obtain the metamaterial to be processed; Step 2: Evaluate the connectivity between adjacent design units in the current metamaterial, screen out adjacent design units whose connectivity does not meet the preset target, and independently combine each pair of lateral adjacent design units and each pair of longitudinal adjacent design units to form a composite unit. Step 3: Construct a slider with the same size as the design unit on each of the composite units, and set the step size of the slider movement, wherein the step size is less than the length of the slider; Step 4: For the area currently covered by the slider, calculate the target performance corresponding to the area covered by the slider using the target physics theory solution method. Use the calculated target performance as an optimization constraint. While keeping the material information of the partially overlapping area between the area currently covered by the slider and the area covered by the slider in the previous step unchanged, construct the topology optimization formula of the slider for optimization and obtain the optimization result. Step 5: Move the slider along the long side of the composite unit it is located by one step length and continue to execute Step 4 until the slider sweeps through the entire area of ​​the composite unit, obtain the optimization result of the composite unit, and execute Step 6. Step 6: Determine whether the preset optimization stopping condition has been met. If yes, output the optimized hypermaterial. If no, return to step 2 to continue iterative optimization.

2. The slider-based performance-driven metamaterial connectivity enhancement method of claim 1, wherein, The metamaterial to be processed is a metamaterial designed using an inverse homogenization method.

3. The slider-based performance-driven metamaterial connectivity enhancement method of claim 1, wherein, Connectivity assessment is based on quantitative calculations of material distribution density or topological connection paths at the interfaces of adjacent design units.

4. The slider-based performance-driven metamaterial connectivity enhancement method of claim 1, wherein, If the metamaterial is a thermal metamaterial, the calculation of the target performance corresponding to the region covered by the slider through the target physics field theory solution method includes: calculating the target thermal conductivity tensor corresponding to the region covered by the slider using the transformation thermal method; If the metamaterial is a mechanical metamaterial, the calculation of the target performance corresponding to the region covered by the slider through the target physics field theory solution method includes: calculating the target elastic tensor corresponding to the region covered by the slider using a mechanical transformation method or a machine learning method.

5. The slider-based performance-driven metamaterial connectivity enhancement method of claim 1, wherein, If the metamaterial is a thermal metamaterial, the topology optimization formula for the slider is expressed as: ； in, Indicates the first Material volume fraction of each slider Indicates the first The area covered by each slider Indicates the first The total number of finite units covered by each slider. Indicates the first The density of a finite number of cells Represents the overall heat conduction tensor matrix. Represents the overall temperature vector. Represents the thermal load vector. Indicates the first The equivalent thermal conductivity tensor of the area covered by each slider. Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first Thermal conductivity of a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux An ideal temperature field generated on a finite element Indicates the first The first macroscopic direction under the action of a unit test heat flux The actual temperature field generated on a finite element Indicates the first The target thermal conductivity tensor of the area covered by each slider. This represents the preset mapping function. Indicates the preset tolerance, subscript and The superscript T represents the different macroscopic directional components of the tensor, and the superscript T indicates the transpose operation of the matrix.

6. The method for enhancing metamaterial connectivity based on slider performance according to claim 1, characterized in that, The preset optimization stopping conditions include one of the following: the connectivity between adjacent design units in the optimized metamaterial meets the preset target, or the number of iterations has reached a preset iteration threshold.

7. The method for enhancing metamaterial connectivity based on slider performance according to claim 1, characterized in that, The optimization processes for each of the composite units are independent of each other and are executed simultaneously.

8. The method for enhancing metamaterial connectivity based on slider performance according to claim 1, characterized in that, The method is applicable to enhancing the connectivity of thermal metamaterials, mechanical metamaterials, acoustic metamaterials, or electromagnetic metamaterials.

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