Pareto genetic algorithm-based method for predicting equivalent mechanical properties of heterogeneous variable-gradient lattice materials

Abstract

The application is a heterogeneous variable gradient lattice material mechanical property equivalent prediction method based on a Pareto genetic algorithm, comprising: recording the equivalent modulus of the lattice structure according to the lattice type and density of the heterogeneous variable gradient lattice material, simulating and analyzing the entity model of the heterogeneous variable gradient lattice material, and recording stress data; obtaining discrete mechanical property optimization degree; traversing all interfaces in the heterogeneous variable gradient lattice material to obtain discrete lattice connectivity; obtaining overall structure comprehensive measurement coefficient according to the number of lattice types and interfaces in the discrete area of the heterogeneous variable gradient lattice material; inputting the discrete mechanical property optimization degree, the discrete lattice connectivity and the overall structure comprehensive measurement coefficient of different types of heterogeneous variable gradient lattice materials into the Pareto genetic algorithm to obtain mechanical property equivalent prediction results. The application can predict the mechanical properties of the heterogeneous variable gradient lattice material under different lattice fillings, and has high efficiency and strong stability.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing and optimization design, and in particular to a method for predicting the equivalent mechanical properties of heterogeneous variable gradient lattice materials based on a Pareto genetic algorithm. Background Art

[0002] Lattice structure is a type of cellular structure, usually composed of a set of spatially periodic units with side-to-side symmetry. Due to its complex geometric configuration, it has not attracted widespread attention until the rapid development of additive manufacturing (AM) in recent years. The design of lattice structures has greatly benefited from polymer powder-based AM technologies such as selective laser sintering (SLS) and multi-jet fusion (MJF), which can build complex parts without support structures. Among them, many lattice structures with excellent physical properties have been studied. At present, these lattice structures have been widely used in engineering fields, such as impact energy absorption, fluid flow control, etc.

[0003] The potential of unit cells with anisotropic properties has been further revealed in the study of functionally graded lattice structures. A single type of unit arrangement may not be sufficient to meet more complex load conditions. In addition, there are a large number of thin rod materials in the low-density design area of ​​the variable-density lattice structure, which can easily lead to manufacturing defects during actual manufacturing. This is also a problem that can be avoided when combining variable-density design with multi-configuration design.

[0004] When studying heterogeneous gradient lattice materials, direct finite element simulation and performance evaluation based on the results are difficult to implement due to the extremely large and complex finite element mesh within the heterogeneous gradient lattice structure. Evaluating the equivalent performance of heterogeneous gradient lattice materials has become a pressing challenge in the optimization design of heterogeneous gradient lattice materials and a key to breaking the current performance ceiling of lattice structures. Summary of the Invention

[0005] In response to the problems existing in the prior art, the present invention provides a method for predicting the equivalent mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm. For the mechanical property evaluation of heterogeneous variable gradient lattice materials, an equivalent prediction method is proposed, which solves the relevant influence coefficients step by step and finally obtains the Pareto optimal solution by weight. The method has high practicality and adaptability.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is: a method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on Pareto genetic algorithm, which specifically includes the following steps:

[0007] Step 1, according to the lattice type and lattice density of the isomeric variable gradient lattice material, record the equivalent modulus of the lattice structure used, and at the same time, the entity model of the isomeric variable gradient lattice material is hexahedron meshing, through the finite element simulation analysis of mechanical properties, record the stress data;

[0008] Step 2, according to the stress data recorded in step 1 and the equivalent modulus of the lattice structure used, obtain the discrete mechanical property optimization degree J1 of the isomeric variable gradient lattice material;

[0009] Step 3, traverse all the interfaces in the isomeric variable gradient lattice material, according to the cross section of the lattice structure on both sides of the interface, solve the cross sectional area and the overlapping cross sectional area, get the discrete lattice connectivity J2 of the isomeric variable gradient lattice material;

[0010] Step 4, according to the filling lattice type and the number of interfaces in the isomeric variable gradient lattice material, solve the comprehensive evaluation coefficient of each discrete area, get the overall structure comprehensive evaluation coefficient J3;

[0011] Step 5, input J1, J2 and J3 of different types of isomeric variable gradient lattice material into Pareto genetic algorithm, get the pareto optimal solution, which is the mechanical property equivalent prediction result of isomeric variable gradient lattice material.

[0012] Further, step 1 includes the following sub steps:

[0013] Step 1.1, according to the lattice type and lattice density interval of the isomeric variable gradient lattice material, record the different types and different densities of lattice structure used in the isomeric variable gradient lattice material, carry out finite element simulation or stiffness matrix calculation on the lattice structure, and get the Young's modulus E 11 , E 22 , E 33 and shear modulus G 12 , G 12 , G 23 of each lattice structure.

[0014] Step 1.2: according to the working load condition of the isomeric variable gradient lattice material, the entity model of the isomeric variable gradient lattice material is hexahedron meshing, and the finite element simulation analysis of mechanical properties is carried out, and the Von-Mises stress value F VM and the six stress values S 11 , S 22 , S 33 , S 12 , S 13 , S 23 of hexahedron of each hexahedron grid unit are recorded.

[0015] Further, step 2 comprises the following sub-steps:

[0016] Step 2.1, any one hexahedral mesh element solves six sub-stress value weight matrix of the hexahedron according to six sub-stress values of the hexahedron Wherein, S 11 , S 22 , S 33 , S 12 , S 13 , S 23 are sub-stress values of the hexahedron, L s is the norm of the six sub-stress values,

[0017] Step 2.2, fill a lattice structure for the hexahedral mesh element to obtain a discrete area, and according to Young's modulus E 11 , E 22 , E 33 and shear modulus G 12 , G 12 , G 23 , the equivalent modulus weight matrix Wherein, L T is the equivalent modulus norm,

[0018] Step 2.3, according to the equivalent modulus of the hexahedral mesh element filled with lattice structure and Von-Mises stress value F VM , the comprehensive bearing capacity of the discrete area is solved According to the six sub-stress value weight matrix of the hexahedral mesh element and the corresponding equivalent modulus weight matrix, the bearing capacity proportion of the discrete area is solved The filling evaluation coefficient of the discrete area is obtained Wherein, L6 is the equivalent modulus norm of the lattice element, is the h column value in the six sub-stress value weight matrix, is the h column value in the equivalent modulus weight matrix;

[0019] Step 2.4, repeat steps 2.1-2.3 to obtain the filling evaluation coefficient of all discrete areas, and solve the discrete mechanical performance optimization degree J1 of the heterogeneous variable gradient lattice material as follows:

[0020]

[0021] Wherein, N is the total number of discrete areas divided in the heterogeneous variable gradient lattice material, and n is the index of N.

[0022] Further, step 3 comprises the following sub-steps:

[0023] Step 3.1, calculate the connectivity between different types and different densities of cells filled in the heterogeneous variable gradient, extract the types and densities of the lattice units on both sides of the connecting surface of all discrete regions, model and form the two types of lattice units through the MC forming code, and display the two cross sections of the spatial intersection of the two cells;

[0024] Step 3.2, convert the two cross sections of the spatial intersection of the two cells into pixel images, calculate the overlapping part of the two types of lattice structures, and calculate the fusion coefficient:

[0025]

[0026] wherein, is the fusion coefficient of the lth interface in the nth discrete region, A A and A B are the areas of the cross sections of the lattice units on both sides of the interface;

[0027] Step 3.3, sum all the interface fusion coefficients to obtain the discrete lattice connectivity J2 of the heterogeneous variable gradient lattice material:

[0028]

[0029] wherein, m represents the total number of interfaces of all discrete regions in the heterogeneous variable gradient lattice material; L n is the number of interfaces possessed by the nth discrete region, and l is the index of L n ; N is the total number of discrete regions divided in the heterogeneous variable gradient lattice material, and n is the index of N.

[0030] Further, step 4 includes the following sub-steps:

[0031] Step 4.1, according to the filling evaluation coefficient M i of each discrete region and the fusion coefficient of each interface in the discrete region, calculate the comprehensive evaluation coefficient M' of each discrete region n :

[0032]

[0033] wherein, L n is the number of interfaces possessed by the nth discrete region, and l is the index of L n ;

[0034] Step 4.2, obtain the overall comprehensive measurement coefficient J3 through the comprehensive evaluation coefficient of each discrete region:

[0035]

[0036] Wherein, N is the total number of discrete regions divided in the heterogeneous gradient lattice material, and n is the index of N.

[0037] Furthermore, the calculation process of the Pareto genetic algorithm in step 5 is:

[0038] P=w1(J1×J2)+w2J3

[0039] Among them, w1 is the total mechanical property evaluation weight of the discrete region, and w2 is the mechanical property evaluation weight of the heterogeneous variable gradient lattice material structure.

[0040] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm.

[0041] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements the equivalent prediction method for the mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm.

[0042] Compared with existing technologies, this invention offers the following advantages: It proposes a method for predicting the equivalent mechanical properties of heterogeneous gradient lattice materials. This method addresses the lack of design theory and algorithms for heterogeneous gradient lattice materials in the lattice research field. It effectively predicts the mechanical properties of heterogeneous gradient lattice materials with different lattice fillings. Compared with traditional finite element iterative optimization methods, this method offers significantly higher design efficiency and more stable prediction results, providing a key design solution for the application of lattice materials in engineering performance optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The present invention is a flowchart of the equivalent prediction method of mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm;

[0044] Figure 2 This is a graph showing equivalent mechanical performance parameter data of multiple types of lattice unit structures in the method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm of the present invention;

[0045] Figure 3 Schematic diagram of load constraint conditions for an example model of the equivalent prediction method for mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm of the present invention. Figure 3 The left figure in the figure shows the load constraint conditions of the example model. Figure 3 The right figure in the figure shows the mechanical simulation results of the example model;

[0046] Figure 4 Fig. 1 is a schematic diagram of the lattice connectivity determination method in the present application; Figure 4 Fig. 1(a) is a cross-sectional view of an example lattice unit A, Figure 4 Fig. 1(b) is a cross-sectional view of an example lattice unit B, Figure 4 Fig. 1(c) is a phase diagram of the example lattice A and the example lattice B;

[0047] Figure 5 Fig. 2 is an analytical diagram of the performance equivalent prediction of the discrete region in the present application;

[0048] Figure 6 Fig. 3 is a model schematic diagram of the present application and the control group. DETAILED DESCRIPTION

[0049] The technical solutions of the present application are further described in detail below in combination with the drawings of the specification.

[0050] As Figure 1 Fig. 4 is a workflow diagram of the heterogeneous variable gradient lattice material mechanical property equivalent prediction method based on the Pareto genetic algorithm in the present application, which specifically includes the following steps:

[0051] Step 1, according to the lattice type and lattice density of the heterogeneous variable gradient lattice material, record the equivalent modulus of the lattice structure used, and at the same time, perform hexahedral mesh division on the solid model of the heterogeneous variable gradient lattice material, record the stress data through mechanical property finite element simulation analysis; specifically including the following sub-steps:

[0052] Step 1.1, according to the lattice type and lattice density interval of the heterogeneous variable gradient lattice material, record the different types and different densities of lattice structures used in the heterogeneous variable gradient lattice material, perform finite element simulation or stiffness matrix calculation on the lattice structure, and obtain the Young's modulus E 11 , E 22 , E 33 and shear modulus G 12 , G 12 , G 23 , Figure 2 Ten lattice units and their corresponding Young's modulus and shear modulus are exemplified

[0053] Step 1.2: according to the working load condition of the heterogeneous variable gradient lattice material, perform hexahedral mesh division on the solid model of the heterogeneous variable gradient lattice material, and perform mechanical property finite element simulation analysis, such as Figure 3 , record the Von-Mises stress value F VM of each hexahedral mesh unit and the six stress values S11 , S 22 , S 33 , S 12 , S 13 , S 23 .

[0054] Step 2, according to the stress data recorded in step 1, the equivalent modulus of the lattice structure used is combined to obtain the discrete mechanical performance optimization degree J1 of the metamorphic variable gradient lattice material, specifically including the following sub-steps:

[0055] Step 2.1, any one hexahedral grid element is solved according to the six stress value weight matrix of the six stress values of the hexahedron Wherein, S 11 , S 22 , S 33 , S 12 , S 13 , S 23 are the stress values of the hexahedron, L s is the norm of the six stress values,

[0056] Step 2.2, fill a lattice structure for the hexahedral grid element to obtain a discrete area, and according to the Young's modulus E 11 , E 22 , E 33 and shear modulus G 12 , G 12 , G 23 of the filled lattice structure, the equivalent modulus weight matrix Wherein, L T is the equivalent modulus norm,

[0057] Step 2.3, according to the equivalent modulus of the hexahedral grid element filled with lattice structure and Von-Mises stress value F VM , the comprehensive pressure bearing capacity of the discrete area is calculated According to the six stress value weight matrix of the hexahedral grid element and the corresponding equivalent modulus weight matrix, the pressure bearing capacity proportion of the discrete area is calculated The filling evaluation coefficient of the discrete area is obtained Wherein, L6 is the equivalent modulus norm of the lattice element, is the h column value in the six stress value weight matrix, is the h column value in the equivalent modulus weight matrix; when M2 is smaller and M1 is larger, the current area filling evaluation coefficient M i calculated is larger, which indicates that the stiffness performance optimization of this discrete area is more ideal;

[0058] Step 2.4, repeat steps 2.1-2.3 to obtain the filling evaluation coefficient of all discrete regions, and calculate the discrete mechanical performance optimization degree J1 of the heterogeneous variable gradient lattice material as:

[0059]

[0060] Wherein, N is the total number of discrete regions divided in the heterogeneous variable gradient lattice material, and n is the index of N.

[0061] Step 3, traverse all interfaces in the heterogeneous variable gradient lattice material, and according to the cross section of the lattice structure on both sides of the interface, solve the cross-sectional area and the overlapping cross-sectional area, and obtain the discrete lattice connectivity J2 of the heterogeneous variable gradient lattice material, which includes the following sub-steps:

[0062] Step 3.1, calculate the connectivity between different types and densities of cells filled in the heterogeneous variable gradient, extract the types and densities of lattice units on both sides of all discrete region connecting surfaces, and model the two types of lattice units through MC forming code to display the two cross sections of the two cells intersecting in space.

[0063] Step 3.2, Figure 4 The cross sections of two different types of lattice units A and B are converted into pixel images, and the overlapping part of A type lattice structure and B type lattice structure is calculated to calculate the fusion coefficient:

[0064]

[0065] Wherein, is the fusion coefficient of the lth interface in the nth discrete region, The smaller the fusion coefficient is, the better the connectivity between A and B in three-dimensional modeling of the two lattice units is; A A and A B are the areas of the cross sections of the lattice units on both sides of the interface;

[0066] Step 3.3, sum all the interface fusion coefficients to obtain the discrete lattice connectivity J2 of the heterogeneous variable gradient lattice material:

[0067]

[0068] Wherein, m represents the total number of all discrete region interfaces in the heterogeneous variable gradient lattice material; L n is the number of interfaces possessed by the nth discrete region, and l is the index of L n ; N is the total number of discrete regions divided in the heterogeneous variable gradient lattice material, and n is the index of N.

[0069] Step 4: According to the lattice type and the number of interfaces of the discrete regions in the heterogeneous gradient lattice material, solve the comprehensive evaluation coefficient of each discrete region to obtain the overall structure comprehensive evaluation coefficient J3; specifically, the following sub-steps are included:

[0070] Step 4.1: Based on the filling evaluation coefficient M of each discrete area i , and the fusion coefficient of each interface in the discrete region Calculate the comprehensive evaluation coefficient M′ of each discrete area n :

[0071]

[0072] Among them, L n is the number of interfaces possessed by the nth discrete region, l is L n The index of . Figure 5 As shown, there are 6 interfaces around the discrete area, L n Then it is equal to 6 at this time;

[0073] Step 4.2: The overall comprehensive evaluation coefficient J3 is obtained by the comprehensive evaluation coefficient of each discrete area:

[0074]

[0075] Wherein, N is the total number of discrete regions divided in the heterogeneous gradient lattice material, and n is the index of N.

[0076] Step 5: Input J1, J2, and J3 of different types of heterogeneous variable gradient lattice materials into the Pareto genetic algorithm to obtain the Pareto optimal solution, which means that the better the mechanical properties of the current structure in theory, the more equivalent the prediction result of the mechanical properties of the heterogeneous variable gradient lattice material;

[0077] The calculation process of the Pareto genetic algorithm in the present invention is:

[0078] P=w1(J1×J2)+w2J3

[0079] Where w1 is the discrete mechanical property evaluation weight for the heterogeneous gradient lattice material structure, and w2 is the overall mechanical property evaluation weight for the heterogeneous gradient lattice material structure. w1 and w2 satisfy w1 + w2 = 1, and are set to 0.5 and 0.5 by default.

[0080] Figure 6The model groups used to test the method are given, and Table 1 gives the equivalent mechanical property prediction results of these test models and the actual simulation results of the models, and the heterogeneous variable gradient lattice structures are obtained by different filling strategies and filling cells of the same model. The same lattice types a, b and c are used in models A and D, models B and E, and models C and F, respectively, and models G, H and I are heterogeneous variable gradient lattice structures constructed by using three lattice types a, b and c. Compared with the uniform lattice structure, the reasonable variable density lattice structure can effectively improve the overall mechanical properties of the structure, and the heterogeneous variable density lattice material can further improve the overall mechanical properties on the basis of the variable density lattice structure. This is specifically reflected in the simulation results of the stiffness and internal energy values, the greater the stiffness and the greater the internal energy, the better the load-bearing performance of the current structure. From the stiffness and internal energy values, it can be found that there is a relationship of A>B>C, D>E>F, I>H>G in the mechanical properties of the same type model group, and A<D, B<E, C<F. The equivalent P value calculated by the design method provided by the application can obtain the same mechanical property comparison and evaluation of the model, and a large amount of analysis and calculation time is saved in the finite element simulation solution. Therefore, the equivalent mechanical property prediction method of the heterogeneous variable gradient lattice material based on the Pareto genetic algorithm provided by the application is a stable and efficient performance solving method.

[0081] The method provided by the application establishes an efficient and accurate equivalent calculation method of lattice materials, quantitatively characterizes the functional characteristics of the heterogeneous variable gradient lattice material, and accurately reflects the influence law of the type and density change of the lattice material on the mechanical properties of the macro model, which is a key method for realizing the macro and micro scale collaborative design of lattice materials.

[0082] Table 1 is the equivalent mechanical property evaluation parameters and equivalent mechanical property prediction results of the method and the control group of the application

[0083]

[0084] In one of the technical solutions of the application, a computer readable storage medium is also provided, which stores a computer program, and the computer program enables a computer to execute the equivalent mechanical property prediction method of the heterogeneous variable gradient lattice material based on the Pareto genetic algorithm.

[0085] In another technical solution of the application, an electronic device is also provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the equivalent mechanical property prediction method of the heterogeneous variable gradient lattice material based on the Pareto genetic algorithm is realized.

[0086] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium which can contain or store programs for use by or in connection with an instruction execution system, apparatus or device. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples of computer storage medium can include one or more wires, portable computer disks, hard drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), fiber optics, compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0087] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software manner depends on the specific application and design constraints of the technical solutions. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0088] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiment. Any technical solution falling within the concept of the present application shall fall within the protection scope of the present application. It should be noted that, for ordinary skilled persons in the art, some improvements and refinements without departing from the principles of the present application shall be considered as falling within the protection scope of the present application.

Claims

1. A method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on Pareto genetic algorithm, characterized in that: The specific steps include: Step 1: Record the equivalent modulus of the lattice structure used according to the lattice type and lattice density of the heterogeneous variable gradient lattice material, perform hexahedral meshing on the solid model of the heterogeneous variable gradient lattice material, and record stress data through finite element simulation analysis of mechanical properties; Step 2: Obtain the discrete mechanical property optimization degree J1 of the heterogeneous variable gradient lattice material based on the stress data recorded in step 1 and the equivalent modulus of the lattice structure used; including the following sub-steps: Step 2.1: For any hexahedral mesh element, solve the six component stress value weight matrix of the hexahedron according to the six component stress values ​​of the hexahedron. Among them, S 11 、S 22 、S 33 、S 12 、S 13 、S 23 are the stress values ​​of the hexahedron, L s is the norm of the six component stress values, Step 2.2: Fill the hexahedral grid unit with a lattice structure to obtain a discrete region. According to the Young's modulus E of the filled lattice structure, 11 、E 22 、E 33 and shear modulus G 12 , G 12 , G 23 Obtain the equivalent modulus weight matrix respectively Among them, L T is the equivalent modulus norm, Step 2.3: Fill the lattice structure with the hexahedral grid cells and the Von-Mises stress value F VM Obtain the comprehensive bearing capacity of the discrete area The bearing capacity ratio of the discrete area is obtained according to the six component stress value weight matrices of the hexahedral grid unit and the corresponding equivalent modulus weight matrix. Get the filling judgment coefficient of the discrete area Where L6 is the equivalent modulus norm of the lattice unit, is the h column value in the six component stress value weight matrix, is the h column value in the equivalent modulus weight matrix; Step 2.4: Repeat steps 2.1-2.3 to obtain the filling evaluation coefficients of all discrete regions and calculate the discrete mechanical property optimization degree J1 of the heterogeneous variable gradient lattice material: Wherein, N is the total number of discrete regions divided in the heterogeneous gradient lattice material, and n is the index of N; Step 3: Traverse all interfaces in the heterogeneous gradient lattice material, and solve the cross-sectional areas of the lattice structures on both sides of the interface and the cross-sectional areas of the overlapping intersections to obtain the discrete lattice connectivity J2 of the heterogeneous gradient lattice material; including the following sub-steps: Step 3.

1. Calculate the connectivity between unit cells of different types and densities within the heterogeneous gradient. Extract the types and densities of the lattice units used on both sides of the connecting surfaces of all discrete regions. Model the two lattice units using the MC modeling code, and display the two cross-sections where the two unit cells intersect in space. Step 3.2: Convert the two cross-sections of the two unit cells that intersect in space into pixel images, calculate the overlapping parts of the two types of lattice structures, and calculate the fusion coefficient: in, is the fusion coefficient of the lth interface in the nth discrete region, A A and A B are the cross-sectional areas of the lattice units on both sides of the interface; Step 3.3: Fusion coefficients of all interfaces The discrete lattice connectivity J2 of the heterogeneous variable gradient lattice material is obtained by summing: Where m represents the total number of interfaces of all discrete regions in the heterogeneous gradient lattice material; L n is the number of interfaces possessed by the nth discrete region, l is L n 's index; Step 4: according to the lattice type and the number of interfaces of the discrete regions in the heterogeneous variable gradient lattice material, solve the comprehensive evaluation coefficient of each discrete region to obtain the overall structure comprehensive evaluation coefficient J3; including the following sub-steps: Step 4.1: Based on the filling evaluation coefficient M of each discrete area i , and the fusion coefficient of each interface in the discrete region Calculate the comprehensive evaluation coefficient M' of each discrete area n : Step 4.2: The overall comprehensive evaluation coefficient J3 is obtained by the comprehensive evaluation coefficient of each discrete area: Step 5: Input J1, J2, and J3 of different types of heterogeneous variable gradient lattice materials into the Pareto genetic algorithm to obtain the Pareto optimal solution, which is the equivalent prediction result of the mechanical properties of the heterogeneous variable gradient lattice material.

2. The method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on Pareto genetic algorithm according to claim 1, characterized in that: Step 1 includes the following sub-steps: Step 1.1: According to the lattice type and lattice density range used in the heterogeneous gradient lattice material, record the different types and densities of lattice structures used in the heterogeneous gradient lattice material, perform finite element simulation or stiffness matrix calculation on the lattice structure, and obtain the Young's modulus E of each lattice structure. 11 、E 22 、E 33 and shear modulus G 12 , G 12 , G 23 ; Step 1.2: According to the working load conditions of the heterogeneous variable gradient lattice material, the solid model of the heterogeneous variable gradient lattice material is divided into hexahedral meshes, and the mechanical properties finite element simulation analysis is performed, and the Von-Mises stress value F of each hexahedral mesh unit is recorded. VM And the six stress components S of the hexahedron 11 、S 22 、S 33 、S 12 、S 13 、S 23 .

3. The method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on Pareto genetic algorithm according to claim 1, characterized in that: The calculation process of the Pareto genetic algorithm in step 5 is: P=w1(J1×J2)+w2J3 Among them, w1 is the total mechanical property evaluation weight of the discrete region, and w2 is the mechanical property evaluation weight of the heterogeneous variable gradient lattice material structure.

4. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the equivalent prediction method for the mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm as described in any one of claims 1 to 3.

5. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for equivalent prediction of mechanical properties of heterogeneous variable gradient lattice materials based on the Pareto genetic algorithm as described in any one of claims 1 to 3 is implemented.

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