Minimum curved surface structure optimization design method, system and equipment and storage medium

Abstract

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of minimal surface structure, and particularly relates to a minimal surface structure optimization design method, system, device and storage medium. BACKGROUND

[0002] With the continuous rapid development of advanced detection technology and the high demand for combat effectiveness, higher requirements are put forward for the development of radar stealth technology to further improve the survivability, penetration capability and depth strike capability of stealth unmanned aerial vehicles and other aviation equipment. Reducing the radar scattering cross section (Radar Cross Section, RCS) plays a dominant role in radar stealth design. As can be seen from the radar distance equation, the radar detection distance mainly depends on the characteristics of the radar itself, the radar scattering cross section of the target and the transmission characteristics of the atmosphere, and the only controllable radar feature is the radar scattering cross section RCS of the aircraft. And the radar detection distance is in 1 / 4 power relationship with RCS, and each 10dB reduction of RCS can reduce the radar action distance by about 44%, and 40dB reduction of RCS can reduce the radar action distance by about 90%, so reducing the RCS of the aircraft is the core of radar stealth design.

[0003] The method for reducing RCS mainly includes two categories of shape design and use of radar wave absorbing material. Among them, the structure type electromagnetic wave absorbing material with the functions of absorbing wave and bearing is integrated by fusing the absorbing material and the bearing structure, and has the dual functions of bearing and absorbing wave, which can effectively alleviate the problems of thick coating, large mass, high risk of falling off, the need for constant temperature hangar and frequent maintenance of the absorbing coating, and has the advantages of good environmental performance, wide absorbing frequency band, high designability, ability to be formed into high value-added components with complex shape, etc. The structure / function integrated collaborative design and more efficient stealth-performance balance can be realized, which has important guiding value for the actual task demand and technical feasibility in engineering application.

[0004] Lightweight is also one of the core requirements in the field of aerospace, aiming to improve the fuel efficiency, load capacity and maneuverability of the aircraft by reducing the structure weight coefficient. The main approaches to achieve lightweight include material lightweight (such as polymer replacing metal), structure lightweight (lattice structure replacing solid structure), and process lightweight (such as polymer fused deposition modeling integrated monolithic structure replacing split assembly structure). Thermoplastic resin-based composite materials, with their excellent high specific strength, high specific stiffness and physical / chemical properties, combined with the design freedom and manufacturing flexibility of fused deposition modeling (FDM) additive manufacturing technology, can quickly shape functional and complex porous lattice structures, effectively reducing the number of parts and design space and improving the degree of freedom of structure design, liberating the manufacturing process and design constraints of lattice structures, achieving high synergy optimization of materials-structure-process, and providing a new solution for the design and application of lightweight and functionally integrated components under complex conditions.

[0005] Currently, most of the structural electromagnetic wave absorbing materials designed with triangles, quadrilaterals and hexagons (such as honeycomb sandwich wave absorbing structure) show obvious anisotropy in 3D space, which has a significant impact on the absorption performance under different electromagnetic wave incidence conditions. Inspired by the biological microstructure in nature (butterfly wings), a new type of minimal surface wave absorbing structure with isotropic characteristics is designed. Minimal surface is a surface with minimum area and zero average curvature that meets certain constraints, and can be periodically extended in three coordinate axis directions. The lattice structure composed of periodically arranged minimal surfaces in space is a triply periodic minimal surface (TPMS) lattice structure.

[0006] In terms of mechanical bearing characteristics, the minimal surface lattice structure not only shows high performance comparable to conventional truss-like lattice structures, such as light weight, high specific strength, and strong energy absorption capacity, but also inherits the smooth surface and uniform curvature radius characteristics of TPMS. The stress distribution is uniform when the TPMS lattice structure is under load, and the self-supporting characteristics are achieved during the additive manufacturing process because each layer can support each other, which can fully utilize the high design and manufacturing freedom of additive manufacturing technology. In terms of electromagnetic wave regulation characteristics, the minimal surface lattice structure has high specific surface area, full connectivity, controllability, porosity, smoothness, and diversity, which can prolong the interaction time of electromagnetic waves and lattice structures and achieve isotropic electromagnetic reflection. However, based on thermoplastic resin-based composite 3D printing technology, the application of minimal surface lattice sandwich structure in the field of electromagnetic wave regulation to realize lightweight / bearing / wave absorbing multifunctional coupled integrated structure has not been systematically mentioned in the prior art. SUMMARY

[0007] The application provides a minimal surface structure optimization design method, system, device and storage medium, which can design a lightweight / bearing / absorbing minimal surface point array sandwich structure with good structure-performance dual characteristics.

[0008] In a first aspect, the application provides a minimal surface structure optimization design method, comprising: determining a multi-objective optimization problem according to design variables and optimization objectives of a three-period minimal surface point array structure; using an adaptive multi-objective engineering optimization method of a Kriging surrogate model to construct an approximate function relationship between the optimization objectives and the design variables; jointly using a Latin hypercube sampling and a sequential quadratic programming optimization algorithm to perform optimization design on the three-period minimal surface point array structure.

[0009] Further, the design variables include a unit cell size, a wall thickness size, a volume fraction and a sandwich height.

[0010] Further, the optimization objectives include a structure mass, a compression characteristic and an absorbing characteristic.

[0011] Further, the step of determining a multi-objective optimization problem according to design variables and optimization objectives of a three-period minimal surface point array structure comprises: obtaining a minimum structure mass and maximum compression characteristic and absorbing characteristic within a constraint condition of the design variables.

[0012] Further, after the step of determining a multi-objective optimization problem according to design variables and optimization objectives of a three-period minimal surface point array structure, the method further comprises: mapping the structure mass, the yield strength and the absorbing bandwidth to a same interval and selecting a weight coefficient to construct an optimization objective function.

[0013] Further, the step of using an adaptive multi-objective engineering optimization method of a Kriging surrogate model to construct an approximate function relationship between the optimization objectives and the design variables comprises: selecting a plurality of sample points in a design interval by using an Optimal LHS method, solving response values of the sample points by using a finite element electromagnetic analysis software and calculating objective function values; using a Kriging surrogate model to establish an approximate relationship between the objective function and the design variables.

[0014] Further, the step of jointly using a Latin hypercube sampling and a sequential quadratic programming optimization algorithm to perform optimization design on the three-period minimal surface point array structure comprises: calculating an expected improvement based on the optimization objective function, and selecting a sample point corresponding to a minimum objective function value as an optimization initial point; The sequence quadratic programming optimization algorithm based on the maximum expected improvement selects the modified design variables to perform the optimization design and obtain the modified design variables; The Kriging model is used to obtain the predicted values corresponding to the modified design variables, and a finite element electromagnetic analysis software is used to calculate the response values; If the convergence is achieved, the optimization is ended, and if the convergence is not achieved, the modified design variables are added to the sample set to re-optimize.

[0015] In a second aspect, the application provides a minimal surface structure optimization design system, comprising: A target determination module is configured to determine a multi-objective optimization problem according to design variables and optimization targets of a three-period minimal surface lattice structure; A function construction module is configured to construct an approximate function relationship between the optimization targets and the design variables by using an adaptive multi-objective engineering optimization method of a Kriging surrogate model; An optimization design module is configured to perform optimization design on the three-period minimal surface lattice structure by combining a Latin hypercube sampling with a sequence quadratic programming optimization algorithm.

[0016] In a third aspect, the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the minimal surface structure optimization design method as described above when executing the computer program.

[0017] In a fourth aspect, the application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the minimal surface structure optimization design method as described above.

[0018] The above technical solutions of the application have the following advantages: The minimal surface structure optimization design method provided by the first aspect of the application can determine a multi-objective optimization problem according to design variables and optimization targets of a three-period minimal surface lattice structure, construct an approximate function relationship between the optimization targets and the design variables by using an adaptive multi-objective engineering optimization method of a Kriging surrogate model, and perform optimization design on the three-period minimal surface lattice structure by combining a Latin hypercube sampling with a sequence quadratic programming optimization algorithm, so as to design a lightweight / bearing / absorbing minimal surface lattice sandwich structure with good structure-performance dual characteristics, fill the minimal surface lattice structure into an absorbing structure to realize integrated structure design and manufacturing of absorbing and bearing, and support fast iteration and performance improvement of the absorbing and bearing structure design.

[0019] It can be understood that the beneficial effects of the above-mentioned second aspect, third aspect and fourth aspect can be referred to the related description in the first aspect, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the specific embodiments or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 The flowchart of the minimum curved surface structure optimization design method provided by the embodiments of the present application; Figure 2 The schematic diagram of different unit cell sizes provided by the embodiments of the present application; Figure 3 The schematic diagram of different wall thickness sizes provided by the embodiments of the present application; Figure 4 The schematic diagram of different sandwich heights provided by the embodiments of the present application; Figure 5 The optimization design flowchart provided by the embodiments of the present application; Figure 6 The TPMS-Gyroid curved surface and dot matrix structure diagram provided by the embodiments of the present application; Figure 7 The TPMS-Gyroid dot matrix structure diagram of different unit cell numbers and wall thickness sizes provided by the embodiments of the present application; Figure 8 The structure diagram of the minimum curved surface structure optimization design system provided by the embodiments of the present application; Figure 9 The structure diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0022] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0023] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0024] In addition, in the description of the present application and the appended claims, the terms "first", "second", "third", etc. are used only to distinguish descriptions and cannot be understood as indicating or implying relative importance.

[0025] Reference to "one embodiment" or "some embodiments" or "one implementation" or "some implementations" described in the present application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearance of the phrases "in one embodiment", "in some embodiments", "in other embodiments", "in additional embodiments", etc. in various places in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically stated. The terms "comprise", "include", "have" and their conjugates mean "including but not limited to", unless otherwise specifically stated. "Multiple" means "two or more".

[0026] How to design and manufacture the TPMS lattice structure by the implicit function modeling method, taking the cell size, wall thickness, volume fraction, gradient direction and other structure parameters as design variables, and taking the structure mass, compression characteristics and wave absorption characteristics as optimization objectives, to design and manufacture a lightweight / bearing / wave-absorbing minimal surface lattice sandwich structure with good structure-performance dual characteristics, is the technical problem to be solved by the present application.

[0027] The specific embodiments of the present application will be further described in detail below in combination with the drawings and examples. The following examples are used to illustrate the present application, but are not used to limit the scope of the present application.

[0028] The embodiment of the present application provides a minimal surface structure optimization design method, as shown in Figure 1 The embodiment of the present application provides a minimal surface structure optimization design method, as shown in

[0029] The approximate function relationship between the optimization target and the design variable is constructed by determining a multi-objective optimization problem according to a design variable and an optimization target of a three-period minimal surface lattice structure, using an adaptive multi-objective engineering optimization method of a Kriging surrogate model, and jointly using a Latin hypercube sampling and a sequential quadratic programming optimization algorithm to optimize the design of the three-period minimal surface lattice structure, so that a lightweight / bearing / absorbing minimal surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the minimal surface lattice structure is filled into an absorbing structure to realize integrated design and manufacturing of the absorbing and bearing structure, and rapid iteration and performance improvement of the absorbing and bearing structure design are supported.

[0030] In some embodiments, the design variable includes a cell size, a wall thickness size, a volume fraction, and a sandwich height.

[0031] In some embodiments, the optimization target includes a structure mass, a compression characteristic, and an absorbing characteristic.

[0032] In some embodiments, the multi-objective optimization problem is determined according to the design variable and the optimization target of the three-period minimal surface lattice structure, including obtaining a minimum structure mass and maximum compression characteristic and absorbing characteristic within a constraint condition of the design variable.

[0033] In some embodiments, after the multi-objective optimization problem is determined according to the design variable and the optimization target of the three-period minimal surface lattice structure, the structure mass, the yield strength, and the absorbing bandwidth are mapped to a same interval, and a weight coefficient is selected to construct an optimization objective function.

[0034] In some embodiments, the adaptive multi-objective engineering optimization method of the Kriging surrogate model is used to construct the approximate function relationship between the optimization target and the design variable, including selecting a plurality of sample points in a design interval by using an Optimal LHS method, solving response values of the sample points by using a finite element electromagnetic analysis software, and calculating objective function values, and establishing an approximate relationship between the objective function and the design variable by using a Kriging surrogate model.

[0035] In some embodiments, the Latin hypercube sampling and the sequential quadratic programming optimization algorithm are jointly used to optimize the design of the three-period minimal surface lattice structure, including calculating an expected improvement based on the optimization objective function, selecting a sample point corresponding to a minimum objective function value as an optimization initial point, selecting a sequential quadratic programming optimization algorithm based on a maximum expected improvement to perform optimization design and obtain modified design variables, obtaining predicted values of the modified design variables by using a Kriging model, calculating response values by using a finite element electromagnetic analysis software, and if convergence is achieved, the optimization is ended, and if convergence is not achieved, the modified design variables are added to the sample set to re-optimize.

[0036] To solve the problems in the prior art, the embodiments of the present application provide a lightweight load-bearing minimal surface structure for wave absorption and an optimization design method thereof. The parameter optimization design and printing manufacturing of the TPMS lattice structure are performed in a hidden function modeling mode. The structural parameters such as the cell size, the wall thickness size, the volume fraction and the interlayer height are taken as design variables. The structural mass, the compression characteristics and the wave absorption characteristics are taken as optimization targets. The adaptive multi-objective engineering optimization method of the Kriging surrogate model is used to construct the approximate function relationship between the design targets and the design variables. The lightweight / load-bearing / wave-absorbing minimal surface lattice interlayer structure with good structure-performance dual characteristics is designed and manufactured by combining the Latin hypercube sampling and the sequential quadratic programming optimization algorithm.

[0037] To achieve the above object, the embodiments of the present application first define the multi-objective optimization design problem of the lightweight / load-bearing / wave-absorbing minimal surface structure. The structural parameters such as the cell size, the wall thickness size, the volume fraction and the interlayer height have obvious influences on the mechanical characteristics and the wave absorption characteristics of the minimal surface structure. The design target is to obtain the structural mass, the compression characteristics and the wave absorption characteristics closer to the design requirements. Therefore, the four structural parameters of the cell size C 1, the wall thickness size T 2, the volume fraction V 3, the interlayer height H 4 are taken as design variables. The structural mass, the compression characteristics and the wave absorption characteristics of the minimal surface lattice interlayer structure are taken as target variables. The multi-objective optimization problem of the structural parameters of the lightweight / load-bearing / wave-absorbing minimal surface lattice interlayer structure can be defined as obtaining the minimum structural mass and the maximum compression characteristics and wave absorption characteristics under the constraint condition of the design variables: In the formula, Structural Weight represents the structural mass, which is abbreviated as SW. Yield Strength represents the yield strength, which is abbreviated as YS. Absorption Bandwidth represents the wave absorption bandwidth, which is abbreviated as AB. x is the design variable, x 1 and x 2 are the lower limit and the upper limit of the design variable. The structural mass SW, the yield strength YS and the wave absorption bandwidth AB are required to be minimized simultaneously in the optimization design. The weighted coefficient summation method is used to convert the multi-objective optimization problem into a single-objective optimization problem: Since the structural mass SW, the yield strength YS and the wave absorption bandwidth AB are three different indexes with large differences in calculation results, it is difficult to accurately select the weight coefficients ω 1, ω 2, ωThe value of 3 allows the structural mass SW, yield strength YS, and absorption bandwidth AB to be mapped to the same interval: [0, 1], facilitating the reasonable selection of weighting coefficients. ω 1. ω 2. ω The value of 3 allows for further improvement of the objective function. Therefore, the multi-objective optimization design problem for lightweight / load-bearing / wave-absorbing minimal curved surface structures can ultimately be defined as follows: In the formula, SW max and SW min These are the structural quality of the sample points. SW The maximum and minimum values; YS max and YS min These are the yield strengths at the sample points. YS The maximum and minimum values; AB max and AB min These are the absorption bandwidths at the sample points. AB The maximum and minimum values. Design variables. x Including unit cell size C 1. Wall thickness T 2. Volume fraction V 3. Mezzanine height H 4 These four structural parameters (such as Figures 2-4 (As shown). Among them, C 1,L and C 1,H These are the unit cell sizes. C 1. Design lower limit and design upper limit; T 2,L and T 2,H These are the wall thickness dimensions. T 2. Design lower limit and design upper limit; V 3,L and V 3,H These are volume fractions. V 3. Design lower limit and design upper limit; H 4,L and H 4,H These are the mezzanine heights H 4. Design lower limit and design upper limit.

[0038] Numerical simulation and multi-objective optimization algorithm: reasonable meshing, boundary conditions and solution settings in the process of numerical simulation. The complete steps of the optimization process based on Kriging surrogate model are as follows, and the sequential quadratic programming algorithm is programmed in MATLAB: ① Define the optimization problem; ② Select n s sample points in the design interval using Optimal LHS method; ③ Solve the response value of each sample point using finite element electromagnetic analysis software CST y ( x i ) and calculate the objective function value f ( x i ); ④ Use Kriging surrogate model to establish the approximate relationship between the objective function and the design variables; ⑤ Calculate the expected improvement f ( x ) based on the function EI ( x ); ⑥ Select a sample point corresponding to the minimum objective function value f ( x i ) as the initial point of optimization; ⑦ Based on the maximum EI select the sequential quadratic programming optimization algorithm to perform optimization design and obtain the modified design variables x k ; ⑧ Use Kriging model to get x k the corresponding predicted value ; ⑨ Use finite element electromagnetic analysis software CST to calculate the response value f ( x k ); ⑩ If it converges, end; if it does not converge, add x k to the sample set and start from step ③.

[0039] The optimization design process is shown in Figure 5 .

[0040] Additive manufacturing technology can rapidly form fully functional and complex porous lattice structures, effectively reducing the number of parts and design space while increasing the freedom of structural design. It liberates the manufacturing process and design constraints of lattice structures, enhancing the application potential of complex lattice structures based on biomimetic or topology optimization design, such as the Triply Periodic Minimal Surfaces (TPMS) lattice structure with a complex and highly symmetrical topology. By using implicit function modeling to perform parameter optimization design and printing of TPMS lattice structures, structural parameters such as unit cell size, wall thickness, volume fraction, and gradient direction are used as design variables, while structural mass, compression characteristics, and wave absorption characteristics are simultaneously used as optimization objectives. This allows for the design and manufacture of lightweight / load-bearing / wave-absorbing minimal surface lattice sandwich structures with excellent structural and performance characteristics. The minimal surface lattice structure can be conformally infilled into wave-absorbing structures to achieve integrated wave-absorbing and load-bearing structural design and manufacturing, supporting rapid iteration and performance improvement in wave-absorbing and load-bearing structure design.

[0041] The following is a description through specific embodiments.

[0042] Example Preliminary structural design: Based on the implicit functions of the TPMS-Gyroid lattice structure and using MATLAB software for programming and calculation, the design and modeling of Gyroid cubes with different wall thicknesses and unit cell sizes were realized. The trigonometric function equations of the Gyroid surface are as follows: In the formula, ( x , y , z ) represents the spatial coordinates of a specific point on the Gyroid surface; parameters c Determines the size of the unit cell; parameters t Controlling the volume enclosed by the Gyroid surface, TPMS-Gyroid surface, such as Figure 6 As shown in (1), the Gyroid surface is a two-sided surface that divides space into two non-intersecting and interwoven subspaces. By giving the Gyroid surface a certain thickness, a solidified thin-walled lattice structure is formed, such as... Figure 6 As shown in (2)~(3). The trigonometric function equations of the TPMS-Gyroid lattice structure are as follows: Large-size TPMS-Gyroid lattice structures can be used in x , y and z The Gyroid units are repeated in the same direction to construct the structure. Based on the above equations, TPMS-Gyroid lattice structures with different numbers of unit cells and wall thicknesses are designed as follows:Figure 7 The results are shown.

[0043] Compression property analysis: Different geometry samples show similar four-stage mechanical responses, but their overall mechanical properties are different. The first stage is the elastic deformation stage, the stress increases linearly with the increase of strain, and the slope represents the compression modulus. The second stage is the elastic-plastic deformation stage, which starts from the deviation of the stress-strain curve from the linear response, and the stress deviation from the linear elastic region of 0.2% of the stress under axial loading is defined as the nominal yield strength. In the elastic-plastic deformation stage, the stress increases nonlinearly with the increase of strain, until the maximum stress, i.e. the ultimate strength, is reached. After reaching the ultimate strength, the stress-strain curve shows a gradual stress drop after a short yield platform period, and the stress-strain curve enters the third stage, i.e. the long platform region of stress fluctuation. Finally, most of the thin walls in the TPMS-Gyroid lattice structure are broken and extruded together, and the stress-strain curve enters the densification stage, where the stress increases sharply with the increase of strain, and enters a rapid stress rising region. There are different deformation mechanisms in each stage. In the early stage of loading, the inclined walls in the unit cell begin to bend under pressure, and the vertical walls begin to stretch and compress under pressure, and the overall structure is in the linear elastic deformation stage. When a certain critical stress is reached, the walls begin to yield and locally collapse or even break due to plastic deformation. Finally, the contact and accumulation of different walls make the overall structure region dense, resulting in a rapid rise of the stress-strain curve.

[0044] Absorption property analysis: Most of the absorption structures designed in triangle, quadrilateral and hexagon show obvious anisotropy in 3D space, and the wave-absorbing sandwich structure with minimal surface lattice has good approximate isotropic wave-absorbing characteristics. In addition, the total area of the curved structure has a significant effect on the length of the current propagation along the surface of the structure. When the path changes from surface propagation to curved surface propagation, the physical length increases, which increases the interaction time between the EM field and the dielectric loss material.

[0045] Multi-objective optimization design: The parameter optimization design and printing manufacturing of the TPMS lattice structure are performed by means of the implicit function modeling method, the structure parameters such as the size of the unit cell, the size of the wall thickness, the volume fraction, and the gradient direction are taken as the design variables, the structure mass, the compression property, and the wave-absorbing property are taken as the optimization objectives, the self-adaptive multi-objective engineering optimization method of the Kriging surrogate model is used, the approximate function relationship between the design objectives and the design variables is constructed, the Latin hypercube sampling and the sequential quadratic programming optimization algorithm are combined, and the structure mass, the compression property, and the wave-absorbing performance are simultaneously optimized. The multi-objective optimization design method proposed in the application can obviously improve the multifunctional coupling characteristics of the minimal surface lattice sandwich structure in bearing / wave-absorbing.

[0046] Corresponding to the minimal surface structure optimization design method described in the above embodiments, the embodiments of the present application also provide a minimal surface structure optimization design system, as shown in Figure 8 The minimal surface structure optimization design system comprises: A target determination module is configured to determine a multi-objective optimization problem according to design variables and optimization targets of a three-period minimal surface lattice structure. A function construction module is configured to construct an approximate function relationship between the optimization targets and the design variables by using an adaptive multi-objective engineering optimization method of a Kriging surrogate model. An optimization design module is configured to perform optimization design on the three-period minimal surface lattice structure by combining Latin hypercube sampling with a sequential quadratic programming optimization algorithm.

[0047] It should be noted that the information interaction, execution process and the like between the above modules / units are based on the same concept as the method embodiments of the present application, and the specific functions and technical effects brought by the same can be referred to the method embodiments part, which will not be repeated here.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units / modules is exemplified, and in actual application, the above functions can be completed by different functional units / modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units / modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above integrated unit can be realized in the form of hardware or software. In addition, the specific names of the functional units / modules are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units / modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0049] The embodiments of the present application also provide an electronic device, as shown in Figure 9 The electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the minimal surface structure optimization design method of the first aspect when executing the computer program.

[0050] In application, the electronic device can include, but is not limited to, a processor and a memory, Figure 9The electronic device is merely an example and does not limit the electronic device, which can include more or fewer components than illustrated, or combine some components, or have different components, such as input / output devices, network access devices, and the like. The input / output devices can include a camera, an audio acquisition / play device, a display screen, and the like. The network access device can include a network module for wireless network with external devices.

[0051] In applications, the processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0052] In applications, the memory can be an internal storage unit of the electronic device in some embodiments, such as a hard disk or a memory of the electronic device. The memory can also be an external storage device of the electronic device in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. The memory can include both the internal storage unit and the external storage device of the electronic device. The memory is used to store an operating system, application programs, a boot loader, data, and other programs, such as program codes of computer programs, and the like. The memory can also be used to temporarily store data that has been output or will be output.

[0053] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in each method embodiment.

[0054] The computer program can be stored in a computer readable storage medium. The computer readable storage medium can be a floppy disk, a USB (Universal Serial Bus) flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic tape, a hard disk, an optical disc, a computer database, or the like.

[0055] Those skilled in the art can understand that the devices and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0056] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, and another point is that the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be through some interface, indirect coupling or communication connection between devices can be electrical, mechanical or other forms.

[0057] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for optimizing the design of minimal surface structures, characterized in that, include: The multi-objective optimization problem is determined based on the design variables and optimization objectives of the three-period minimal surface lattice structure. An adaptive multi-objective engineering optimization method using the Kriging surrogate model is used to construct an approximate functional relationship between the optimization objective and the design variables; A combined Latin hypercube sampling and sequential quadratic programming optimization algorithm is used to optimize the design of a three-period minimal surface lattice structure.

2. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, The design variables include unit cell size, wall thickness, volume fraction, and interlayer height.

3. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, The optimization objectives include structural quality, compression characteristics, and wave absorption characteristics.

4. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, The determination of the multi-objective optimization problem based on the design variables and optimization objectives of the three-period minimal surface lattice structure includes: Within the constraints of the design variables, the goal is to achieve the minimum structural mass and the maximum compressibility and absorption characteristics.

5. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, After determining the multi-objective optimization problem based on the design variables and optimization objective of the three-period minimal surface lattice structure, the following steps are also included: By mapping structural mass, yield strength, and absorption bandwidth to the same interval and selecting weighting coefficients, an optimization objective function is constructed.

6. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, The adaptive multi-objective engineering optimization method utilizing the Kriging surrogate model constructs an approximate functional relationship between the optimization objective and the design variables, including: In the design interval, the Optimal LHS method is used to select multiple sample points, and the finite element electromagnetic analysis software is used to solve the response value of each sample point and calculate the objective function value. The Kriging surrogate model is used to establish an approximate relationship between the objective function and the design variables.

7. The method for optimizing the design of minimal surface structures as described in claim 1, characterized in that, The combined Latin hypercube sampling and sequential quadratic programming optimization algorithm is used to optimize the design of a three-period minimal surface lattice structure, including: Based on the expected improvement calculated from the optimization objective function, a sample point corresponding to the minimum objective function value is selected as the initial optimization point; Based on the maximum expectation improvement selection sequence quadratic programming optimization algorithm, the optimization design is performed and the modified design variables are obtained; The predicted values ​​corresponding to the modified design variables are obtained using the Kriging model, and the response values ​​are calculated using finite element electromagnetic analysis software. If convergence is achieved, the optimization ends; if convergence is not achieved, the modified design variables are added to the sample set and the optimization is repeated.

8. A minimal surface structure optimization design system, characterized in that, include: The objective determination module is used to determine the multi-objective optimization problem based on the design variables and optimization objectives of the three-period minimal surface lattice structure. The function construction module is used to construct an approximate functional relationship between the optimization objective and the design variables using the adaptive multi-objective engineering optimization method of the Kriging surrogate model; The optimization design module is used to combine Latin hypercube sampling and sequential quadratic programming optimization algorithms to optimize the design of three-period minimal surface lattice structures.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the minimal surface structure optimization design method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the minimal surface structure optimization design method as described in any one of claims 1 to 7.

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