A method and system for three-gradient filling lattice structure arrangement of unmanned aerial vehicle structural members

Abstract

The application relates to a method and system for setting a three-gradient filling lattice structure of a structure part of an unmanned aerial vehicle; the method comprises the following steps: modeling a three-gradient minimum triple-curved surface lattice Gyroid structure and initializing; determining an n-th relative density; taking a unit cell with an (n-1)-th relative density as a center position, constructing a unit cell with a unit granularity and an n-th relative density around the center position; judging whether a first stop condition is met; when the first stop condition is met, using metal laser 3D printing to print the modeled structure part, and testing the compression mechanical property of the printed structure part. Through adaptive three-gradient structure setting, the unmanned aerial vehicle structure part can accurately configure material and lattice structure parameters according to actual stress conditions and functional requirements at different positions, so that the overall performance and competitiveness of the unmanned aerial vehicle are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of three-gradient filling lattice structure design of unmanned aerial vehicle structural parts, and particularly relates to a method and system for setting a three-gradient filling lattice structure of an unmanned aerial vehicle structural part. BACKGROUND

[0002] With the wide application of unmanned aerial vehicle technology in many fields such as military, civil and scientific research, the requirements for the performance of unmanned aerial vehicles are also increasing. As a key component, the structural part of the unmanned aerial vehicle not only needs to meet the lightweight design requirements to improve the flight efficiency and endurance, but also needs to have sufficient strength and stability to ensure safety and reliability under complex flight conditions.

[0003] Traditional unmanned aerial vehicle structural part design mostly adopts solid materials or simple honeycomb structures. Although solid materials can ensure a certain strength, they often result in a large overall weight of the structural part, limiting the performance improvement of the unmanned aerial vehicle. The honeycomb structure reduces the weight to some extent, but the optimization space of its mechanical properties is limited, and it is difficult to meet different requirements under various conditions at the same time.

[0004] In recent years, lattice structure as a new type of lightweight structure has gradually attracted attention. Lattice structure is composed of regularly arranged rods or nodes in three-dimensional space, has higher specific strength and specific stiffness than traditional structures, and can maintain good mechanical properties while reducing weight. However, the existing lattice structure still has some deficiencies when applied to unmanned aerial vehicle structural parts. Under the condition of limited performance of 3D printers and strict requirements of stress field distribution of structural parts, the adopted lattice structure setting method is often relatively fixed and limited, lacking adaptability adjustment, and failing to adapt to the maximum extent under the limited performance and requirement restrictions. For example, single lattice structure parameter design cannot fully adapt to the complex and variable load conditions of unmanned aerial vehicles in different parts, such as the wing root needs to bear large bending moment and shear force, while the wing end mainly bears small tension and pressure. The traditional uniform lattice structure cannot be optimized for these different stress areas, resulting in low material utilization efficiency, either excessive strength in some areas causing weight waste, or insufficient strength in key stress areas affecting the reliability of the overall structure.

[0005] In addition, as unmanned aerial vehicles develop towards multifunctionalization and high performance, new requirements are put forward for the heat dissipation and energy absorption functions of their structural components. The comprehensive performance of existing lattice structures still needs to be further improved in these aspects, and it is difficult to meet the growing application requirements of unmanned aerial vehicles. Therefore, there is an urgent need for an innovative design method for unmanned aerial vehicle structural components that can fully combine the advantages of lattice structures and overcome the shortcomings of existing technologies, achieving lightweight, high-strength, and multifunctionalization of structural components to meet the needs of modern unmanned aerial vehicle technology development. Based on the above problems, the present application sets up a self-adaptive three-gradient structure, so that the unmanned aerial vehicle structural component can accurately configure the material and lattice structure parameters at different positions according to the actual stress condition and functional requirement, so that the optimization process does not destroy the existing and verified mechanical performance framework, avoiding the introduction of new and unpredictable stress concentration risks due to parameter adjustment, thereby significantly improving the overall performance and competitiveness of unmanned aerial vehicles, as well as the engineering practicability. SUMMARY

[0006] In order to solve the above problems in the prior art, the present application proposes a three-gradient filling lattice structure setting method and system for unmanned aerial vehicle structural components, which comprises:

[0007] Step S1: Perform three-gradient minimum triple-curve lattice Gyroid structure modeling and initialization; establish a unit cell at the center position with a size of a unit granularity and a relative density of a 0th relative density; set the initial value of the iteration value n to 1; obtain the initial values of the lowest relative density, the highest relative density, the gain factor, the adjustment coefficient, or the density enhancement coefficient;

[0008] Step S2: Determine the nth relative density; construct unit cells with a size of a unit granularity and a relative density of the nth relative density around the center position of the (n-1)th relative density unit cell; so that the nth relative density unit cell wraps the (n-1)th relative density unit cell;

[0009] Step S3: Determine whether the first stopping condition is met, if yes, proceed to the next step, otherwise, set n = n + 1 and return to step S2; the first stopping condition is that n is greater than or equal to a quantity threshold;

[0010] Step S3E: Determine whether the second stopping condition is met, the second stopping condition is that the average relative density is equal to a preset relative density or belongs to a preset relative density range, if met, proceed to the next step, otherwise, adjust the initial values of the gain factor, the adjustment coefficient, or the density enhancement coefficient to control the steepness of the change position or the granularity of the change, and return to step S1;

[0011] Step S4: Use metal laser 3D printing to model the structural component;

[0012] Step S5: Perform compressive mechanical property testing on the printed structure.

[0013] Furthermore, determining the nth relative density specifically involves: setting... It is the relative density enhancement coefficient.

[0014] Furthermore, determining the nth relative density specifically involves: determining the nth relative density based on key parameters and the (n-1)th relative density; the key parameters are stress field distribution parameters, and the nth relative density is determined based on the stress field distribution parameters of the structural component, their gradient changes, and the (n-1)th relative density.

[0015] Furthermore, the nth relative density is determined based on the following equations (1) and (2); where: It is the nth relative density, It is a temporary variable. It is the stress value at the nth relative density position. It is the stress value at the (n-1)th relative density position; It is the adjustment coefficient; and These are the minimum and maximum stress values ​​in the structural component, respectively. and These are the minimum and maximum relative densities;

[0016] (1)

[0017] (2).

[0018] Furthermore, the adjustment coefficient is determined based on the change in stress gradient; specifically, the adjustment coefficient is determined based on the following formula (3) or (4);

[0019] (3);

[0020] (4).

[0021] Furthermore, the determination of the nth relative density specifically involves: performing a smooth S-shaped transition mapping on the key parameters, so that the relative density increases slowly before the inflection point and increases rapidly after the inflection point; further, the nth relative density is determined based on the following equations (8) and (9); where: k is a gain factor used to control the steepness of the change position; Yes, the key parameter value for n relative density is at that location; the S value is the key parameter for the inflection point. It is a temporary variable; when the key parameter s is the stress value hour, The inflection point parameter value is the mean stress value; when the key parameter s is the distance field parameter to the boundary... hour, The inflection point parameter value is the mean value of the distance field parameter;

[0022] (8)

[0023] (9).

[0024] A platform for setting up a three-gradient filled dot matrix structure for UAV structural components, the platform being used to implement the aforementioned method for setting up a three-gradient filled dot matrix structure for UAV structural components.

[0025] A server for setting up a three-gradient filled dot matrix structure for UAV structural components includes a processor coupled to a memory. The memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the method for setting up a three-gradient filled dot matrix structure for UAV structural components is implemented.

[0026] A system for setting up a three-gradient filled dot matrix structure for UAV structural components, the system being used to implement the method for setting up a three-gradient filled dot matrix structure for UAV structural components.

[0027] A computer-readable storage medium includes a program that, when run on a computer, causes the computer to perform the method for setting up a three-gradient filled lattice structure for unmanned aerial vehicle (UAV) structural components.

[0028] The beneficial effects of this invention include:

[0029] (1) It solves the problem that traditional UAV structural component design cannot simultaneously achieve lightweight, high strength, and multi-functionality. To a certain extent, it optimizes the stress distribution under structural stress, thus achieving increased strength and energy absorption capacity;

[0030] (2) By setting up a hierarchical lattice structure based on multi-parameter control of key parameters, multi-objective optimization of lattice structure setting is achieved, ensuring that the design generated by each adjustment is manufacturable and has strong engineering feasibility. Adaptive adjustment strategies or joint adjustments are adopted for different relative density errors to achieve rapid and linear adjustment of average density, which is direct and efficient. This ensures that while meeting macroscopic weight indicators, the material distribution and stress field achieve a higher degree of matching. During the adjustment process, the core principle of material distribution based on stress level is not changed. High stress areas always correspond to higher density, and low stress areas correspond to lower density. This ensures that the optimization process does not destroy the existing and verified mechanical performance framework and avoids the introduction of new and unpredictable stress concentration risks due to parameter adjustment. Attached Figure Description

[0031] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to unduly limit the invention. In the drawings:

[0032] Figure 1 This is a schematic diagram of the method for setting a three-gradient filled lattice structure for UAV structural components provided by the present invention.

[0033] Figure 2 This is a two-dimensional schematic diagram of the relative density distribution of a single cell in a three-gradient structure provided in an embodiment of the present invention.

[0034] Figure 3 This is a schematic diagram showing the comparison results of the compressive mechanical performance curves in the performance test of this invention.

[0035] Figure 4 This is a schematic diagram showing the comparison results of peak compressive strength of the compressive mechanical property curves in the performance test of this invention.

[0036] Figure 5 This is a schematic diagram showing the comparison results of compression energy absorption curves in the performance test of this invention.

[0037] Figure 6 This is a schematic diagram showing the comparison results of compression energy absorption values ​​under different strains in the performance test of this invention. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only for explaining the invention and do not constitute a complete description. The present invention proposes a method and system for setting a three-gradient filled lattice structure for UAV structural components, as shown in the accompanying drawings. Figure 1 As shown, the method includes the following steps:

[0039] Step S1: Model the Gyroid structure of the three-gradient minimum triplet surface lattice and initialize it; establish a unit cell at the center position with a size of unit particle size and a relative density of the 0th relative density; set the initial value of the iteration value n to 1; obtain the initial values ​​of the minimum relative density, maximum relative density, and gain factor (or adjustment coefficient, density enhancement coefficient); further: the unit particle size is 2.5×2.5×2.5mm. 3 The center position is the geometric center or the center of gravity.

[0040] Preferred method: Use nTop software for Gyroid structure modeling;

[0041] Preferably, when the structural component is symmetrical, the center position is the geometric center position; when the structural component is asymmetrical, the center position is the center of gravity position.

[0042] Preferred: The 0th relative density is a preset value; for example: 10%;

[0043] Preferred initial values: minimum relative density between 2% and 10%, maximum relative density between 50% and 95%; initial gain factor between 0.05 and 0.1, for example, k=0.08 indicates a moderate steepness of change; required error within 1%.

[0044] Step S2: Using the (n-1)th relative density unit cell as the center, uniformly construct around it. There are unit cells with a size of unit particle size and a relative density of n; such that the nth relative density unit cell encloses the (n-1)th relative density unit cell;

[0045] Preferred: Determine the nth relative density such that the nth relative density is greater than the (n-1)th relative density; obviously, in the first iteration, the expansion is centered on the cell with the 0th relative density, such that the cell with the 1st relative density wraps around the cell with the 0th relative density;

[0046] The determination of the nth relative density specifically involves: setting... This is the relative density enhancement coefficient; the relative density enhancement coefficient is a preset value, for example: 1%~10%;

[0047] Preferred settings ;in: and These are the minimum and maximum distance field parameters in the structural components, respectively. and These are the minimum and maximum relative densities. Of course, this indicates the case where the relative density changes uniformly, which is also easy to implement in engineering practice. As 3D printing capabilities are enhanced, this value can be adjusted in fine granularity. However, it is obvious that the more complex the change of this enhancement coefficient, the more it will lead to an increase in printing time and other overhead.

[0048] Alternative: Set the initial value for the relative density enhancement factor. or ;

[0049] Alternative: The nth relative density is determined based on the key parameters and the (n-1)th relative density; specifically: the key parameters are stress field distribution parameters, and the nth relative density is determined based on the stress field distribution parameters of the structural component and the (n-1)th relative density; further: the nth relative density is determined based on the following equations (1) and (2); where: It is the nth relative density, , , It is a temporary variable. It is the stress value at the nth relative density position. It is the stress value at the (n-1)th relative density position; It is an adjustment coefficient. By adjusting the overall relative density in the later stage, the changes can be balanced as much as possible, so as to reduce the average of the overall relative density without producing drastic changes in relative density. and These are the minimum and maximum stress values ​​in the structural component, respectively. and These are the minimum and maximum relative densities;

[0050] Preferred method: Obtain stress values ​​at various locations of the structural component based on FEA analysis;

[0051] (1)

[0052] (2)

[0053] Preferred: Set adjustment coefficient The initial value is 0.95;

[0054] Preferred values: The minimum and maximum relative densities are preset values; for example, they are equal to 5% and 50% respectively; 50% is a relatively dense lattice, and 100% will form a solid shell structure;

[0055] Preferred method: Determine the adjustment coefficient based on the stress value gradient change; specifically: determine the adjustment coefficient based on the following formula (3) or (4);

[0056] (3);

[0057] (4);

[0058] Alternatively: The determination of the nth relative density based on key parameters and the (n-1)th relative density specifically involves: the key parameters being stress field distribution parameters, distance field parameters to the boundary, and distance field parameters to key points; the nth relative density is determined based on one or more of the key parameters mentioned above, based on the key parameters of the structural component and / or the (n-1)th relative density; considering that high-stress areas require higher density to bear the load, low-stress areas can reduce density to reduce weight, and the outer surface of the structural component usually has high stress and requires a solid shell to connect and provide surface quality, the distance field parameters at the boundary are crucial parameters; the density can gradually decrease from the shell inwards; further: the nth relative density is determined based on equations (5) and (6), or equations (2), (6), and (7); wherein: It is the distance field parameter at the nth relative density position, that is, the distance from the center position. It is the distance field parameter at the (n-1)th relative density position; and These are the minimum and maximum distance field parameters in the structural components, respectively.

[0059] (5)

[0060] (6)

[0061] (7)

[0062] Alternatively: The determination of the nth relative density based on the key parameters and the (n-1)th relative density is specifically: a smooth S-shaped transition mapping is performed on the key parameters so that the relative density increases slowly before the inflection point and increases rapidly after the inflection point; further, the nth relative density is determined based on the following equations (8) and (9); where: k is a gain factor used to control the steepness of the change position; Yes, n is the key parameter value at the relative density location; the S value is the key parameter at the inflection point; when using this method, it tends to keep the relative density smoothly low in the low-stress region, and once the stress exceeds a certain threshold, the relative density rises rapidly; when hour, ,therefore, ; and when hour, ,therefore, ; and when exist When the relative density is near a certain value, a smooth and rapid transition occurs.

[0063] (8)

[0064] (9)

[0065] Preferred: It can be seen that when the key parameter s is the stress value hour, The inflection point parameter value is the mean stress value; when the key parameter s is the distance field parameter to the boundary... hour, The inflection point parameter value is the mean of the distance field parameters; of course, this inflection point parameter value can also be other significant values ​​of the key parameters.

[0066] Obviously, when considering complex factors, the above key parameters can be considered in combination with the S-shaped transition mapping; at this time, the alternative is: the determination of the nth relative density based on the key parameters and the (n-1)th relative density is specifically: the determination of the nth relative density based on equation (10);

[0067] (10)

[0068] For some special structural components, such as motor mounting points and arm connections where there are concentrated loads, the relative density needs to be radially reduced outward from the center based on the location of the key points. For high curvature areas such as rounded corners and bends, stress tends to concentrate, requiring higher density. Conversely, the relative density can be reduced. In this case, the key parameters to be considered also include the distance field parameters and curvature field parameters of the key points.

[0069] Step S3: Determine whether the first cutoff condition is met. If it is, proceed to the next step; otherwise, set n=n+1 and return to step S2.

[0070] Preferably, the first cutoff condition is that n is greater than or equal to the quantity threshold; the quantity threshold is a preset value; for example, n=5, 7, etc.

[0071] Preferably, after executing step S3, step S3E is also included: determining whether the second cutoff condition is met. The second cutoff condition is that the average relative density is equal to or falls within the preset relative density range. If met, proceed to the next step; otherwise, adjust the gain factor (or adjustment coefficient, density enhancement coefficient). The former is used to control the steepness of the change position, and the latter (in parentheses) is used to control the granularity of the change, and return to step S1. When the average relative density error is within 5%, the final adjustment purpose can often be achieved by adjusting the gain factor (or adjustment coefficient, density enhancement coefficient); suitable for fine-tuning situations.

[0072] The adjustment of the gain factor (or adjustment coefficient, density enhancement coefficient) specifically involves: decreasing the value of k to make the S-curve smoother, at which point the relative density change will increase slowly over a wider stress range instead of changing abruptly; recalculating whether the average offset density meets the cutoff condition; increasing the value of k to make the S-curve steeper, at which point the relative density change will increase rapidly over a narrower stress range, and recalculating whether the average offset density meets the second cutoff condition. Since the adjustment between relative density and gain factor ultimately depends on the change in the stress field, the result of a single adjustment method is unpredictable. Therefore, the final suitable relative density allocation method can be determined by trying different directions; for example, decreasing k... Afterward, the curve flattens out. In the 20MPa low-stress region, the relative density is actually higher than before; in the 80MPa high-stress region, the relative density is lower than before, thus making the overall density distribution more uniform and reducing the average relative density. The range of the high-density region is compressed, but the minimum and maximum relative densities remain unchanged. Decreasing the adjustment coefficient and density enhancement coefficient makes the change in relative density relatively slow, which is a slow process throughout. Increasing the adjustment coefficient and density enhancement coefficient makes the change in relative density relatively fast, which is a fast process throughout. After adjusting the gain factor (or adjustment coefficient, density enhancement coefficient), the average relative density is recalculated to see if it meets the second cutoff condition, until the first and second cutoff conditions are finally met. The adjustment direction of the adjustment coefficient and density enhancement coefficient on the relative density is relatively clear.

[0073] Alternatively: Step S3E specifically involves: determining whether the second cutoff condition is met, whereby the average relative density is equal to or falls within a preset relative density range. If met, proceed to the next step; otherwise, jointly adjust the initial values ​​of the minimum and maximum relative densities and return to step S1. When the average relative density error is outside 5%, it is necessary to adjust the gain factor k value or... The value is used to jointly adjust the minimum and maximum relative densities to achieve the final operating principle: by scaling the entire relative density output range proportionally, the overall average density of the structure can be predictably controlled, thereby achieving fast and stable convergence when facing large errors, avoiding tedious fine-tuning in the wrong direction.

[0074] The joint adjustment of minimum and maximum relative density is specifically as follows: if the average relative density is greater than the preset relative density or falls within the preset relative density range, then both the minimum and maximum relative density are reduced simultaneously; if the average relative density is less than the preset relative density or falls within the preset relative density range, then both the minimum and maximum relative density are increased simultaneously; after the average relative density error reaches 5%, the initial value of the gain factor (or adjustment coefficient, density enhancement coefficient) is adjusted.

[0075] Preferred method: Adjust the minimum and maximum relative densities according to the error ratio; the larger the error, the larger the adjustment range. During the adjustment process, the minimum relative density should be significantly smaller than the maximum relative density; for example, the difference should be at least greater than 10%.

[0076] Preferred approach: When the error is less than or equal to 5%, adjust the gain factor (or adjustment coefficient, density enhancement coefficient) separately (step S3E), or adjust the gain factor while simultaneously adjusting the minimum relative density to be significantly less than the maximum relative density. In this case, the target is approached quickly with large steps in the early stage of adjustment, and then finely adjusted with small steps in the later stage. This not only greatly accelerates the convergence speed, but also effectively prevents oscillations near the optimal solution, ensuring the stability and robustness of the entire optimization process.

[0077] Preferred method: During joint adjustment, the adjustment amplitude is gradually reduced as the number of adjustments increases, while maintaining the adjustment amplitude in the intermediate stage. When the second cutoff condition is about to be met, a fine-tuned small-amplitude adjustment is performed. By combining the first and second cutoff conditions, when the relative density deviation is large, the average relative density is rapidly and linearly adjusted by simultaneously scaling the minimum and maximum relative densities, which is direct and efficient. When the relative density approaches the preset value, the gain factor (or adjustment coefficient, density enhancement coefficient) is finely adjusted to change the gradient shape of the relative density distribution, thereby ensuring that the material distribution and stress field achieve a higher degree of matching while meeting the macroscopic weight index.

[0078] Step S4: Use metal laser 3D printing to print the modeled structural parts.

[0079] Preferred method: Use an EOSM290 metal laser 3D printer, with Ti-6Al-V powder of 15~53μm size; printing parameters: laser power 200W, spot size 80μm, laser scanning speed 1000mm / s, layer thickness 50μm, scanning spacing 100μm; scanning rotation between adjacent layers 90°.

[0080] Step S5: Perform compressive mechanical property testing on the printed structure;

[0081] Preferred: The testing equipment is the INSTRON8801 universal mechanical performance testing machine, the testing standard is ISO13314:2011, and the strain parameter is 1.0 mm / min;

[0082] Example 1

[0083] Taking the cutoff condition n=4 as an example, the minimum triplet surface lattice Gyroid trigradation structure is constructed for modeling; (See appendix) Figure 2 A two-dimensional schematic diagram of the relative density distribution of a single cell in a three-gradient structure is shown.

[0084] Step SA1: Model the Gyroid structure of the three-gradient minimum triplet surface lattice; establish a unit cell at the center of the modeled structure, with a size of unit grain size and a relative density of 10%;

[0085] Step SA2: Using the unit cell of the first relative density as the center, uniformly construct around it. Each Gyroid cell has a unit particle size and a relative density of 15%, such that the 15% relative density cell encloses the 10% relative density cell.

[0086] Step SA3: Using a single cell with a relative density of 15% as the center, generate [the following] around it. Each Gyroid cell has a unit particle size and a relative density of 20%, such that the 20% relative density cell encloses the 15% relative density cell.

[0087] Step SA4: Based on the previously generated Centered on a single cell, it generates around it... A Gyroid unit cell with dimensions of 2.5 × 2.5 × 2.5 mm³ has a relative density of 25%. This results in the unit cell with a relative density of 20% encapsulating... One cell;

[0088] Step SA5: Based on the previously generated Centered on a single cell, it generates around it... Gyroid unit cells with dimensions of 2.5 × 2.5 × 2.5 mm³ and a relative density of 30% were observed. These 30% relative density unit cells enclosed the center. The minimum triplet surface lattice Gyroid three-gradient structure with n=4 is now modeled, with an average relative density of 23.8%. The density of this structure changes gradually and corresponds to the direction of stress development.

[0089] Step SA6: Print the sample of this invention using an EOS M290 metal laser 3D printer. The powder used is Ti-6Al-V with a powder size of 15~53μm. The printing parameters used are: laser power of 200 watts, spot size of 80 micrometers, laser scanning speed of 1000 mm / s, layer thickness of 50 micrometers, and scanning spacing of 100 micrometers. The scanning between adjacent layers is rotated 90 degrees.

[0090] Step SA7: Perform compressive mechanical property testing on the printed sample. The testing equipment is INSTRON8801 universal mechanical performance tester, the testing standard is ISO13314:2011, and the strain parameter is 1.0 mm / min.

[0091] Performance tests were conducted on a density-equilibrium three-gradient structure with the same density and the three-gradient filled lattice structure of the present invention, as shown in the attached figure. Figure 3 As shown, the Gyroid three-gradient structure of the present invention has higher strength and slightly higher elastic modulus than the existing uniform Gyroid structure; as shown in the attached figure. Figure 4 As shown, the Gyroid three-gradient structure of the present invention has a compressive strength that is approximately 33% higher than that of the existing uniform Gyroid structure; as shown in the attached figure. Figure 5 As shown, the Gyroid three-gradient structure of the present invention exhibits higher energy absorption at each strain level than the existing uniform Gyroid structure; as shown in the attached figure. Figure 6 As shown, with increasing strain capacity, the energy absorption of the Gyroid trigradation structure continuously increases from 10.46% at the 10% strain level compared to the existing homogeneous Gyroid structure, reaching 19.52% higher at 40% strain.

[0092] Based on the same inventive concept, the present invention also provides a system for setting up a three-gradient filled dot matrix structure for UAV structural components, the system being used to implement the above-described method for setting up a three-gradient filled dot matrix structure for UAV structural components.

[0093] Based on the same inventive concept, the present invention also provides a structural component for unmanned aerial vehicles as a limitation thereof.

[0094] A gradient-filled lattice structure setting server is used to implement the above-described method for setting a three-gradient-filled lattice structure for UAV structural components.

[0095] Based on the same inventive concept, the present invention also provides a device for setting a three-gradient filled dot matrix structure for UAV structural components, the device being used to implement the above-mentioned method for setting a three-gradient filled dot matrix structure for UAV structural components.

[0096] Based on the same inventive concept, the present invention also provides a platform for setting up a three-gradient filled dot matrix structure for UAV structural components, the platform being used to implement the above-mentioned method for setting up a three-gradient filled dot matrix structure for UAV structural components.

[0097] A computer program (also referred to as a program, software, software application, script, or code) can be written in any form of programming language, including assembly or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but does not necessarily, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to said program, or in multiple co-located files (e.g., a file storing one or more modules, subroutines, or code portions). A computer program can be deployed to execute on a single computer or on multiple computers located at a single site or distributed across multiple sites and interconnected by a communications network.

[0098] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0099] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxesFigure 1 The function specified in one or more boxes.

[0101] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for setting a three-gradient filled lattice structure for UAV structural components, characterized in that, The method includes: Step S1: Model the Gyroid structure of the three-gradient minimum triplet surface lattice and initialize it; establish a unit cell at the center position with a size of unit particle size and a relative density of the 0th relative density; Set the initial value of the iteration value n to 1; obtain the initial values ​​of the minimum relative density, maximum relative density, gain factor, adjustment coefficient, or density enhancement coefficient; Step S2: Determine the nth relative density; using the unit cell of the (n-1)th relative density as the center, uniformly construct [something] around it. A unit cell with a size of unit particle size and a relative density of the nth relative density; such that the nth relative density unit cell encloses the (n-1)th relative density unit cell; The determination of the nth relative density specifically involves: determining the nth relative density based on key parameters and the (n-1)th relative density; the key parameters are stress field distribution parameters, and the nth relative density is determined based on the stress field distribution parameters of the structural component, their gradient changes, and the (n-1)th relative density. Furthermore, the nth relative density is determined based on the following equations (1) and (2); where: It is the nth relative density, It is a temporary variable. It is the stress value at the nth relative density position. It is the stress value at the (n-1)th relative density position; It is the adjustment coefficient; and These are the minimum and maximum stress values ​​in the structural component, respectively. and These are the minimum and maximum relative densities; （1） （2）； Step S3: Determine whether the first cutoff condition is met. If yes, proceed to the next step; otherwise, set n=n+1 and return to step S2. The first cutoff condition is that n is greater than or equal to the quantity threshold. Step S3E: Determine whether the second cutoff condition is met. The second cutoff condition is that the average relative density is equal to the preset relative density or belongs to the preset relative density range. If it is met, proceed to the next step. Otherwise, adjust the initial values ​​of the gain factor, adjustment coefficient, or density enhancement coefficient to control the steepness of the change position or the granularity of the change, and return to step S1. Step S4: Use metal laser 3D printing to print the modeled structural parts; Step S5: Perform compressive mechanical property testing on the printed structure.

2. The method for setting a three-gradient filled lattice structure for UAV structural components according to claim 1, characterized in that, The determination of the nth relative density specifically involves: setting... It is the relative density enhancement coefficient.

3. The method for setting a three-gradient filled lattice structure for UAV structural components according to claim 2, characterized in that, The adjustment coefficient is determined based on the stress gradient change; specifically, the adjustment coefficient is determined based on the following formula (3) or (4); （3）； （4）。 4. The method for setting a three-gradient filled lattice structure for UAV structural components according to claim 3, characterized in that, The determination of the nth relative density is specifically as follows: a smooth S-shaped transition mapping is performed on the key parameters so that the relative density increases slowly before the inflection point and increases rapidly after the inflection point; further, the nth relative density is determined based on the following formulas (8) and (9); where: k is a gain factor used to control the steepness of the change position; Yes, the key parameter value for n relative density is at that location; the S value is the key parameter for the inflection point. It is a temporary variable; when the key parameter s is the stress value hour, The inflection point parameter value is the mean stress value; when the key parameter s is the distance field parameter to the boundary... hour, The inflection point parameter value is the mean value of the distance field parameter; （8） （9）。 5. A device for setting a three-gradient filled lattice structure for UAV structural components, characterized in that, The device is used to implement the method for setting up a three-gradient filled lattice structure for UAV structural components as described in any one of claims 1-4.

6. A server for setting up a three-gradient filled lattice structure for UAV structural components, characterized in that, The method includes a processor coupled to a memory, the memory storing program instructions, which, when executed by the processor, implement the method for setting a three-gradient filled lattice structure for UAV structural components as described in any one of claims 1-4.

7. A three-gradient filled lattice structure setting system for UAV structural components, characterized in that, The system is used to implement the method for setting up a three-gradient filled lattice structure for UAV structural components as described in any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, Includes a program that, when run on a computer, causes the computer to perform any one of the methods for setting up a three-gradient filled lattice structure for UAV structural components as described in any one of claims 1-4.

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