Method and device for determining structural parameters of anti-collision beam, anti-collision beam and vehicle

Abstract

The invention discloses an anti-collision beam structure parameter determination method and device, an anti-collision beam and a vehicle, and the method comprises the steps: obtaining the prediction data of the mechanical property and energy absorption performance of deformation under a set collision working condition when a reinforcement layer with a candidate thickness is applied to the anti-collision beam through a prediction model, and carrying out the prediction of the mechanical property and energy absorption performance from a plurality of groups of candidate structure parameters based on the prediction data; and target structure parameters of the anti-collision beam are determined. By applying the prediction model, the target structure parameters enabling the mechanical and energy absorption characteristics of the anti-collision beam to reach the standard are efficiently determined.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and in particular to a method and device for determining structural parameters of an anti-collision beam, an anti-collision beam and a vehicle. Background Art

[0002] The door anti-collision beam is a key structural component in the car body. Its main function is to absorb and disperse the impact energy in the side collision of the vehicle to ensure the safety of the passengers in the car.

[0003] With the advancement of research technology, there are more and more materials and selection types for door anti-collision beams. When designing the structure of door anti-collision beams, in order to determine the appropriate structural parameters to achieve a multi-faceted balance of needs in terms of strength, energy absorption and lightweight, it is necessary to design a large number of comparative experiments to determine the optimal anti-collision beam structural parameters. Therefore, it is costly and time-consuming. Summary of the invention

[0004] The present application provides a method and device for determining the structural parameters of an anti-collision beam, an anti-collision beam and a vehicle, so as to solve the technical problems of high cost and long time consumption in the existing anti-collision beam structure design.

[0005] In view of the above problems, the present application is proposed to provide a method and device for determining structural parameters of an anti-collision beam, an anti-collision beam and a vehicle that overcome the above problems or at least partially solve the above problems.

[0006] In a first aspect, a method for determining structural parameters of an anti-collision beam is provided, comprising:

[0007] Acquire multiple sets of candidate structural parameters of an anti-collision beam, wherein the anti-collision beam comprises a beam body and a strengthening layer formed on the beam body, and the multiple sets of candidate structural parameters comprise multiple sets of candidate thicknesses of the strengthening layer;

[0008] For each group of candidate structural parameters, the candidate structural parameters are input into a preset prediction model to obtain prediction data, wherein the prediction data includes mechanical properties and energy absorption properties of the anti-collision beam corresponding to the candidate structural parameters when deformed under a set collision condition;

[0009] Based on the prediction data corresponding to each group of candidate structural parameters, the target structural parameters of the anti-collision beam are determined from the multiple groups of candidate structural parameters.

[0010] Optionally, the determining the target structural parameters of the anti-collision beam from the multiple groups of candidate structural parameters based on the prediction data corresponding to each group of candidate structural parameters includes:

[0011] The prediction data corresponding to the multiple groups of candidate structural parameters are traversed one by one in an incremental manner until a preset end condition is met; the end condition is that the mechanical properties of the anti-collision beam corresponding to the candidate structural parameters that are deformed under a set collision condition are less than or equal to a preset mechanical property threshold, and the energy absorption performance is greater than or equal to a preset energy absorption performance threshold;

[0012] The candidate thickness of the strengthening layer corresponding to the prediction data satisfying the end condition is used as the target structural parameter of the anti-collision beam.

[0013] Optionally, the prediction model is obtained by the following operations:

[0014] Construct an initial polynomial regression prediction model;

[0015] Acquire sample data, the sample data including: multiple groups of sample structural parameters, and mechanical properties and energy absorption properties of the anti-collision beam sample corresponding to each group of sample structural parameters when deformed under set collision conditions;

[0016] The initial polynomial regression prediction model is trained based on the sample data to obtain the prediction model.

[0017] Optionally, the mechanical properties and energy absorption properties of the anti-collision beam samples corresponding to each group of sample structural parameters when deformed under set collision conditions are obtained by the following operations:

[0018] For each set of sample structural parameters, a finite element model of the anti-collision beam sample corresponding to the sample structural parameters is constructed; based on the finite element model of the anti-collision beam sample corresponding to the sample structural parameters, a collision simulation is performed to determine the mechanical properties and energy absorption performance of the anti-collision beam sample when deformed under set collision conditions.

[0019] Optionally, the beam body has at least one region to be strengthened, the strengthening layer is formed in the region to be strengthened, and the region to be strengthened is obtained by the following operations:

[0020] Constructing a finite element model of the beam;

[0021] Based on the finite element model of the beam body, a collision simulation is performed to determine at least one target deformation area of ​​the beam body that deforms under a set collision condition, wherein a maximum stress of the target deformation area is greater than or equal to a preset stress threshold and a maximum deformation amount of the target deformation area is greater than or equal to a preset deformation threshold;

[0022] The target deformation region is used as the region to be strengthened.

[0023] In a second aspect, a device for determining structural parameters of an anti-collision beam is provided, comprising:

[0024] an acquisition unit, configured to acquire a candidate structural parameter set of an anti-collision beam, wherein the anti-collision beam comprises a beam body and a strengthening layer formed on the beam body, wherein the candidate structural parameter set comprises a plurality of groups of candidate thicknesses of the strengthening layer;

[0025] A traversal unit, used for traversing the candidate thicknesses of the strengthening layer in the candidate structural parameter set one by one in an incremental manner until a preset end condition is met, and during the traversal process, for the candidate thicknesses of the strengthening layer traversed, the candidate thicknesses of the strengthening layer are input into a preset prediction model to obtain prediction data, the prediction data including a predicted value of mechanical properties and a predicted value of energy absorption performance of the anti-collision beam deformed under a set collision condition corresponding to the candidate thicknesses of the strengthening layer, and the end condition is that the predicted value of mechanical properties in the prediction data is less than or equal to a preset upper limit value, and the predicted value of energy absorption performance is greater than or equal to a preset lower limit value;

[0026] A determination unit is used to use the candidate thickness of the strengthening layer corresponding to the prediction data that meets the end condition as the target structural parameter of the anti-collision beam.

[0027] In a third aspect, the present application also provides an anti-collision beam, the structural parameters of which are determined by the method of the first aspect, wherein the anti-collision beam includes a beam body and a reinforcement layer formed on the beam body, and the structural parameters of the anti-collision beam include the thickness of the reinforcement layer.

[0028] Optionally, the strengthening layer is a carbon nanotube material, and the carbon nanotube material is processed on the surface of the beam body by a laser cladding process. The laser cladding process parameters are: laser power is 1000-1500W, scanning speed is 2-5mm / s, laser beam diameter is 0.3-0.5mm, cladding layer thickness is 0-0.3mm, and cladding zone temperature is 400-600°C.

[0029] In a fourth aspect, the present application also provides a vehicle, comprising the anti-collision beam of the third aspect.

[0030] In a fifth aspect, the present application also provides a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the server executes the method provided in the first aspect.

[0031] In a sixth aspect, the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the computer executes the method provided in the first aspect.

[0032] In a seventh aspect, the present application also provides a computer program product, including a computer program, which, when executed by a computer, enables the computer to execute the method provided in the first aspect.

[0033] The technical solution provided by this application has at least the following technical effects or advantages:

[0034] The method and device for determining the structural parameters of the anti-collision beam provided by the present application, and the anti-collision beam and vehicle use a prediction model to obtain prediction data of the mechanical properties and energy absorption properties of deformation of a reinforcement layer of a candidate thickness applied to the anti-collision beam under set collision conditions, and determine the target structural parameters of the anti-collision beam from multiple sets of candidate structural parameters based on the prediction data. The present application uses a prediction model to efficiently determine the target structural parameters that make the mechanical and energy absorption properties of the anti-collision beam meet the standards.

[0035] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0037] Figure 1 The method for determining the structural parameters of the anti-collision beam in the embodiment of the present application is as follows: Figure 1 ;

[0038] Figure 2 A flowchart for constructing a prediction model in an embodiment of the present application;

[0039] Figure 3 The method for determining the structural parameters of the anti-collision beam in the embodiment of the present application is as follows: Figure 2 ;

[0040] Figure 4 This is a flow chart of a method for determining an area to be strengthened according to an embodiment of the present application;

[0041] Figure 5 This is a schematic diagram of the structure of the door anti-collision beam in the embodiment of the present application;

[0042] Figure 6 This is a schematic diagram of the deformation of the aluminum alloy beam after a simulated collision in an embodiment of the present application;

[0043] Figure 7This is a schematic diagram of the optimization process of the anti-collision beam structural parameters in the embodiment of the present application;

[0044] Figure 8 It is a wireframe diagram of a device for determining the structural parameters of an anti-collision beam in an embodiment of the present application;

[0045] Fig. 9 It is a wireframe diagram of a determination unit of a device for determining structural parameters of an anti-collision beam in an embodiment of the present application;

[0046] Fig.10 This is a schematic diagram of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0047] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings.

[0048] Various structural schematic diagrams according to embodiments of the present application are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may further design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0049] It should be noted that, in this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0050] In the present application, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0051] In order to better understand the above-mentioned technical scheme, the above-mentioned technical scheme will be described in detail below in combination with specific implementation methods. It should be understood that the embodiments of the present disclosure and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0052] The door anti-collision beam is a key structural component in the car body. Its main function is to absorb and disperse the impact energy in the side collision of the vehicle to ensure the safety of the passengers in the car. Traditional door anti-collision beams are mostly made of aluminum alloy or hot stamping high-strength steel. Although these materials have high strength and rigidity, they still have certain limitations in terms of energy absorption and lightweight performance.

[0053] Taking the anti-collision beam made of pure aluminum alloy or pure high-strength steel as an example, the current design of door anti-collision beams faces the following technical challenges:

[0054] 1. Balance between lightweight and strength: Traditional aluminum alloy or hot-stamped high-strength steel anti-collision beams often have difficulty in taking into account both energy absorption capacity and lightweight requirements while improving strength.

[0055] 2. Although carbon nanotubes have excellent reinforcement properties, the cost of full material coverage is high and the process is complex. It is important to achieve performance improvement and cost control through local reinforcement.

[0056] 3. Difficulty in designing the thickness of the reinforcement layer: How to scientifically design the thickness of the reinforcement layer to meet the requirements of strength, energy absorption and lightweight is the core problem of existing technology.

[0057] In view of this, this application provides a method for determining the structural parameters of an anti-collision beam, please refer to Figure 1 , Figure 1 This is a flow chart of a method for determining the structural parameters of an anti-collision beam in an embodiment of the present application. The method includes:

[0058] S101, obtaining multiple sets of candidate structural parameters of an anti-collision beam. The anti-collision beam includes a beam body and a strengthening layer formed on the beam body, and the multiple sets of candidate structural parameters include multiple sets of candidate thicknesses of the strengthening layer;

[0059] S102, for each group of candidate structural parameters, input the candidate structural parameters into a preset prediction model to obtain prediction data. The prediction data includes the mechanical properties and energy absorption properties of the anti-collision beam corresponding to the candidate structural parameters when deformed under set collision conditions;

[0060] S103 . Determine target structural parameters of the anti-collision beam from multiple groups of candidate structural parameters based on the prediction data corresponding to each group of candidate structural parameters.

[0061] Each group of candidate thicknesses of the reinforcement layer of the anti-collision beam is within a preset thickness range. For the reinforcement layer, in step S101, the preset thickness range is traversed according to a preset step size to obtain multiple groups of candidate thicknesses of the reinforcement layer, and the multiple groups of candidate thicknesses of the reinforcement layer are used as multiple groups of candidate structural parameters of the anti-collision beam.

[0062] Different candidate structural parameters will result in different mechanical and energy absorption properties of the corresponding anti-collision beams under collision conditions. If bench tests are used to verify the actual mechanical and energy absorption properties of the anti-collision beams corresponding to each candidate structural parameter one by one, it will require very high manpower, material and time costs.

[0063] In step S102, the prediction model can be used to quickly predict the mechanical properties and energy absorption performance of the anti-collision beam corresponding to each set of candidate structural parameters under set working conditions, such as the stress, deformation and energy absorption of the anti-collision beam corresponding to each set of candidate structural parameters under set collision working conditions, with high efficiency and low cost.

[0064] It is understandable that the prediction model needs to be trained to grasp the functional relationship between the candidate structural parameters and the mechanical and energy absorption performance of the beam body of the anti-collision beam.

[0065] In some optional embodiments, such as Figure 2 As shown, Figure 2 This is a flowchart of the prediction model construction in the embodiment of the present application. The prediction model is obtained by the following operations:

[0066] S201, constructing an initial polynomial regression prediction model;

[0067] S202, obtaining sample data. The sample data includes: multiple groups of sample structural parameters, and mechanical properties and energy absorption properties of the anti-collision beam sample corresponding to each group of sample structural parameters when deformed under set collision conditions; wherein the multiple groups of sample structural parameters include multiple groups of candidate thicknesses of the strengthening layer of the anti-collision beam sample;

[0068] S203: Train the initial polynomial regression prediction model based on the sample data to obtain a prediction model.

[0069] It can be understood that when a collision occurs, the stress, deformation and energy absorption of the anti-collision beam are concentrated in at least one area. The stress output by the collision simulation here is the maximum stress in the area where the anti-collision beam sample generates stress under the set collision conditions, and the output deformation is the maximum deformation in the area where the anti-collision beam sample generates deformation under the set collision conditions.

[0070] Therefore, in step S202, the stress, deformation and energy absorption of the anti-collision beam sample corresponding to each set of sample structural parameters under the set collision working condition are obtained by the following operation:

[0071] For each set of sample structural parameters, a finite element model of the anti-collision beam sample corresponding to the sample structural parameters is constructed; based on the finite element model of the anti-collision beam sample corresponding to the sample structural parameters, a collision simulation is performed to determine the mechanical properties and energy absorption performance of the anti-collision beam sample that undergoes deformation under set collision conditions, specifically referring to the maximum stress, maximum deformation and energy absorption of the anti-collision beam sample that undergoes deformation under set collision conditions corresponding to each set of sample structural parameters.

[0072] In step S102, for each set of candidate structural parameters, each set of candidate structural parameters is input into the prediction model of the prediction model, that is, the predicted values ​​of stress, deformation and energy absorption of the anti-collision beam corresponding to each set of candidate structural parameters under the set collision working conditions, that is, the prediction data, are obtained.

[0073] The polynomial regression prediction model introduces polynomial terms to model the nonlinear characteristics between data, and is a regression analysis method for fitting nonlinear relationships. In step S202, the performance (stress, deformation, and energy absorption) data of the anti-collision beam corresponding to the thickness of different reinforcement layers is obtained using the finite element model of the anti-collision beam sample, and the operation of the above step S203 can be performed. The nonlinear relationship between the thickness of the reinforcement layer and the performance of the anti-collision beam is established through the polynomial regression prediction model, and the prediction model is trained to obtain the prediction model. The trained prediction model can predict the stress, deformation, and energy absorption of the anti-collision beam under the set collision conditions at any given reinforcement layer thickness. The core advantage of this process is that it can predict thickness configurations that have not been simulated, thereby improving calculation efficiency.

[0074] In summary, the prediction data includes the stress prediction value, deformation prediction value and energy absorption prediction value of the anti-collision beam corresponding to each set of candidate structural parameters under the set collision conditions.

[0075] like Figure 3 As shown, Figure 3 The method for determining the structural parameters of the anti-collision beam in the embodiment of the present application is as follows: Figure 2 Step S103 specifically includes the following operations:

[0076] S301, traverse the prediction data corresponding to multiple groups of candidate structural parameters one by one in an incremental manner until a preset end condition is met; wherein the end condition is that the mechanical properties of the anti-collision beam corresponding to the candidate structural parameters that undergo deformation under set collision conditions are less than or equal to a preset mechanical property threshold, and the energy absorption performance is greater than or equal to a preset energy absorption performance threshold.

[0077] S302: taking the candidate thickness of the strengthening layer corresponding to the prediction data satisfying the end condition as the target structural parameter of the anti-collision beam.

[0078] In step S301, the mechanical properties specifically refer to stress and deformation, and the energy absorption performance specifically refers to energy absorption. Therefore, the mechanical properties of the anti-collision beam corresponding to the candidate structural parameters that is deformed under the set collision conditions are less than or equal to the preset mechanical property threshold. Specifically, the stress of the anti-collision beam corresponding to the candidate structural parameters that is deformed under the set collision conditions is less than or equal to the preset stress upper limit, the deformation is less than or equal to the preset deformation upper limit, and the energy absorption is greater than or equal to the preset energy absorption lower limit.

[0079] During the traversal process, once the predicted data that meets the end conditions is found, the thickness of the reinforcement layer corresponding to the predicted data is output as the target structural parameter of the anti-collision beam. Therefore, in the operation of S302, the candidate thickness of the reinforcement layer corresponding to the predicted data that meets the end conditions is the thickness of the minimum reinforcement layer that makes the mechanical and energy absorption properties of the anti-collision beam meet the predicted conditions, thereby achieving a multi-faceted balance of the mechanical properties, energy absorption performance and lightweight requirements of the anti-collision beam.

[0080] It is understandable that the purpose of the traversal is to obtain the optimal material size of the anti-collision beam with stress, deformation and energy absorption within the limit range and the lightest weight when experiencing a collision event. Therefore, the upper limit of stress and deformation and the lower limit of energy absorption must be limited.

[0081] In summary, by setting a reinforcement layer on the beam body of the anti-collision beam, the limitation of the anti-collision beam adopting a single material solution is overcome. The solution provided by the embodiment of the present application can provide a thickness of the reinforcement layer with excellent performance, so that the anti-collision beam has more excellent mechanical and energy absorption performance, while meeting the lightweight requirements. By traversing the predicted data corresponding to the candidate thickness of the reinforcement layer on the beam body, the minimum thickness that makes the mechanical performance and energy absorption performance of the anti-collision beam meet the standards under the set collision conditions can be quickly determined, while ensuring excellent mechanical and energy absorption characteristics, reducing the weight of the anti-collision beam, and achieving a multi-faceted balance of needs in terms of strength, energy absorption and lightweight.

[0082] In some optional embodiments, the beam body has at least one region to be strengthened, and the strengthening layer is formed in the region to be strengthened, such as Figure 4 As shown, Figure 4 This is a flow chart of the method for determining the area to be strengthened in the embodiment of the present application. The area to be strengthened is obtained by the following operations:

[0083] S401, constructing a finite element model of the beam;

[0084] S402, performing collision simulation based on the finite element model of the beam body, and determining at least one target deformation region of the beam body that deforms under a set collision condition, wherein the maximum stress of the target deformation region is greater than or equal to a preset stress threshold and the maximum deformation of the target deformation region is greater than or equal to a preset deformation threshold;

[0085] S403: Taking the target deformation area as the area to be strengthened.

[0086] Through finite element simulation analysis, the areas with poor strength of the anti-collision beam are identified as the areas to be strengthened. By processing the strengthening layer in the areas to be strengthened, the performance of the key stress-bearing parts of the door anti-collision beam is targetedly improved. At the same time, the overall weight is effectively controlled, taking into account both performance and cost, and meeting the dual needs of modern automobiles for safety and lightweight.

[0087] To make the purpose, technical solutions and advantages of the present invention more clear, the following Figures 5 to 6 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] The following is a complete solution for the optimization design of an anti-collision beam using a reinforcement layer using the method of the above embodiment. Different from the traditional overall material reinforcement solution, this application only reinforces the key stress-bearing parts through simulation analysis and process optimization to achieve the balance between mechanical properties, energy absorption performance and lightweight.

[0089] 1. Material Design

[0090] like Figure 5 As shown, Figure 5 a is a schematic diagram of a car door. Figure 5 b is a schematic diagram of the anti-collision beam on the door. Figure 5 c is a partial cross-sectional view of the area to be optimized in the anti-collision beam. Figure 5 It can be seen that the vehicle door 500 includes an anti-collision beam 501 , and the anti-collision beam 501 includes a beam body 5011 and a reinforcement layer 5012 formed on the beam body 5011 .

[0091] The beam body 5011 is made of 6-series or 7-series aluminum alloy, so that the beam body 5011, as the base material of the door anti-collision beam, has high strength and excellent lightweight performance, which is suitable for the lightweight design requirements of automobiles. The strengthening layer 5012 is made of carbon nanotube composite material, which significantly improves the local impact resistance and energy absorption capacity.

[0092] Carbon nanotubes have become one of the ideal reinforcement materials due to their excellent mechanical properties (tensile strength of 50 to 200 GPa and elastic modulus higher than 1 TPa) and excellent energy absorption capacity. At present, carbon nanotube-reinforced composites have been widely used in aerospace and high-end equipment, but the local optimization design in automotive structural components is still in the exploration and research and development stage.

[0093] 2. Process design

[0094] Laser cladding technology is used to heat carbon nanotubes to a molten state and directly deposit them on the surface of the aluminum alloy beam 5011 to form a strengthening layer. Carbon nanotube materials are evenly distributed in key stress-bearing areas to ensure sufficient reinforcement in stress-concentrated areas while avoiding unnecessary weight addition in non-critical areas.

[0095] The laser cladding process parameters are: laser power of 1000-1500W, scanning speed of 2-5mm / s, laser beam diameter of 0.3-0.5mm, cladding layer thickness of 0-0.3mm, and cladding zone temperature of 400-600℃.

[0096] Laser cladding technology is a high-precision, high-efficiency local deposition process that can achieve efficient composites of carbon nanotubes and aluminum alloys. This technology not only has the conditions for industrial implementation, but also can accurately control the distribution of the enhanced area, providing a new technical path for the local enhancement design of automobile anti-collision beams.

[0097] 3. Structural optimization

[0098] Ansys Workbench and Solidworks Simulation platforms were used for simulation analysis. First, the stress distribution and deformation of the door anti-collision beam under collision conditions were simulated to determine the areas that needed to be strengthened. Figure 5 As shown, the end connection area and the middle area with the largest force of the beam body 5011 are determined as the areas to be strengthened, and a strengthening layer 5012 is designed at the end and the middle of the beam body 5011.

[0099] Secondly, when performing local optimization design on the door anti-collision beam, based on the stress, deformation, and energy absorption results output by the simulation, the polynomial regression model is used to fit the effect of different thicknesses on performance to determine the optimal strengthening layer thickness for each area.

[0100] The specific steps are as follows:

[0101] 3.1. Collision simulation conditions:

[0102] Collision speed: 50km / h (typical door side collision speed)

[0103] Collision force: 50kN (set simulated collision force, taking into account the impact of the actual collision)

[0104] Simulation analysis: simulate the force distribution, deformation, energy absorption, maximum stress and damage area of ​​the door anti-collision beam.

[0105] 3.2 Material model and physical parameters:

[0106] According to the actual geometric shape of the door anti-collision beam, a three-dimensional model is established. The model includes the door frame, the anti-collision beam body (aluminum alloy or high-strength steel) and the carbon nanotube reinforcement layer, such as Figure 1 The parameter settings are as follows:

[0107]

[0108] Note: The physical parameters of carbon nanotubes take into account the comprehensive mixing of carbon nanotubes along the length and radius of the tube.

[0109] 3.3 Stress distribution and deformation analysis:

[0110] Through simulation analysis, the following data of the door anti-collision beam during the collision can be obtained:

[0111] Maximum stress: The maximum stress value during a collision, used to evaluate whether the anti-collision beam can withstand the collision force.

[0112] Maximum deformation: The maximum deformation after a collision is used to evaluate the deformation capacity and collision energy absorption capacity of the structure.

[0113] Energy absorption: The total energy absorbed by the anti-collision beam during a collision is a key indicator to measure the energy absorption performance of the structure.

[0114] Through simulation analysis, the stress distribution and deformation of the door anti-collision beam during the collision process can be determined.

[0115] The results are as follows Figure 6 As shown in the figure, the two ends of the door anti-collision beam are the areas where stress and deformation are most concentrated and local damage is prone to occur. Therefore, the two end areas need to have high impact resistance and rigidity to ensure that the door will not be seriously deformed in a side collision. The second is the center. Although the center also bears a large collision force, the force distribution is relatively uniform. Compared with the stress concentration at the connection of the beam end, the stress value of the center stress area is relatively moderate, but it still needs to be strengthened. It is understandable that Figure 6 The upper left corner is a schematic diagram of the correspondence between deformation degree and color, and the lower end is a ruler with the unit of ruler in mm.

[0116] 4. Basis and optimization analysis of thickness setting

[0117] like Figure 7As shown, when the optimization starts, several sets of reinforcement layer thickness and corresponding anti-collision beam performance simulation data are input, the relationship between the reinforcement layer thickness and the anti-collision beam performance is fitted using a polynomial regression model, the thickness setting is initialized, and iteration begins. During the iteration process, the prediction model is used to predict the anti-collision beam performance under the current reinforcement layer thickness, and it is determined whether the predicted anti-collision beam performance meets the end condition. If not, the thickness of the reinforcement layer is updated and iterated again. If it is satisfied, the current reinforcement layer thickness and the corresponding anti-collision beam performance are output, and the process ends.

[0118] In this way, based on the simulation analysis results, the nonlinear relationship between thickness and performance indicators (stress, deformation, energy absorption) is fitted through simulation data, and a prediction model is constructed using polynomial regression. The performance requirements defined in the rule table (such as stress upper limit, deformation upper limit, energy absorption lower limit) are gradually optimized within the set thickness range. By traversing each thickness combination, predicting the corresponding performance value and verifying whether it meets the design requirements, the optimal thickness and corresponding performance indicators that meet the performance requirements are finally output, realizing thickness optimization design for different areas. The following is the specific process:

[0119] 4.1. Definition of rule table:

[0120] Use the rule table to define the performance requirements (stress, deformation, energy absorption requirements) of each area and the corresponding strengthening layer thickness range. Taking the carbon nanotube reinforced aluminum alloy anti-collision beam as an example, the rule table is as follows:

[0121] area Maximum stress requirement Deformation requirements Energy absorption requirements Strengthening layer thickness Beam end connection ≤500MPa ≤15mm ≥450J ≤2mm Central stress area ≤400MPa ≤20mm ≥400J ≤2mm

[0122] This rule table was established through multiple simulations, experiments and material performance analysis to ensure that the thickness of the reinforcement layer in each area can meet the design requirements while avoiding unnecessary over-design (such as excessively thick reinforcement layers) to achieve the goal of lightweighting.

[0123] 4.2. Input of simulation data:

[0124] Input more than 10 sets of simulation data results of stress, deformation and energy absorption of the anti-collision beam under different strengthening layer thicknesses. Each area (beam end connection and central stress area) contains performance data under different thicknesses. The simulation data format is as follows: area, thickness, stress value corresponding to each thickness, deformation value corresponding to each thickness, and energy absorption value corresponding to each thickness.

[0125] 4.3. Fitting the polynomial regression model:

[0126] For each area to be strengthened, polynomial regression is used to fit the relationship between stress, deformation, energy absorption and thickness. Polynomial regression can capture nonlinear relationships, making the model more realistic. Stress, deformation and energy absorption are fitted for each area to obtain a polynomial regression model. PolynomialFeatures is used to expand the input data to adapt it to polynomial regression (i.e., high-order terms of thickness are introduced into the model).

[0127] 4.4. Thickness optimization:

[0128] The process of selecting the optimal thickness is based on the following points:

[0129] Model Prediction: Use a polynomial model to predict stress, deformation, and energy absorption at the current thickness.

[0130] Performance verification: For each thickness, verify whether it meets the design requirements, that is, the stress does not exceed the maximum stress (upper limit of stress), the deformation does not exceed the maximum deformation (upper limit of deformation), and the energy absorption is greater than or equal to the minimum requirement (lower limit of energy absorption). If it meets the requirements, the thickness and its performance value will be returned immediately.

[0131] Iterative selection: Starting from the minimum thickness, gradually increase the thickness (step length 0.05mm), and perform prediction and verification until the minimum thickness that meets all performance requirements is found. If the current thickness meets these conditions, the thickness and corresponding performance index are returned; if the conditions are not met, the thickness continues to increase until the maximum thickness is reached.

[0132] 4.5. Output the optimal thickness and its corresponding performance:

[0133] After the optimization process, the optimal thickness of each area is output, and the stress, deformation and energy absorption values ​​at this thickness are returned. If the requirements are not met, the maximum thickness is output. These results help understand how to set different reinforcement layer thicknesses in different areas to achieve the best balance of strength, energy absorption and lightness.

[0134] 5. Conclusion and analysis:

[0135] By fitting the simulation data with polynomial regression, the thickness of the reinforcement layer of the door anti-collision beam can be optimized under given performance requirements. The optimization goal is to maximize safety (stress, deformation and energy absorption) while achieving lightweight (avoiding excessively thick reinforcement layers). The results show that for carbon nanotube reinforced aluminum alloy, a reinforcement layer thickness of 0.3mm is most suitable for the end connection of the anti-collision beam. The reinforcement layer thickness of 0.2mm is suitable for the central stress area, where the stress is relatively uniform. The thickness of 0.2mm can not only meet the design requirements, but also avoid material waste, effectively improving the overall lightweight performance.

[0136] The following table compares the stress and deformation of door anti-collision beams made of different materials under the same collision conditions:

[0137]

[0138] From the table above, we can see that compared with pure aluminum alloy, the maximum stress of aluminum alloy processed with carbon nanotube strengthening layer, that is, carbon nanotube reinforced aluminum alloy, has increased by about 100%, the maximum deformation has decreased by 36%, and the energy absorption efficiency has increased by about 65%. Compared with pure high-strength steel, carbon nanotube reinforced aluminum alloy not only has obvious advantages in deformation and energy absorption, but also has a smaller damage area during collision, which can effectively improve the safety of the overall structure.

[0139] It can be seen that after the introduction of carbon nanotube reinforced materials, the impact resistance, energy absorption capacity and deformation resistance of the door anti-collision beam have been significantly improved, especially in terms of lightweight and improved impact resistance and energy absorption capacity.

[0140] In summary, the method for determining the structural parameters of the anti-collision beam provided in the embodiment of the present application has significant advantages in terms of material performance, process feasibility and cost control, and can effectively meet the dual needs of lightweight and safety development of modern automobiles.

[0141] (1) Through the local reinforcement layer, such as the carbon nanotube reinforcement layer reinforcement design, the strength and energy absorption performance of the door anti-collision beam are effectively improved, while maintaining the lightweight advantage and avoiding the weight increase and material waste caused by full material reinforcement. The local reinforcement design reduces the use of high-performance composite materials, thereby reducing material and processing costs and improving economic efficiency.

[0142] (2) The nonlinear relationship between the thickness of the reinforcement layer and the performance of the anti-collision beam (stress, deformation, and energy absorption) was established through simulation data, and a prediction model was constructed using the polynomial regression method to achieve a step-by-step optimization process of the thickness. Combined with the performance requirements defined in the rule table, the performance values ​​of the thickness combinations were gradually predicted and verified, and finally the optimal thickness and its corresponding performance indicators that met the design requirements were output.

[0143] (3) This application is not only applicable to vehicle door anti-collision beams, but can also be applied to the local enhancement design of other key safety components such as vehicle door rings, B-pillar patch plates, battery packs, front bumpers, headlights, reflectors, fenders, radiator grilles, and protruding parts on the side of the vehicle body. It has strong universality and has promoted the widespread application and industrial promotion of this technology in the automotive industry.

[0144] Based on the same inventive concept, the embodiment of the present application provides a device for determining the structural parameters of an anti-collision beam, such as Figure 8 As shown, the anti-collision beam structural parameter determination device 800 includes:

[0145] An acquisition unit 801 is used to acquire multiple sets of candidate structural parameters of the anti-collision beam. The anti-collision beam includes a beam body and a strengthening layer formed on the beam body, and the multiple sets of candidate structural parameters include multiple sets of candidate thicknesses of the strengthening layer;

[0146] The prediction unit 802 is used to input the candidate structural parameters into a preset prediction model for each group of candidate structural parameters to obtain prediction data. The prediction data includes the mechanical properties and energy absorption properties of the anti-collision beam corresponding to the candidate structural parameters when deformed under the set collision conditions;

[0147] The determination unit 803 is used to determine the target structural parameters of the anti-collision beam from multiple groups of candidate structural parameters based on the prediction data corresponding to each group of candidate structural parameters.

[0148] In some optional embodiments, such as Fig. 9 As shown, the determining unit 803 includes:

[0149] A traversal unit 901 is used to traverse the prediction data corresponding to the multiple groups of candidate structural parameters one by one in an incremental manner until a preset end condition is met; wherein the end condition is that the mechanical properties of the anti-collision beam corresponding to the candidate structural parameters that are deformed under the set collision condition are less than or equal to a preset mechanical property threshold, and the energy absorption performance is greater than or equal to a preset energy absorption performance threshold;

[0150] The value taking unit 902 is used to take the candidate thickness of the strengthening layer corresponding to the prediction data satisfying the end condition as the target structural parameter of the anti-collision beam.

[0151] The device for determining the structural parameters of the anti-collision beam provided in the embodiment of the present application can be used to execute the operations S101 to S103 provided in the above embodiment, and quickly determine the optimal thickness of the reinforcement layer of the beam body of the anti-collision beam, so as to achieve a multi-faceted balance in terms of lightweight, mechanical and energy absorption performance of the anti-collision beam.

[0152] Regarding the above-mentioned device, the specific functions of each unit therein have been described in detail in the embodiment of the method for determining the structural parameters of the anti-collision beam provided in this specification, and will not be elaborated here.

[0153] Based on the same inventive concept, an embodiment of the present application provides an anti-collision beam, and the structural parameters of the anti-collision beam are obtained by using the method of the above embodiment. The anti-collision beam includes a beam body and a strengthening layer formed on the beam body, and the structural parameters of the anti-collision beam include the thickness of the strengthening layer.

[0154] Based on the same inventive concept, an embodiment of the present application provides a vehicle, including the anti-collision beam implemented above.

[0155] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a computer, the computer executes the method provided in the above embodiment. The specific coding of the computer program is described in detail in the embodiment of the above-mentioned method for determining the structural parameters of the anti-collision beam, and will not be elaborated here.

[0156] The present application also provides a server, such as Fig.10 As shown, the server 1000 includes a memory 1001, a processor 1002, and a computer program 1003 stored in the memory 1001 and executable on the processor 1002. When the processor 1002 executes the computer program 1003, the server 1000 executes the method provided in the above embodiment.

[0157] The embodiments of the present application also provide a computer program product, including a computer program. When the computer program is executed, the computer executes the method provided in the above embodiments.

[0158] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0160] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

Claims

1. A method for determining the structural parameters of an anti-collision beam, characterized in that: include: Acquire multiple groups of candidate structural parameters of an anti-collision beam, wherein the anti-collision beam comprises a beam body and a strengthening layer formed on the beam body, and the multiple groups of candidate structural parameters comprise multiple groups of candidate thicknesses of the strengthening layer; For each group of candidate structural parameters, the candidate structural parameters are input into a preset prediction model to obtain prediction data, wherein the prediction data includes mechanical properties and energy absorption properties of the anti-collision beam corresponding to the candidate structural parameters when deformed under a set collision condition; Based on the prediction data corresponding to each group of candidate structural parameters, the target structural parameters of the anti-collision beam are determined from the multiple groups of candidate structural parameters.

2. The method for determining the structural parameters of an anti-collision beam according to claim 1, characterized in that: The step of determining target structural parameters of the anti-collision beam from the plurality of sets of candidate structural parameters based on the prediction data corresponding to each set of candidate structural parameters includes: The prediction data corresponding to the multiple groups of candidate structural parameters are traversed one by one in an incremental manner until a preset end condition is met; the end condition is that the mechanical properties of the anti-collision beam corresponding to the candidate structural parameters that are deformed under a set collision condition are less than or equal to a preset mechanical property threshold, and the energy absorption performance is greater than or equal to a preset energy absorption performance threshold; The candidate thickness of the strengthening layer corresponding to the prediction data satisfying the end condition is used as the target structural parameter of the anti-collision beam.

3. The method for determining the structural parameters of an anti-collision beam according to claim 1, characterized in that: The prediction model is obtained by the following operations: Construct an initial polynomial regression prediction model; Acquire sample data, the sample data including: multiple groups of sample structural parameters, and mechanical properties and energy absorption properties of the anti-collision beam sample corresponding to each group of sample structural parameters when deformed under set collision conditions; The initial polynomial regression prediction model is trained based on the sample data to obtain the prediction model.

4. The method for determining the structural parameters of an anti-collision beam according to claim 3, characterized in that: The mechanical properties and energy absorption performance of the anti-collision beam samples corresponding to each group of sample structural parameters deformed under the set collision conditions are obtained by the following operations: For each set of sample structural parameters, a finite element model of the anti-collision beam sample corresponding to the sample structural parameters is constructed; based on the finite element model of the anti-collision beam sample corresponding to the sample structural parameters, a collision simulation is performed to determine the mechanical properties and energy absorption performance of the anti-collision beam sample when deformed under set collision conditions.

5. The method for determining the structural parameters of an anti-collision beam according to claim 1, characterized in that: The beam body has at least one area to be strengthened, the strengthening layer is formed in the area to be strengthened, and the area to be strengthened is obtained by the following operations: Constructing a finite element model of the beam; Based on the finite element model of the beam body, a collision simulation is performed to determine at least one target deformation area of ​​the beam body that deforms under a set collision condition, wherein a maximum stress of the target deformation area is greater than or equal to a preset stress threshold and a maximum deformation amount of the target deformation area is greater than or equal to a preset deformation threshold; The target deformation region is used as the region to be strengthened.

6. A device for determining the structural parameters of an anti-collision beam, characterized in that: include: an acquisition unit, configured to acquire a candidate structural parameter set of an anti-collision beam, wherein the anti-collision beam comprises a beam body and a strengthening layer formed on the beam body, wherein the candidate structural parameter set comprises a plurality of groups of candidate thicknesses of the strengthening layer; A traversal unit, used for traversing the candidate thicknesses of the strengthening layer in the candidate structural parameter set one by one in an incremental manner until a preset end condition is met, and during the traversal process, for the candidate thicknesses of the strengthening layer traversed, the candidate thicknesses of the strengthening layer are input into a preset prediction model to obtain prediction data, the prediction data including a predicted value of mechanical properties and a predicted value of energy absorption performance of the anti-collision beam deformed under a set collision condition corresponding to the candidate thicknesses of the strengthening layer, and the end condition is that the predicted value of mechanical properties in the prediction data is less than or equal to a preset upper limit value, and the predicted value of energy absorption performance is greater than or equal to a preset lower limit value; A determination unit is used to use the candidate thickness of the strengthening layer corresponding to the prediction data that meets the end condition as the target structural parameter of the anti-collision beam.

7. An anti-collision beam, characterized in that: The structural parameters of the anti-collision beam are determined by the method described in any one of claims 1 to 5, wherein the anti-collision beam comprises a beam body and a reinforcement layer formed on the beam body, and the structural parameters of the anti-collision beam include the thickness of the reinforcement layer.

8. A vehicle, characterized in that: Including the anti-collision beam as claimed in claim 7.

9. A server 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, the server is caused to perform the method according to any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the computer is caused to perform the method according to any one of claims 1 to 5.

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