Body-in-white lightweight design method and device, storage medium

Abstract

The application provides a body-in-white lightweight design method, equipment and a storage medium, the design method comprises the following steps: obtaining a body-in-white finite element model, identifying the body-in-white finite element model, obtaining a current basic scheme, the current basic scheme comprising a plurality of components and characteristic parameters corresponding to the plurality of components; reading a lightweight related data set, adjusting the characteristic parameters of each component of the current basic scheme according to the data set, and obtaining a lightweight scheme; wherein the data set comprises a body structure data set, and the body structure data set comprises a plurality of components and characteristic parameters corresponding to the plurality of components. The method adjusts the component characteristic parameters based on the data set to reduce weight, the design process is automatically executed, the work efficiency is high, the computing power is saved, and the data set provides data support for lightweight design.

Description

Technical Field

[0001] This invention belongs to the field of automotive design, and specifically relates to a lightweight design method for a body-in-white and an electronic device and computer-readable storage medium that enable the lightweight design method for a body-in-white. Background Technology

[0002] Automotive lightweighting refers to reducing the weight of a vehicle while ensuring its safety performance, such as driving safety, crashworthiness, and shock resistance. Lightweighting of the automotive structure can effectively reduce fuel consumption and emissions, and is of great significance for improving the energy efficiency and range of new energy vehicles. With the significant reduction in the R&D cycle of new energy vehicles, more stringent requirements have been placed on the quality and efficiency of lightweighting efforts.

[0003] The methods of automotive lightweighting include material lightweighting, structural lightweighting, and process lightweighting. Material lightweighting means using lighter materials; process lightweighting means adopting lightweight processes; structural lightweighting means optimizing the number and size of body parts. Existing automotive structural lightweighting design mainly relies on the design experience of engineers, and the final lightweight product design scheme is obtained through repeated trial and error. This working method has the following problems: (1) Limited data support. Historical design schemes are difficult to represent with data and cannot provide explicit design guidance. The optimization method based on engineer experience requires high engineer capabilities; (2) Low work efficiency. When performing lightweighting simulation analysis of the body-in-white, there are many different combinations of components. Manual operation and editing are cumbersome and inefficient; (3) High computational power consumption. When it is impossible to accurately propose an optimization scheme, the trial and error method leads to a lot of invalid calculations, which occupies resources and consumes computational power. Summary of the Invention

[0004] The purpose of this invention is to solve the problems of insufficient data support, low work efficiency, and high computing power consumption in existing lightweight design methods for automotive structures.

[0005] To address the aforementioned technical problems, the present invention discloses a lightweight design method for a body-in-white, comprising: S1: acquiring a finite element model of the body-in-white, identifying the finite element model of the body-in-white, and obtaining a current basic scheme, wherein the current basic scheme includes multiple components and feature parameters corresponding to the multiple components; S2: reading a lightweight-related dataset, adjusting the feature parameters of each component of the current basic scheme according to the dataset, and obtaining a lightweight scheme; wherein the dataset includes a body structure dataset, wherein the body structure dataset includes multiple components and feature parameters corresponding to the multiple components.

[0006] By adopting the above technical solution, lightweight design is carried out by adjusting the feature parameters of components based on the dataset, rather than based on engineers' experience. This improves the intelligence of the lightweight design method, automates the design process, increases work efficiency, saves computing power, and the dataset provides data support for lightweight design.

[0007] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a lightweight design method for a body-in-white, wherein the characteristic parameter includes thickness; step S2 includes adjusting the thickness of each component of the current basic scheme to obtain a lightweight scheme.

[0008] According to another specific embodiment of the present invention, a lightweight design method for a body-in-white is disclosed in the present invention. Step S2 includes: reading a body structure dataset, obtaining the minimum thickness or average thickness of each component based on the body structure dataset, and adjusting the thickness of each component to the minimum thickness or average thickness.

[0009] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a lightweight design method for a body-in-white, wherein the dataset further includes a scheme dataset, the scheme dataset includes multiple historical lightweight schemes and a body structure dataset corresponding to each historical lightweight scheme; step S2 includes: reading the scheme dataset and adjusting the thickness of each component to the thickness of a component in at least one historical lightweight scheme.

[0010] According to another specific embodiment of the present invention, an embodiment of the present invention discloses a lightweight design method for a body-in-white. The dataset further includes a scheme dataset, which includes: multiple historical lightweight schemes, a body structure dataset and performance parameters corresponding to each historical lightweight scheme, a historical basic scheme corresponding to each historical lightweight scheme, and a body structure dataset and performance parameters corresponding to each historical basic scheme. Step S2 includes: reading the scheme dataset to obtain multiple historical lightweight schemes and corresponding multiple historical basic schemes; obtaining the thickness change rate of each component based on the thickness of each component in each lightweight scheme and the thickness of each component in the corresponding historical basic scheme; obtaining the performance parameter change rate based on the performance parameters of each lightweight scheme and the performance parameters of the corresponding historical basic scheme; obtaining the weight coefficient of each component based on the thickness change rate and the performance parameter change rate of each component; and determining the lightweight scheme based on the weight coefficient and the thickness range.

[0011] According to another specific embodiment of the present invention, a lightweight design method for a body-in-white is disclosed in this embodiment. Step S1, the step of identifying the finite element model of the body-in-white, includes: extracting feature parameters of each component in the finite element model of the body-in-white, the feature parameters including mass, size, and position; comparing the mass of each component with a mass threshold, if the mass of a component is greater than the mass threshold, it is determined as a component to be optimized; classifying the components to be optimized according to their feature parameters, into symmetrical components and asymmetrical components; and further identifying the components to be optimized according to their category and feature parameters.

[0012] According to another specific embodiment of the present invention, a lightweight design method for a body-in-white is disclosed in the present invention. The characteristic parameters further include thickness, material, correlation, surface area, and volume. The step of further identifying the component to be optimized includes: identifying the name of the component to be optimized according to the classification and characteristic parameters of the component to be optimized.

[0013] According to another specific embodiment of the present invention, a lightweight design method for a body-in-white is disclosed in the present invention. The position in the feature parameters includes the centroid coordinates and corner coordinates. The step of further identifying the parts to be optimized further includes: selecting multiple groups of similar parts to be optimized from the parts to be optimized, and identifying the relative position of each group of parts to be optimized according to the centroid coordinates and corner coordinates of the parts to be optimized.

[0014] According to another specific embodiment of the present invention, a lightweight design method for a body-in-white disclosed in the present invention further includes step S1: after obtaining multiple components and their corresponding feature parameters, reading the body structure dataset, and comparing the feature parameters of each component with the feature parameters of the corresponding component in the body structure dataset to verify the identification results.

[0015] The present invention also discloses an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the lightweight body-in-white design method provided by the present invention.

[0016] The embodiments of the present invention also disclose a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the lightweight body-in-white design method provided by the present invention. Attached Figure Description

[0017] Figure 1 This is a flowchart of the lightweight body-in-white design method provided by the present invention;

[0018] Figure 2 This is a flowchart of the step of identifying the finite element model of the body-in-white in step S1 of the lightweight body-in-white design method provided by the present invention;

[0019] Figure 3 This is a schematic diagram of the spatial envelope of components in the lightweight body-in-white design method provided by the present invention;

[0020] Figure 4 This is a flowchart of one implementation of the process of obtaining a lightweight solution in step S2 of the lightweight body-in-white design method provided by the present invention;

[0021] Figure 5 This is a flowchart of another implementation of the process of obtaining a lightweight solution in step S2 of the lightweight body-in-white design method provided by the present invention;

[0022] Figure 6 This is a flowchart of another implementation of the process of obtaining a lightweight solution in step S2 of the lightweight body-in-white design method provided by the present invention;

[0023] Figure 7 This is a flowchart of the further post-processing after obtaining the lightweight solution in the lightweight body-in-white design method provided by the present invention. Detailed Implementation

[0024] Automotive lightweighting methods include material lightweighting, structural lightweighting, and process lightweighting. Existing technologies utilize finite element analysis for lightweight optimization design; however, automotive finite element models are complex and involve large amounts of data—for example, a body-in-white typically has over 300 components. Furthermore, current technologies for automotive structural lightweighting design primarily rely on engineers' design experience, employing trial-and-error methods to arrive at the final lightweight product design solution. This approach suffers from limited data support, low efficiency, and high computational consumption.

[0025] This invention addresses the aforementioned problems by providing a lightweight design method for a body-in-white (BIS). Based on a finite element model of the BIS, it optimizes the structure of BIS components to achieve vehicle lightweighting. Specifically, by automatically identifying the BIS finite element model, multiple components and their corresponding feature parameters are obtained, leading to the current basic design of the BIS itself. The feature parameters of the BIS components are then adjusted according to a pre-built dataset to obtain a lightweight design. By adjusting components based on a dataset rather than engineer experience, the lightweight design method becomes more intelligent, automating the design process, increasing efficiency, reducing meaningless trial-and-error algorithms, saving computational resources, and providing data support for the lightweight design process.

[0026] To better understand the lightweight design method and equipment for body-in-white provided in this application, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Example 1

[0028] The lightweight design method for body-in-white provided by this invention, such as Figure 1 As shown, the design method includes: S1: Obtaining the finite element model of the body-in-white, identifying the finite element model of the body-in-white, and obtaining the current basic scheme. The current basic scheme includes multiple components and the feature parameters corresponding to the multiple components; S2: Reading the lightweight related dataset, adjusting the feature parameters of each component of the current basic scheme according to the dataset, and obtaining the lightweight scheme; wherein the lightweight related dataset includes the body structure dataset, which includes multiple components and the feature parameters corresponding to the multiple components.

[0029] The lightweight design of body-in-white components in this invention is based on a lightweight-related dataset. Therefore, it is necessary to pre-construct a lightweight-related dataset (hereinafter referred to as the dataset). The dataset may include historical weight reduction design schemes for the overall body of similar vehicle models, historical schemes for all component structures of similar vehicle models, and lightweight schemes for some or all component structures input by engineers based on experience; any data that can be used for lightweight design of body-in-white components can be stored in the dataset.

[0030] The dataset includes a vehicle body structure dataset, which comprises multiple components of the vehicle body and their corresponding feature parameters. These feature parameters include mass, material, dimensions, location, thickness, area, and volume. Specifically, the vehicle body structure dataset can be obtained from automotive CAE (Computer Aided Engineering) model data. Although the model data is unstructured, the information of its individual components is structured. Some specific details of the vehicle body structure dataset are shown in Table 1.

[0031] Table 1 Example of vehicle body structure dataset

[0032]

[0033] In one specific implementation, the dataset further includes a scheme dataset, which comprises multiple historical lightweighting schemes. Each historical lightweighting scheme includes multiple components and their corresponding feature parameters (i.e., a vehicle body structure dataset). Additionally, it may include the performance parameters of the body-in-white corresponding to each historical lightweighting scheme, the historical base scheme corresponding to each historical lightweighting scheme, and the vehicle body structure dataset and performance parameters corresponding to each historical base scheme. The performance parameters can be one or multiple parameters, forming structured data. Specific partial scheme datasets are shown in Table 2.

[0034] Table 2 Example of Scheme Dataset

[0035] Project Name Basic Solution Lightweight solution Vehicle body name Performance 1 Performance 2 AA Base AA-Base X1 Y1 AA Base P1 AA-Base-P1 X2 Y2 AA Base P2 AA-Base-P2 X3 Y3 BB Base BB-Base X4 Y4 BB P1 P3 BB-P1-P3 X5 Y5

[0036] In this dataset example, project AA has a base model named Base, with the corresponding body structure table named AA-Base (i.e., the table name in the body structure dataset). The dataset also includes the performance results X1 and Y1 corresponding to this base solution. The second column represents the lightweight solution P1 proposed based on the Base model, with the corresponding table name in the body structure dataset named AA-Base-P1. The performance results X2 and Y2 corresponding to this lightweight solution model are also shown in the table. The table exemplifies that there are two performance parameters, but in application, the dataset may include multiple performance parameters depending on the actual situation. The naming rules for the table names in the body structure dataset can specifically include the project name, the corresponding base solution name, and the lightweight solution name. For example, in the table name AA-Base-P2 in the body structure dataset in Table 1, AA represents the project name, Base represents the corresponding base solution name, and P1 represents the lightweight solution name.

[0037] After constructing the lightweight-related dataset, lightweight design of the body-in-white can be carried out based on the dataset. The steps S1-S2 of the design method are explained in detail below.

[0038] S1: By importing the finite element model of the body-in-white of the car to be optimized into the recognition software, the finite element model of the body-in-white of the car to be optimized is obtained; the recognition software identifies each component in the finite element model of the body-in-white to obtain the current basic scheme of the car to be optimized. The current basic scheme includes multiple components and the characteristic parameters corresponding to multiple components. The multiple components can be all the components in the finite element model of the body-in-white, or some key components, or components with large mass or volume. The characteristic parameters can be, but are not limited to, all the parameters exemplified in Table 2 above.

[0039] In one specific implementation, such as Figure 2 As shown, the steps for identifying the finite element model of the body-in-white include extracting the component feature parameters from the finite element model of the body-in-white, screening out the components to be optimized, classifying the components to be optimized, and further identifying the components to be optimized.

[0040] In the step of extracting feature parameters for each component in the finite element model of the body-in-white, the feature parameters include mass, size, and position. Specifically, since many components in a car are irregularly shaped, the dimensional parameters of the components can be characterized using spatial envelope dimensions, such as... Figure 3A schematic diagram of the spatial envelope of a component is shown. The spatial cube formed by two corner points A and B constitutes the spatial envelope. The envelope information includes the coordinates (X, Y, Z coordinates) of the two corner points. The length (L), width (W), and height (H) of the spatial envelope can be obtained by the difference between the coordinates of the two corner points, thereby obtaining the dimensional parameters (i.e., length, width, and height) of the component's spatial envelope. Furthermore, the positional parameters of the component can include the coordinates of a corner point within the spatial envelope (as shown by corner point A in the figure), and the positional parameters can also include the coordinates of the component's centroid.

[0041] like Figure 2 As shown, the step of screening components to be optimized specifically involves comparing the mass of each component with a mass threshold. If the mass of a component is greater than the mass threshold, it is selected as a component to be optimized. This step performs an initial screening of components, eliminating those with a mass less than or equal to the mass threshold, i.e., eliminating lightweight components that have little effect on weight reduction. It should be noted that if a step of screening components to be optimized is performed in this invention, only the components to be optimized identified in this step will be adjusted in the subsequent process of obtaining a lightweight solution; the eliminated components will not be adjusted.

[0042] The specific steps for classifying the components to be optimized are as follows: based on the characteristic parameters of the components, they are divided into symmetrical and asymmetrical components. Specifically, the components are initially classified into two main categories: symmetrical and asymmetrical components. When the Y-coordinate of the component's center of mass is close to 0 (e.g., between -1 and 1), the component can be considered located in the middle of the vehicle body (i.e., penetrating the Y=0 plane), and is thus classified as asymmetrical. Examples include front and rear anti-collision beams, upper and lower crossbeams of the windshield, front and rear windshields, roof, and spare tire compartment. When two components have opposite Y-coordinates of their centers of mass and very little difference in mass or size, they are classified as symmetrical components. Examples include energy-absorbing boxes, front and rear longitudinal beams, side panels, inner and outer panels of A / B pillars, and inner and outer panels of door sills.

[0043] The further identification steps for the components to be optimized specifically involve more accurately identifying the components based on their category and characteristic parameters, such as further identifying the functional type of the components to be optimized and distinguishing similar components to be optimized.

[0044] In one specific implementation, the feature parameters also include thickness, material, relationship with other components, surface area, and area. The step of further identifying the component to be optimized includes: identifying the name of the component to be optimized based on its classification, size, location, thickness, material, relationship, surface area, and area. Specifically, a feature combination method is used to further identify each component. The identification of a particular component may utilize all feature parameters, or it may only utilize some feature parameters. For example, in the asymmetric category, if a component has a length, width, and height greater than 800mm, a thickness greater than or equal to 3mm, and a material elastic modulus less than 10000, it can be identified as a windshield. Then, based on the centroid position feature, the component with the smaller centroid X-coordinate is the front windshield, and the component with the larger centroid X-coordinate is the rear windshield. In the symmetrical component category, if it has the largest surface area among the symmetrical components, a thickness less than 1mm, and a length greater than 1000mm, it can be identified as a side panel. Using the above method, functional components of the body-in-white, such as front and rear longitudinal beams, side panels, windshields, windshield crossbeams, A / B pillars, floor, seat crossbeams, door sills, and spare tire compartment, can be identified.

[0045] In one specific implementation, the position in the feature parameters includes centroid coordinates and corner coordinates. The step of further identifying the component to be optimized also includes: selecting multiple groups of similar components to be optimized, identifying the relative position of each group of components based on their centroid coordinates and corner coordinates, and performing refined identification to distinguish similar components. For example, the inner panel, outer panel, and reinforcing plate can be finely identified using centroid coordinates and corner coordinates. Taking the inner / outer panel of the left B-pillar as an example, after identifying the B-pillar, the relative position of the inner / outer panel can be identified using the centroid Y-coordinate and the corner Y-coordinate, further determining that the component with the smaller centroid Y-coordinate and corner Y-coordinate is the outer panel of the B-pillar; conversely, for the right side, the component with the larger centroid Y-coordinate and corner Y-coordinate is the outer panel of the B-pillar.

[0046] Using the methods described above, the algorithm can automatically identify each component of the finite element model of the body-in-white, obtaining multiple components to be optimized and their feature parameters. The identification process is highly intelligent and fully automated, improving design efficiency. Furthermore, the identification method can also be used to construct a body structure dataset.

[0047] In one specific implementation, step S1 further includes: after obtaining multiple components and their corresponding feature parameters, reading the vehicle body structure dataset, comparing the feature parameters of each component with the feature parameters of the corresponding component in the vehicle body structure dataset to verify the recognition result. If the verification fails, the erroneous result can be re-identified to improve the accuracy of the recognition process. After recognition and verification are completed, the data that is confirmed to be correct can be stored in the vehicle body structure dataset.

[0048] S2: Read the dataset and adjust the feature parameters of each component in the current base solution based on the dataset to obtain a lightweight solution. For example, the feature parameters of a component can be adjusted by referring to the feature parameters of the component with the lightest weight in the historical data of the dataset. Furthermore, one or more of the component's size, thickness, and material can be adjusted. After obtaining the lightweight solution, the lightweight solution and its related data can be stored in the dataset.

[0049] In one specific implementation, the characteristic parameter includes thickness. In this step, the thickness of each component in the current base scheme is adjusted based on the dataset to obtain a lightweight scheme. More specifically, the thickness of the components can be adjusted based on the historical thickness of each component, or the thickness of all components can be adjusted as a whole based on a certain historical lightweight scheme.

[0050] Furthermore, in one specific implementation, the process of obtaining a lightweight solution includes: reading a vehicle body structure dataset, and statistically obtaining the minimum or average thickness of each component in historical designs based on the dataset; successively adjusting the thickness of each component to the minimum or average thickness to obtain a lightweight solution. Specifically, as... Figure 4 As shown, first, the name of a component in the current basic scheme is read. Then, using SQL, all thicknesses of that component in the vehicle body structure dataset are queried. Based on the query results, the minimum or average thickness of that component is calculated. The thickness of that component in the current basic scheme is then adjusted to the minimum or average thickness based on the calculation results. Next, the name of the next component in the current basic scheme is read, and the above steps are repeated until the thickness of all components to be optimized is adjusted, thus obtaining a lightweight scheme. Adjusting the minimum thickness of components in historical schemes can generate an extreme lightweight scheme.

[0051] In one specific implementation, the dataset includes a scheme dataset, which comprises multiple historical lightweighting schemes and a corresponding vehicle body structure dataset for each historical lightweighting scheme. For example... Figure 5 As shown, the process of obtaining a lightweight solution includes: reading the solution dataset, calling a historical lightweight solution for the same vehicle model, replicating that lightweight solution, and adjusting the thickness of each component in the current base solution to match the thickness of the components in that lightweight solution. Specifically, as... Figure 5 As shown, the algorithm calls a historical lightweight solution from the solution dataset, reads the thickness of a component in the historical lightweight solution, searches for the corresponding component in the current basic solution based on the read component name, and adjusts the thickness of that component in the current basic solution to match the thickness of that component in the lightweight solution. The algorithm then reads the thickness of the next component in the historical lightweight solution and repeats the above steps until the thickness of all components to be optimized is adjusted, thus obtaining the lightweight solution.

[0052] In one specific implementation, the dataset includes a scheme dataset, which comprises: multiple historical lightweighting schemes, a vehicle body structure dataset and performance parameters corresponding to each historical lightweighting scheme, a historical base scheme corresponding to each historical lightweighting scheme, and a vehicle body structure dataset and performance parameters corresponding to each historical base scheme. Specifically, the performance parameters include vehicle weight and parameters characterizing vehicle structural performance, such as structural stiffness and strength. The process of obtaining lightweighting schemes includes: as follows... Figure 6 As shown, the solution dataset is read to obtain multiple historical lightweighting solutions and their corresponding historical baseline solutions. Based on the thickness of each component in each lightweighting solution and the thickness of each component in the corresponding historical baseline solutions, the thickness change rate of each component is obtained. Based on the performance parameters of the body-in-white in each lightweighting solution and the performance parameters of the body-in-white in the corresponding historical baseline solutions, the performance parameter change rate of the body-in-white is obtained. Based on the thickness change rate and the performance parameter change rate of each component, the weight coefficient of each component is obtained. The lightweighting solution is determined based on the weight coefficients and the thickness range. The thickness range refers to the range between the minimum and maximum allowable thickness of a component, which can be determined based on historical component thickness data and experience.

[0053] Specifically, changes in the thickness of each component will lead to changes in the corresponding performance parameters. Since the changes in each component have different weights of influence on the performance parameter changes, the following expression can be obtained.

[0054]

[0055] Where, ΔT 11 C1 is the thickness variation rate of component 1 in the first scheme, and C1 is the weighting coefficient of component 1. Similarly, ΔT... mn Let C be the thickness change rate of the nth component in the mth scheme. n Let ΔP be the weighting coefficient of the nth component, and ΔP1 be the rate of change of a certain performance parameter of the body-in-white in the first scheme. m Let be the rate of change of a certain performance parameter of the body-in-white for the m-th scheme.

[0056] Based on the above expression, the weighting coefficients of the impact of thickness changes on the performance parameter are obtained through logistic regression training. Further, the name of a component in the current base scheme is read, and the component's thickness is adjusted according to its weighting coefficient and thickness range. Specifically, if the performance parameter is a structural performance-related parameter, the smaller the component weighting coefficient, the greater the component thickness can be adjusted, such as adjusting it to the minimum thickness within the range; while the larger the component weighting coefficient, the component thickness can be slightly reduced or not adjusted at all. If the performance parameter is a vehicle weight parameter, the larger the component weighting coefficient, the greater the component thickness can be adjusted. After adjustment, the thickness of the next component in the current base scheme is read, and the above steps are repeated until the thickness of all components to be optimized is adjusted, thus obtaining a lightweight solution.

[0057] In this embodiment, the weight coefficient of each component's influence on performance parameters is obtained based on the dataset. Then, the thickness of the component is adjusted according to the weight coefficient and the thickness range to obtain a lightweight solution. When designing a car for lightweighting, the influence of the adjustment of different components on the car's performance parameters is considered. The thickness of the component is adjusted according to the magnitude of the influence. This lightweight design method takes into account the car's performance while lightweighting the car, ensuring good car performance.

[0058] Furthermore, this step automatically adjusts the thickness of each component in the basic design based on the dataset to achieve lightweight design and obtain a lightweight solution. The entire process is automated and highly efficient.

[0059] It should be noted that after step S2 is completed and the lightweight solution is obtained, further post-processing can be performed, including calculating the performance parameters of the lightweight solution and storing the lightweight solution and its related data (including the characteristic parameters of the components and the performance parameters of the solution) in the dataset to update the dataset. This enables the representation and storage of historical lightweight design solutions using data, providing more data support for automotive lightweight design.

[0060] Specifically, after determining the lightweight solution, such as Figure 7 As shown, the lightweight scheme processing script is invoked to add boundary conditions, including model constraints, loads, and calculation cards. It automatically generates calculation models for each performance characteristic and submits them for calculation. After calculation, automatic post-processing is performed to retrieve the performance parameter results. Automatic post-processing involves using an automated script to read the analysis results, including stiffness, displacement, and modal parameters. The lightweight scheme and its related data are imported into a centralized dataset, adding a historical lightweight scheme record to the dataset. This lightweight scheme data includes the performance parameters of the body-in-white. Simultaneously, the structural dataset is updated, adding a structural data table.

[0061] Example 2

[0062] The present invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the lightweight body-in-white design method provided in Embodiment 1.

[0063] Example 3

[0064] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the lightweight body-in-white design method provided in Embodiment 1.

[0065] It should be noted that, in addition to the specific embodiments described above, those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Although the description of the present invention is presented in conjunction with preferred embodiments, this does not mean that the features of the invention are limited to these embodiments. On the contrary, the purpose of describing the invention in conjunction with the embodiments is to cover other options or modifications that may be derived based on the claims of the present invention. To provide a deep understanding of the invention, many specific details are included in the above description, and the invention may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of the invention, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0066] It should be noted that similar reference numerals and letters in this specification are similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0067] In the description of this embodiment, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this embodiment based on the specific circumstances.

[0068] While the present invention has been illustrated and described with reference to certain preferred embodiments, those skilled in the art should understand that the above description is a further detailed explanation of the invention in conjunction with specific embodiments, and should not be construed as limiting the specific implementation of the invention to these descriptions. Various changes in form and detail can be made by those skilled in the art, including several simple deductions or substitutions, without departing from the spirit and scope of the invention.

Claims

1. A lightweight design method for a body-in-white, characterized in that, The design method includes: S1: Obtain the finite element model of the body-in-white, identify the finite element model of the body-in-white, and obtain the current basic scheme; the current basic scheme includes multiple components and the feature parameters corresponding to the multiple components; S2: Read the lightweight-related dataset, and adjust the feature parameters of each component of the current basic solution according to the dataset to obtain the lightweight solution; wherein The dataset includes a vehicle body structure dataset, which includes multiple components and corresponding feature parameters for each component.

2. The lightweight design method for body-in-white as described in claim 1, characterized in that, The characteristic parameter includes thickness; step S2 includes: adjusting the thickness of each component of the current basic solution to obtain the lightweight solution.

3. The lightweight design method for body-in-white as described in claim 2, characterized in that, Step S2 includes: Read the vehicle body structure dataset and obtain the minimum or average thickness of each component based on the vehicle body structure dataset; adjust the thickness of each component to the minimum or average thickness.

4. The lightweight design method for body-in-white as described in claim 2, characterized in that, The dataset also includes a scheme dataset, which includes multiple historical lightweighting schemes and a vehicle body structure dataset corresponding to each historical lightweighting scheme; step S2 includes: Read the dataset of the proposed solution and adjust the thickness of each component to the thickness of at least one component in the historical lightweighting solution.

5. The lightweight design method for body-in-white as described in claim 2, characterized in that, The dataset also includes a scheme dataset, which includes: multiple historical lightweighting schemes, a vehicle body structure dataset and performance parameters corresponding to each historical lightweighting scheme, a historical basic scheme corresponding to each historical lightweighting scheme, and a vehicle body structure dataset and performance parameters corresponding to each historical basic scheme; step S2 includes: Read the solution dataset to obtain multiple historical lightweight solutions and corresponding multiple historical basic solutions; Based on the thickness of each component in each of the lightweight solutions and the thickness of each component in the corresponding historical baseline solution, the thickness change rate of each component is obtained; Based on the performance parameters of each of the lightweight solutions and the performance parameters of the corresponding historical baseline solutions, the rate of change of performance parameters is obtained; The weighting coefficients of each component are obtained based on the thickness change rate and the performance parameter change rate of each component. The lightweighting scheme is determined based on the weighting coefficients and thickness range.

6. The lightweight body-in-white design method according to any one of claims 1-5, characterized in that, In step S1, the step of identifying the finite element model of the body-in-white includes: Extract the feature parameters of each component in the finite element model of the body-in-white, including mass, size, and position; The mass of each component is compared with a mass threshold. If the mass of a component is greater than the mass threshold, it is determined to be a component to be optimized. The components to be optimized are classified into symmetrical components and asymmetrical components based on the characteristic parameters of the components to be optimized. The component to be optimized is further identified based on its category and the characteristic parameters.

7. The lightweight design method for body-in-white as described in claim 6, characterized in that, The feature parameters also include thickness, material, correlation, surface area, and volume; the steps for further identification of the component to be optimized include: The name of the component to be optimized is identified based on the classification of the component to be optimized and the feature parameters.

8. The lightweight design method for body-in-white as described in claim 7, characterized in that, The location in the feature parameters includes centroid coordinates and corner coordinates; the step of further identifying the component to be optimized also includes: Multiple similar groups of components to be optimized are selected from the components to be optimized, and the relative positions of each group of components to be optimized are identified based on the centroid coordinates and corner coordinates of the components to be optimized.

9. The lightweight design method for body-in-white as described in claim 6, characterized in that, Step S1 further includes: after obtaining multiple components and the feature parameters corresponding to the multiple components, reading the vehicle body structure dataset, and comparing the feature parameters of each component with the feature parameters of the corresponding component in the vehicle body structure dataset to verify the recognition result.

10. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the lightweight body-in-white design method according to any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lightweight body-in-white design method as described in any one of claims 1 to 9.

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