Method for evaluating wheel design based on model simulation and wheel design evaluation system

Abstract

The present invention discloses a method and a system for evaluating wheel design based on model simulation, belonging to the technical field of wheel design evaluation. In the existing wheel evaluation scheme, the initial wheel structure needs to be obtained first before the wheel can be optimized, which affects the efficiency of wheel design evaluation. A method for evaluating wheel design based on model simulation according to the present invention can directly evaluate the wheel design according to the two-dimensional wheel design image by creating a wheel design acquisition model, a simulated wheel generation model, a numerical simulation model, and a wheel evaluation model. Therefore, the efficiency of wheel design evaluation is effectively improved, and the wheel R & D and design cycle is shortened. Further, by applying the method of the present invention, industrial designers can timely evaluate the engineering performance of the two-dimensional wheel design, so as to directly screen out the optimal conceptual design scheme, and can improve the wheel design scheme according to the evaluation results, thereby effectively improving the wheel design quality and reducing the wheel R & D time.

Description

Technical Field

[0001] The present invention relates to a wheel design evaluation method and a wheel design evaluation system based on model simulation, and belongs to the technical field of wheel design evaluation. Background Art

[0002] Chinese Patent Application (Publication No.: CN117574552A) discloses an optimized integrated design method for wheels, which includes: obtaining an initial wheel structure; according to the initial wheel structure, selecting a first integrated optimization strategy composed of at least one method selected from the group consisting of a topology optimization method, a parametric shape optimization method, a material structure integrated optimization method, etc., performing an optimized integrated conceptual design of the wheel, and obtaining a wheel model that meets the requirements in terms of comprehensive evaluation of weight and reliability; according to the wheel model that meets the requirements in terms of comprehensive evaluation of weight and reliability, selecting a second integrated optimization strategy composed of at least one method selected from the group consisting of a free dimension optimization method, a free shape optimization method, etc., performing an optimized integrated detailed design of the wheel; according to the detailed design, performing rapid load and boundary condition loading based on a grid, and performing wheel styling verification; obtaining a final lightweight wheel structure.

[0003] For the above-mentioned solution, it is necessary to obtain the initial wheel structure first to perform the optimization of the wheel. However, in the existing industrial design mode, what industrial designers usually give is a two-dimensional creative design image of the wheel, resulting in the inability to directly apply the above-mentioned solution for wheel design optimization evaluation, affecting the efficiency of wheel design evaluation, making the wheel R & D design cycle longer, and being unfavorable for the popularization and use of the wheel design evaluation solution.

[0004] The information disclosed in this background art is only used to understand the background of the inventive concept of the present invention, and thus it may include information that does not constitute prior art. Summary of the Invention

[0005] Aiming at the above problems or one of the above problems, the first object of the present invention is to provide a wheel design evaluation method and a wheel design evaluation system based on model simulation. By creating a wheel design acquisition model, a simulation wheel generation model, a numerical simulation model, and a wheel evaluation model, the wheel design can be directly evaluated according to the two-dimensional wheel design image, thus effectively improving the efficiency of wheel design evaluation, shortening the wheel R & D design cycle, and being conducive to the popularization and use of the wheel design evaluation solution.

[0006] Aiming at the above problems or one of the above problems, the second object of the present invention is to provide a wheel design evaluation method and a wheel design evaluation system based on model simulation, which can timely evaluate the engineering performance of the two-dimensional wheel design, so as to directly screen out the optimal conceptual design scheme, and can quickly improve the wheel design scheme according to the evaluation results, thus effectively improving the wheel design quality and reducing the wheel design R & D time.

[0007] To achieve one of the above objects, the first technical solution of the present invention is as follows:

[0008] A method for evaluating wheel design based on model simulation, comprising the following steps:

[0009] Step 1: Through a pre-created wheel design acquisition model, obtain a two-dimensional wheel design image and collect wheel design distribution characteristics;

[0010] Step 2: Use a pre-created simulation wheel generation model, based on an edge detection algorithm, to process the two-dimensional wheel design image and the wheel design distribution characteristics to generate a wheel simulation object;

[0011] Step 3: Use a pre-created numerical simulation model to perform numerical calculations on the wheel simulation object to obtain the mass data and natural frequency data of the wheel simulation object;

[0012] Step 4: Based on a pre-created wheel evaluation model, calculate the wheel stiffness information according to the natural frequency data and the mass data, and judge the wheel design in the two-dimensional wheel design image according to the stiffness information to complete the evaluation of the wheel design based on model simulation.

[0013] By creating a wheel design acquisition model, a simulation wheel generation model, a numerical simulation model, and a wheel evaluation model, the present invention can directly evaluate the wheel design according to the two-dimensional wheel design image, thus effectively improving the efficiency of wheel design evaluation, shortening the wheel R & D design cycle, and facilitating the popularization and use of the wheel design evaluation scheme.

[0014] Furthermore, when applying the method of the present invention, compared with the prior art, industrial designers can timely evaluate the engineering performance of the two-dimensional wheel design, so as to directly screen out the optimal conceptual design scheme, and can quickly improve the wheel design scheme according to the evaluation results, thus effectively improving the wheel design quality and reducing the wheel design R & D time.

[0015] As a preferred technical measure:

[0016] Step 1: The method for obtaining one or more two-dimensional wheel design images and collecting wheel design distribution characteristics through a pre-created wheel design acquisition model is as follows:

[0017] Use a deep learning algorithm to construct a generative adversarial network for wheel design;

[0018] Use the generative adversarial network to learn the wheel characteristic parameters to generate one or more two-dimensional wheel design images;

[0019] Based on the wheel material distribution characteristics, perform topology optimization on the two-dimensional wheel design image to obtain a new two-dimensional wheel design image;

[0020] Collect the features of the new two-dimensional wheel design image to obtain the wheel design distribution features.

[0021] As a preferred technical measure:

[0022] Step 2: Use the pre-created simulation wheel generation model. Based on the edge detection algorithm, process the two-dimensional wheel design image and the wheel design distribution features. The method for generating the wheel simulation object is as follows:

[0023] Use the edge detection algorithm to capture the smooth and clear boundaries in the two-dimensional wheel design image to obtain the wheel edge coordinate point data;

[0024] Based on the wheel design distribution features, extract the spoke cross-section and the rim cross-section;

[0025] According to the range distribution of the spoke cross-section and the rim cross-section, sort and group the wheel edge coordinate point data to obtain the edge coordinate array;

[0026] The edge coordinate array includes the spoke cross-section coordinates and the rim cross-section coordinates;

[0027] According to the spoke cross-section coordinates and using the spline curve algorithm, draw the cross-section of the spoke, and then rotate the cross-section of the spoke to create a spoke body;

[0028] According to the rim cross-section coordinates and using the spline curve algorithm, draw the cross-section of the rim, and then rotate the cross-section of the rim to create a rim body;

[0029] Combine the rim body and the spoke body together to generate the wheel simulation object.

[0030] As a preferred technical measure:

[0031] The method for using the edge detection algorithm to capture the smooth and clear boundaries in the two-dimensional wheel design image to obtain the wheel edge coordinate point data is as follows:

[0032] First, through the image processing algorithm for the two-dimensional wheel design image, obtain the maximum and minimum values of the abscissa and ordinate of the wheel outer edge to obtain four extreme coordinates;

[0033] The four extreme coordinates include the first minimum and the first maximum of the abscissa, and the second minimum and the second maximum of the ordinate;

[0034] Based on the four extreme coordinates, in the two-dimensional wheel design image, determine the cropping area; the cropping area is a rectangle, the upper left corner coordinates of which are the first minimum and the second minimum, and the lower right corner coordinates of which are the first maximum and the second maximum;

[0035] Crop the two-dimensional wheel design image according to the cropping area, and only retain the image content within this cropping area to obtain a wheel image without blank margins;

[0036] Use an edge detection algorithm to identify the boundaries of the wheel image without blank margins according to the brightness change rate, and obtain the gradient information of all pixels;

[0037] Judge the gradient information according to the preset gradient threshold, and screen out the pixel points that constitute the boundary to capture the smooth and clear boundary in the two-dimensional wheel design image;

[0038] Extract the coordinates of the pixel points that constitute the boundary as the wheel edge coordinate point data.

[0039] As a preferred technical measure:

[0040] The method of using an edge detection algorithm to identify the boundaries of the wheel image without blank margins according to the brightness change rate is as follows:

[0041] Set a binary image and a marking mechanism according to the edge detection algorithm;

[0042] The marking mechanism is that edge pixels are marked as white or 1, and non-edge pixels are marked as black or 0;

[0043] Based on the brightness change rate, set two circular masks, including a rim mask and a hub mask;

[0044] The radius of the rim mask is smaller than the outer diameter of the rim, and the radius of the hub mask is larger than the inner diameter of the hub;

[0045] Assign values to the binary image according to the rim mask and the hub mask, which specifically includes the following content:

[0046] Set the pixel values outside the rim mask area to 0, that is, black, to remove the edge of the rim;

[0047] Set the pixel values inside the hub mask area to 0, that is, white, to remove the edge of the hub, so that only the edge of the spoke area remains in the wheel image, thereby completing the boundary recognition of the wheel image.

[0048] As a preferred technical measure:

[0049] The method of combining the rim body and the spoke body to generate a wheel simulation object is as follows:

[0050] Create a reference section passing through the center of the wheel and parallel to the axis of the wheel;

[0051] Use the reference section to intercept the rim body and the spoke body to obtain a wheel body section;

[0052] Extract the sectional curve of the wheel body section, obtain the contour lines of the spokes and the rim, and convert the sectional curve into coordinate data to obtain the cross-sectional coordinate data of the spokes and the cross-sectional coordinate data of the rim;

[0053] Screen the head and tail coordinate points from the cross-sectional coordinate data of the spokes and the cross-sectional coordinate data of the rim, and connect the head and tail coordinate points to form a closed contour line;

[0054] Rotate the contour line to form an original vehicle body;

[0055] Draw the edge shape of the spokes according to the cross-sectional coordinate data of the spokes;

[0056] Create a spoke-shaped object based on the edge shape of the spokes;

[0057] Cut the original vehicle body according to the spoke-shaped object to obtain a vehicle body with the shape of the spokes, that is, the wheel simulation object.

[0058] As a preferred technical measure:

[0059] Step 3, use the previously created numerical simulation model to perform numerical calculations on the wheel simulation object to obtain the mass data and natural frequency data of the wheel simulation object. The method is as follows:

[0060] Set the material property data and mesh density according to the wheel simulation object;

[0061] The material property data includes wheel material characteristics, Young's modulus, Poisson's ratio, density, and shear modulus;

[0062] Mesh the wheel simulation object according to the mesh density to obtain a second-order tetrahedral mesh;

[0063] Process the second-order tetrahedral mesh based on the material property data to obtain the mass data;

[0064] And combine the mass data to perform modal analysis on the wheel simulation object to obtain the natural frequency of the lateral mode.

[0065] As a preferred technical measure:

[0066] Step 4, based on the previously created wheel evaluation model, calculate the wheel stiffness information according to the natural frequency data and the mass data, and judge the wheel design in the two-dimensional wheel design image according to the stiffness information. The method is as follows:

[0067] Set the stiffness constraint conditions according to the correlation between the wheel stiffness and the road noise and the preset stiffness requirement standard;

[0068] Calculate the stiffness information of several wheel simulation objects according to the natural frequency data and the mass data;

[0069] According to the stiffness constraint conditions, each stiffness information is judged to obtain the stiffness information that meets the stiffness requirements;

[0070] The stiffness information that meets the stiffness requirements is corresponded with the two-dimensional wheel design image to obtain the two-dimensional wheel design image that meets the design requirements, so as to screen out one or more wheel designs that meet the requirements.

[0071] To achieve one of the above purposes, the second technical solution of the present invention is:

[0072] A method for evaluating wheel design based on model simulation, including the following:

[0073] Obtain the wheel design distribution characteristics;

[0074] Based on the wheel design distribution characteristics, generate a number of two-dimensional wheel design images to form diverse wheel designs;

[0075] Use the edge detection algorithm to process a number of two-dimensional wheel design images to generate a number of wheel simulation objects;

[0076] Perform numerical simulation calculations on each wheel simulation object to obtain mass data and natural frequency data;

[0077] According to the natural frequency data and mass data, calculate a number of wheel stiffness information;

[0078] According to the stiffness information, screen out one or more wheel designs that meet the stiffness requirements.

[0079] To achieve one of the above purposes, the third technical solution of the present invention is:

[0080] A system for evaluating wheel design based on model simulation, which includes:

[0081] One or more processors;

[0082] A storage device for storing one or more programs;

[0083] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method for evaluating wheel design based on model simulation.

[0084] Compared with the prior art solutions, the present invention has the following beneficial effects:

[0085] By creating a wheel design acquisition model, a simulation wheel generation model, a numerical simulation model, and a wheel evaluation model, the present invention can directly evaluate the wheel design based on a two-dimensional wheel design image, thus effectively improving the efficiency of wheel design evaluation, shortening the wheel R & D design cycle, and facilitating the popularization and use of the wheel design evaluation scheme.

[0086] Furthermore, by applying the method of the present invention, compared with the prior art, industrial designers can timely evaluate the engineering performance of a two-dimensional wheel design, thereby directly screening out the optimal conceptual design scheme, and can quickly improve the wheel design scheme according to the evaluation results, thus effectively improving the wheel design quality and reducing the wheel design R & D time. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 is a schematic flowchart of a wheel design evaluation method of the present invention;

[0088] Figure 2 is a two-dimensional wheel design image in an embodiment of the present invention;

[0089] Figure 3 are seven two-dimensional wheel design images in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0090] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0091] On the contrary, the present invention covers any alternatives, modifications, equivalent methods and solutions made within the spirit and scope of the present invention defined by the claims. Further, in order to enable the public to better understand the present invention, some specific details are described in detail in the following detailed description of the present invention. Those skilled in the art can fully understand the present invention without the description of these details.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0093] As Figure 1 shown, the first specific embodiment of the wheel design evaluation method based on model simulation of the present invention:

[0094] A wheel design evaluation method based on model simulation includes the following steps:

[0095] Step 1: Obtain a two-dimensional wheel design image through a pre-created wheel design acquisition model, and collect the wheel design distribution characteristics;

[0096] Step 2: Use a pre-created simulation wheel generation model to process the two-dimensional wheel design image and the wheel design distribution characteristics based on an edge detection algorithm to generate a wheel simulation object;

[0097] Step 3: Use a pre-created numerical simulation model to perform numerical calculations on the wheel simulation object to obtain the mass data and natural frequency data of the wheel simulation object;

[0098] Step 4: Based on a pre-created wheel evaluation model, calculate the wheel stiffness information according to the natural frequency data and the mass data, and judge the wheel design in the two-dimensional wheel design image according to the stiffness information to complete the wheel design evaluation based on model simulation.

[0099] The second specific embodiment of the wheel design evaluation method based on model simulation of the present invention:

[0100] A wheel design evaluation method based on model simulation, including the following:

[0101] Obtain the wheel design distribution characteristics;

[0102] Based on the wheel design distribution characteristics, generate a plurality of two-dimensional wheel design images to form a diversified wheel design;

[0103] Use an edge detection algorithm to process a plurality of two-dimensional wheel design images to generate a plurality of wheel simulation objects;

[0104] Perform numerical simulation calculations on each wheel simulation object to obtain the mass data and natural frequency data;

[0105] Calculate a plurality of wheel stiffness information according to the natural frequency data and the mass data;

[0106] According to the stiffness information, screen out one or more wheel designs that meet the stiffness requirements.

[0107] The third specific embodiment of the wheel design evaluation method based on model simulation of the present invention:

[0108] A wheel design evaluation method based on model simulation, combining technologies such as deep learning algorithms, computer-aided design (abbreviation: CAD), and computer-aided engineering (abbreviation: CAE), and its model training process is divided into the following four stages:

[0109] In the first stage, a deep generative model is used to generate various two-dimensional wheel designs to produce diverse two-dimensional wheel design images.

[0110] In the second stage, a simulation wheel generation model is constructed, which generates a wheel simulation object based on the two-dimensional wheel design image.

[0111] In the third stage, a numerical simulation model is constructed, and numerical simulation calculations are performed using the wheel simulation object generated in the second stage to obtain the mass of the wheel simulation object and collect the natural frequency of the wheel.

[0112] In the fourth stage, the two-dimensional wheel design and its mass and natural frequency are used as labels to train a Convolutional Neural Network (CNN), so that the trained convolutional neural network can take the two-dimensional wheel design image as input and output the predicted natural frequency and mass, thereby evaluating the engineering performance of the conceptual wheel design image.

[0113] In this embodiment, in the first stage, the method for generating the two-dimensional design is as follows:

[0114] To generate various two-dimensional wheel designs, the present invention adopts a deep generative design method, which combines a generative adversarial network with topology optimization. Topology optimization is used to optimize the material distribution to ensure the optimal engineering performance of the design, while the generative adversarial network generates new designs with more diversity and aesthetic features by learning the distribution of existing designs. The core of the combination is that the generated design is first generated by the generative adversarial network and then further optimized for its engineering performance through topology optimization. This iterative process ensures that the generated design is both innovative and able to meet actual engineering requirements.

[0115] The generative adversarial network includes a generator and a discriminator. The generator generates wheel design data according to the wheel distribution characteristics. The discriminator couples the wheel design data with the existing wheel design data to obtain a number of wheel design images.

[0116] In this embodiment, in the second stage, the method for generating the wheel simulation object is as follows:

[0117] In the first step, an edge detection algorithm is used to capture smooth and clear boundaries in the two-dimensional wheel design image to obtain the spoke edge coordinate points.

[0118] In the second step, the spoke edge coordinate points obtained in the first step are sorted and grouped to form a spline curve of the wheel.

[0119] In the third step, according to the spline curve of the wheel, the automated generation of the wheel simulation object is realized.

[0120] In this example, the method for obtaining the spoke edge coordinate points is as follows:

[0121] Since the wheel images generated in the first stage have blank margins at the edges, in order to obtain a more compact image that only contains the wheel, these blank margins need to be removed. First, the maximum and minimum values of the coordinates and coordinates of the outer edge of the wheel are obtained through image processing techniques.

[0122] Next, using the minimum value Xmin and maximum value Xmax of the obtained x - coordinates, and the minimum value Ymin and maximum value Ymax of the y - coordinates, a cropping region is defined in the original image. This cropping region is a rectangle, and its boundaries are determined by these four coordinate values, that is, the upper - left corner coordinates of the rectangle are (Xmin, Ymin), and the lower - right corner coordinates are (Xmax, Ymax).

[0123] Finally, the original image is cropped, only the image content within this cropping region is retained, and the image is scaled to pixel size. In this way, a wheel image without blank margins can be obtained, which only contains the wheel itself. The overall process is as follows:

[0124] In a two - dimensional wheel design image, the pixels with a large rate of change in brightness are the edges of the image, and the rate of change in its brightness is defined as the gradient in mathematics. To identify these boundaries, first calculate the gradient of each pixel. The gradient calculation depends on the rate of change in brightness of each pixel point in the horizontal and vertical directions of the image, and can be approximately realized by calculating the first - order derivatives in these two directions. The formula for the gradient is as follows:

[0125]

[0126]

[0127]

[0128] where, is the rate of change in brightness of the pixel point in the horizontal direction in the wheel design image, is the rate of change in brightness of the pixel point in the vertical direction in the wheel design image, is the gray - scale value of any pixel point in the wheel design image, and Similarly, and represent the abscissa and ordinate of the pixel point in the wheel design image respectively.

[0129] After obtaining the gradient information of the image, it is possible to determine which pixel points form the boundary according to a preset gradient threshold. The choice of the threshold determines the sensitivity and accuracy of edge detection, and varies due to various factors such as the specific content of the image, lighting conditions, noise level, etc. In this application, the gradient threshold is set to 100.

[0130] In this embodiment, an edge detection algorithm is used to extract the edges of the wheel image to generate a binary image, where the edge pixels are marked as white or 1, and the non-edge pixels are marked as black or 0. To remove the edges of the rim and hub, two circular masks are defined: one for the rim and the other for the hub. The radius of the rim mask should be slightly smaller than the outer diameter of the rim, and the radius of the hub mask should be slightly larger than the inner diameter of the hub.

[0131] Apply these two circular masks to the binary image after edge detection. For the rim mask, set the pixel values outside the mask area to 0 (i.e., black) to remove the edges of the rim, as shown in Figure 2 . For the hub mask, set the pixel values inside the mask area to 0 to remove the edges of the hub.

[0132] After the above processing, only the edges of the spoke area remain in the image; finally, traverse the processed binary image, extract the coordinates of all white pixels (i.e., spoke edges), and save these coordinates to a plain text file to obtain the spoke edge coordinate points. Each coordinate point can be represented as form.

[0133] In this embodiment, the edge detection algorithm is an edge detection method with an operator Sobel, which detects the edges in the wheel design image by calculating the gradient values in the horizontal and vertical directions of the wheel design image.

[0134] In this embodiment, the spoke edge coordinate points obtained in the first step are sorted and grouped, and its algorithm flow is as follows:

[0135] Step 21, store all coordinate points in array A;

[0136] Take the first coordinate (X0, Y0) from array A as the initial value and delete this point from array A;

[0137] Create an array B to store the grouped points, and store the initial value coordinate (X0, Y0) in the i-th group of array B, where i is zero at this time.

[0138] Step 22, perform iterative loop calculation until array A is empty, and the process is as follows:

[0139] Declare the initial value as a fixed point;

[0140] Traverse each point in array A and use the Euclidean distance formula to calculate the distance between the current point and the fixed point. For two points on a two-dimensional plane and , the distance between them can be calculated by the following formula:

[0141]

[0142] Find the point closest to the fixed point, declare it as the new initial value, and remove it from array A;

[0143] Calculate the distance between the fixed point and the initial value (i.e., the closest point):

[0144] If this distance is less than or equal to the threshold (set to 5 pixels here), store the initial value in the i-th group of array B.

[0145] Otherwise, create a new group (group i + 1) and store the initial value in group i + 1.

[0146] Since each new initial value is removed and assigned to the corresponding group, no point will belong to multiple groups simultaneously. Through the above steps, the spoke edge coordinate points can be effectively sorted and grouped.

[0147] In this example, in the third step, to generate a specific wheel simulation object, its algorithm flow is as follows:

[0148] Step 31, Select a wheel with a model number of 18 inches and obtain the wheel simulation object of this wheel.

[0149] Step 32, Create a cross-section plane that intersects the wheel simulation object, and its cross-section passes through the center of the wheel and is parallel to the axis of the wheel.

[0150] Step 33, Intersect the cross-section plane with the wheel simulation object to intercept the cross-section geometries of the spokes and the rim.

[0151] Step 34, Obtain the cross-section curves (the contour lines of the spokes and the rim) and convert the cross-section curves into coordinate point form to obtain the cross-section information.

[0152] Perform 3D CAD modeling according to the spoke shape in the disc view and the cross-section information;

[0153] Step 35, Load the cross-section coordinate data of the spokes and the rim and draw the cross-section, which includes the following content:

[0154] Use the spline algorithm to connect the cross-section coordinate points of the spokes and the rim end to end to form a closed contour line.

[0155] Rotate the contour line and the cross-section by 180 degrees to form an original vehicle body.

[0156] Step 36: Load the coordinate data of the spoke edge and draw the spoke shape, which includes the following:

[0157] Use the spline algorithm to connect the points of each group head to tail to draw the spoke edge shape;

[0158] Then extrude the spoke edge shape to create a spoke-shaped object. Set the extrusion direction to the vehicle body direction. Designate the spoke-shaped object as the tool body and the spoke body as the target body. Subsequently, cut the original vehicle body to remove the intersecting part of the two objects, obtaining a vehicle body with a spoke shape, which is the wheel simulation object.

[0159] In this example, in the third stage, the method of performing numerical simulation calculations using the wheel simulation object generated in the second stage is as follows:

[0160] Step 301: Use the meshing algorithm to process the wheel simulation object to generate a finite element mesh, namely a second-order tetrahedral mesh. Set the maximum size of the mesh to 6 mm. At the same time, set the material properties as follows: The wheel manufacturing material is an aluminum wheel, with a Young's modulus of 73500, a Poisson's ratio of 0.33, a density of , and a shear modulus of 0.001.

[0161] Step 302: Calculate the mass of the wheel simulation object based on the second-order tetrahedral mesh .

[0162] Step 303: Perform modal analysis using the free modal algorithm to obtain the results of the modal analysis and the natural frequency of the lateral mode .

[0163] Step 304: The stiffness of the wheel refers to the ability of the wheel to resist deformation when subjected to an external force. Since the natural frequency is proportional to the stiffness and inversely proportional to the mass, the stiffness of the wheel can be obtained using the following formula with the mass and natural frequency obtained in the above steps:

[0164]

[0165] Among them, is the natural frequency, is the stiffness to be solved, is the mass.

[0166] Step 305: The shape of the wheel spokes has a direct and significant impact on the overall engineering performance of the wheel, and this correlation can be quantitatively evaluated through stiffness analysis. The stiffness of the wheel is a key indicator to measure its ability to resist deformation under external forces, which is directly related to the driving stability and safety of the vehicle. Specifically, if the wheel stiffness is too high, excessive vibrations may occur during driving, affecting the riding comfort of passengers; on the contrary, if the stiffness is insufficient, the wheel is prone to excessive deformation, thus threatening driving safety. Therefore, the design of the wheel spoke shape is crucial for ensuring that the wheel has appropriate stiffness.

[0167] At the same time, the stiffness standards of different manufacturers involve multiple factors and are constantly changing. Therefore, the specific threshold of stiffness should be selected according to the actual situation.

[0168] In this embodiment, in the fourth stage, the method of training the convolutional neural network with the two-dimensional wheel design and its mass and natural frequency as tags is as follows:

[0169] Construct a convolutional neural network and store the two-dimensional wheel design images, natural frequencies, and masses in pairs as the training data for deep learning.

[0170] Rotate each wheel design image generated in the first stage by 72 degrees, 144 degrees, 216 degrees, and 288 degrees and flip it left and right for 10 times for augmentation. The augmented samples are used for training to avoid overfitting. At this time, the natural frequency and mass remain unchanged because rotation and left-right flipping do not affect the modal analysis and mass results.

[0171] The convolutional neural network model of this embodiment can quickly predict the natural frequency and mass of the wheel only based on the two-dimensional design drawing, and then calculate the wheel stiffness through relevant equations. During the design process, the manufacturer can set the lower limit of the stiffness of each mode as a design constraint according to the correlation between the stiffness and road noise in each mode, and use a computer equipped with a high-performance accelerator for evaluation and calculation. Through experiments, it is known that it takes only 0.66 seconds on average to evaluate and predict a wheel design image, significantly shortening the iteration cycle in traditional industrial design.

[0172] Therefore, the evaluation method of the present invention enables automobile manufacturers to quickly evaluate the stiffness of the wheels, and quickly screen and eliminate designs that do not meet the requirements according to the manufacturer's own stiffness standards. At the same time, it can efficiently select a candidate solution for the subsequent detailed design stage, thus ensuring that the design process is both efficient and meets the manufacturer's requirements.

[0173] Furthermore, with the present invention, a large amount of three-dimensional wheel design data can be automatically generated, and its engineering performance can be evaluated in a timely manner. In the conceptual design stage, industrial designers and engineers can utilize the engineering performance results of the present invention to obtain numerous three-dimensional wheel simulation objects and discuss the conceptual design candidate solutions applicable to the detailed design stage. The deep learning model proposed by the present invention can predict the CAE results of two-dimensional wheel designs, enabling industrial designers to promptly understand the engineering performance of two-dimensional conceptual sketches.

[0174] A specific embodiment of applying the present invention to evaluate 7 wheel design drawings:

[0175] Applying the wheel design evaluation method based on model simulation of the present invention to evaluate 7 wheel design images, which includes the following:

[0176] First, select 7 wheel design images, as shown in Figure 3 , and then conduct simulation evaluation. The evaluation results are as follows:

[0177] The true mass values are respectively: 9.96; 10.71; 11.80; 10.83; 10.40; 10.05; 11.40;

[0178] The true frequency values are respectively: 814.2; 846.4; 849.6; 803.2; 795.4; 779.1; 801.6.

[0179] Meanwhile, manually evaluate the engineering performance of the above 7 wheel design images. The manual evaluation results are as follows:

[0180] The mass predictions are respectively: 9.90; 10.32; 11.43; 10.78; 10.20; 9.83; 11.09;

[0181] The frequency predictions are respectively: 806.5; 837.5; 824.0; 794.8; 785.2; 782.2; 796.0.

[0182] Therefore, it can be seen that the mass prediction error of the present invention is within 3%, and the frequency prediction error is within 1.5%, indicating that the solution of the present invention is practical and has high accuracy.

[0183] Furthermore, through the above mass and frequency evaluations, the stiffness of the wheel can be calculated. Therefore, in the design process, applying the present invention can accurately and timely feedback the engineering performance of the two-dimensional conceptual sketch of the wheel. At the same time, the manufacturer can set the lower limit of the modal stiffness as a design constraint condition to quickly screen out the wheels that do not meet the requirements.

[0184] An equipment embodiment of applying the method of the present invention:

[0185] An electronic device, comprising:

[0186] One or more processors;

[0187] A storage device for storing one or more programs;

[0188] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the above-mentioned method for evaluating wheel design based on model simulation.

[0189] An embodiment of a computer medium applying the method of the present invention:

[0190] A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-mentioned method for evaluating wheel design based on model simulation.

[0191] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0192] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or / and one or more of the blocks

[0193] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or / and one or more of the blocks

[0194] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the process Figure 1 one process or multiple processes or / and boxes Figure 1 steps for the functions specified in one box or multiple boxes.

[0195] The model in this application is an object that constitutes an objective description of the morphological structure with the help of an entity or a virtual representation. The object is not equal to an object and is not limited to entities and virtuals. It can be a data processing function, a software program, a processing mode, a usage method, an operation mode, a workflow, an application process, an electronic hardware, a circuit module, a processing system, a system imitation, or a simulation object.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art can still modify or equivalently replace the specific implementation manners of the present invention. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. A wheel design evaluation method based on model simulation, characterized in that: The following steps are involved: Step 1: Obtain a two-dimensional wheel design image and collect wheel design distribution features through a wheel design acquisition model created in advance; Step 2: Using the previously created simulated wheel generation model, based on the edge detection algorithm, the two-dimensional wheel design image and the wheel design distribution characteristics are processed to generate a wheel simulation object; the method is as follows: Use edge detection algorithm to capture smooth and clear boundaries in the two-dimensional wheel design image and obtain wheel edge coordinate point data; Extract spoke and rim sections based on wheel design distribution features; According to the range distribution of the spoke cross section and the rim cross section, the wheel edge coordinate point data are sorted and grouped to obtain an edge coordinate array; The edge coordinate array includes the spoke cross-section coordinates and the rim cross-section coordinates; According to the spoke cross-section coordinates and using the spline curve algorithm, draw the cross-section of the spoke, and then rotate the cross-section of the spoke to create a spoke body; According to the rim cross-section coordinates and using a spline curve algorithm, the cross-section of the rim is drawn, and then the cross-section of the rim is rotated to create a rim body; Combine the rim body and the spoke body together to generate a wheel simulation object; Step 3, using the numerical simulation model created in advance, numerically calculate the wheel simulation object to obtain mass data and natural frequency data of the wheel simulation object; Step 4: Based on the wheel evaluation model created in advance, the wheel stiffness information is calculated according to the natural frequency data and mass data, and the wheel design in the two-dimensional wheel design image is judged based on the stiffness information to complete the wheel design evaluation based on model simulation.

2. A wheel design evaluation method based on model simulation as claimed in claim 1, characterized in that: Step 1: Using the wheel design acquisition model created in advance, one or more two-dimensional wheel design images are acquired, and the method for acquiring wheel design distribution features is as follows: Use deep learning algorithms to build a generative adversarial network for wheel design; Using a generative adversarial network, the wheel characteristic parameters are learned to generate one or more two-dimensional wheel design images; Based on the distribution characteristics of wheel materials, topology optimization is performed on the two-dimensional wheel design image to obtain a new two-dimensional wheel design image; Feature collection is performed on the new two-dimensional wheel design image to obtain the wheel design distribution characteristics.

3. A wheel design evaluation method based on model simulation as claimed in claim 1, characterized in that: The method of using edge detection algorithm to capture smooth and clear boundaries in the two-dimensional wheel design image and obtain wheel edge coordinate point data is as follows: Firstly, the image processing algorithm is used to process the two-dimensional wheel design image, and the maximum and minimum values ​​of the horizontal and vertical coordinates of the outer edge of the wheel are obtained to obtain four extreme value coordinates; The four extreme coordinates include a first minimum value and a first maximum value of the abscissa, and a second minimum value and a second maximum value of the ordinate; Based on the four extreme value coordinates, a cropping area is determined in the two-dimensional wheel design image; the cropping area is a rectangle, the coordinates of the upper left corner are the first minimum value and the second minimum value, and the coordinates of the lower right corner are the first maximum value and the second maximum value; The two-dimensional wheel design image is cropped according to the cropping area, and only the image content within the cropping area is retained to obtain a wheel image without blank margins; Using edge detection algorithm, the boundary of wheel image without blank margin is identified according to the brightness change rate, and the gradient information of all pixels is obtained; According to the preset gradient threshold, the gradient information is judged and the pixel points constituting the boundary are screened out to capture the smooth and clear boundary in the two-dimensional wheel design image; The coordinates of the pixel points constituting the boundary are extracted as wheel edge coordinate point data.

4. A wheel design evaluation method based on model simulation as claimed in claim 3, characterized in that: Using the edge detection algorithm, the method for identifying the boundary of the wheel image without blank margins according to the brightness change rate is as follows: According to the edge detection algorithm, set up a binary image and marking mechanism; The marking mechanism is that edge pixels are marked as white or 1, and non-edge pixels are marked as black or 0; Based on the brightness change rate, two circular masks are set, which include a rim mask and a hub mask; The radius of the rim mask is smaller than the outer diameter of the rim, and the radius of the hub mask is larger than the inner diameter of the hub; According to the rim mask and the hub mask, the binary image is assigned a value, which specifically includes the following contents: Set the pixel values ​​outside the rim mask area to 0, i.e. black, to remove the edge of the rim; The pixel values ​​in the hub mask area are set to 0, i.e. white, to remove the edge of the hub, so that only the edge of the spoke area remains in the wheel image, thereby completing the boundary recognition of the wheel image.

5. A wheel design evaluation method based on model simulation as claimed in claim 1, characterized in that: The method of combining the rim body and the spoke body to generate a wheel simulation object is as follows: Create a reference section passing through the wheel center and parallel to the wheel axis; Using the reference section, cut the rim body and the spoke body to obtain the wheel body section; Extracting the cross-sectional curve of the wheel body cross section, obtaining the contour lines of the spoke and the rim, and converting the cross-sectional curve into coordinate data to obtain the cross-sectional coordinate data of the spoke and the cross-sectional coordinate data of the rim; Selecting the first and last coordinate points from the cross-sectional coordinate data of the spoke and the cross-sectional coordinate data of the rim, and connecting the first and last coordinate points end to end to form a closed contour line; Rotate the outline to form an original car body; Draw the spoke edge shape based on the cross-sectional coordinate data of the spoke; Create spoke-shaped objects based on the spoke edge shape; According to the spoke-shaped object, the original vehicle body is cut to obtain a vehicle body with a spoke shape, that is, a wheel simulation object.

6. A wheel design evaluation method based on model simulation as claimed in claim 1, characterized in that: Step 3: Use the numerical simulation model created in advance to perform numerical calculations on the wheel simulation object to obtain the mass data and natural frequency data of the wheel simulation object as follows: According to the wheel simulation object, set the material property data and mesh density; Material property data include wheel material characteristics, Young’s modulus, Poisson’s ratio, density, and shear modulus; According to the mesh density, the wheel simulation object is meshed to obtain a second-order tetrahedral mesh; Based on the material property data, the second-order tetrahedral mesh is processed to obtain quality data; Combined with the mass data, modal analysis is performed on the wheel simulation object to obtain the natural frequency of the lateral mode.

7. A wheel design evaluation method based on model simulation as claimed in claim 1, characterized in that: Step 4: Based on the wheel evaluation model created in advance, the wheel stiffness information is calculated according to the natural frequency data and mass data, and the wheel design in the two-dimensional wheel design image is judged according to the stiffness information as follows: According to the correlation between wheel stiffness and road noise and the preset stiffness requirement standard, stiffness constraint conditions are set; According to the natural frequency data and mass data, the stiffness information of several wheel simulation objects is calculated; According to the stiffness constraint conditions, each stiffness information is judged to obtain stiffness information that meets the stiffness requirements; The stiffness information that meets the stiffness requirements is matched with the two-dimensional wheel design image to obtain the two-dimensional wheel design image that meets the design requirements, thereby screening out one or more wheel designs that meet the requirements.

8. A wheel design evaluation method based on model simulation, characterized in that: Includes the following: Obtain wheel design distribution characteristics; Based on the wheel design distribution characteristics, several two-dimensional wheel design images are generated to form diversified wheel designs; Using edge detection algorithm, several two-dimensional wheel design images are processed to generate several wheel simulation objects; the method is as follows: Use edge detection algorithm to capture smooth and clear boundaries in the two-dimensional wheel design image and obtain wheel edge coordinate point data; Extract spoke and rim sections based on wheel design distribution features; According to the range distribution of the spoke cross section and the rim cross section, the wheel edge coordinate point data are sorted and grouped to obtain an edge coordinate array; The edge coordinate array includes the spoke cross-section coordinates and the rim cross-section coordinates; According to the spoke cross-section coordinates and using the spline curve algorithm, draw the cross-section of the spoke, and then rotate the cross-section of the spoke to create a spoke body; According to the rim cross-section coordinates and using a spline curve algorithm, the cross-section of the rim is drawn, and then the cross-section of the rim is rotated to create a rim body; Combine the rim body and the spoke body together to generate a wheel simulation object; Perform numerical simulation calculations on each wheel simulation object to obtain mass data and natural frequency data; According to the natural frequency data and mass data, several wheel stiffness information is calculated; One or more wheel designs meeting the stiffness requirement are screened out based on the stiffness information.

9. A wheel design evaluation system based on model simulation, characterized in that: It includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a wheel design evaluation method based on model simulation as described in any one of claims 1-8.

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