A multi-technology fusion wheel NVH performance optimization method

Abstract

The application provides a wheel hub NVH performance optimization method fusing multiple technologies, and relates to the technical field of automobile part structure design control. An initial wheel hub three-dimensional model is acquired, an intrinsic structure description model and a design variable set are constructed through structure feature analysis, a geometric deformation control body of an optimization region is constructed based on the intrinsic structure description model, a deformation model is generated by driving the control body displacement adjustment through the design variable, the deformation model is input into a pre-trained performance prediction model to obtain an NVH performance prediction result, the design variable is iteratively adjusted by an optimization algorithm combined with a preset constraint, and a target wheel hub three-dimensional model is output after a convergence condition is met. A closed-loop optimization process is formed, the wheel hub NVH performance optimization and iterative update can be efficiently completed under the premise that the topological structure is unchanged, the research and development cycle is shortened, and the design cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of automotive component structural design and control technology, and in particular to a wheel hub NVH performance optimization method that integrates multiple technologies. Background Technology

[0002] As the automotive industry continues to demand higher levels of driving comfort, lightweighting, and structural reliability, the wheel hub, as a key rotating component that bears and transmits alternating loads, directly determines the overall vehicle vibration and noise level and driving quality through its NVH (noise, vibration, and harshness) performance.

[0003] Currently, wheel hub NVH performance optimization generally adopts an iterative development model combining engineering experience with finite element simulation. Designers manually adjust local geometric parameters, and then verify performance indicators through numerical simulations such as modal analysis and frequency response analysis. This model has significant technical drawbacks: the adjustable range of geometric configuration is constrained by the predetermined topology, resulting in insufficient continuity and controllability of morphological changes; the design adjustment and performance analysis stages are disconnected, failing to form an efficient closed-loop optimization mechanism; in scenarios where multiple NVH indicators are coupled with constraints such as strength and mass, frequent high-computational-cost simulation verifications are required, leading to lengthy optimization iteration cycles, high design trial-and-error costs, and difficulty in obtaining the optimal configuration with multi-constraint coordination within a limited development cycle, thus restricting the efficiency and automation level of wheel hub NVH optimization design.

[0004] Therefore, it is necessary to propose a wheel hub NVH performance optimization method that integrates multiple technologies to solve or at least alleviate the above-mentioned defects. Summary of the Invention

[0005] The main objective of this invention is to provide a wheel hub NVH performance optimization method that integrates multiple technologies, so as to solve the problem that it is difficult to obtain the optimal configuration with multiple constraints within a limited development cycle in the existing technology.

[0006] To achieve the above objectives, this invention provides a wheel hub NVH performance optimization method integrating multiple technologies, comprising the following steps: S1. Obtain an initial three-dimensional model of the wheel hub, perform structural feature analysis on the initial three-dimensional model of the wheel hub, extract the geometric features of the initial three-dimensional model of the wheel hub, and construct an intrinsic structural description model and a set of design variables based on the geometric features; S2, construct the geometric deformation control body of the region to be optimized based on the intrinsic structure description model, and extract the design variables associated with the geometric deformation control body from the set of design variables; S3, Apply displacement adjustment to the geometric deformation control body according to the design variables to generate the deformed three-dimensional model of the wheel hub; S4, input the deformed three-dimensional model of the wheel hub into the pre-trained performance prediction model to obtain the NVH performance prediction result of the wheel hub; wherein, the NVH performance prediction result of the wheel hub includes one of the modal frequency, vibration response amplitude, and sound pressure level; S5, based on the predicted NVH performance of the wheel hub and combined with the preset constraints, the design variables are adjusted using an optimization algorithm, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables; S6, in response to the current meeting of the iteration convergence condition, output the current deformed wheel hub 3D model as the target wheel hub 3D model; wherein, the iteration convergence condition is that the current deformed wheel hub 3D model meets the preset performance or the current iteration optimization number reaches the preset iteration number threshold.

[0007] Preferably, step S1 specifically includes the following steps: S11, Obtain the initial three-dimensional model of the wheel hub; wherein, the initial three-dimensional model of the wheel hub includes the spoke structure, the rim structure, and the machined surface structure; S12, Perform topological analysis on the initial three-dimensional model of the wheel hub to extract geometric parameters; wherein, the geometric parameters include spoke thickness, rim width, inner and outer rim radii, rim fillet radius, machined surface dimensions, and spatial position parameters; S13, construct an intrinsic structure description model and a set of design variables based on the geometric parameters.

[0008] Preferably, the step S3 is preceded by the following step: Establish the displacement mapping relationship between the design variables and each control point in the geometric deformation control body; The displacement vector of each control point is defined based on the displacement mapping relationship.

[0009] Preferably, step S3 specifically includes the following steps: S31, map the node coordinates of each control point from the world coordinate system to the parametric coordinate system; S32, interpolate the displacement of each control point based on the displacement vector of each control point to obtain the deformed parameter coordinates of each control point; S33 generates a deformed 3D model of the wheel hub based on the parameter coordinates of each control point after deformation.

[0010] Preferably, step S5 specifically includes the following steps: S51, construct an objective function based on the predicted NVH performance of the wheel hub to obtain the deviation value between the predicted result and the target result; S52, Define the constraints for the design variables; S53, Based on the constraints and the objective function, the adjusted design variables are updated using an optimization algorithm; S54, return the adjusted design variables to step S3 for iterative optimization.

[0011] Preferably, when the wheel hub NVH performance is predicted as modal frequencies, the objective function is: in, These are the weighting coefficients; To predict modal frequencies; For the target modal frequency, Indicates the number of performance metrics.

[0012] Preferably, when the predicted NVH performance of the wheel hub is the vibration response amplitude, the objective function is: in, These are the weighting coefficients; To predict the amplitude of the vibration response; The target vibration response amplitude, Indicates the number of performance metrics.

[0013] Preferably, the performance prediction model in step S4 adopts one of the following: support vector regression model, random forest regression model, and multilayer perceptron neural network model.

[0014] Preferably, in step S32, Bernstein polynomials or B-spline basis functions are used to interpolate the displacement of each control point.

[0015] Preferably, the constraints include at least one of the following: upper and lower limit constraints of design variables, quality constraints, and strength constraints.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a wheel hub NVH performance optimization method integrating multiple technologies. By integrating structural feature recognition, geometric deformation control, data-driven prediction, and intelligent optimization algorithms, it constructs an integrated closed-loop optimization system for wheel hub NVH performance, encompassing "identification-deformation-prediction-optimization." Through the cooperation of an intrinsic structure description model and a geometric deformation control body, continuous and precise control of the geometry of the area to be optimized can be achieved while preserving the original topology and assembly interface, ensuring the engineering manufacturability and assembly compatibility of the optimization results. A pre-trained performance prediction model replaces repetitive numerical simulations in the iterative process, significantly reducing the computational overhead of performance evaluation and greatly improving iterative convergence efficiency. Relying on optimization algorithms to automatically complete the iterative optimization of design variables, it can quickly match preset NVH performance targets while meeting multiple constraints such as strength and mass, effectively shortening the wheel hub product development cycle, reducing design trial-and-error costs, and enhancing the automation level and engineering application value of wheel hub NVH optimization design. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the optimization method in one embodiment of the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Please see the appendix Figure 1 The present invention provides a method for optimizing the NVH performance of a wheel hub that integrates multiple technologies, comprising the following steps: S1. Obtain an initial 3D model of the wheel hub, perform structural feature analysis on the initial 3D model of the wheel hub, extract the geometric features of the initial 3D model of the wheel hub, and construct an intrinsic structural description model and a set of design variables based on the geometric features; by obtaining the initial 3D model of the wheel hub and performing structural feature analysis on it, the boundary representation data of the model is traversed to complete the topological deconstruction, identify the solid surfaces, boundary curves and feature functional areas, extract the geometric features of key parts, and then construct an intrinsic structural description model based on the extracted geometric features according to the structural hierarchy and assembly constraint relationship, while selecting adjustable geometric parameters to form a set of design variables; transform the continuous 3D geometric entity into a structured description system with parameterized correlation, clarify the one-to-one mapping relationship between geometric features and design variables, and provide a complete parameterized driving carrier for subsequent geometric deformation control and optimization iteration, so that the wheel hub geometric configuration can be quantitatively and controllably modified through the numerical adjustment of design variables, replacing the traditional manual operation mode of modifying the geometric model one by one, and improving the accuracy, traceability and efficiency of geometric adjustment.

[0024] The intrinsic structure description model is used to organize geometric features hierarchically according to the order of "overall envelope - main structure - local functional features - transitional features," and establish topological connections and parameter associations between features. In this model, each type of geometric feature corresponds to a set of geometric control parameters, and the upper-level feature parameters drive the synchronous update of lower-level features through the association relationship. Based on the hierarchy and parameter association of this model, adjustable geometric parameters that directly affect structural performance and process are selected to form a set of design variables, and each design variable is mapped one-to-one with the geometric control parameters in the intrinsic structure description model, thereby realizing the quantitative driving of the wheel hub geometry.

[0025] Preferably, step S1 specifically includes the following steps: S11, Obtain the initial 3D model of the wheel hub; wherein, the initial 3D model of the wheel hub includes the spoke structure, the rim structure, and the machined surface structure; the model is a standard format 3D solid model, containing complete geometric surfaces, assembly interfaces, and machining datums, with no missing geometric surfaces and structural features; the complete wheel hub solid geometric model is used as the geometric datum for the entire optimization process, covering the geometric shape of the main load-bearing area and machining area of ​​the wheel hub, ensuring that the geometric features extracted subsequently can fully cover the key structural parts that affect NVH performance, avoiding the problem of incomplete feature extraction and insufficient optimization coverage due to missing model structure, and ensuring the consistency between the optimization results and the actual product structure.

[0026] S12, perform topological analysis on the initial 3D model of the wheel hub to extract geometric parameters. These geometric parameters include spoke thickness, rim width, inner and outer rim radii, rim fillet radius, machined surface dimensions, and spatial position parameters. Traverse the model's boundary representation data to identify solid surfaces, edges, vertices, and feature functional zones, measuring and extracting geometric parameters including spoke thickness, rim width, inner and outer rim radii, rim fillet radius, machined surface dimensions, and spatial position parameters one by one. Through topological analysis, the continuous 3D geometric entity is decomposed into quantifiable discrete parameters, accurately locating the dimensional parameters and spatial position information of each key structure. This provides clear parameterized input for the subsequent construction of the intrinsic structure description model, realizing the transformation from geometric entities to adjustable parameters.

[0027] S13, construct an intrinsic structure description model and a set of design variables based on the geometric parameters; by constructing the intrinsic structure description model, clarify the spatial positional association and constraint relationship between each parameter, and at the same time, select independently adjustable parameters from the geometric parameters and include them in the set of design variables, and label the corresponding geometric parts and physical meanings of each parameter; thereby integrating the decomposed geometric parameters into an intrinsic structure description model with internal logical associations, and forming a set of design variables that can be iteratively updated, clarifying the corresponding mapping relationship between each design variable and the geometric entity, so that subsequent adjustments to the design variables can be accurately mapped to the dimensional changes of the corresponding geometric structure, ensuring the accuracy and consistency of parameterized adjustments.

[0028] S2, construct a geometric deformation control body for the region to be optimized based on the intrinsic structure description model, and extract design variables associated with the geometric deformation control body from the set of design variables; construct an envelope-like three-dimensional geometric deformation control body around the region to be optimized based on the intrinsic structure description model, forming a control grid composed of multiple sets of spatial control points; wherein, the geometric deformation control body can be constructed according to the spokes, rims, and machined surface areas, by constructing a three-dimensional control point array in the region to be optimized as the geometric deformation control body, and extract design variables associated with the geometric deformation control body from the set of design variables according to the deformation transmission path matching, such as the normal displacement variable of the spoke area control points corresponding to the change in spoke thickness, and the radial displacement variable of the rim boundary control points corresponding to the change in rim width; thus, using the geometric deformation control body as the execution carrier of spatial deformation, the scalar value change of the design variables is converted into the spatial displacement adjustment amount of the control body, establishing the transmission path between the design parameters and spatial geometric deformation, so that the geometric shape of the region to be optimized can produce continuous and smooth deformation following the adjustment of the design variables, while limiting the deformation range, avoiding unexpected changes in the geometric structure of non-optimized areas, and ensuring the stability of the overall topology and assembly interface of the wheel hub.

[0029] It is worth mentioning that the three-dimensional control point array is a spatial array of points arranged within the geometric deformation control body according to characteristic regions such as spokes, rims, or machined surfaces. This array is used to discretize local structural changes in the wheel hub into positional changes of control points in corresponding regions. Thus, design variables such as spoke thickness and rim width can be directly mapped to the displacement of corresponding control points along the normal or radial direction. This coordinated movement of the control point array drives continuous geometric deformation in the region to be optimized, forming the geometric deformation control body.

[0030] S3, Apply displacement adjustment to the geometric deformation control body according to the design variables to generate a deformed 3D model of the wheel hub; Drive the wheel hub geometric entity nodes within the coverage area of ​​the control body to generate linkage displacement through the volume deformation algorithm, and reconstruct the 3D entity model after updating the coordinates of all geometric nodes to generate a deformed 3D model of the wheel hub; Thus, the spatial displacement of the geometric deformation control body drives the corresponding region of the wheel hub geometric entity to generate linkage deformation, transforming the parameterized design variable values ​​into intuitive 3D geometric shape changes, and completing the quantitative adjustment of the local geometric configuration while keeping the original topology of the wheel hub unchanged, so that each set of design variable values ​​can generate a unique, continuous and manufacturable deformed wheel hub 3D model, providing compliant and usable geometric input for subsequent performance prediction.

[0031] Furthermore, the step S3 is preceded by the following step: Establish a displacement mapping relationship between the design variables and each control point in the geometric deformation control body; define the displacement vector of each control point based on the displacement mapping relationship; thus, for each design variable, match the corresponding control point group in the geometric deformation control body, clarify the displacement direction and displacement ratio of the control points corresponding to the unit value change of the design variable, establish a deterministic displacement mapping relationship between the design variable and the control point displacement, and then calculate the three-dimensional displacement components of each control point according to the current value of the design variable, and define the displacement vector of each control point; in order to convert the scalar value of the design variable into the spatial displacement of the control point, clarify the movement direction and movement amplitude of the control point corresponding to the adjustment of each design variable, so that the numerical change of the design variable can be accurately converted into the spatial deformation action of the geometric deformation control body, avoiding problems such as geometric misalignment and morphological distortion during the deformation process, and ensuring the controllability and accuracy of geometric deformation.

[0032] Preferably, step S3 specifically includes the following steps: S31 maps the node coordinates of each control point from the world coordinate system to the parameter coordinate system; through parameter domain normalization transformation, the three-dimensional absolute coordinates of each control point in the world coordinate system are converted into normalized parameter coordinates in the parameter domain, thus obtaining the position parameters in the corresponding parameter coordinate system. This step transforms the absolute spatial coordinates in the Cartesian coordinate system into normalized coordinates in the parameter domain, eliminating the influence of spatial scale and position on deformation calculation, and providing a unified parameter domain calculation benchmark for subsequent interpolation calculations based on basis functions. This enables the displacements of control points with different spatial locations and different sizes to be calculated under a unified parameter system, improving the versatility and stability of deformation calculation.

[0033] S32: Based on the displacement vector of each control point, interpolation calculation is performed on the displacement of each control point to obtain the deformed parameter coordinates of each control point. Based on the displacement vector of each control point, the corresponding interpolation basis function is selected, and interpolation operation is performed on the displacement of all geometric points in the parameter domain to calculate the displacement increment corresponding to each geometric point in the parameter domain. After superimposing the displacement increment onto the original parameter coordinates, the deformed parameter coordinates of each geometric point are obtained. In this way, the displacement of discrete control points is diffused to the entire deformation control body area through interpolation operation, so that all geometric points within the control body coverage area can obtain the corresponding displacement adjustment amount, realizing continuous and smooth deformation in the spatial area and avoiding geometric surface abrupt changes, sharp corners and other morphological defects that do not meet manufacturing requirements caused by the displacement of discrete control points.

[0034] S33 generates a deformed 3D model of the wheel hub based on the parameter coordinates of each control point after deformation; updates the spatial coordinates of all corresponding geometric nodes in the 3D model of the wheel hub with the coordinates of all geometric points that have completed the deformation calculation, reconstructs the solid surface and topological connection relationship, and generates a complete deformed 3D solid model of the wheel hub with continuous surfaces; thus completing the conversion from parameter domain calculation results to actual geometric model, outputting a standard 3D model with complete topological structure that can be directly used for subsequent analysis, without the need for manual repair of geometric defects, ensuring that the deformed model can be directly connected to the subsequent performance prediction stage, and maintaining the automated operation of the optimization process.

[0035] S4, input the deformed 3D model of the wheel hub into the pre-trained performance prediction model to obtain the NVH performance prediction result of the wheel hub; wherein, the NVH performance prediction result of the wheel hub includes one of modal frequency, vibration response amplitude, and sound pressure level; input the geometric feature parameters corresponding to the deformed 3D model of the wheel hub into the pre-trained performance prediction model, the model completes the calculation based on the pre-fitted geometric feature-NVH performance mapping relationship, and outputs the corresponding NVH performance prediction result of the wheel hub, wherein the NVH performance prediction result of the wheel hub includes one of modal frequency, vibration response amplitude, and sound pressure level; this step relies on the pre-trained performance prediction model to establish a fast mapping relationship between geometric features and NVH performance indicators, replacing the traditional finite element simulation calculation process, quickly outputting the NVH performance evaluation result of the corresponding geometric configuration, significantly reducing the performance evaluation calculation cost in the iteration process, shortening the execution cycle of a single round of optimization, and providing computing power support for high-frequency iterative optimization. It can be understood that different sample databases are constructed when training the model for different prediction results, and an appropriate prediction model is selected, and an appropriate prediction function is constructed as the pre-trained performance prediction model after training.

[0036] S5, based on the predicted NVH performance of the wheel hub and combined with preset constraints, the design variables are adjusted using an optimization algorithm, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables. Based on the predicted NVH performance of the wheel hub and combined with preset constraints such as size, mass, and strength, the values ​​of the design variables are automatically adjusted by the optimization algorithm with the goal of reducing performance deviation. The adjusted design variables are then returned to step S3, and the adjusted design variables are used as a new round of input to drive the geometric deformation control body to deform, repeating the process of model deformation and performance prediction. This step uses the performance prediction results as the basis for optimization feedback, and combined with preset constraints, automatically searches for better design variable values ​​through the optimization algorithm, forming a closed-loop iterative link of geometric deformation, performance prediction, and variable update. Multiple rounds of optimization can be completed without manual intervention. At the same time, the feasible domain of the design variables is limited by the constraints to ensure that the geometric configuration generated during the iteration process always meets the engineering boundary requirements, avoiding design schemes that are unmanufacturable or do not meet mechanical requirements.

[0037] Furthermore, step S5 specifically includes the following steps: S51, construct an objective function based on the wheel hub NVH performance prediction results to obtain the deviation value between the prediction result and the target result; construct an objective function that reflects the deviation value based on the currently obtained wheel hub NVH performance prediction results and target results, and quantify the deviation value between the prediction result and the target result through the numerical value of the objective function, so that the optimization algorithm can accurately judge the merits of the design scheme based on the magnitude of the deviation value.

[0038] S52, define the constraints of the design variables; wherein the constraints include at least one of the following: upper and lower limit constraints, quality constraints, and strength constraints of the design variables: upper and lower limit constraints of the design variables are used to limit the adjustment range of geometric dimensions to match the feasible size range of casting and machining processes; quality constraints are used to ensure that the optimized wheel hub meets the vehicle's lightweight index; strength constraints are used to ensure that the stress level of the wheel hub under rated load does not exceed the allowable value of the material; these constraints define the feasible range of optimization from multiple dimensions such as geometric dimension boundaries, overall quality index, and structural mechanical performance, avoiding the occurrence of a solution that meets the single performance standard but fails other engineering indicators during the optimization process, and ensuring the engineering usability and feasibility of the final output solution. By defining the feasible value range of the design variables and additional engineering constraints, engineering requirements such as geometric dimension boundaries and performance baselines are transformed into constraint rules that the optimization algorithm can recognize, limiting the search space of the optimization iteration, avoiding the optimization algorithm from generating design variable values ​​that exceed the engineering allowable range, and ensuring that all candidate solutions have engineering feasibility during the iteration process.

[0039] S53, based on the constraints and the objective function, the optimized algorithm is used to update the adjusted design variables. Thus, within the feasible region defined by the constraints, the optimization algorithm, guided by minimizing the objective function value, updates the design variable values ​​through its built-in optimization logic, and outputs the adjusted design variable combination. The optimization algorithm automatically updates the design variable values, replacing the manual trial-and-error parameter adjustment method. The global optimization algorithm can search the optimal region within the feasible region over a large area, while the local optimization algorithm can perform a fine search near the optimal region. This enables the algorithm to quickly locate a better solution in a high-dimensional design space, improving optimization efficiency and the final optimization effect.

[0040] S54, return the adjusted design variables to step S3 for iterative optimization; finally, return the adjusted design variables output by the optimization algorithm as the input for the next round to step S3, restart the complete process of geometric deformation, performance prediction and variable update, continuously feed the performance prediction results obtained in each round back to the optimization algorithm, guide the variable adjustment in the next round, form a closed-loop iterative mechanism, gradually reduce the deviation between the performance prediction results and the target value through multiple rounds of iteration, continuously improve the performance of the design scheme, and output the final scheme after the convergence condition is met.

[0041] S6, in response to the current meeting of the iteration convergence condition, output the current deformed wheel hub 3D model as the target wheel hub 3D model; wherein, the iteration convergence condition is that the current deformed wheel hub 3D model meets the preset performance or the current iteration optimization number reaches the preset iteration number threshold.

[0042] To facilitate those skilled in the art, the implementation process is described below for three different performance prediction results: modal frequency, vibration response amplitude, and sound pressure level. Taking hub modal frequency optimization as an example: Step 1: First, obtain the initial CAD 3D model of the wheel hub (i.e., the initial 3D model of the wheel hub), which includes the spoke structure, rim structure, and machined surface structure. Perform topology analysis on the initial 3D model of the wheel hub to identify solid surfaces, boundary curves, and key feature regions to extract the following geometric parameters: spoke thickness, rim width, inner and outer rim radii, rim fillet radius, machined surface dimensions, and spatial position parameters. Based on the above geometric parameters and the initial 3D model of the wheel hub, establish an intrinsic structural description model, define the above geometric parameters as design variables, and establish a geometric constraint relationship matrix (design variable set). The design variable set is denoted as: ;in, In this embodiment, the structural design variable vector is represented. ;in, For the thickness of the spokes, The width of the rim. The inner radius of the wheel rim. The outer radius of the wheel rim. The radius of the rim fillet is 1. This refers to the dimensions of the machined surface.

[0043] Step Two: To achieve controllable adjustment of the structural geometry, a geometric deformation control body is constructed in the spoke, rim, and machined surface areas. This is mainly achieved by creating a three-dimensional control point array in the area to be optimized. Then, to prepare for subsequent displacement adjustments, a mapping relationship between design variables and control point displacements is established: changes in spoke thickness correspond to the normal displacement of control points in the spoke region, changes in rim width correspond to the radial displacement of control points at the rim boundary, and changes in fillet radius correspond to the local smooth displacement of control points in the transition region; the control point displacement vector is defined as follows: ;in, It is a deterministic mapping function.

[0044] in, To design a deterministic linear mapping function between the design variables and the control point displacements, i.e. , J This is the mapping coefficient matrix determined based on the characteristic region where the control points are located and the deformation direction. To design the variable change.

[0045] Step 3: Map the node coordinates of the CAD 3D model from the world coordinate system to the parametric coordinate system; interpolate the control point displacements using Bernstein polynomials or B-spline basis functions; Bernstein polynomials have global support properties, where the displacement of a single control point affects the geometry of the entire parametric domain, making them suitable for scenarios with uniform deformation of the region to be optimized; B-spline basis functions have local support properties, where the displacement of a single control point only affects the region within its adjacent node interval, resulting in higher deformation control precision, making them suitable for scenarios with fine-tuning in the local area; the two basis functions can be flexibly selected according to the deformation requirements of the region to be optimized, balancing the smoothness of deformation with the accuracy of control, adapting to different optimization and adjustment needs; after calculation, the new coordinates of any node after deformation are obtained: in, These are the normalized local coordinates of the geometric deformation control volume in the three parametric directions, with values ​​ranging from [value range missing]. ; These represent the degrees of the Bernstein polynomials in the three parameter directions, which is equivalent to reducing the number of control points in the corresponding directions by one. These are the control point indices for the three parameter directions. , , ; , , These are the Bernstein basis functions of corresponding orders in the three parameter directions; To control the index in the grid The coordinates of the control points; coordinates of internal parameters of the control volume Spatial coordinates after adjustment by control point displacement.

[0046] In this way, the coordinates of all geometric points that have completed the deformation calculation are updated to update the spatial coordinates of all corresponding geometric nodes in the 3D model of the wheel hub, the solid surface and topological connection relationship are reconstructed, and a complete 3D solid model of the deformed wheel hub with continuous surface is generated.

[0047] Step 4: Construct a sample database, which includes: a design variable vector. The modal frequency vector obtained from the corresponding finite element analysis Establish a machine learning prediction model, wherein the model is one of the following: multivariate linear regression model, support vector regression model (SVR), random forest regression model, or multilayer perceptron neural network model (MLP); pre-train the prediction model using a sample database to construct a prediction function: ; in, For the trained prediction model, To predict modal frequencies.

[0048] Step 5: Construct the objective function: in, These are the weighting coefficients; To predict modal frequencies; The target modal frequency.

[0049] Set constraints: Design variable upper and lower limit constraints: Quality constraints: ; and strength constraints ;in, This represents the minimum value of the current design variables. This represents the maximum value of the current design variable. For current design variables The corresponding quality, For the maximum permissible mass, The maximum equivalent stress under the preset load conditions. The allowable stress of the material is used. Based on the predicted NVH performance of the wheel hub and the preset constraints, an optimization algorithm is adopted. The optimization algorithm can be one of the following: genetic algorithm (GA), particle swarm optimization algorithm (PSO), differential evolution algorithm (DE), or gradient optimization method. After the new design variable values ​​are calculated, the design variables are updated and adjusted, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables. The process is repeated.

[0050] Step Six: When the iteration termination condition is met; in this embodiment, the following condition must be met: in, For the first The objective function value for hub modal frequency optimization is obtained in the next iteration. For the first The objective function value for hub modal frequency optimization is obtained in the next iteration. In this embodiment, the convergence threshold can be set to... or .

[0051] Once the above conditions are met, the optimal design variables are output, and a 3D model of the target wheel hub is generated based on the optimized design variables.

[0052] Taking the amplitude of wheel hub vibration response as an example: Steps one and two are similar to the implementation of hub modal frequency optimization, and will not be repeated here.

[0053] Step 3: Convert the original geometric point coordinates Mapping to parameter space coordinates Geometric deformation calculations were performed using B-spline basis functions in the form of three-dimensional tensor products. The coordinates of the deformed points are as follows: in, For parameter direction The last time was B-spline basis functions; For parameter direction The last time was B-spline basis functions; For parameter direction The last time was B-spline basis functions; These are the original control points; This refers to the displacement of the control point.

[0054] The B-spline basis functions are defined using the Cox–deBoor recursive formula: when hour: when hour: in, It is a node vector.

[0055] Then, for all geometric point coordinates that have completed the deformation calculation, update the spatial coordinates of all corresponding geometric nodes in the 3D model of the wheel hub, reconstruct the solid surface and topological connection relationship, and generate a complete 3D solid model of the deformed wheel hub with continuous surfaces.

[0056] Step 4: Construct a sample database, which includes: a design variable vector. The output is the key node at the target frequency. Vibration response amplitude The amplitude of the vibration response is defined by the dynamic equation: in, For inertial force, For the damping force term, This is the elastic restoring force term; The response amplitude in the frequency domain is ;in, Let be the structure frequency response function matrix, representing the structure at frequency under unit excitation. Steady-state response characteristics at the location.

[0057] A machine learning prediction model is established using supervised learning, and the model is one of the following: Support Vector Regression (SVR), Random Forest Regression, or Multilayer Perceptron Neural Network (MLP). The prediction model is pre-trained using a sample database to construct a prediction function. ; in, The trained machine learning prediction model approximates the frequency response calculation results described above. For the target frequency, For key nodes at target frequency The predicted vibration response amplitude vector is given below. This represents the number of critical nodes.

[0058] Step 5: Construct the objective function: in, These are the weighting coefficients; For predicting the amplitude; The target amplitude.

[0059] Set constraints: Design variable upper and lower limit constraints: and meet quality constraints The optimization algorithm, based on the predicted NVH performance of the wheel hub and combined with preset constraints, employs a two-stage strategy: For the global search stage, a genetic algorithm or particle swarm optimization algorithm is used to obtain a globally approximate optimal solution; for the local fine-tuning stage, gradient descent is used for a fine search near the global solution. After calculating new design variable values, the design variables are updated and adjusted, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables, repeating the iteration process repeatedly.

[0060] Step Six: When the iteration termination condition is met; in this embodiment, the following condition must be met: in, For the first The objective function value for hub NVH performance optimization in the next iteration. For the first The objective function value for hub NVH performance optimization in the next iteration. In this embodiment, the convergence threshold can be set to... It can also be based on the optimization target magnitude. Select from the range.

[0061] Once the above conditions are met, the optimal design variables are output, and a 3D model of the target wheel hub is generated based on the optimized design variables.

[0062] Taking wheel hub sound pressure level as an example: Steps one, two, and three are similar to the implementation of the hub vibration response amplitude optimization, and will not be repeated here.

[0063] Step 4: Construct a sample database, which includes: a design variable vector. The output is the sound pressure level frequency response. The formula for calculating sound pressure level is: in, This refers to the sound pressure level. .

[0064] Construct a regression model, which is one of the following: Support Vector Regression (SVR), Random Forest Regression, Multilayer Perceptron Neural Network (MLP), or Gaussian Process Regression; pre-train the prediction model using a sample database to construct a prediction function: ; in, The frequency response calculation results are approximated by a machine learning model for the trained regression prediction model. For frequency, Design variables Corresponding structure at frequency The predicted sound pressure level at the location.

[0065] Step 5: Construct the objective function: in, These are the weighting coefficients; To predict sound pressure level; For the target sound pressure level, Indicates the number of performance metrics.

[0066] Set constraints: Design variable upper and lower limit constraints: and meet quality constraints Strength constraints The optimization algorithm, based on the predicted NVH performance of the wheel hub and combined with preset constraints, employs a two-stage strategy: the first stage uses a genetic algorithm or particle swarm optimization for global search; the second stage uses a quasi-Newton method or finite difference gradient method for local fine-tuning. After calculating new design variable values, the design variables are updated and adjusted, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables, repeating the iteration process repeatedly.

[0067] Step Six: When the iteration termination condition is met; in this embodiment, the following condition must be met: in, For the first The sound pressure level is optimized in the next iteration to obtain the objective function value. For the first The sound pressure level is optimized in the next iteration to obtain the objective function value. In this embodiment, the convergence threshold can be set to... Alternatively, it can be based on the magnitude of the objective function. Select from the range.

[0068] Once the above conditions are met, the optimal design variables are output, and a 3D model of the target wheel hub is generated based on the optimized design variables.

[0069] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for optimizing the NVH performance of wheel hubs by integrating multiple technologies, characterized in that, Includes the following steps: S1. Obtain an initial three-dimensional model of the wheel hub, perform structural feature analysis on the initial three-dimensional model of the wheel hub, extract the geometric features of the initial three-dimensional model of the wheel hub, and construct an intrinsic structural description model and a set of design variables based on the geometric features; S2, construct the geometric deformation control body of the region to be optimized based on the intrinsic structure description model, and extract the design variables associated with the geometric deformation control body from the set of design variables; S3, Apply displacement adjustment to the geometric deformation control body according to the design variables to generate the deformed three-dimensional model of the wheel hub; S4, input the deformed three-dimensional model of the wheel hub into the pre-trained performance prediction model to obtain the NVH performance prediction result of the wheel hub; wherein, the NVH performance prediction result of the wheel hub includes one of the modal frequency, vibration response amplitude, and sound pressure level; S5, based on the predicted NVH performance of the wheel hub and combined with the preset constraints, the design variables are adjusted using an optimization algorithm, and the process returns to step S3 to iteratively optimize the geometric deformation control body based on the adjusted design variables; S6, in response to the current meeting of the iteration convergence condition, output the current deformed wheel hub 3D model as the target wheel hub 3D model; wherein, the iteration convergence condition is that the current deformed wheel hub 3D model meets the preset performance or the current iteration optimization number reaches the preset iteration number threshold.

2. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11, Obtain the initial three-dimensional model of the wheel hub; wherein, the initial three-dimensional model of the wheel hub includes the spoke structure, the rim structure, and the machined surface structure; S12, Perform topological analysis on the initial three-dimensional model of the wheel hub to extract geometric parameters; wherein, the geometric parameters include spoke thickness, rim width, inner and outer rim radii, rim fillet radius, machined surface dimensions, and spatial position parameters; S13, construct an intrinsic structure description model and a set of design variables based on the geometric parameters.

3. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 1, characterized in that, The step preceding step S3 includes the following steps: Establish the displacement mapping relationship between the design variables and each control point in the geometric deformation control body; The displacement vector of each control point is defined based on the displacement mapping relationship.

4. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 3, characterized in that, Step S3 specifically includes the following steps: S31, map the node coordinates of each control point from the world coordinate system to the parametric coordinate system; S32, interpolate the displacement of each control point based on the displacement vector of each control point to obtain the deformed parameter coordinates of each control point; S33 generates a deformed 3D model of the wheel hub based on the parameter coordinates of each control point after deformation.

5. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 1, characterized in that, Step S5 specifically includes the following steps: S51, construct an objective function based on the predicted NVH performance of the wheel hub to obtain the deviation value between the predicted result and the target result; S52, Define the constraints for the design variables; S53, Based on the constraints and the objective function, the adjusted design variables are updated using an optimization algorithm; S54, return the adjusted design variables to step S3 for iterative optimization.

6. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 5, characterized in that, When the NVH performance of the wheel hub is predicted as modal frequencies, the objective function is: in, These are the weighting coefficients; To predict modal frequencies; For the target modal frequency, Indicates the number of performance metrics.

7. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 5, characterized in that, When the NVH performance of the wheel hub is predicted as the vibration response amplitude, the objective function is: in, These are the weighting coefficients; To predict the amplitude of the vibration response; The target vibration response amplitude, Indicates the number of performance metrics.

8. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 5, characterized in that, In step S4, the performance prediction model adopts one of the following: support vector regression model, random forest regression model, or multilayer perceptron neural network model.

9. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 4, characterized in that, In step S32, Bernstein polynomials or B-spline basis functions are used to interpolate the displacement of each control point.

10. The wheel hub NVH performance optimization method integrating multiple technologies according to claim 1, characterized in that, The constraints include at least one of the following: upper and lower limit constraints on design variables, quality constraints, and strength constraints.

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