Method, system and device for testing performance of noise-reducing and shock-absorbing sealing components for automobiles

Abstract

The application discloses a performance testing method, system and equipment for a noise-reducing and shock-absorbing sealing component of an automobile, and relates to the technical field of performance testing.The method comprises the following steps: performing multi-axis servo testing on the noise-reducing and shock-absorbing sealing component of the automobile to obtain a multi-working-condition original sensing sequence; positioning a contact interface to extract an interface contact state and performing viscoelastic separation to obtain a complex stiffness characteristic group; performing frequency-variable compression modulus inversion to construct a wide-frequency damping characteristic curve, and determining an initial NVH performance baseline; introducing a whole-vehicle boundary condition to perform multi-physical-field coupling correction, and generating a full-working-condition performance evaluation report of the noise-reducing and shock-absorbing sealing component of the automobile.The technical problems that, in the prior art, a multi-axis composite loading working condition cannot be simulated, accurate analysis of viscoelastic separation of a material is lacking, and the accuracy of NVH performance of a sealing component under a vehicle running state is insufficient are solved, and the technical effects of improving the simulation accuracy and evaluation effectiveness of the NVH performance of the sealing component under a full working condition are achieved.

Description

Technical Field

[0001] This invention relates to the technical field of performance testing, specifically to performance testing methods, systems, and equipment for automotive noise reduction and vibration damping sealing components. Background Technology

[0002] With the rapid development of the automotive industry, sealing components, such as door seals, engine mounts, and shock absorbers, which affect the NVH (noise, vibration, and harshness) performance of automobiles, are of paramount importance. They play an irreplaceable role in attenuating road impacts, isolating wind noise, and suppressing structural resonance. However, the working environment of automotive sealing components is extremely complex. They need to withstand the coupled effects of multiple factors, including multiaxial alternating loads, a wide temperature range (-40℃ to +100℃), humid heat aging, and chemical corrosion. In particular, for viscoelastic rubber materials, their mechanical behavior exhibits significant frequency and temperature dependence, i.e., temperature equivalent effects. This makes it difficult to accurately predict the dynamic stiffness and damping characteristics of sealing components under broadband excitation. Traditional performance evaluation of sealing components often relies on basic material tests such as uniaxial tension and compression set, or on-road testing of finished products. These methods differ significantly from the broadband random vibration and multiaxial load spectrum of real vehicles, making it difficult to reproduce the multiaxial stress state under actual working conditions. Furthermore, they cannot effectively isolate the viscous and elastic contributions of components in dynamic response, resulting in insufficiently accurate complex stiffness characteristics that are difficult to support subsequent simulation analysis.

[0003] Therefore, current technologies suffer from technical problems such as the inability to simulate multi-axis composite loading conditions and the lack of accurate analysis of material viscoelastic separation, resulting in insufficient accuracy of NVH performance of sealing components under vehicle operating conditions. Summary of the Invention

[0004] This application provides a performance testing method, system, and equipment for automotive noise reduction and vibration damping sealing components. It solves the technical problems in the prior art that the inability to simulate multi-axis composite loading conditions and the lack of accurate analysis of material viscoelastic separation lead to insufficient accuracy of the NVH performance of sealing components under vehicle operation. It achieves the technical effect of improving the simulation accuracy and evaluation effectiveness of NVH performance of sealing components under all operating conditions.

[0005] This application provides a performance testing method for automotive noise reduction and vibration damping sealing components. The method includes: performing multi-axis servo testing on the automotive noise reduction and vibration damping sealing components to obtain original sensing sequences under multiple operating conditions; locating the contact interface, extracting the interface contact state, and performing viscoelastic separation on the original sensing sequences under multiple operating conditions to obtain a complex stiffness feature set; performing frequency-varying compression modulus inversion according to the complex stiffness feature set to construct a broadband damping characteristic curve and determine the initial NVH performance baseline of the automotive noise reduction and vibration damping sealing components; introducing vehicle boundary conditions to perform multi-physics field coupling correction on the initial NVH performance baseline and generating a full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing components.

[0006] In one possible implementation, multi-axis servo testing is performed on automotive noise reduction and vibration damping sealing components to obtain original sensing sequences under multiple operating conditions. The method includes: constructing a standard road spectrum database to analyze the operating loads on the automotive noise reduction and vibration damping sealing components and extracting multiple load profiles; traversing the automotive noise reduction and vibration damping sealing components to segment strain regions and determine key test points; simulating based on the multiple load profiles, setting multiple loading conditions and performing multi-axis servo testing on the key test points according to the multiple load profiles to obtain a test data set; iteratively optimizing the load spectrum based on the test data set, setting a target load spectrum, and performing bidirectional testing according to the target load spectrum to obtain the original sensing sequences under multiple operating conditions.

[0007] In a possible implementation, the load spectrum is iteratively optimized based on the test data set to set a target load spectrum. The method includes: performing environmental factor decoupling analysis based on the test data set to determine multiple test interference components, including temperature drift interference data and humidity hysteresis interference data; analyzing the material properties of automotive noise reduction and vibration damping sealing components, and performing PID control on the temperature control box according to the temperature drift interference data and the component material properties to determine a compensation temperature profile; analyzing the hydrophilic modification characteristics of automotive noise reduction and vibration damping sealing components, and performing injection feedforward control according to the humidity hysteresis interference data and the hydrophilic modification characteristics to determine a compensation humidity gradient; and adjusting the load spectrum based on the compensation temperature profile and the compensation humidity gradient to set the target load spectrum.

[0008] In a possible implementation, the method of locating the contact interface, extracting the interface contact state, and performing viscoelastic separation on the original multi-condition sensing sequence to obtain a complex stiffness feature set includes: activating a first laser displacement sensor to track the normal compression of the contact interface in real time to obtain a first point cloud dataset; activating a second laser confocal sensor to scan the tangential slip of the contact interface at the micrometer level to obtain a second point cloud dataset; spatially registering the first point cloud dataset and the second point cloud dataset to generate a registration dataset; constructing a transient contact mesh and mapping the registration dataset to the transient contact mesh to generate a dynamic contact stress cloud map; extracting interface contact state information based on the dynamic contact stress cloud map; performing parameter identification on the original multi-condition sensing sequence and the dynamic contact stress cloud map according to the interface contact state information to generate a viscoelastic separation result; introducing a standard relaxation modulus database; traversing the viscoelastic separation result and combining it with the standard relaxation modulus database to perform time-temperature equivalent translation to construct a temperature-frequency superposition curve; and performing frequency domain extraction on the original sensing sequence based on the temperature-frequency superposition curve to obtain the complex stiffness feature set.

[0009] In a possible implementation, the process of constructing the transient contact mesh includes: performing a three-dimensional analysis on a noise reduction and vibration damping sealing component for automobiles to construct a three-dimensional geometric model of the component; traversing the three-dimensional geometric model of the component to discretize the contact surface area to obtain multiple finite element meshes, each of the multiple finite element meshes containing at least one spatial coordinate point; performing time analysis on the multiple finite element meshes based on the multiple spatial coordinate points to obtain multiple contact state parameters; loading and recording the multiple contact state parameters according to time changes to construct the transient contact network.

[0010] In a possible implementation, the initial NVH performance baseline of automotive noise reduction and vibration damping sealing components is determined by performing frequency-varying compressive modulus inversion according to the complex stiffness feature set and constructing a wideband damping characteristic curve. The method includes: traversing the complex stiffness feature set to extract complex stiffness data at multiple frequency points; mapping the complex stiffness data to a frequency domain complex plane for plotting to construct a complex plane curve; performing virtual and real peak detection based on the complex plane curve to determine multiple relaxation spectrum peak frequencies; dividing the frequency range according to the multiple relaxation spectrum peak frequencies to obtain frequency variation law parameters; repeatedly approximating and solving the complex stiffness feature set based on the frequency variation law parameters to obtain the compressive modulus function relationship and loss function relationship; superimposing and fusing the compressive modulus function relationship and the loss function relationship to generate a wideband damping characteristic curve; extracting damping peak data and modulus plateau value data according to a preset characteristic frequency based on the wideband damping characteristic curve; integrating the damping peak data and modulus plateau value data to construct the initial NVH performance baseline.

[0011] In a possible implementation, the complex stiffness feature set is repeatedly approximated and solved based on the frequency variation law parameters to obtain the compressibility modulus function relationship and the loss function relationship. The method includes: performing forward modeling on the complex stiffness feature set based on the frequency variation law parameters to obtain multiple theoretical complex stiffness values; calculating the deviation between the multiple theoretical complex stiffness values ​​and the complex stiffness feature set to obtain data deviation values; adjusting the frequency variation law parameters in reverse according to the data deviation values, updating the multiple theoretical complex stiffness values ​​according to the adjustment results, and iterating to obtain the inversion solution result; calculating the compressibility modulus according to the frequency points based on the inversion solution result to obtain the compressibility modulus function relationship; performing virtual-real derivation calculation based on the compressibility modulus function relationship to obtain the virtual-real ratio relationship, and using the virtual-real ratio relationship as the loss function relationship.

[0012] In one possible implementation, vehicle boundary conditions are introduced to perform multi-physics coupling correction on the initial NVH performance baseline, generating a full-condition performance evaluation report for automotive noise reduction and vibration damping sealing components. The method includes: introducing vehicle boundary conditions, which include an upper limit for peak damping, a lower limit for peak damping, an upper limit for low-frequency modulus, a lower limit for low-frequency modulus, an upper limit for high-frequency modulus, and a lower limit for high-frequency modulus; comparing the initial NVH performance baseline with the vehicle boundary conditions item by item to determine whether the initial NVH performance baseline is... Under the vehicle boundary conditions: S1: Compare the damping peak data in the initial NVH performance baseline with the upper limit and lower limit of the damping peak; S2: Compare the low-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit and lower limit of the low-frequency modulus; S3: Compare the high-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit and lower limit of the high-frequency modulus; If the damping peak data simultaneously satisfies the condition of being greater than or equal to the upper limit and lower limit of the low-frequency modulus... If the damping peak value is less than or equal to the damping peak value, and the low-frequency modulus plateau value is simultaneously greater than or equal to the low-frequency modulus lower limit and less than or equal to the low-frequency modulus upper limit, and the high-frequency modulus plateau value is simultaneously greater than or equal to the high-frequency modulus lower limit and less than or equal to the high-frequency modulus upper limit, then the initial NVH performance baseline is determined to be within the vehicle boundary conditions. A first-level correction instruction is then generated, and the cross-sectional geometry of the sealing component is topologically optimized using the first-level correction instruction to generate the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component. If any one of the damping peak value, the low-frequency modulus plateau value, or the high-frequency modulus plateau value does not meet its corresponding upper and lower limit ranges, then the initial NVH performance baseline is determined to be outside the vehicle boundary conditions. A second-level correction instruction is then generated, and the material parameters of the sealing component are input into the finite element simulation model for acoustic-structure interaction correction using the second-level correction instruction to generate the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component.

[0013] This application also provides a performance testing system for automotive noise reduction and vibration damping sealing components. The system includes: a sensor sequence acquisition module for performing multi-axis servo testing on the automotive noise reduction and vibration damping sealing components to acquire original sensor sequences under multiple operating conditions; a viscoelastic separation module for locating contact interfaces, extracting interface contact states, and performing viscoelastic separation on the original sensor sequences under multiple operating conditions to obtain a complex stiffness feature set; an initial performance baseline determination module for performing frequency-varying compression modulus inversion according to the complex stiffness feature set, constructing a broadband damping characteristic curve, and determining the initial NVH performance baseline of the automotive noise reduction and vibration damping sealing components; and a performance evaluation report generation module for introducing vehicle boundary conditions to perform multi-physics field coupling correction on the initial NVH performance baseline and generating a full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing components.

[0014] This application also provides an electronic device, including: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement a performance testing method for automotive noise reduction and vibration damping sealing components.

[0015] This application proposes a performance testing method, system, and equipment for automotive noise reduction and vibration damping sealing components. The method involves multi-axis servo testing to acquire original sensing sequences under multiple operating conditions; locating the contact interface to extract its contact state and performing viscoelastic separation to obtain complex stiffness feature sets; constructing a broadband damping characteristic curve through frequency-varying compressive modulus inversion to determine the initial NVH performance baseline; and introducing vehicle boundary conditions for multi-physics coupling correction to generate a full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing components. This addresses the technical problems in existing technologies, such as the inability to simulate multi-axis composite loading conditions and the lack of precise analysis of material viscoelastic separation, which leads to insufficient accuracy in NVH performance simulation of sealing components under vehicle operating conditions. The method effectively improves the accuracy and effectiveness of NVH performance simulation and evaluation of sealing components under all operating conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic flowchart of a performance testing method for automotive noise reduction and vibration damping sealing components provided in this application embodiment.

[0018] Figure 2A schematic diagram of the performance testing system for automotive noise reduction and vibration damping sealing components provided in this application embodiment.

[0019] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0020] Explanation of reference numerals in the attached figures: Sensing sequence acquisition module 10, viscoelastic separation module 20, initial performance baseline determination module 30, performance evaluation report generation module 40, input device 401, processor 402, memory 403, output device 404. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] This application provides a performance testing method for automotive noise reduction and vibration damping sealing components, such as... Figure 1 As shown, the method includes:

[0023] Step S100: Perform multi-axis servo testing on the noise reduction and vibration damping sealing components for automobiles to obtain the original sensing sequences under multiple operating conditions.

[0024] Step S100 further includes: constructing a standard road spectrum database to perform operational load analysis on automotive noise reduction and vibration damping sealing components, and extracting multiple load profiles; traversing the automotive noise reduction and vibration damping sealing components to segment strain regions and determine key test points; simulating based on the multiple load profiles, setting multiple loading conditions and performing multi-axis servo tests on the key test points according to the multiple load profiles to obtain test data sets; iteratively optimizing the load spectrum based on the test data sets, setting a target load spectrum, performing bidirectional tests according to the target load spectrum, and obtaining original sensing sequences for multiple working conditions.

[0025] Preferably, by operating the vehicle on a test track or actual road, and using sensors installed on the axles, suspension, and other parts to measure different road conditions such as bumpy roads, cobblestone roads, and highways, as well as different driving conditions, time history data representing the loads experienced by the vehicle when driving on actual roads are collected and stored in advance. A standard road spectrum database is established, and the operating load analysis of automotive noise reduction and vibration damping sealing components is performed. This includes analyzing the dynamic loads that the components actually bear in the vehicle environment. Through signal processing such as rain flow counting and peak extraction, typical load segments that have the greatest impact on the life and performance of the sealing components are selected from the standard road spectrum database to determine multiple load profiles, which can then be used for bench testing load-time curves.

[0026] Preferably, the automotive noise reduction and vibration damping sealing component is segmented by finite element simulation analysis (such as applying standard load) or pre-analysis based on the component geometry. Based on the strain degree and mode (such as compression, shear, bending) of each region under stress, it is divided into multiple different regions. According to the results of strain region segmentation, the hot spots with the largest strain, the most concentrated stress, or the most likely fatigue failure on the component are identified as key test points for deploying test sensors.

[0027] Preferably, multiple loading conditions are set using multiple load profiles as input, which simulate real stress test conditions. For example, the condition of the sealing strip being compressed instantly when a vehicle goes over a speed bump or the condition of the sealing strip being subjected to tangential shear force when a vehicle turns is set. Then, multi-axis servo tests are performed on key test points according to multiple load profiles. Specifically, according to the set loading conditions, multi-dimensional dynamic loads are applied to key test points, such as applying vertical compression and lateral shear at the same time, and raw signals such as force, displacement, and acceleration are collected in real time to form a test data set. Then, the load spectrum is iteratively optimized based on the test data set. This involves comparing the test data set with the load profile. Due to factors such as test fixtures and component nonlinearity, there is a deviation between the actual load generated by the test bench and the theoretical target load. The input test drive signal is continuously adjusted to make the actual load applied to the sealing component infinitely close to the target load profile. The target load spectrum is then determined through continuous correction. Tensile and compression tests are then performed to simulate the dynamic process of the sealing component being repeatedly squeezed and released in actual operation. The original sensing sequence of multiple working conditions that best reflects its true service state is obtained according to the target load spectrum test.

[0028] Furthermore, step S100 also includes: performing environmental factor decoupling analysis based on the test data set to determine multiple test interference components, including temperature drift interference data and humidity hysteresis interference data; analyzing the material characteristics of automotive noise reduction and vibration damping sealing components, and performing PID adjustment on the temperature control box according to the temperature drift interference data and the material characteristics of the components to determine a compensation temperature profile; analyzing the hydrophilic modification characteristics of automotive noise reduction and vibration damping sealing components, and performing injection feedforward control according to the humidity hysteresis interference data and the hydrophilic modification characteristics to determine a compensation humidity gradient; and adjusting the load spectrum based on the compensation temperature profile and the compensation humidity gradient to set the target load spectrum.

[0029] Preferably, the test data set is subjected to environmental factor decoupling analysis through orthogonal experimental design or frequency domain filtering to separate the real response generated by mechanical loading from the spurious response caused by environmental changes (such as material softening due to increased temperature or material expansion due to increased humidity). The influence of each environmental factor on the test results is quantified individually, and multiple test interference components are identified, including quantitative calculation of temperature drift interference data and humidity hysteresis interference data. Temperature drift interference data refers to the spurious component introduced into the test signal due to the zero-point drift of the test sensor or the change in the stiffness of the sealing component material caused by the fluctuation of ambient temperature. Humidity hysteresis interference data refers to the change in mechanical response caused by changes in ambient humidity, especially the volume expansion or modulus decrease of hygroscopic materials after absorbing water. Humidity transfer and material moisture absorption take time, thus exhibiting obvious hysteresis characteristics, that is, the material performance changes lag after temperature changes.

[0030] Preferably, the material properties of automotive noise reduction and vibration damping sealing components are analyzed to determine the material's temperature change characteristics, such as the glass transition temperature and storage modulus as a function of temperature. Then, temperature drift interference data is combined with the material's temperature change characteristics to calculate the ambient chamber temperature change that can offset temperature interference. A PID algorithm is then used to precisely control and adjust the temperature control chamber to determine the compensation temperature profile, ensuring it operates according to the pre-calculated compensation temperature profile. This ensures that the material performance remains consistent with the target operating conditions during the test, thereby eliminating test errors caused by temperature fluctuations. The hydrophilic modification characteristics of automotive noise reduction and vibration damping sealing components are analyzed to determine the material's wet-change characteristics, including the material's water absorption rate, saturated moisture absorption rate, and the changes in modulus and damping performance after moisture absorption. Since humidity response has a lag, humidity lag interference data is combined with the material's wet-change characteristics and spray feedforward control is implemented. This includes predicting humidity interference in advance, determining the compensation humidity gradient, and then controlling the spray humidification component to dynamically adjust the humidity of the test environment according to the compensation humidity gradient. This compensates for impending material performance changes and ensures that humidity interference is accurately offset during the test. Finally, the compensated temperature profile and compensated humidity gradient are used as boundary constraints to compensate and correct the original load driving signal, and the target load spectrum is finally determined to ensure that the effective mechanical excitation applied to the component during actual testing is not disturbed, so that the test results can truly reflect the mechanical performance of the component.

[0031] Step S200: Locate the contact interface, extract the interface contact state, and perform viscoelastic separation on the original sensing sequence under multiple working conditions to obtain a complex stiffness feature group.

[0032] Step S200 further includes: activating a first laser displacement sensor to track the normal compression of the contact interface in real time to obtain a first point cloud dataset; activating a second laser confocal sensor to scan the tangential slip of the contact interface at the micrometer level to obtain a second point cloud dataset; spatially registering the first point cloud dataset and the second point cloud dataset to generate a registration dataset; constructing a transient contact mesh and mapping the registration dataset to the transient contact mesh to generate a dynamic contact stress cloud map; extracting interface contact state information based on the dynamic contact stress cloud map; performing parameter identification on the original multi-condition sensing sequence and the dynamic contact stress cloud map according to the interface contact state information to generate a viscoelastic separation result; introducing a standard relaxation modulus database; traversing the viscoelastic separation result and combining it with the standard relaxation modulus database to perform time-temperature equivalent translation to construct a temperature-frequency superposition curve; and performing frequency domain extraction on the original sensing sequence according to the temperature-frequency superposition curve to obtain the complex stiffness feature group.

[0033] Preferably, a first laser displacement sensor is used for normal tracking to track the normal compression of the contact interface (such as the contact surface between the sealing strip and the body sheet metal) in the vertical direction in real time. That is, by scanning, spatial coordinate points representing the interface contour changing over time are obtained to obtain a first point cloud dataset, which is used to describe the compression status of the sealing component in the vertical direction. A second laser confocal sensor is used for tangential scanning to track the minute slippage of the contact interface in the horizontal direction in real time to obtain a second point cloud dataset representing the tangential displacement, which is used to describe the displacement status of the sealing component in the horizontal direction. Then, the first point cloud dataset and the second point cloud dataset are spatially registered through coordinate transformation and unified into the same coordinate system to ensure that the deformation data describing the same physical point in different directions can be accurately aligned, generating a registered dataset.

[0034] Preferably, a transient contact mesh is established using finite element analysis to depict the dynamic changes of the sealing component over time, describing the geometric changes of the contact interface. A registration dataset containing three-dimensional deformation information is mapped onto the transient contact network. Combining the material's constitutive relation and mechanical equilibrium equations, the contact stress at each mesh node is calculated, generating a dynamic contact stress cloud map to visually display the real-time pressure distribution at the contact interface. Then, key state parameters such as the magnitude of the contact pressure, the change in contact area, and the extent of the slip zone are extracted from the dynamic contact stress cloud map to determine the interface contact state information. This information is then combined with the original multi-condition sensing sequences for parameter identification. Viscous and elastic parameters are solved using the least squares method, decomposing the original total response into a purely elastic response (the recoverable energy storage portion) and a purely viscous response (the energy dissipation portion), ultimately determining the viscoelastic separation result.

[0035] Preferably, a standard relaxation modulus database is used to pre-store standard data on the relaxation modulus of the material at different temperatures, such as curves showing stress relaxation over time. Since the mechanical properties of polymer materials are equivalent to time and temperature (e.g., high temperature is equivalent to a long time, and low temperature is equivalent to a short time), the viscoelastic separation results measured at a certain temperature within a limited frequency range are time-temperature equivalently shifted along the frequency axis and stitched together to form a temperature-frequency superposition curve covering a wider frequency range. This curve describes how material properties change over a wider frequency band. Finally, based on the temperature-frequency superposition curve, the original sensing sequence is converted to the frequency domain using Fourier transform for analysis. The complex stiffness characteristics of the material at different vibration frequencies are extracted to obtain a complex stiffness feature set, containing complete information on the changes of the elastic part of the energy storage stiffness and the viscous part of the energy dissipation stiffness with frequency, to describe the dynamic characteristics of the sealing component.

[0036] Furthermore, step S200 also includes performing three-dimensional analysis on the automotive noise reduction and vibration damping sealing component to construct a three-dimensional geometric model of the component; traversing the three-dimensional geometric model of the component to discretize the contact surface area to obtain multiple finite element meshes, each of the multiple finite element meshes containing at least one spatial coordinate point; performing time analysis on the multiple finite element meshes based on the multiple spatial coordinate points to obtain multiple contact state parameters; loading and recording the multiple contact state parameters according to time changes to construct the transient contact network.

[0037] Preferably, using 3D computer-aided design software, based on the product's design drawings or by reverse scanning the physical object, a precise geometric model of the automotive noise reduction and vibration damping sealing component is created, generating a digital 3D solid model that accurately describes all geometric features of the sealing component, such as its external dimensions, cross-sectional shape, and lip structure, i.e., the component's 3D geometric model. Then, the contact surface area is discretized using the component's 3D geometric model, dividing the surface area that contacts the mating part (such as the door sheet metal) into multiple tiny geometric units to obtain multiple finite element meshes. Each finite element mesh contains at least one spatial coordinate point, which serves as the basic location point for calculating deformation and stress. Multiple spatial coordinate points are used to perform time analysis on multiple finite element meshes. That is, during the simulation loading process, the position of each spatial coordinate point moves with the change of time, and its stress and strain also change. Then, the finite element solver is used to calculate the contact state parameters of each node at different times, including at least the contact pressure, contact state (such as adhesion, slip, or separation), and slip distance. Finally, the multiple contact state parameters are loaded and recorded according to the time change and associated with the corresponding mesh nodes to determine the transient contact network, which is used to describe the evolution of contact interface pressure and motion over time.

[0038] Step S300: Perform frequency-varying compression modulus inversion according to the complex stiffness characteristic group, construct a wideband damping characteristic curve, and determine the initial NVH performance baseline of the automotive noise reduction and vibration damping sealing component.

[0039] Step S300 further includes: traversing the complex stiffness feature group to extract complex stiffness data at multiple frequency points; mapping the complex stiffness data to a complex plane in the frequency domain for plotting to construct a complex plane curve; performing virtual and real peak detection based on the complex plane curve to determine multiple relaxation spectrum peak frequencies; dividing the frequency range according to the multiple relaxation spectrum peak frequencies to obtain frequency variation law parameters; repeatedly approximating and solving the complex stiffness feature group based on the frequency variation law parameters to obtain the compression modulus function relationship and loss function relationship; superimposing and fusing the compression modulus function relationship and the loss function relationship to generate a broadband damping characteristic curve; extracting damping peak data and modulus plateau value data according to a preset characteristic frequency based on the broadband damping characteristic curve; integrating the damping peak data and modulus plateau value data to construct the initial NVH performance baseline.

[0040] Preferably, multiple discrete frequency points are selected from the complex stiffness feature set, and the complex stiffness data corresponding to each frequency point is extracted, including the real part of energy storage stiffness and the imaginary part of energy dissipation stiffness. The real part of energy storage stiffness is used as the abscissa, and the imaginary part of energy dissipation stiffness is used as the ordinate. The complex stiffness data of each frequency point is plotted as a point on a two-dimensional plane. The points of different frequencies are connected in sequence to form a complex plane curve (also known as a Nyquist curve), which is used to describe the trajectory of the stiffness vector as a function of frequency. Then, the real and imaginary peak values ​​of the complex plane curve are detected to identify the feature points where the imaginary part of energy dissipation stiffness has a local maximum or the feature points where the rate of change of the real part of energy storage stiffness is the largest. These correspond to the frequencies at which the molecular chain segments inside the material undergo transformation. The frequency corresponding to the detected peak position is then used as the peak frequency of the relaxation spectrum, indicating that the damping energy dissipation capacity of the material reaches its strongest at this frequency.

[0041] Preferably, the frequency range (e.g., 0.1Hz to 1000Hz) is divided into multiple continuous intervals using multiple relaxation spectrum peak frequencies as boundary points. Within each interval, the trend of complex stiffness variation with frequency is analyzed to obtain parameters such as slope and inflection point. Based on the current frequency variation parameters and a viscoelastic constitutive model (e.g., the generalized Maxwell model), the theoretical complex stiffness value is calculated forward and compared with the actual measured complex stiffness characteristic set. The deviation between the two is calculated, and then the frequency variation parameters are adjusted backward based on the calculated deviation. The forward calculation is then repeated iteratively until the deviation between the theoretical and measured values ​​is less than a set deviation threshold, obtaining the compression modulus function relationship and the loss function relationship. The compression modulus function relationship describes the continuous change of the material's elastic energy storage modulus with frequency, and the loss function relationship describes the continuous change of the material's damping energy dissipation modulus with frequency. The compression modulus function relationship and the loss function relationship are superimposed and fused to generate a broadband damping characteristic curve, fully revealing the stiffness and damping characteristics of the sealing component at any frequency. Finally, based on preset characteristic frequency points that are crucial to the overall vehicle NVH performance, such as engine idling frequency and tire cavity resonance frequency, corresponding key values ​​are extracted from the wideband damping characteristic curve, including damping peak data and modulus plateau value data. Among them, damping peak data is the maximum value of the loss factor within a specific frequency range, and modulus plateau value data is the value where the energy storage modulus tends to be stable in the low-frequency and high-frequency ranges. Then, the key characteristic data such as low-frequency modulus value, high-frequency modulus value, and damping value at a specific frequency are summarized and integrated to generate an initial NVH performance baseline representing the NVH performance of the component under ideal conditions.

[0042] Furthermore, step S300 also includes: performing forward modeling of the complex stiffness feature set based on the frequency variation law parameters to obtain multiple theoretical complex stiffness values; calculating the deviation between the multiple theoretical complex stiffness values ​​and the complex stiffness feature set to obtain data deviation values; adjusting the frequency variation law parameters in reverse according to the data deviation values, updating the multiple theoretical complex stiffness values ​​according to the adjustment results, and iterating to obtain inversion solution results; calculating the compression modulus according to the frequency points based on the inversion solution results to obtain the compression modulus function relationship; performing virtual-real derivation calculation based on the compression modulus function relationship to obtain the virtual-real ratio relationship, and using the virtual-real ratio relationship as the loss function relationship.

[0043] Preferably, the frequency variation law parameter refers to the material model parameters to be solved, such as relaxation time and weighting coefficients. These are used as inputs to the viscoelastic mechanics model to calculate the complex stiffness of the material at different frequencies, obtaining multiple theoretical complex stiffness values, representing the theoretically predicted complex stiffness of the current material parameters. Then, these multiple theoretical complex stiffness values ​​are compared point-by-point with the complex stiffness feature set. The deviation between the two is calculated using the least squares method, which may include real part error, imaginary part error, or modulus error, thus obtaining a data deviation value. The smaller the deviation value, the closer the current parameters are to the actual situation; the larger the deviation value, the less accurate the predicted complex stiffness. Based on the data deviation value, the gradient descent method is used to calculate the direction and amount of modification for the frequency variation law parameter, making the next theoretical calculation value closer to the measured value. The adjustment results are then used to update multiple theoretical values. The complex stiffness value is recalculated through forward modeling to determine the comparison deviation. The iteration continues until the deviation value is less than the preset tolerance or the number of iterations reaches the upper limit. The convergent parameter combination is the inversion solution. Then, the inversion solution is substituted into the mechanical model, and the compression modulus is calculated at continuous frequency points to obtain the compression modulus function relationship, that is, the mathematical expression or curve of the compression modulus changing with frequency, representing the elastic energy storage characteristics of the material. Based on the linear viscoelastic theory, the material's loss factor has a fixed mathematical relationship with the real and imaginary parts of the complex modulus, usually the ratio of the imaginary part to the real part. The imaginary-real ratio relationship is then derived by using the compression modulus function relationship to derive the ratio of the real part to the imaginary part. This is used as the loss function relationship to describe the change law of the material's damping characteristics with frequency and serves as a core indicator for evaluating the vibration reduction and noise reduction capabilities of components.

[0044] Step S400: Introduce vehicle boundary conditions to perform multi-physics field coupling correction on the initial NVH performance baseline, and generate a full-condition performance evaluation report for automotive noise reduction and vibration damping sealing components.

[0045] Step S400 further includes introducing vehicle boundary conditions, which include an upper limit value for peak damping, a lower limit value for peak damping, an upper limit value for low-frequency modulus, a lower limit value for low-frequency modulus, an upper limit value for high-frequency modulus, and a lower limit value for high-frequency modulus; comparing the initial NVH performance baseline with the vehicle boundary conditions item by item to determine whether the initial NVH performance baseline is within the vehicle boundary conditions: S1: comparing the peak damping data in the initial NVH performance baseline with the upper limit value for peak damping and the lower limit value for peak damping; S2: comparing the low-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit value for low-frequency modulus and the lower limit value for low-frequency modulus; S3: comparing the high-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit value for high-frequency modulus and the lower limit value for high-frequency modulus; if the peak damping data simultaneously satisfies being greater than or equal to the lower limit value for peak damping and less than or equal to the upper limit value for peak damping, and the low-frequency modulus... If the peak damping data, the low-frequency modulus plateau value data, and the high-frequency modulus plateau value data simultaneously satisfy the conditions of being greater than or equal to the lower limit of the low-frequency modulus and less than or equal to the upper limit of the high-frequency modulus, then the initial NVH performance baseline is determined to be within the vehicle boundary conditions. A first-level correction instruction is then generated, and topology optimization is performed on the cross-sectional geometry of the sealing component using this first-level correction instruction to generate the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component. If any one of the damping peak data, the low-frequency modulus plateau value data, or the high-frequency modulus plateau value data fails to meet its corresponding upper and lower limit ranges, then the initial NVH performance baseline is determined to be outside the vehicle boundary conditions. A second-level correction instruction is then generated, and the material parameters of the sealing component are input into the finite element simulation model using this second-level correction instruction for acoustic-structure interaction correction, generating the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component.

[0046] Preferably, vehicle boundary conditions are introduced, that is, a set of key performance index thresholds for the sealing component are extracted, including the upper limit of damping peak value, the lower limit of damping peak value, the upper limit of low-frequency modulus, the lower limit of low-frequency modulus, the upper limit of high-frequency modulus, and the lower limit of high-frequency modulus. These values ​​constrain the acceptable range of the three key performance indicators, including the damping peak value, which specifies the upper and lower limits of the energy dissipation capacity of the sealing component near the resonant frequency; the low-frequency modulus, which specifies the upper and lower limits of the stiffness of the sealing component under low-frequency large amplitude conditions (such as going over speed bumps); and the high-frequency modulus, which specifies the upper and lower limits of the stiffness of the sealing component under high-frequency small amplitude conditions (such as wind noise and road noise). The initial NVH performance baseline was compared with the vehicle boundary conditions item by item, including comparing the damping peak data in the initial NVH performance baseline with the upper and lower limits of the damping peak; comparing the low-frequency modulus plateau value data in the initial NVH performance baseline with the upper and lower limits of the low-frequency modulus; and comparing the high-frequency modulus plateau value data in the initial NVH performance baseline with the upper and lower limits of the high-frequency modulus.

[0047] Preferably, if the damping peak data simultaneously meets the conditions of being greater than or equal to the lower limit of the damping peak and less than or equal to the upper limit of the damping peak, and the low-frequency modulus plateau data simultaneously meets the conditions of being greater than or equal to the lower limit of the low-frequency modulus and less than or equal to the upper limit of the low-frequency modulus, and the high-frequency modulus plateau data simultaneously meets the conditions of being greater than or equal to the lower limit of the high-frequency modulus and less than or equal to the upper limit of the high-frequency modulus, that is, the damping, low-frequency modulus, and high-frequency modulus are all within the window, then the initial NVH performance baseline is determined to be at the vehicle boundary condition. This indicates that the material properties of the component itself are very close to the requirements of the vehicle, with only minor deviations or the need to match specific installation spaces. In this way, a first-level correction instruction is generated, that is, without changing the material formula or basic properties, only the geometric features such as the cross-sectional shape, lip angle, and wall thickness distribution of the sealing component are finely adjusted. The first-level correction instruction performs topology optimization on the cross-sectional geometric features of the sealing component. For example, by adding a sealing lip or adjusting the wall thickness of the foam tube, the contact pressure and local stiffness are finely adjusted, and a full-condition performance evaluation report of the automotive noise reduction and vibration damping sealing component is generated.

[0048] Preferably, if any of the damping peak data, low-frequency modulus plateau value data, or high-frequency modulus plateau value data fails to meet its corresponding upper and lower limit ranges, it is determined that the initial NVH performance baseline is not within the vehicle boundary conditions. This indicates that the material properties of the component itself cannot meet the vehicle requirements by simply changing its shape, and there is a fundamental performance gap. In this case, a secondary correction instruction is generated to instruct the adjustment of material parameters and acoustic-structure interaction correction. That is, the material parameters of the sealing component are input into the finite element simulation model, which includes both the solid structure (the sealing component itself) and the acoustic cavity (the air layer between the door and the body). Acoustic-structure interaction analysis is performed to simulate how sound is transmitted to the vehicle interior through structural vibration. The simulation guides fundamental adjustments to the material formulation, such as changing the glass transition temperature of the damping material. Finally, an acoustically verified full-condition performance evaluation report of the automotive noise reduction and vibration damping sealing component is generated, ensuring the accuracy and effectiveness of the NVH performance simulation of the sealing component under full-condition conditions.

[0049] In the above text, refer to Figure 1 The performance testing method for automotive noise reduction and vibration damping sealing components according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 A performance testing system for automotive noise reduction and vibration damping sealing components according to embodiments of the present invention is described.

[0050] The performance testing system for automotive noise reduction and vibration damping sealing components according to embodiments of the present invention addresses the technical problems in the prior art, namely, the inability to simulate multi-axis composite loading conditions and the lack of accurate analysis of material viscoelastic separation, leading to insufficient accuracy in the NVH performance of sealing components under vehicle operating conditions. This system achieves the technical effect of improving the simulation accuracy and evaluation effectiveness of NVH performance of sealing components under all operating conditions. Figure 2 As shown, the performance testing system for automotive noise reduction and vibration damping sealing components includes: a sensor sequence acquisition module 10, a viscoelastic separation module 20, an initial performance baseline determination module 30, and a performance evaluation report generation module 40.

[0051] The sensor sequence acquisition module 10 is used to perform multi-axis servo testing on automotive noise reduction and vibration damping sealing components to acquire original sensor sequences under multiple operating conditions; the viscoelastic separation module 20 is used to locate the contact interface, extract the interface contact state, and perform viscoelastic separation on the original sensor sequences under multiple operating conditions to obtain a complex stiffness feature set; the initial performance baseline determination module 30 is used to perform frequency-varying compression modulus inversion according to the complex stiffness feature set, construct a wideband damping characteristic curve, and determine the initial NVH performance baseline of the automotive noise reduction and vibration damping sealing components; the performance evaluation report generation module 40 is used to introduce vehicle boundary conditions to perform multi-physics field coupling correction on the initial NVH performance baseline and generate a full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing components.

[0052] The specific configuration of the sensor sequence acquisition module 10 will be described in detail below. The sensor sequence acquisition module 10 further includes: constructing a standard road spectrum database to perform operational load analysis on automotive noise reduction and vibration damping sealing components, extracting multiple load profiles; traversing the automotive noise reduction and vibration damping sealing components to segment strain regions and determine key test points; simulating based on the multiple load profiles, setting multiple loading conditions and performing multi-axis servo testing on the key test points according to the multiple load profiles to obtain test data sets; iteratively optimizing the load spectrum based on the test data sets, setting a target load spectrum, and performing bidirectional testing according to the target load spectrum to obtain the original sensor sequence for multiple working conditions.

[0053] The specific configuration of the sensor sequence acquisition module 10 will be described in detail below. The sensor sequence acquisition module 10 further includes: performing environmental factor decoupling analysis based on the test data set to determine multiple test interference components, including temperature drift interference data and humidity hysteresis interference data; analyzing the material characteristics of automotive noise reduction and vibration damping sealing components, and performing PID control on the temperature control box according to the temperature drift interference data and the material characteristics of the components to determine a compensated temperature profile; analyzing the hydrophilic modification characteristics of automotive noise reduction and vibration damping sealing components, and performing injection feedforward control according to the humidity hysteresis interference data and the hydrophilic modification characteristics to determine a compensated humidity gradient; and adjusting the load spectrum based on the compensated temperature profile and the compensated humidity gradient to set the target load spectrum.

[0054] The specific configuration of the viscoelastic separation module 20 will be described in detail below. The viscoelastic separation module 20 further includes: activating a first laser displacement sensor to track the normal compression of the contact interface in real time, obtaining a first point cloud dataset; activating a second laser confocal sensor to scan the tangential slip of the contact interface at the micrometer level, obtaining a second point cloud dataset; spatially registering the first and second point cloud datasets to generate a registration dataset; constructing a transient contact mesh, mapping the registration dataset to the transient contact mesh, and generating a dynamic contact stress cloud map; extracting interface contact state information based on the dynamic contact stress cloud map, and performing parameter identification on the original multi-condition sensing sequence and the dynamic contact stress cloud map according to the interface contact state information to generate a viscoelastic separation result; introducing a standard relaxation modulus database, traversing the viscoelastic separation result and combining it with the standard relaxation modulus database for time-temperature equivalent translation, and constructing a temperature-frequency superposition curve; and performing frequency domain extraction on the original sensing sequence based on the temperature-frequency superposition curve to obtain the complex stiffness feature group.

[0055] The specific configuration of the viscoelastic separation module 20 will be described in detail below. The viscoelastic separation module 20 further includes: performing three-dimensional analysis on the automotive noise reduction and vibration damping sealing component to construct a three-dimensional geometric model of the component; discretizing the contact surface area by traversing the three-dimensional geometric model of the component to obtain multiple finite element meshes, each of which contains at least one spatial coordinate point; performing time analysis on the multiple finite element meshes based on the multiple spatial coordinate points to obtain multiple contact state parameters; loading and recording the multiple contact state parameters according to time changes to construct the transient contact network.

[0056] The specific configuration of the initial performance baseline determination module 30 will be described in detail below. The initial performance baseline determination module 30 further includes: traversing the complex stiffness feature group to extract complex stiffness data at multiple frequency points; mapping the complex stiffness data to a frequency domain complex plane for plotting to construct a complex plane curve; performing virtual and real peak detection based on the complex plane curve to determine multiple relaxation spectrum peak frequencies; dividing the frequency range according to the multiple relaxation spectrum peak frequencies to obtain frequency variation law parameters; repeatedly approximating and solving the complex stiffness feature group based on the frequency variation law parameters to obtain the compression modulus function relationship and loss function relationship; superimposing and fusing the compression modulus function relationship and the loss function relationship to generate a broadband damping characteristic curve; extracting damping peak data and modulus plateau value data according to a preset characteristic frequency based on the broadband damping characteristic curve; integrating the damping peak data and the modulus plateau value data to construct the initial NVH performance baseline.

[0057] The specific configuration of the initial performance baseline determination module 30 will be described in detail below. The initial performance baseline determination module 30 further includes: performing forward modeling of the complex stiffness feature set based on the frequency variation law parameters to obtain multiple theoretical complex stiffness values; calculating the deviation between the multiple theoretical complex stiffness values ​​and the complex stiffness feature set to obtain data deviation values; adjusting the frequency variation law parameters in reverse according to the data deviation values, iterating the multiple theoretical complex stiffness values ​​based on the adjustment results to obtain inversion solution results; calculating the compression modulus according to frequency points based on the inversion solution results to obtain the compression modulus function relationship; performing virtual-real derivation calculation based on the compression modulus function relationship to obtain the virtual-real ratio relationship, and using the virtual-real ratio relationship as the loss function relationship.

[0058] The specific configuration of the performance evaluation report generation module 40 will be described in detail below. The performance evaluation report generation module 40 further includes: introducing vehicle boundary conditions, which include an upper limit value for damping peak, a lower limit value for damping peak, an upper limit value for low-frequency modulus, a lower limit value for low-frequency modulus, an upper limit value for high-frequency modulus, and a lower limit value for high-frequency modulus; comparing the initial NVH performance baseline with the vehicle boundary conditions item by item to determine whether the initial NVH performance baseline is within the vehicle boundary conditions: S1: comparing the damping peak data in the initial NVH performance baseline with the upper limit value for damping peak and the lower limit value for damping peak; S2: comparing the low-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit value for low-frequency modulus and the lower limit value for low-frequency modulus; S3: comparing the high-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit value for high-frequency modulus and the lower limit value for high-frequency modulus; if the damping peak data simultaneously satisfies being greater than or equal to the lower limit value for damping peak and less than or equal to the upper limit value for damping peak, and the If the low-frequency modulus plateau value data simultaneously satisfies both the lower limit and upper limit of the low-frequency modulus and the upper limit of the high-frequency modulus, then the initial NVH performance baseline is determined to be within the vehicle boundary conditions. A first-level correction instruction is then generated, and topology optimization is performed on the cross-sectional geometry of the sealing component using this instruction to generate the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component. If any one of the damping peak data, the low-frequency modulus plateau value data, or the high-frequency modulus plateau value data fails to meet its corresponding upper and lower limit ranges, then the initial NVH performance baseline is determined to be outside the vehicle boundary conditions. A second-level correction instruction is then generated, and the material parameters of the sealing component are input into the finite element simulation model for acoustic-structure interaction correction using this instruction to generate the full-condition performance evaluation report for the automotive noise reduction and vibration damping sealing component.

[0059] The performance testing system for automotive noise reduction and vibration damping sealing components provided in this embodiment of the invention can execute the performance testing method for automotive noise reduction and vibration damping sealing components provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0060] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 401, a processor 402, a memory 403, and an output device 404. The processor 402 may be one or more; the memory 403 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0061] The memory 403 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage medium can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, for storing software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the performance testing method for automotive noise reduction and vibration damping sealing components in this embodiment of the invention. The processor 402 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 403, thereby realizing the above-mentioned performance testing method for automotive noise reduction and vibration damping sealing components.

[0062] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0063] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A performance testing method for automotive noise reduction and vibration damping sealing components, characterized in that, The method includes: Multi-axis servo testing was performed on automotive noise reduction and vibration damping sealing components to obtain raw sensor sequences under multiple operating conditions. The interface contact state is extracted by locating the contact interface, and viscoelastic separation is performed on the original sensing sequence under multiple working conditions to obtain a complex stiffness feature group. Based on the complex stiffness characteristic group, frequency-varying compression modulus inversion is performed, and a wideband damping characteristic curve is constructed to determine the initial NVH performance baseline of automotive noise reduction and vibration damping sealing components; The initial NVH performance baseline is corrected by multi-physics coupling by introducing vehicle boundary conditions, and a full-condition performance evaluation report of automotive noise reduction and vibration damping sealing components is generated. The complex stiffness feature group is traversed to extract complex stiffness data at multiple frequency points. The complex stiffness data is then mapped to the frequency domain complex plane for plotting, and a complex plane curve is constructed. Based on the complex plane curve, virtual and real peak values ​​are detected to determine multiple relaxation spectrum peak frequencies; The frequency range is divided according to the multiple relaxation spectrum peak frequencies to obtain frequency variation parameters. Based on the frequency variation law parameters, the complex stiffness characteristic set is repeatedly approximated and solved to obtain the compressibility modulus function relationship and the loss function relationship; The compression modulus function relationship and the loss function relationship are superimposed and fused to generate a wideband damping characteristic curve. Based on the broadband damping characteristic curve, damping peak data and modulus plateau value data are extracted according to a preset characteristic frequency. The damping peak data and modulus plateau value data are then integrated to construct the initial NVH performance baseline.

2. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 1, characterized in that, Multi-axis servo testing was performed on automotive noise reduction and vibration damping sealing components to obtain raw sensor sequences under multiple operating conditions. The methods included: A standard road spectrum database was constructed to perform operational load analysis on automotive noise reduction and vibration damping sealing components, and multiple load profiles were extracted. Strain regions were segmented across automotive noise reduction and vibration damping sealing components to determine key test points; Simulations were performed based on the multiple load profiles, and multiple loading conditions were set up to perform multi-axis servo tests on the key test points according to the multiple load profiles to obtain test data sets. Based on the test data set, iterative optimization of the load spectrum is performed, a target load spectrum is set, and bidirectional testing is conducted according to the target load spectrum to obtain the original sensing sequence under multiple operating conditions.

3. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 2, characterized in that, Iterative optimization of the load spectrum based on the test data set, and setting of the target load spectrum, includes the following methods: Based on the test data set, environmental factor decoupling analysis was performed to determine multiple test interference components, including temperature drift interference data and humidity hysteresis interference data. Based on the analysis of the material properties of automotive noise reduction and vibration damping sealing components, the temperature control box is PID-regulated according to the temperature drift interference data and the material properties of the components to determine the compensation temperature profile. Based on the analysis of the hydrophilic modification characteristics of automotive noise reduction and vibration damping sealing components, injection feedforward control is performed according to the humidity hysteresis interference data and the hydrophilic modification characteristics to determine the compensation humidity gradient. The load spectrum is adjusted based on the compensated temperature profile and the compensated humidity gradient, and the target load spectrum is set.

4. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 1, characterized in that, The method includes: locating the contact interface, extracting the interface contact state, and performing viscoelastic separation on the original multi-condition sensing sequence to obtain a complex stiffness feature set. The first laser displacement sensor is activated to track the normal compression of the contact interface in real time and obtain the first point cloud dataset. The second laser confocal sensor is activated to scan the tangential slip of the contact interface at the micrometer level and obtain the second point cloud dataset. Spatial registration is performed between the first point cloud dataset and the second point cloud dataset to generate a registration dataset; A transient contact mesh is constructed, and the registration dataset is mapped to the transient contact mesh to generate a dynamic contact stress cloud map. Based on the dynamic contact stress cloud map, the interface contact state information is extracted. According to the interface contact state information, the original sensing sequence of the multi-condition and the dynamic contact stress cloud map are used for parameter identification to generate viscoelastic separation results. A standard relaxation modulus database is introduced, and the viscoelastic separation results are traversed and combined with the standard relaxation modulus database to perform time-temperature equivalent translation, thereby constructing a temperature-frequency superposition curve. The original sensing sequence is extracted in the frequency domain based on the temperature-frequency superposition curve to obtain the complex stiffness feature set.

5. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 4, characterized in that, The process of constructing transient contact meshes includes the following methods: Three-dimensional analysis was performed on automotive noise reduction and vibration damping sealing components, and a three-dimensional geometric model of the components was constructed. The contact surface area is discretized by traversing the three-dimensional geometric model of the component to obtain multiple finite element meshes, each of which contains at least one spatial coordinate point. Time analysis is performed on the multiple finite element meshes based on multiple spatial coordinate points to obtain multiple contact state parameters. The multiple contact state parameters are loaded and recorded according to the time change to construct the transient contact network.

6. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 1, characterized in that, Based on the frequency variation parameters, the complex stiffness characteristic set is repeatedly approximated and solved to obtain the compressibility modulus function relationship and the loss function relationship. The method includes: Based on the frequency variation law parameters, a forward modeling calculation of the complex stiffness feature group is performed to obtain multiple theoretical complex stiffness values; The deviation values ​​are calculated by comparing the multiple theoretical complex stiffness values ​​with the complex stiffness characteristic group to obtain the data deviation values; Based on the data deviation value, the frequency variation law parameter is adjusted in reverse, and the multiple theoretical complex stiffness values ​​are updated iteratively based on the adjustment result to obtain the inversion solution result. Based on the inversion solution results, the compressibility modulus is calculated according to the frequency points to obtain the compressibility modulus function relationship; Based on the aforementioned compression modulus function relationship, a virtual-real derivation calculation is performed to obtain the virtual-real ratio relationship, which is then used as the loss function relationship.

7. The performance testing method for automotive noise reduction and vibration damping sealing components as described in claim 1, characterized in that, The initial NVH performance baseline is corrected by multiphysics coupling by introducing vehicle boundary conditions, and a full-condition performance evaluation report of automotive noise reduction and vibration damping sealing components is generated. The method includes: Vehicle boundary conditions are introduced, which include an upper limit for peak damping, a lower limit for peak damping, an upper limit for low-frequency modulus, a lower limit for low-frequency modulus, an upper limit for high-frequency modulus, and a lower limit for high-frequency modulus. The initial NVH performance baseline is compared item by item with the vehicle boundary conditions to determine whether the initial NVH performance baseline is within the vehicle boundary conditions: S1: Compare the damping peak data in the initial NVH performance baseline with the upper limit of the damping peak and the lower limit of the damping peak; S2: Compare the low-frequency modulus plateau value data in the initial NVH performance baseline with the upper limit value of the low-frequency modulus and the lower limit value of the low-frequency modulus; S3: Compare the high-frequency modulus plateau value data in the initial NVH performance baseline with the high-frequency modulus upper limit value and the high-frequency modulus lower limit value; If the damping peak data simultaneously satisfies the condition of being greater than or equal to the lower limit of the damping peak and less than or equal to the upper limit of the damping peak, and the low-frequency modulus plateau value data simultaneously satisfies the condition of being greater than or equal to the lower limit of the low-frequency modulus and less than or equal to the upper limit of the low-frequency modulus, and the high-frequency modulus plateau value data simultaneously satisfies the condition of being greater than or equal to the lower limit of the high-frequency modulus and less than or equal to the upper limit of the high-frequency modulus, then it is determined that the initial NVH performance baseline is at the vehicle boundary condition, and a first-level correction instruction is generated. The cross-sectional geometric features of the sealing component are then topologically optimized using the first-level correction instruction to generate the full-condition performance evaluation report of the automotive noise reduction and vibration damping sealing component. If any of the damping peak data, the low-frequency modulus plateau value data, or the high-frequency modulus plateau value data does not meet its corresponding upper and lower limit ranges, then it is determined that the initial NVH performance baseline is not within the vehicle boundary conditions. In this case, a secondary correction instruction is generated, and the material parameters of the sealing component are input into the finite element simulation model through the secondary correction instruction for acoustic-structure interaction correction, thereby generating the full-condition performance evaluation report of the automotive noise reduction and vibration damping sealing component.

8. A performance testing system for automotive noise reduction and vibration damping sealing components, characterized in that, The system is used to implement the performance testing method for automotive noise reduction and vibration damping sealing components according to any one of claims 1 to 7, the system comprising: The sensor sequence acquisition module is used to perform multi-axis servo testing on automotive noise reduction and vibration damping sealing components and acquire original sensor sequences under multiple working conditions. The viscoelastic separation module is used to locate the contact interface, extract the interface contact state, and perform viscoelastic separation on the original multi-condition sensing sequence to obtain a complex stiffness feature group. The initial performance baseline determination module is used to perform frequency-varying compression modulus inversion according to the complex stiffness characteristic group, construct a wideband damping characteristic curve, and determine the initial NVH performance baseline of the automotive noise reduction and vibration damping sealing component. The performance evaluation report generation module is used to introduce vehicle boundary conditions to perform multi-physics field coupling correction on the initial NVH performance baseline, and generate a full-condition performance evaluation report for automotive noise reduction and vibration damping sealing components.

9. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the performance testing method for the automotive noise reduction and vibration damping sealing component according to any one of claims 1-7.

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