Automobile lamp vibration test method and device

Abstract

The invention provides an automobile lamp vibration test method and device, an automobile lamp comprises a wire harness connector and a PCB, and the method comprises the steps: constructing a finite element model and a friction model corresponding to the wire harness connector and the PCB; inputting a preset mixed vibration excitation spectrum into the finite element model, and outputting first stress modal data; inputting the first stress modal data into a friction model, and if a risk feature is detected in an output result of the friction model, determining second stress modal data corresponding to the risk feature; and inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report.

Description

Technical Field

[0001] The present application relates to the technical field of lamp testing, and in particular to a method and device for vibration testing of automobile lamps. Background Art

[0002] In the vibration failure analysis of automotive lamps, the failure rate of wiring harness connectors is significantly higher than that of other components, becoming an important factor affecting the reliability of lamps. Since cars are subject to vibrations of different frequencies and intensities during driving, the connection stability and electrical performance of wiring harness connectors are easily affected, resulting in malfunction of lamps. At present, the test method commonly used in the industry mainly relies on vibration testing of physical samples. However, this traditional method has the disadvantages of long cycle and high cost, and at the stage when the product is basically finalized, there is limited room for modification, making it difficult to effectively optimize the design. Summary of the invention

[0003] The present application provides a vibration test method and device for automobile lamps, which are used to test the design rationality of wiring harness connectors during the design stage, and have high modification flexibility and test efficiency.

[0004] In a first aspect, an embodiment of the present application provides a vibration test method for an automobile lamp, wherein the automobile lamp comprises: a wiring harness connector and a PCB board, and the method comprises: Constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; Inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data; Inputting the first stress modal data into the friction model, and if a risk feature is detected in an output result of the friction model, determining second stress modal data corresponding to the risk feature; The second stress modal data is input into the finite element model, optimization is performed, and an optimization report is output.

[0005] In a second aspect, an embodiment of the present application provides a vibration testing device for an automobile lamp, the device comprising: A model building module, used to build a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; A stress analysis module, used for inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data; a friction analysis module, configured to input the first stress modal data into the friction model, and if a risk feature is detected in an output result of the friction model, determine second stress modal data corresponding to the risk feature; The result output module is used to input the second stress modal data into the finite element model, perform optimization, and output an optimization report.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the automobile lamp vibration testing method as described in any one of the embodiments of the present application when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the automobile lamp vibration test method as described in any one of the embodiments of the present application.

[0008] The embodiment of the present application provides a vibration test method for automobile lamps, the automobile lamps include: a wiring harness connector and a PCB board, the method includes: constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; inputting a preset mixed vibration excitation spectrum into the finite element model, and outputting first stress modal data; inputting the first stress modal data into the friction model, and if a risk feature is detected in the output result of the friction model, determining the second stress modal data corresponding to the risk feature; inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report. Through the above method, the finite element model and the friction model corresponding to the wiring harness connector and the PCB board are respectively constructed, the stress mode between the two is analyzed by the finite element model, the first stress modal data is output, and then the friction effect between the two is detected by the first stress modal data and the friction model, the second stress modal data with abnormal friction effect is located, the second stress modal data with abnormal friction effect is optimized by the second stress modal data and the finite element model, and the optimization report is output, so that the compatibility between the wiring harness connector and the PCB board can be analyzed in the design stage, with high efficiency and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A schematic flow chart of a vibration test method for an automobile lamp provided in an embodiment of the present application; Figure 2 A schematic block diagram of an automobile lamp vibration testing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0013] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0015] See also Figure 1 , Figure 1 FIG. 1 is a schematic flow chart of a vibration test method for automobile lamps provided in an embodiment of the present application. Figure 1 As shown, the specific steps of the automobile lamp vibration test method include: S101-S104.

[0016] S101, constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board.

[0017] Exemplarily, based on the geometric features, material properties and assembly relationships of the wiring harness connector and the PCB board, a multi-physics field coupled finite element model and friction model are established. The finite element model must cover the terminal structure of the connector, the shell geometry, the substrate layer and copper layer routing of the PCB board, and define the nonlinear mechanical behavior of the contact pair. The geometric modeling adopts parametric design, the terminal size tolerance is controlled at ±0.05mm, and the PCB mounting hole position tolerance is ±0.1mm. The material properties are calibrated by tensile testing and dynamic mechanical analyzer (DMA): the terminal material is phosphor bronze (elastic modulus 110GPa, Poisson's ratio 0.33), and the shell material is PA66-GF30 (elastic modulus 8.5GPa, thermal expansion coefficient 5×10 -5 / ℃, and the PCB substrate is FR-4 (elastic modulus 22GPa, glass transition temperature 130℃). The friction parameters of the contact interface are obtained through micro-motion wear test. The test conditions cover 5-2000Hz vibration frequency and 0-200MPa contact pressure. The dynamic friction coefficient (0.18-0.25), static friction coefficient (0.25-0.30), wear coefficient (1.2×10 -6 mm³ / (N·m)) and contact resistance change rate (0.8mΩ / μm). The meshing of the finite element model adopts a hexahedron-dominated strategy, and the contact area is locally encrypted to 0.1mm to ensure the calculation accuracy of the stress gradient.

[0018] There is a hardness difference between the terminal contact surface (phosphor bronze, hardness HV210) and the PCB gold finger (hard gold plating, HV140). Vibration causes micro-displacement of 30-50μm, resulting in: contact resistance fluctuation (ΔR>50mΩ); wear debris accumulates to form an insulating layer (contact failure after 1000 vibration cycles).

[0019] The friction model integrates the Archard wear equation and electrical contact degradation theory, and calibrates the mapping relationship between wear depth and impedance change through experimental data.

[0020] S102, inputting a preset hybrid vibration excitation spectrum into a finite element model, and outputting first stress modal data.

[0021] Illustratively, in an embodiment of the present application, a finite element model is used to perform qualitative and quantitative analysis of stress to simulate the distribution of stress between the wiring harness connector and the PCB board. Stress analysis can be performed after the design stage is completed, thereby improving analysis efficiency.

[0022] For example, the hybrid vibration excitation spectrum is generated by combining three parts: random vibration spectrum (frequency 5-2000Hz, power spectrum density 0.01-3g² / Hz), engine second-order harmonic vibration (frequency 88Hz, amplitude 1.2g) and transient impact waveform (peak acceleration 15g, duration 50ms). After the finite element model is loaded with the excitation spectrum, the Newmark-β method is used for time domain integration, and the time step is set to 1×10 -5seconds, and calculate the acceleration response matrix. The time domain response is decomposed into frequency domain stress components through short-time Fourier transform (STFT): random vibration component (5-2000Hz broadband distribution), harmonic component (88Hz narrowband peak) and transient component (impact energy concentrated at 10-500Hz). Modal analysis extracts the first 8 natural frequencies (80Hz, 89Hz, 105Hz, 122Hz, 150Hz, 178Hz, 195Hz, 210Hz), and calculates the vibration mode distribution and participation factor of each mode. The risk identification module locates the edge of the PCB mounting hole (coordinate x=35mm, y=12mm) at 89Hz. The stress peak is 120MPa, which exceeds the yield strength of PA66-GF30 (110MPa), and is marked as a high-risk area.

[0023] S103: input the first stress modal data into the friction model, and if a risk feature is detected in the output result of the friction model, determine the second stress modal data corresponding to the risk feature.

[0024] For example, it is difficult to identify possible design problems only through finite element models, which often reverts to reliance on professionals and makes it difficult to form qualitative and quantitative standards. Therefore, while fully leveraging the finite element model's technology for accurate stress analysis, a friction model is connected to conduct qualitative and quantitative analysis of the friction effect on the stress process and identify stress risk areas.

[0025] For example, the second stress modal data includes the spatial coordinates of the risk area (x=35±0.5mm, y=12±0.3mm), stress amplitude (80-120MPa) and frequency characteristics (main frequency 89Hz, energy share 65%). The friction model calculates the wear depth of the contact surface based on the Archard wear equation. The formula parameters are calibrated through historical test data, and the impedance change trend is predicted by combining the electrical contact degradation model. The micro-displacement of the contact interface (30-50μm) is solved by the implicit dynamics algorithm, and the time step is set to 5×10 -6 seconds. The risk judgment standard is set as: contact resistance fluctuation ΔR ≥ 5mΩ or coating wear depth ≥ 0.6μm. When a risk is detected, the model output includes the risk position coordinates, stress gradient ( =15MPa / mm) and the second stress modal data of frequency domain energy distribution.

[0026] S104, inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report.

[0027] Exemplarily, the identified stress risk area corresponds to the second stress modal data, and optimization can be performed in the finite element model based on the second stress modal data to obtain preliminary modification indicators, thereby improving test efficiency.

[0028] Exemplarily, based on the risk coordinates and stress gradients in the second stress modal data, the connector installation position (Δx=+1.2mm, Δy=-0.8mm) is adjusted to avoid the resonance zone, and trapezoidal reinforcement ribs (bottom width 1.5mm, height 0.8mm) are added around the PCB mounting holes. The updated finite element model recalculates the modal parameters, and the stress peak value of the original risk point (x=35mm, y=12mm) is reduced from 120MPa to 72MPa, and the modal participation factor is reduced from 0.85 to 0.42. The optimization report generates a three-dimensional frequency response surface, showing that the 88Hz energy attenuation rate is ≥40%, and outputs a stress attenuation cloud map, marking the conditions that satisfy Δσ≤88MPa (0.8σ yield ). The tolerance sensitivity matrix indicates that the installation position tolerance must be controlled within ±0.15mm. Exceeding this range will result in a frequency deviation of ≥5Hz.

[0029] The natural frequency of the cantilever beam structure of the wiring harness connector (80-120Hz) forms a frequency-multiplying relationship with the bending mode of the PCB board (150-200Hz). The second-order vibration of the engine (44Hz×2=88Hz for a four-cylinder engine) is likely to excite a composite resonance, amplifying the amplitude of the wiring harness connector by 3.2 times.

[0030] The embodiment of the present application provides a vibration test method for automobile lamps, the automobile lamps include: a wiring harness connector and a PCB board, the method includes: constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; inputting a preset mixed vibration excitation spectrum into the finite element model, and outputting first stress modal data; inputting the first stress modal data into the friction model, and if a risk feature is detected in the output result of the friction model, determining the second stress modal data corresponding to the risk feature; inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report. Through the above method, the finite element model and the friction model corresponding to the wiring harness connector and the PCB board are respectively constructed, the stress mode between the two is analyzed by the finite element model, the first stress modal data is output, and then the friction effect between the two is detected by the first stress modal data and the friction model, the second stress modal data with abnormal friction effect is located, the second stress modal data with abnormal friction effect is optimized by the second stress modal data and the finite element model, and the optimization report is output, so that the compatibility between the wiring harness connector and the PCB board can be analyzed in the design stage, with high efficiency and high accuracy.

[0031] In order to more clearly introduce the technical solution of the present application, the technical solution of the present application will be introduced through specific embodiments below. It should be noted that the specific embodiments are used to expand the technical solution of the present application, but are not intended to limit the present application.

[0032] In some embodiments, before the preset hybrid vibration excitation spectrum is input into the finite element model and the first stress modal data is output, the method further includes: S105 - S106 .

[0033] S105. Determine triaxial vibration parameters, engine order vibration components, and road impact transient waveforms according to a preset vibration test standard.

[0034] For example, the triaxial vibration parameters refer to the vibration characteristic parameters in the three directions of X, Y, and Z, including amplitude, frequency, and phase information. The engine order vibration component refers to the harmonic vibration component that is an integer multiple of the engine speed, mainly including the first-order, second-order, and other fundamental frequency components. The road impact transient waveform simulates the transient impact response generated when the vehicle encounters bumps, potholes, and other road conditions during driving.

[0035] For example, based on international standards (such as ISO 16750-3, SAE J2380) and actual vehicle operating conditions, the quantitative indicators of triaxial vibration parameters, engine order vibration components and road impact transient waveforms are defined. The triaxial vibration parameters need to cover the typical vibration environment throughout the vehicle life cycle: the vertical (Z-axis) vibration magnitude is the largest (PSD 0.1-3g² / Hz), the lateral (Y-axis) is the second largest (PSD 0.05-1.5g² / Hz), and the longitudinal (X-axis) is the smallest (PSD 0.01-0.8g² / Hz). The engine order vibration component is determined according to the powertrain characteristics. The four-cylinder engine is mainly second-order vibration (speed range 800-6000rpm corresponding to frequency 26.7-200Hz), and the six-cylinder engine is mainly third-order vibration. The road impact transient waveform is extracted through the measured road spectrum data. Typical waveforms include speed bump impact (peak value 15g, duration 50ms) and gravel road impact (peak value 8g, duration 100ms). Parameter setting needs to take into account the high-frequency vibrations unique to new energy vehicles (such as the 800-2000Hz component caused by motor howling).

[0036] S106. Performing time-frequency domain composite excitation on triaxial vibration parameters, engine order vibration components, and road impact transient waveforms to generate a mixed vibration excitation spectrum, which includes a power spectrum density matrix, order component vectors, and impact waveform functions.

[0037] The triaxial vibration parameters are converted into a power spectral density matrix (PSD matrix), the engine order components are converted into harmonic vectors (amplitude, phase, frequency), and the road impact waveform is decomposed into time-frequency energy distribution through wavelet transform. The composite excitation algorithm adopts a weighted superposition strategy: the random vibration component accounts for 60%-70% of the overall energy, the harmonic component accounts for 20%-30%, and the transient impact accounts for 5%-10%. Time-frequency synchronization is ensured by a phase alignment algorithm to avoid energy cancellation. The generated hybrid vibration excitation spectrum contains three core modules: PSD matrix (frequency resolution 1Hz), order component vector (order resolution 0.1 order) and impact waveform function (time resolution 0.1ms).

[0038] In some embodiments, a preset hybrid vibration excitation spectrum is input into a finite element model, and first stress modal data is output, including: S1021-S1027.

[0039] S1021. Perform prestressed modal solution on the finite element model, calculate the first N-order modal parameters, and generate a modal parameter set. The modal parameter set includes: a natural frequency set, a modal vibration shape matrix, and a modal mass matrix, where N ≥ 8 and covers a frequency range of 5-2000 Hz.

[0040] The assembly preload (such as terminal crimping force 80-120N) and thermal stress field (temperature range -40℃ to +125℃) are applied to the finite element model, and the Lanczos algorithm is used to solve the structural mode under the prestressed state. The modal parameter set contains the first N-order (N≥8) natural frequency set, and the frequency range covers 5-2000Hz to ensure that all potential resonance risk frequency bands are covered; the modal vibration matrix records the displacement vector of the node under each order mode, with a spatial resolution of 0.1mm; the modal mass matrix is ​​the normalized mass distribution, which is used to quantify the energy contribution of each order mode. During the solution process, the safety margin of the modal frequency and the excitation frequency must be checked, and the interval is required to be ≥15%. If frequency overlap is detected (such as a certain modal frequency of 89Hz and the second-order excitation of the engine 88Hz), it is marked as a high-risk mode. The modal parameter set is stored in HDF5 format to support subsequent analysis module calls. The power spectral density matrix describes the frequency distribution characteristics of random vibration and contains the energy distribution information of the vibration signal; the order component vector represents the periodic vibration component related to the speed and reflects the operating characteristics of the mechanical system; the impact waveform function represents the time domain characteristics of the transient impact load.

[0041] S1022. Superimpose the power spectrum density matrix, the order component vector and the impulse waveform function in the frequency domain to generate a combined excitation matrix.

[0042] Exemplarily, energy-weighted superposition is performed on three types of excitation components in the frequency domain: random vibration components are weighted according to the PSD matrix, harmonic components are interpolated according to order amplitude, and transient impact components are mapped according to wavelet energy distribution. The dimensions of the combined excitation matrix are frequency × spatial direction × time, the frequency resolution is 1Hz, and the spatial direction includes the three axes XYZ. The superposition algorithm needs to avoid energy distortion caused by frequency band overlap, such as suppressing random vibration components near the 88Hz engine order frequency (±5Hz) to prevent resonance energy amplification. In one embodiment, the random vibration energy near 88Hz was not suppressed, resulting in the model-predicted stress value (110MPa) being lower than the measured value (128MPa), and the prediction error of the corrected combined excitation matrix was reduced to less than 5%.

[0043] S1023. Perform time domain integration on the combined excitation matrix to generate an acceleration response matrix.

[0044] For example, the Newmark-β numerical integration method is used to solve the combined excitation matrix in the time domain, and the time step is set to 5×10 according to the highest frequency component (2000 Hz). -6 seconds to ensure accurate analysis of high-frequency components. The nonlinear characteristics of the material (such as the strain rate hardening effect of the plastic shell) and the dynamic friction behavior of the contact interface (the dynamic friction coefficient varies with speed) are considered during the integration process. The acceleration response matrix records the three-dimensional acceleration time history data of each node, with a spatial resolution of 0.1mm and a time length covering a complete vibration cycle (such as 2×10 7 The data storage uses block compression technology, and the amount of data generated by a single calculation is about 10TB, which requires the support of a distributed storage system.

[0045] S1024. Perform a short-time Fourier transform on the acceleration response matrix, extract the random vibration stress component corresponding to the power spectrum density matrix, the harmonic stress component corresponding to the order component vector, and the transient stress component corresponding to the impact waveform function, and construct a four-dimensional stress modal tensor.

[0046] Exemplarily, a Hanning window (window length 50ms, overlap rate 75%) is used to perform a short-time Fourier transform (STFT) on the acceleration response matrix, with a time and frequency resolution of 5ms and 1Hz, respectively. The random vibration component is extracted by 5-2000Hz broadband filtering, the harmonic component is locked to the order frequency (such as 88Hz±1Hz), and the transient component is extracted by matching the time-energy characteristics of the impact waveform. The dimensions of the four-dimensional stress modal tensor are frequency×spatial coordinate×time×stress type, where stress types include normal stress, shear stress, and equivalent Mises stress. The tensor data is processed by a parallel computing architecture, and a single transformation takes about 12 hours (based on a 100-node cluster).

[0047] S1025. Perform principal component analysis on the four-dimensional stress modal tensor and extract the first m principal component stress modal vectors, where m=3 and the contribution rate is ≥85%.

[0048] For example, the principal components are calculated through the covariance matrix, and the first three principal components (PC1-PC3) with a cumulative contribution rate ≥ 85% are retained. The principal component vector represents the key stress distribution pattern: PC1 corresponds to the overall bending stress, PC2 corresponds to the torsional stress, and PC3 corresponds to the local contact stress. The load matrix quantifies the contribution of each node stress to the principal component and is used to locate high-risk areas. The analysis results are visualized in the form of a heat map, and the high-contribution areas (such as PC3 load value ≥ 0.7) are marked as optimization focuses.

[0049] S1026. Perform a tensor product operation on the principal component stress modal vector and the modal vibration shape matrix to generate a modal-stress coupling matrix.

[0050] For example, the tensor product operation dynamically associates the principal component stress distribution (spatial mode) with the modal vibration mode (displacement mode) to generate a three-dimensional coupling matrix (modal order × principal component × spatial node). The matrix elements represent the contribution intensity of a specific mode to the local stress. For example, the coupling coefficient of the second-order mode (89Hz) at the root of the terminal is 0.75, indicating that the stress here is mainly excited by this mode. The coupling matrix is ​​optimized through sparse storage technology, and the memory usage is reduced to 30% of the original data.

[0051] S1027. Perform singular value decomposition on the modal-stress coupling matrix, and output a first stress modal data set, where the first stress modal data set includes: a principal stress distribution cloud diagram, a modal participation factor, and a frequency eigenvalue.

[0052] For example, singular value decomposition (SVD) extracts singular values ​​and left and right singular vectors of the modal-stress coupling matrix, and generates principal stress distribution cloud diagram (area corresponding to the maximum singular value), modal participation factor (left singular vector amplitude) and frequency eigenvalue (right singular vector frequency distribution). The data set is encapsulated in JSON format, including metadata such as risk coordinates, stress amplitude, frequency labels, etc., and supports interactive 3D visualization and downstream analysis module calls.

[0053] In some embodiments, constructing the finite element model and friction model corresponding to the wiring harness connector and the PCB board includes: S1011-S1017.

[0054] S1011. Obtain material parameters of the wiring harness connector and material parameters of the PCB board, wherein the material parameters of the wiring harness connector include elastic modulus, Poisson's ratio, density, hardness coefficient, surface roughness and conductivity, and the material parameters of the PCB board include elastic modulus, Poisson's ratio, density, laminate structure parameters, surface coating thickness and conductivity of the substrate material.

[0055] For example, the material parameters are obtained through experimental testing and fusion of supplier data: the elastic modulus of the connector terminal material phosphor bronze is 110GPa, Poisson's ratio is 0.33, and density is 8.8g / cm³; the elastic modulus of the shell material PA66-GF30 is 8.5GPa, and the thermal expansion coefficient is 5×10 -5 / ℃; the elastic modulus of PCB substrate FR-4 is 22GPa and the glass transition temperature is 130℃. The surface characteristic parameters include the roughness of the terminal gold plating layer Ra≤0.4μm (measured by white light interferometer) and the contact surface conductivity of 2.44μΩ·cm (calibrated by four-probe method). The data is stored in the material database and is structured according to ISO 10350 standard.

[0056] S1012. Conduct friction tests on the wiring harness connectors and PCB boards, measure and record the friction parameter data set within the frequency range of 5-2000 Hz and the stress range of 0-200 MPa, and the friction parameter data set includes dynamic friction coefficient, static friction coefficient, wear coefficient, contact stiffness and contact resistance.

[0057] For example, the fretting wear test was performed on an electromagnetic vibration table with a frequency range of 5-2000 Hz (step size 50 Hz), a contact pressure of 0-200 MPa (step size 20 MPa), and a cycle number of 2×10 7 times. Measure the dynamic friction coefficient (0.18-0.25), static friction coefficient (0.25-0.30), wear coefficient (1.2×10 -6 mm³ / (N·m)) and contact resistance (initial value 10mΩ, failure threshold 15mΩ). The test data is stored in a frequency-pressure grid with a grid density of 10Hz×20MPa, and the total data volume is about 25,000 records.

[0058] S1013. Divide the friction parameter data set into a first sub-parameter data set and a second sub-parameter data set.

[0059] For example, the first sub-dataset (accounting for 70%) is used for model construction, covering the basic parameters of the full frequency-pressure range, and ensuring balanced data distribution according to the stratified sampling strategy. The second sub-dataset (accounting for 30%) is used for verification, focusing on high-risk working conditions (such as 88Hz, 100MPa) and boundary conditions (such as extreme temperature -40℃). After the data set is divided, the KS test is used to verify the consistency of the statistical distribution to ensure the unbiasedness of the training set and the verification set.

[0060] S1014. Using the first sub-parameter data set, a basic friction model based on the Archard wear model is established. The basic friction model is used to describe the relationship between friction depth and contact pressure, relative sliding speed and cumulative time.

[0061] For example, the Archard model describes the relationship between wear depth and contact pressure, sliding distance and material hardness. The formula parameters are fitted by nonlinear regression of the first sub-dataset. When the model is extended to vibration conditions, the sliding distance is calculated by multiplying the micro-displacement (30-50μm) by the frequency. The dynamic correction module adjusts the wear coefficient according to the frequency (for example, the coefficient increases by 20% when >500Hz). The model output is a wear depth time history curve with a resolution of 10 4 Cycle / point.

[0062] S1015. Combine the basic friction model with the measured contact resistance data to establish a contact resistance prediction model that takes frequency dependence into account.

[0063] For example, the contact resistance model integrates the effects of wear depth, oxide layer growth, and contact area change, and determines the frequency weight coefficient through multivariate regression analysis. In the low frequency band (<100Hz), the oxide layer rupture leads to a decrease in resistance, while in the high frequency band (>500Hz), the accumulation of wear debris leads to an increase in resistance. The model output is a resistance-cycle number curve, which is updated synchronously with the wear depth data.

[0064] S1016. Integrate the basic friction model and contact resistance prediction model to construct a friction-vibration coupling model. The friction-vibration coupling model is used to describe the mapping relationship between mass, damping and stiffness parameters.

[0065] For example, the friction-vibration coupling model dynamically relates the friction behavior and vibration response through state variables (wear depth, contact resistance). The mass matrix contains the material loss caused by wear, the damping matrix integrates the friction energy dissipation and viscoelastic effects, and the stiffness matrix reflects the contact interface degradation. The solver uses an implicit-explicit hybrid algorithm, and the iteration step is synchronized with the vibration period (10 4 cycle / step), and update material parameters in real time.

[0066] S1017. Verify and calibrate the friction-vibration coupling model according to the second sub-parameter data set to generate a friction model. The friction model is used to describe the corresponding relationship between the model parameters and the frequency and stress.

[0067] For example, the second sub-dataset is used to verify the accuracy of the model, and the key indicators include wear depth error ≤10% and contact resistance error ≤5%. The parameter calibration uses the Levenberg-Marquardt optimization algorithm, and the objective function is to minimize the root mean square error (RMSE) between the predicted and measured data. After verification, the model is extended to untested conditions (such as high temperature 125°C), and key influencing parameters are identified through sensitivity analysis.

[0068] In some embodiments, the first stress modal data is input into the friction model, and if a risk feature is detected in the output result of the friction model, the second stress modal data corresponding to the risk feature is determined, including: S1031-S1034.

[0069] S1031. Perform time-frequency decomposition on the second stress modal data to obtain a stress time series matrix and a stress frequency characteristic matrix.

[0070] Exemplarily, the second stress modal data is decomposed into a time domain stress series (time resolution 0.1ms) and a frequency domain energy distribution (frequency resolution 1Hz) by wavelet transform. The time domain series captures the instantaneous effect of transient impact, and the frequency domain energy distribution identifies the resonant frequency band and harmonic components. The decomposition results are stored as a three-dimensional array of time × frequency × space coordinates, which is used to drive the high-precision solution of the friction model.

[0071] S1032. According to the stress time series matrix and the stress frequency characteristic matrix, the friction model is numerically solved to obtain a friction response characteristic set.

[0072] For example, an implicit-explicit hybrid solver is used: the implicit Newmark method is used to deal with the nonlinearity of contact stiffness, and the explicit Runge-Kutta method is used to solve the friction state transition. The calculation step size is 5×10 -6 seconds, output wear depth, contact resistance, and friction time history data.

[0073] S1033. Extract features from the friction response feature set to obtain a risk feature indicator set, which includes: maximum friction depth, critical contact resistance threshold, energy dissipation rate, and stress concentration factor.

[0074] For example, four types of risk indicators are extracted from the friction response data: maximum wear depth (threshold 0.6μm), contact resistance critical value (ΔR≥5mΩ), energy dissipation rate (≥0.3J / cycle), and stress concentration factor (≥2.5). The indicator exceeding limit area is marked by a spatial clustering algorithm (DBSCAN), with a cluster radius of 0.2mm and a noise point filtering threshold of 5%.

[0075] S1034. Perform time-space mapping on the risk characteristic indicator set to obtain second stress modal data corresponding to the risk characteristics.

[0076] Exemplarily, the time-space mapping algorithm associates the risk index with the original stress modal data to generate a data set containing risk coordinates, stress gradients, main frequency energy, and modal participation factors. The data dimensions are risk points × characteristic parameters, which are used to guide the directional optimization of the finite element model and generate an optimization priority list through topological analysis.

[0077] In some embodiments, before inputting the second stress modal data into the finite element model, performing optimization, and outputting the optimization report, the method further includes: S107.

[0078] S107. In the finite element model, multiple optimization matrices are generated according to the first stress modal data, specifically: S1071-S1073.

[0079] S1071. Perform three-dimensional Kriging interpolation processing on the natural frequency and the spatial coordinates to generate a frequency-space mapping matrix.

[0080] For example, the three-dimensional Kriging interpolation algorithm establishes a continuous mapping relationship between spatial coordinates (x, y, z) and natural frequencies, the variation function uses a Gaussian model, and the interpolation grid resolution is 0.1 mm. The matrix is ​​used to quantify the impact of position changes on frequency. For example, when the frequency gradient in a certain area is ≥50 Hz / mm, a position offset of 0.2 mm can result in a frequency change of ≥10 Hz.

[0081] S1072. Perform cubic spline interpolation processing on the modal participation factor and the stress amplitude to generate a modal-stress correlation matrix.

[0082] For example, cubic spline interpolation establishes a nonlinear relationship between the modal participation factor and the stress amplitude, with a node spacing of 0.1 order. The matrix reveals the influence of the mode on the local stress. For example, when the participation factor of a certain mode increases from 0.5 to 0.8, the stress increase increases nonlinearly to 120%, and the upper limit of the participation factor needs to be constrained.

[0083] S1073. Perform Hilbert-Huang transform on the frequency response eigenvalues, extract the intrinsic mode function, and generate a frequency domain energy distribution matrix.

[0084] For example, the Hilbert-Huang transform extracts the intrinsic mode function (IMF) of the frequency response eigenvalue, and calculates the energy integral of each IMF to generate a matrix. The matrix identifies the high-frequency energy concentration area (such as 800-2000Hz), and guides the priority attenuation of high-risk frequency band energy during excitation spectrum correction to suppress resonance risk.

[0085] In some embodiments, the second stress modal data is input into the finite element model, optimization is performed, and an optimization report is output, including: S1041-S1045.

[0086] S1041. Match the risk position coordinates in the second stress modal data with the frequency-space mapping matrix, and calculate the position sensitivity index of each risk point, where the position sensitivity index is determined by the weighted average of the frequency responses in the spatial neighborhood.

[0087] For example, the position sensitivity index is calculated by weighted averaging the frequency response values ​​of the neighborhood (±0.5mm cube), with the weight decaying exponentially with distance (attenuation coefficient 0.3 / mm). The index quantifies the intensity of the impact of position change on frequency. For example, a sensitivity index of 1.5 means that a position offset of 1mm causes a frequency change of 1.5Hz, which is used to guide the optimization of the installation position.

[0088] S1042. Construct a constrained optimization equation based on the modal-stress association matrix, and output optimized modal stress gradient parameters.

[0089] Exemplarily, the constrained optimization equation limits the stress gradient change to ≤ 0.8σ yield (e.g. 88MPa), the objective function is to minimize the mean square error between the actual stress and the target stress. The sequential quadratic programming (SQP) algorithm handles nonlinear constraints, and the convergence tolerance is set to 10 -6 , with a maximum number of iterations of 200. The optimization variables include modal participation factor, installation position offset and rib geometry parameters.

[0090] S1043, inputting the optimized modal stress gradient parameters into the frequency domain energy distribution matrix, adjusting the power spectrum density component of the hybrid vibration excitation spectrum through an exponential decay function, and generating an updated excitation spectrum.

[0091] For example, based on the frequency domain energy distribution matrix, exponential attenuation (attenuation coefficient γ = 0.05) is applied to the high-risk frequency band (such as 89Hz ± 5Hz) to reduce the power spectrum density component of this frequency band. The updated excitation spectrum is generated into a time domain waveform through inverse Fourier transform, which is used for iterative verification of the finite element model to form a closed-loop optimization process.

[0092] S1044. Perform robust verification on the optimized modal stress gradient parameters to obtain verification results.

[0093] For example, the Monte Carlo simulation generates 1000 sets of parameter samples containing manufacturing tolerance disturbances (δ~N(0,0.05²)), calculates the coefficient of variation (CV=σ / μ) of the objective function (stress mean square error), and selects the optimization solution with CV≤0.1. At the same time, the constraint conditions (Δσ≤0.8σ yield ) is effective within the 99% confidence interval and solutions that do not meet the robustness requirements are eliminated.

[0094] S1045. Output an optimization report based on the optimized modal stress gradient parameters, the updated excitation spectrum and the verification results.

[0095] For example, the optimization report integrates modal parameters, updated excitation spectrum and robustness verification results. The three-dimensional frequency response surface compares the energy attenuation rate before and after optimization, the stress attenuation cloud map identifies the safe area (Δσ≤88MPa), and the sensitivity matrix marks the tolerance sensitive parameters (such as the installation position tolerance must be ≤±0.15mm). The report is presented through an interactive visualization platform, supporting multi-dimensional data drilling and process parameter feedback.

[0096] See also Figure 2 , Figure 2 2 is a schematic block diagram of an automobile lamp vibration test device provided by an embodiment of the present application, wherein the automobile lamp vibration test device 200 is used to execute the aforementioned automobile lamp vibration test method. The automobile lamp vibration test device 200 can be configured in a server.

[0097] Among them, the server can be an independent server or a server cluster, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN) and big data and artificial intelligence platforms.

[0098] like Figure 2 As shown, the automobile lamp vibration testing device 200 includes: a model building module 201 , a stress analysis module 202 , a friction analysis module 203 and a result output module 204 .

[0099] A model building module, used to build a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; A stress analysis module, used for inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data; a friction analysis module, configured to input the first stress modal data into the friction model, and if a risk feature is detected in an output result of the friction model, determine second stress modal data corresponding to the risk feature; The result output module is used to input the second stress modal data into the finite element model, perform optimization, and output an optimization report.

[0100] An embodiment of the present application provides an electronic device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement any one of the automobile lamp vibration testing methods in the embodiments of the present application when executing the computer program.

[0101] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements any one of the automobile lamp vibration testing methods in the embodiments of the present application.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A vibration test method for automobile lamps, characterized in that: The automobile lamp comprises: a wiring harness connector and a PCB board, and the method comprises: Constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; Inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data; Inputting the first stress modal data into the friction model, and if a risk feature is detected in an output result of the friction model, determining second stress modal data corresponding to the risk feature; The second stress modal data is input into the finite element model, optimization is performed, and an optimization report is output.

2. The automobile lamp vibration testing method according to claim 1, characterized in that: Before inputting the preset hybrid vibration excitation spectrum into the finite element model and outputting the first stress modal data, the method further includes: Determine triaxial vibration parameters, engine order vibration components and road impact transient waveforms according to preset vibration test standards; The triaxial vibration parameters, the engine order vibration components and the road impact transient waveform are subjected to composite excitation in the time-frequency domain to generate a mixed vibration excitation spectrum, which includes: a power spectrum density matrix, an order component vector and an impact waveform function.

3. The automobile lamp vibration testing method according to claim 2, characterized in that: The step of inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data comprises: Performing prestress modal solution on the finite element model, calculating the first N-order modal parameters, and generating a modal parameter set, wherein the modal parameter set includes: a natural frequency set, a modal vibration shape matrix, and a modal mass matrix, wherein N ≥ 8 and covers a frequency range of 5-2000 Hz; Superimposing the power spectrum density matrix, the order component vector and the impulse waveform function in the frequency domain to generate a combined excitation matrix; Performing time domain integration on the combined excitation matrix to generate an acceleration response matrix; Performing a short-time Fourier transform on the acceleration response matrix, respectively extracting the random vibration stress component corresponding to the power spectrum density matrix, the harmonic stress component corresponding to the order component vector, and the transient stress component corresponding to the impact waveform function, and constructing a four-dimensional stress modal tensor; Performing principal component analysis on the four-dimensional stress modal tensor, extracting the first m principal component stress modal vectors, where m=3 and the contribution rate is ≥85%; Performing a tensor product operation on the principal component stress modal vector and the modal vibration shape matrix to generate a modal-stress coupling matrix; The modal-stress coupling matrix is ​​subjected to singular value decomposition to output a first stress modal data set, wherein the first stress modal data set includes: a principal stress distribution cloud diagram, a modal participation factor, and a frequency eigenvalue.

4. The automobile lamp vibration testing method according to claim 1, characterized in that: The step of constructing the finite element model and the friction model corresponding to the wiring harness connector and the PCB board includes: Obtaining material parameters of the wiring harness connector and material parameters of the PCB board, wherein the material parameters of the wiring harness connector include elastic modulus, Poisson's ratio, density, hardness coefficient, surface roughness and conductivity, and the material parameters of the PCB board include elastic modulus, Poisson's ratio, density, laminated structure parameters, surface coating thickness and conductivity of the substrate material; Performing a friction test on the wiring harness connector and the PCB board, measuring and recording a friction parameter data set within a frequency range of 5-2000 Hz and a stress range of 0-200 MPa, wherein the friction parameter data set includes a dynamic friction coefficient, a static friction coefficient, a wear coefficient, a contact stiffness, and a contact resistance; dividing the friction parameter data set into a first sub-parameter data set and a second sub-parameter data set; Using the first sub-parameter data set, a basic friction model based on the Archard wear model is established, wherein the basic friction model is used to describe the relationship between friction depth and contact pressure, relative sliding speed and cumulative time; Combining the basic friction model with measured contact resistance data to establish a contact resistance prediction model that takes frequency dependence into account; Integrating the basic friction model and the contact resistance prediction model to construct a friction-vibration coupling model, wherein the friction-vibration coupling model is used to describe: a mapping relationship among mass, damping and stiffness parameters; The friction-vibration coupling model is verified and parameterized according to the second sub-parameter data set to generate a friction model, wherein the friction model is used to describe: the corresponding relationship between model parameters and frequency and stress.

5. The automobile lamp vibration testing method according to claim 1, characterized in that: The step of inputting the first stress modal data into the friction model and determining the second stress modal data corresponding to the risk feature if a risk feature is detected in an output result of the friction model comprises: Performing time-frequency decomposition on the second stress modal data to obtain a stress time series matrix and a stress frequency characteristic matrix; According to the stress time series matrix and the stress frequency characteristic matrix, the friction model is numerically solved to obtain a friction response characteristic set; Extracting features from the friction response feature set to obtain a risk feature indicator set, wherein the risk feature indicator set includes: maximum friction depth, critical contact resistance threshold, energy dissipation rate, and stress concentration factor; The risk characteristic indicator set is subjected to time-space mapping to obtain second stress modal data corresponding to the risk characteristic.

6. The automobile lamp vibration testing method according to claim 1, characterized in that: Before inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report, the method further includes: In the finite element model, multiple optimization matrices are generated according to the first stress modal data, specifically: Perform three-dimensional Kriging interpolation processing on the natural frequency and spatial coordinates to generate a frequency-space mapping matrix; The modal participation factors and stress amplitudes are processed by cubic spline interpolation to generate the modal-stress correlation matrix; Perform Hilbert-Huang transform on the frequency response eigenvalues, extract the intrinsic mode function, and generate the frequency domain energy distribution matrix.

7. The automobile lamp vibration testing method according to claim 6, characterized in that: The step of inputting the second stress modal data into the finite element model, performing optimization, and outputting an optimization report includes: Matching the risk position coordinates in the second stress modal data with the frequency-space mapping matrix, and calculating a position sensitivity index of each risk point, wherein the position sensitivity index is determined by a weighted average of frequency responses in a spatial neighborhood; Constructing a constrained optimization equation based on the modal-stress association matrix, and outputting optimized modal stress gradient parameters; Inputting the optimized modal stress gradient parameters into the frequency domain energy distribution matrix, adjusting the power spectrum density component of the hybrid vibration excitation spectrum through an exponential decay function, and generating an updated excitation spectrum; Performing robust verification on the optimized modal stress gradient parameters to obtain verification results; An optimization report is outputted according to the optimized modal stress gradient parameters, the updated excitation spectrum and the verification results.

8. A vibration test device for automobile lamps, characterized in that: The automobile lamp comprises: a wiring harness connector and a PCB board, and the automobile lamp vibration test device is used to perform the automobile lamp vibration test method according to any one of claims 1 to 7, and the automobile lamp vibration test device comprises: A model building module, used to build a finite element model and a friction model corresponding to the wiring harness connector and the PCB board; A stress analysis module, used for inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data; a friction analysis module, configured to input the first stress modal data into the friction model, and if a risk feature is detected in an output result of the friction model, determine second stress modal data corresponding to the risk feature; The result output module is used to input the second stress modal data into the finite element model, perform optimization, and output an optimization report.

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