Automobile lamp vibration test method and device

Abstract

The present application provides a vibration testing method and device for an automotive lamp. The automotive lamp includes: 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 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 the output result of the friction model, determining 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.

Description

Technical Field

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

[0002] In vibration failure analysis of automotive lamps, wiring harness connectors have a significantly higher failure rate than other components, making them a significant factor affecting lamp reliability. Because vehicles are subject to vibrations of varying frequencies and intensities during driving, the connection stability and electrical performance of wiring harness connectors are easily affected, leading to lamp malfunction. Currently, the industry's commonly used testing method relies primarily on vibration testing of physical samples. However, this traditional approach suffers from long cycle times and high costs. Furthermore, during the finalized product phase, the scope for modification is limited, making it difficult to effectively optimize the design. Summary of the Invention

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

[0004] In a first aspect, an embodiment of the present application provides a vibration testing method for an automotive lamp, wherein the automotive lamp includes a wiring harness connector and a PCB board. The method includes:

[0005] Constructing a finite element model and a friction model corresponding to the wiring harness connector and the PCB board;

[0006] Inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data;

[0007] 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;

[0008] The second stress modal data is input into the finite element model, optimization is performed, and an optimization report is output.

[0009] In a second aspect, an embodiment of the present application provides a vibration testing device for an automobile lamp, the device comprising:

[0010] 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;

[0011] A stress analysis module, configured to input a preset hybrid vibration excitation spectrum into the finite element model and output first stress modal data;

[0012] 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;

[0013] 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.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device including a memory and a processor;

[0015] The memory is used to store computer programs;

[0016] The processor is configured 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.

[0017] 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 testing method as described in any one of the embodiments of the present application.

[0018] An embodiment of the present application provides a vibration testing method for an automotive lamp. The automotive lamp includes: 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 hybrid vibration excitation spectrum into the finite element model to output first stress modal data; inputting the first stress modal data into the friction model, and if a risk feature is detected in the output of the friction model, determining 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, finite element models and friction models corresponding to the wiring harness connector and the PCB board are respectively constructed, the stress pattern between the two is analyzed using the finite element model, and the first stress modal data is output. The friction effect between the two is then detected using the first stress modal data and the friction model, and the second stress modal data with abnormal friction effect is located. The second stress modal data and the finite element model are optimized and an optimization report is output. This method enables the compatibility analysis between the wiring harness connector and the PCB board to be performed during the design phase, with high efficiency and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A schematic flow chart of a vibration testing method for an automobile lamp provided in an embodiment of the present application;

[0021] Figure 2 A schematic block diagram of an automotive lamp vibration testing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0023] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0024] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It should be further understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] See also Figure 1 , Figure 1 This 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.

[0027] S101. Construct a finite element model and a friction model corresponding to the wiring harness connector and the PCB board.

[0028] For example, based on the geometric features, material properties and assembly relationship 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 were obtained through fretting wear testing. The test conditions covered a vibration frequency of 5-2000Hz and a contact pressure of 0-200MPa. The dynamic friction coefficient (0.18-0.25), static friction coefficient (0.25-0.30), and wear coefficient (1.2×10 -6 mm³ / (N·m)) and contact resistance change rate (0.8mΩ / μm). The finite element model meshing adopts a hexahedron-dominated strategy, with the contact area locally refined to 0.1mm to ensure the accuracy of stress gradient calculations.

[0029] 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 fluctuations (ΔR>50mΩ); wear debris accumulates to form an insulating layer (contact failure after 1000 vibration cycles).

[0030] 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.

[0031] S102 : Input a preset hybrid vibration excitation spectrum into a finite element model, and output first stress modal data.

[0032] For example, 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 phase is completed, thereby improving analysis efficiency.

[0033] 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 shock 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 -5 The acceleration response matrix is calculated within 10 seconds. The short-time Fourier transform (STFT) is used to decompose the time-domain response into frequency-domain stress components: random vibration (wideband distribution of 5-2000 Hz), harmonics (narrowband peak at 88 Hz), and transients (impact energy concentrated between 10 and 500 Hz). Modal analysis extracts the first eight natural frequencies (80 Hz, 89 Hz, 105 Hz, 122 Hz, 150 Hz, 178 Hz, 195 Hz, and 210 Hz) and calculates the mode shape distribution and participation factor for each mode. The risk identification module locates the peak stress of 120 MPa at 89 Hz at the edge of the PCB mounting hole (coordinates x = 35 mm, y = 12 mm), exceeding the yield strength of PA66-GF30 (110 MPa), marking it as a high-risk area.

[0034] S103: Input the first stress modal data into the friction model. 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.

[0035] For example, it is difficult to identify possible design problems only through finite element models, which often reverts to relying on professionals and makes it difficult to form qualitative and quantitative standards. Therefore, in order to give full play to the finite element model's technology for accurate analysis of stress, a friction model is connected to conduct qualitative and quantitative analysis of the friction effect on the stress process and identify stress risk areas.

[0036] 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 The risk judgment criteria are set as: contact resistance fluctuation ΔR ≥ 5mΩ or coating wear depth ≥ 0.6μm. When a risk is detected, the model output includes the risk location coordinates, stress gradient ( =15MPa / mm) and the second stress modal data of energy distribution in the frequency domain.

[0037] S104: Input the second stress modal data into the finite element model, perform optimization, and output an optimization report.

[0038] Illustratively, 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.

[0039] For example, 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 meet 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 offset of ≥5Hz.

[0040] 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 very likely to excite a composite resonance, amplifying the amplitude of the wiring harness connector by 3.2 times.

[0041] An embodiment of the present application provides a vibration testing method for an automotive lamp. The automotive lamp includes: 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 hybrid vibration excitation spectrum into the finite element model to output first stress modal data; inputting the first stress modal data into the friction model, and if a risk feature is detected in the output of the friction model, determining 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, finite element models and friction models corresponding to the wiring harness connector and the PCB board are respectively constructed, the stress pattern between the two is analyzed using the finite element model, and the first stress modal data is output. The friction effect between the two is then detected using the first stress modal data and the friction model, and the second stress modal data with abnormal friction effect is located. The second stress modal data and the finite element model are optimized and an optimization report is output. This method enables the compatibility analysis between the wiring harness connector and the PCB board to be performed during the design phase, with high efficiency and high accuracy.

[0042] 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.

[0043] In some embodiments, before inputting the preset hybrid vibration excitation spectrum into the finite element model and outputting the first stress modal data, the method further includes: S105 - S106 .

[0044] S105. Determine triaxial vibration parameters, engine order vibration components, and road impact transient waveforms according to a preset vibration test standard.

[0045] For example, triaxial vibration parameters refer to vibration characteristic parameters in the X, Y, and Z directions, including amplitude, frequency, and phase information. Engine-order vibration components refer to harmonic vibration components that are integer multiples of the engine speed, primarily including first-order and second-order fundamental frequency components. Road impact transient waveforms simulate the transient impact response generated when a vehicle encounters bumps, potholes, and other road conditions while driving.

[0046] For example, based on international standards (such as ISO 16750-3 and SAE J2380) and actual vehicle operating conditions, quantitative indicators for triaxial vibration parameters, engine-order vibration components, and road impact transient waveforms are defined. Triaxial vibration parameters must cover typical vibration environments throughout the vehicle's lifecycle: vertical (Z-axis) vibration magnitude is highest (PSD 0.1-3 g² / Hz), followed by lateral (Y-axis) vibration (PSD 0.05-1.5 g² / Hz), and longitudinal (X-axis) vibration is lowest (PSD 0.01-0.8 g² / Hz). Engine-order vibration components are determined based on powertrain characteristics. Four-cylinder engines are primarily characterized by second-order vibration (with a speed range of 800-6000 rpm corresponding to a frequency of 26.7-200 Hz), while six-cylinder engines are primarily characterized by third-order vibration. Road impact transient waveforms are extracted from measured road spectrum data. Typical waveforms include speed bump impact (peak 15 g, duration 50 ms) and gravel road impact (peak 8 g, duration 100 ms). Parameter settings need to take into account the high-frequency vibrations unique to new energy vehicles (such as the 800-2000Hz component caused by motor howling).

[0047] S106. Perform time-frequency domain composite excitation on the triaxial vibration parameters, engine order vibration components, and road impact transient waveforms to generate a hybrid vibration excitation spectrum. The hybrid vibration excitation spectrum includes a power spectrum density matrix, order component vectors, and impact waveform functions.

[0048] 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, and frequency), and the road shock waveform is decomposed into a time-frequency energy distribution using a wavelet transform. A composite excitation algorithm employs a weighted superposition strategy: random vibration components account for 60%-70% of the total energy, harmonic components for 20%-30%, and transient shocks for 5%-10%. Time-frequency synchronization is ensured by a phase alignment algorithm to avoid energy cancellation. The resulting hybrid vibration excitation spectrum consists of three core modules: the PSD matrix (frequency resolution 1Hz), the order component vectors (order resolution 0.1), and the shock waveform function (time resolution 0.1ms).

[0049] In some embodiments, inputting a preset hybrid vibration excitation spectrum into a finite element model and outputting first stress modal data includes: S1021-S1027.

[0050] 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.

[0051] The finite element model is subjected to an assembly preload (e.g., a terminal crimping force of 80-120N) and a thermal stress field (temperature range -40°C to +125°C). The Lanczos algorithm is used to solve the structural modes in the prestressed state. The modal parameter set consists of the first N natural frequencies (N ≥ 8) covering the frequency range of 5-2000 Hz, ensuring coverage of all potential resonance risk bands. The modal shape matrix records the displacement vectors of the nodes under each mode with a spatial resolution of 0.1 mm. The modal mass matrix is a normalized mass distribution, which quantifies the energy contribution of each mode. During the solution process, the modal frequencies are checked for safety margins relative to the excitation frequencies, requiring a spacing of ≥ 15%. If frequency overlap is detected (e.g., a modal frequency of 89 Hz with an engine second-order excitation of 88 Hz), the mode is marked as a high-risk mode. The modal parameter set is stored in HDF5 format to support subsequent analysis modules. 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 components 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.

[0052] 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.

[0053] Exemplarily, three types of excitation components are energy-weighted and superimposed 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 the wavelet energy distribution. The dimensions of the combined excitation matrix are frequency × spatial direction × time, with a frequency resolution of 1 Hz and spatial directions encompassing the X, Y, and Z axes. The superposition algorithm must avoid energy distortion caused by overlapping frequency bands. For example, random vibration components are suppressed near the 88 Hz engine order frequency (±5 Hz) to prevent resonant energy amplification. In one embodiment, the random vibration energy near 88 Hz was not suppressed, resulting in the model-predicted stress value (110 MPa) being lower than the measured value (128 MPa). After correction, the prediction error of the combined excitation matrix was reduced to less than 5%.

[0054] S1023. Perform time domain integration on the combined excitation matrix to generate an acceleration response matrix.

[0055] 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 based on the highest frequency component (2000 Hz). -6seconds, ensuring accurate analysis of high-frequency components. The integration process takes into account 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 velocity). 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 Data storage uses block compression technology, and a single calculation generates approximately 10TB of data, requiring support from a distributed storage system.

[0056] S1024. Perform a short-time Fourier transform on the acceleration response matrix to 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.

[0057] For example, a short-time Fourier transform (STFT) of the acceleration response matrix is performed using a Hanning window (50 ms window length, 75% overlap), with time and frequency resolutions of 5 ms and 1 Hz, respectively. Random vibration components are extracted using a 5-2000 Hz broadband filter, harmonic components are locked to the order frequency (e.g., 88 Hz ± 1 Hz), and transient components are extracted by matching the time-energy characteristics of the impact waveform. The four-dimensional stress modal tensor has the dimensions of frequency × spatial coordinate × time × stress type, where stress types include normal stress, shear stress, and equivalent Mises stress. The tensor data is processed using a parallel computing architecture, with a single transformation taking approximately 12 hours (based on a 100-node cluster).

[0058] 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%.

[0059] For example, principal components are calculated using the covariance matrix, and the first three principal components (PC1-PC3) with cumulative contributions ≥ 85% are retained. The principal component vectors characterize key stress distribution patterns: PC1 corresponds to global bending stress, PC2 to torsional stress, and PC3 to local contact stress. The load matrix quantifies the contribution of each node stress to the principal components and is used to identify high-risk areas. The analysis results are visualized as a heat map, with high-contribution areas (e.g., PC3 load values ≥ 0.7) marked as optimization priorities.

[0060] 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.

[0061] For example, a tensor product operation dynamically correlates the principal component stress distribution (spatial mode) with the modal vibration mode (displacement mode), generating a three-dimensional coupling matrix (modal order × principal component × spatial node). The matrix elements represent the contribution of a specific mode to the local stress. For example, the coupling coefficient of the second-order mode (89 Hz) at the terminal root is 0.75, indicating that the stress there is primarily driven by this mode. The coupling matrix is optimized using sparse storage techniques, reducing memory usage to 30% of the original data.

[0062] S1027. Perform singular value decomposition on the modal-stress coupling matrix and output a first stress modal data set. The first stress modal data set includes: a principal stress distribution cloud map, a modal participation factor, and a frequency eigenvalue.

[0063] For example, singular value decomposition (SVD) extracts the singular values and left and right singular vectors of the modal-stress coupling matrix, generating a principal stress distribution cloud (region corresponding to the largest singular value), modal participation factors (left singular vector amplitudes), and frequency eigenvalues (right singular vector frequency distribution). The dataset is encapsulated in JSON format and includes metadata such as risk coordinates, stress amplitudes, and frequency labels, supporting interactive 3D visualization and downstream analysis module invocation.

[0064] In some embodiments, constructing the finite element model and friction model corresponding to the wiring harness connector and the PCB board includes: S1011-S1017.

[0065] S1011. Obtain material parameters of the wiring harness connector and 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.

[0066] 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, thermal expansion coefficient is 5×10 -5 / °C; the elastic modulus of the FR-4 PCB substrate is 22 GPa, and the glass transition temperature is 130°C. Surface characteristic parameters include the roughness of the terminal gold plating layer Ra ≤ 0.4 μm (measured by white light interferometry) and the contact surface conductivity of 2.44 μΩ·cm (calibrated by a four-probe method). Data is stored in a material database structured according to ISO 10350 standards.

[0067] S1012. Conduct friction tests on the wiring harness connectors and PCB boards, measuring and recording friction parameter data sets within the frequency range of 5-2000 Hz and the stress range of 0-200 MPa. The friction parameter data sets include the dynamic friction coefficient, static friction coefficient, wear coefficient, contact stiffness, and contact resistance.

[0068] 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, totaling approximately 25,000 records.

[0069] S1013. Divide the friction parameter data set into a first sub-parameter data set and a second sub-parameter data set.

[0070] For example, the first subset (70%) was used for model construction, covering basic parameters across the full frequency-pressure range, using a stratified sampling strategy to ensure balanced data distribution. The second subset (30%) was used for validation, focusing on high-risk operating conditions (e.g., 88 Hz, 100 MPa) and boundary conditions (e.g., extreme temperature of -40°C). After the dataset was divided, the KS test was used to verify statistical consistency of the distribution, ensuring the unbiasedness of the training and validation sets.

[0071] S1014. Establish a basic friction model based on the Archard wear model using the first sub-parameter data set. The basic friction model is used to describe the relationship between friction depth and contact pressure, relative sliding speed, and cumulative time.

[0072] 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 subset of data sets. 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 is increased by 20% when >500Hz). The model output is a wear depth time curve with a resolution of 10 4 Second cycle / point.

[0073] 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.

[0074] For example, the contact resistance model integrates the effects of wear depth, oxide layer growth, and contact area change, and uses multivariate regression analysis to determine the frequency weighting coefficient. In the low-frequency range (<100Hz), oxide layer breakdown leads to a decrease in resistance, while in the high-frequency range (>500Hz), wear debris accumulation leads to an increase in resistance. The model output is a resistance-cycle curve, which is updated synchronously with the wear depth data.

[0075] S1016. Integrate the basic friction model and the 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.

[0076] For example, the friction-vibration coupling model dynamically relates friction behavior and vibration response through state variables (wear depth, contact resistance). The mass matrix includes material loss due to wear, the damping matrix integrates friction energy dissipation and viscoelastic effects, and the stiffness matrix reflects the degradation of the contact interface. The solver uses an implicit-explicit hybrid algorithm, with the iteration step synchronized with the vibration period (10 4 cycles / steps), and update material parameters in real time.

[0077] S1017. Verify and calibrate the friction-vibration coupling model based on 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.

[0078] For example, the model accuracy was verified using the second subset of the dataset, with key metrics including wear depth error ≤ 10% and contact resistance error ≤ 5%. Parameter calibration employed the Levenberg-Marquardt optimization algorithm, with the objective function minimizing the root mean square error (RMSE) between the predicted and measured data. After verification, the model was extended to untested operating conditions (e.g., high temperature of 125°C), and sensitivity analysis was performed to identify key influencing parameters.

[0079] In some embodiments, the first stress modal data is input into the friction model. 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.

[0080] S1031. Perform time-frequency decomposition on the second stress modal data to obtain a stress time series matrix and a stress frequency characteristic matrix.

[0081] For example, the second stress modal data is decomposed into a time-domain stress series (with a time resolution of 0.1 ms) and a frequency-domain energy distribution (with a frequency resolution of 1 Hz) using a wavelet transform. The time-domain series captures the transient effects of the transient impact, while the frequency-domain energy distribution identifies resonant frequency bands and harmonic components. The decomposition results are stored as a three-dimensional array of time × frequency × spatial coordinates, which is used to drive the high-precision solution of the friction model.

[0082] S1032. Numerically solve the friction model based on the stress time series matrix and the stress frequency characteristic matrix to obtain a friction response characteristic set.

[0083] For example, an implicit-explicit hybrid solver is used: the implicit Newmark method is used to deal with the contact stiffness nonlinearity, 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.

[0084] S1033. Extract features from the friction response feature set to obtain a risk feature indicator set, where the risk feature indicator set includes: maximum friction depth, critical contact resistance threshold, energy dissipation rate, and stress concentration factor.

[0085] For example, four risk indicators were extracted from the friction response data: maximum wear depth (threshold 0.6 μm), contact resistance threshold (ΔR ≥ 5 mΩ), energy dissipation rate (≥ 0.3 J / cycle), and stress concentration factor (≥ 2.5). Areas exceeding these limits were marked using a spatial clustering algorithm (DBSCAN), with a cluster radius of 0.2 mm and a noise point filtering threshold of 5%.

[0086] S1034. Perform spatiotemporal mapping on the risk characteristic indicator set to obtain second stress modal data corresponding to the risk characteristics.

[0087] For example, a spatiotemporal mapping algorithm associates risk indicators with raw stress modal data, generating a dataset containing risk coordinates, stress gradients, dominant frequency energies, and modal participation factors. This data, dimensional as risk points × characteristic parameters, is used to guide directional optimization of the finite element model and generate an optimization priority list through topological analysis.

[0088] 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.

[0089] S107. In the finite element model, multiple optimization matrices are generated according to the first stress modal data, specifically: S1071-S1073.

[0090] S1071. Perform three-dimensional Kriging interpolation processing on the natural frequency and the spatial coordinates to generate a frequency-space mapping matrix.

[0091] For example, a 3D kriging interpolation algorithm establishes a continuous mapping between spatial coordinates (x, y, z) and natural frequencies. A Gaussian model is used as the variogram, and the interpolation grid resolution is 0.1 mm. The matrix quantifies the effect of position changes on frequency. For example, if the frequency gradient in a region is ≥50 Hz / mm, a position shift of 0.2 mm can result in a frequency change of ≥10 Hz.

[0092] S1072. Perform cubic spline interpolation processing on the modal participation factors and stress amplitudes to generate a modal-stress correlation matrix.

[0093] For example, cubic spline interpolation establishes a nonlinear relationship between modal participation factors and stress amplitudes, with node spacing of 0.1 order. The matrix reveals the influence of modes on local stresses. For example, when the participation factor of a certain modal increases from 0.5 to 0.8, the stress amplitude increases nonlinearly to 120%, necessitating a constraint on the upper limit of the participation factor.

[0094] S1073. Perform Hilbert-Huang transform on the frequency response eigenvalues, extract the intrinsic mode function, and generate a frequency domain energy distribution matrix.

[0095] For example, the Hilbert-Huang transform extracts the intrinsic mode functions (IMFs) of the frequency response eigenvalues, and the energy integral of each IMF is calculated to generate a matrix. This matrix identifies high-frequency energy concentrations (e.g., 800-2000 Hz), guiding the excitation spectrum correction to prioritize attenuation of energy in high-risk frequency bands and suppress resonance risks.

[0096] 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.

[0097] 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. The position sensitivity index is determined by the weighted average of the frequency responses in the spatial neighborhood.

[0098] For example, the position sensitivity index is calculated by weighted averaging the frequency response values of a neighborhood (±0.5mm cube), with the weights decaying exponentially with distance (attenuation coefficient 0.3 / mm). The index quantifies the impact of position changes on frequency. For example, a sensitivity index of 1.5 indicates that a 1mm position offset results in a 1.5Hz frequency change. This index is used to guide installation position optimization.

[0099] S1042. Construct a constrained optimization equation based on the modal-stress correlation matrix, and output the optimized modal stress gradient parameters.

[0100] For example, 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.

[0101] S1043. Input the optimized modal stress gradient parameters into the frequency domain energy distribution matrix, adjust the power spectrum density component of the hybrid vibration excitation spectrum through an exponential decay function, and generate an updated excitation spectrum.

[0102] For example, based on the frequency domain energy distribution matrix, exponential attenuation (attenuation coefficient γ = 0.05) is applied to high-risk frequency bands (e.g., 89 Hz ± 5 Hz) to reduce the power spectral density component in this frequency band. The updated excitation spectrum is then transformed into a time-domain waveform through an inverse Fourier transform for iterative verification of the finite element model, forming a closed-loop optimization process.

[0103] S1044. Perform robust verification on the optimized modal stress gradient parameters to obtain verification results.

[0104] For example, Monte Carlo simulation generates 1000 sets of parameter samples with manufacturing tolerance perturbations (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.

[0105] S1045. Output an optimization report based on the optimized modal stress gradient parameters, the updated excitation spectrum, and the verification results.

[0106] For example, the optimization report integrates modal parameters, updated excitation spectra, and robustness verification results. A three-dimensional frequency response surface compares the energy attenuation rate before and after optimization, a stress attenuation contour map identifies the safe zone (Δσ ≤ 88 MPa), and a sensitivity matrix identifies tolerance-sensitive parameters (e.g., installation position tolerance ≤ ±0.15 mm). The report is presented via an interactive visualization platform, supporting multi-dimensional data drilling and process parameter feedback.

[0107] See also Figure 2 , Figure 2 FIG2 is a schematic block diagram of an automobile lamp vibration test device provided in an embodiment of the present application. The automobile lamp vibration test device 200 is used to perform the aforementioned automobile lamp vibration test method. The automobile lamp vibration test device 200 can be configured in a server.

[0108] Among them, the server can be an independent server, a server cluster, or 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 (CDNs), and big data and artificial intelligence platforms.

[0109] 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 .

[0110] 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;

[0111] A stress analysis module, configured to input a preset hybrid vibration excitation spectrum into the finite element model and output first stress modal data;

[0112] 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;

[0113] 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.

[0114] 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 an automobile lamp vibration testing method as described in any one of the embodiments of the present application when executing the computer program.

[0115] An embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements a vibration test method for an automobile lamp as described in any one of the embodiments of the present application.

[0116] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A vibration test method for automobile lamps, characterized in that: The automobile lamp includes: a wiring harness connector and a PCB board, and the method includes: Constructing finite element models corresponding to the wiring harness connector and the PCB board, and obtaining material parameters of the wiring harness connector and 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 and 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 dynamic friction coefficient, static friction coefficient, wear coefficient, contact stiffness and contact resistance; dividing the friction parameter data set into a first sub- A parameter data set and a second sub-parameter data set; using the first sub-parameter data set to establish a basic friction model based on the Archard wear model, 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 considers frequency dependence; integrating the basic friction model and the 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; verifying and calibrating 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 model parameters and frequency and stress; 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, wherein: 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 based on preset vibration test standards; The triaxial vibration parameters, the engine order vibration components and the road impact transient waveform are subjected to time-frequency domain composite excitation to generate a hybrid 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, wherein: The step of inputting a preset hybrid vibration excitation spectrum into the finite element model and outputting first stress modal data comprises: Performing a prestressed 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, where N is ≥ 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, 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 to extract 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; Singular value decomposition is performed on the modal-stress coupling matrix to 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.

4. The automobile lamp vibration testing method according to claim 1, wherein: 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, includes: Performing time-frequency decomposition on the second stress modal data to obtain a stress time series matrix and a stress frequency characteristic matrix; numerically solving the friction model according to the stress time series matrix and the stress frequency characteristic matrix 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; Performing spatiotemporal mapping on the risk characteristic indicator set to obtain second stress modal data corresponding to the risk characteristic.

5. The automobile lamp vibration testing method according to claim 1, wherein: 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 on the natural frequency and spatial coordinates to generate a frequency-space mapping matrix; The modal participation factors and stress amplitudes are interpolated using cubic spline to generate the modal-stress correlation matrix. Perform Hilbert-Huang transform on the frequency response eigenvalues, extract the intrinsic mode functions, and generate the frequency domain energy distribution matrix.

6. The automobile lamp vibration testing method according to claim 5, wherein: 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 for each risk point, where the position sensitivity index is determined by a weighted average of frequency responses within a spatial neighborhood; Constructing a constrained optimization equation based on the modal-stress correlation 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 spectral density component of the hybrid vibration excitation spectrum by 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.

7. A vibration test device for automobile lamps, characterized in that: The automobile lamp comprises: a wiring harness connector and a PCB board. The automobile lamp vibration testing device is used to perform the automobile lamp vibration testing method according to any one of claims 1 to 6. The automobile lamp vibration testing device comprises: A model building module is used to build a finite element model corresponding to the wiring harness connector and the PCB board, and to obtain material parameters of the wiring harness connector and 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 and surface coating thickness and conductivity of the substrate material; a friction test is performed on the wiring harness connector and the PCB board, and a friction parameter data set is measured and recorded within a frequency range of 5-2000 Hz and a stress range of 0-200 MPa, wherein the friction parameter data set includes dynamic friction coefficient, static friction coefficient, wear coefficient, contact stiffness and contact resistance; the friction parameter data set is divided into Divided into a first sub-parameter data set and a second sub-parameter data set; using the first sub-parameter data set to establish a basic friction model based on the Archard wear model, 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 the measured contact resistance data to establish a contact resistance prediction model that considers frequency dependence; integrating the basic friction model and the 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; verifying and calibrating 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 model parameters and frequency and stress; A stress analysis module, configured to input a preset hybrid vibration excitation spectrum into the finite element model and output 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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