Construction method of high-precision whole vehicle fatigue simulation database and related equipment

Abstract

The invention relates to the technical field of computer-aided engineering, in particular to a construction method of a high-precision whole vehicle fatigue simulation database and related equipment. The method comprises the following steps: acquiring parameters of a test sample, setting parameters of a fatigue machine and fatigue parameters of the test sample; performing modeling processing on the structure of the test sample according to the parameters of the test sample to obtain an initial CAE model; performing fatigue analysis processing on the initial CAE model according to fatigue machine setting parameters to obtain simulation data; comparing the simulation data with the fatigue parameters of the test sample piece; when the data difference exists, performing parameter adjustment on the initial CAE model, and then performing fatigue analysis processing and comparison until the data difference does not exist; when the data difference does not exist, obtaining a target CAE model; and establishing a database according to the parameters of the target CAE model to obtain a fatigue simulation database. The simulation analysis accuracy can be improved, problems can be accurately found in the automobile design stage, and physical prototype testing is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer-aided engineering, and in particular, to a construction method of a high-precision whole-vehicle fatigue simulation database and related equipment. BACKGROUND

[0002] In the modern automobile manufacturing industry, structural adhesives are widely used between many components and parts of automobiles, and their role is to enhance the stability and reliability of the structure. For example, in the connection of the body frame, the combination of the door and the body, and the fixation of the engine components, structural adhesives play a key role. With the continuous development of the automobile industry, the requirements for automobile performance are increasing, which makes automobile manufacturers increasingly rely on simulation analysis technology to predict and optimize various performances of automobiles in the product design stage.

[0003] In the simulation analysis process, in order to make the simulation results as close to the actual situation as possible, accurate material parameters need to be input. For structural adhesives, their basic properties such as density, Young's modulus, and Poisson's ratio are important basis for simulation analysis. However, the existing technology has obvious defects: on the one hand, when directly inputting the parameters of the adhesive in real life for simulation, the fatigue performance and other performances exhibited are often inaccurate. This is because the structural adhesive in actual application will have complex changes in performance under different environmental conditions (such as temperature, humidity), stress state, and contact with different materials, and the existing simulation model fails to fully consider these factors. On the other hand, the current simulation data acquisition method lacks systematicness and accuracy. Most methods only rely on empirical data or simple laboratory tests, without establishing a perfect simulation parameter adjustment mechanism and a comprehensive simulation database. For example, some studies only conduct a limited number of experimental tests on a specific type of structural adhesive at room temperature, and directly apply these test results to simulation analysis, ignoring the differences between actual use scenarios and laboratory environments, or only considering the stiffness parameters of the adhesive, without involving key issues such as fatigue life prediction of the adhesive under cyclic loading and crack propagation mechanism. This simple processing method leads to a large deviation between the simulation results and the actual situation, and cannot provide reliable reference for automobile manufacturing.

[0004] In summary, the technical problems in the related art need to be improved. SUMMARY

[0005] The main purpose of the embodiments of the present application is to propose a construction method of a high-precision whole-vehicle fatigue simulation database and related equipment, which can improve the accuracy of simulation analysis, accurately find problems in the automobile design stage, and reduce physical prototype testing.

[0006] To achieve the above object, one aspect of the embodiment of the present application proposes a method for constructing a high-precision whole vehicle fatigue simulation database, which comprises the following steps:

[0007] Obtaining parameters of a test sample, fatigue machine setting parameters and test sample fatigue parameters;

[0008] Modeling the structure of the test sample according to the parameters of the test sample to obtain an initial CAE model;

[0009] Performing fatigue analysis on the initial CAE model according to the fatigue machine setting parameters to obtain simulation data;

[0010] Comparing the simulation data with the test sample fatigue parameters;

[0011] When there is a data difference, performing parameter adjustment on the initial CAE model, then performing fatigue analysis and comparison, until there is no data difference;

[0012] When there is no data difference, obtaining a target CAE model;

[0013] Obtaining parameters of a part in the test sample, fatigue machine part setting parameters and part fatigue parameters when there is no data difference;

[0014] Modeling the structure of the part according to the parameters of the part to obtain an initial CAE part model;

[0015] Performing fatigue analysis on the initial CAE part model according to the fatigue machine part setting parameters to obtain part simulation data;

[0016] Comparing the part simulation data with the part fatigue parameters;

[0017] When there is a part data difference, performing parameter adjustment on the initial CAE part model, then performing fatigue analysis and comparison, until there is no part data difference;

[0018] When there is no part data difference, obtaining a target CAE part model;

[0019] Performing database construction according to the parameters of the target CAE model and the target CAE part model to obtain a fatigue simulation database;

[0020] Among them, the test sample and the part are both provided with structural glue.

[0021] In some embodiments, the method further comprises:

[0022] Obtaining structural glue fatigue data;

[0023] adding the structural adhesive fatigue data to fatigue damage parameters and crack propagation parameters, and associating actual working load spectrum to obtain a closed-loop data system;

[0024] establishing an intelligent matching engine according to the closed-loop data system.

[0025] In some embodiments, the structural adhesive fatigue data includes stress-strain curves, fatigue life S-N curves, and crack propagation rates.

[0026] The fatigue damage parameters include Goodman correction coefficients and Miner linear cumulative damage rules.

[0027] The crack propagation parameters include Paris law coefficients.

[0028] In some embodiments, the method further comprises:

[0029] retrieving new working condition parameters through the intelligent matching engine to obtain high-similarity historical cases;

[0030] extracting material parameters, fatigue parameters, and fatigue machine setting parameters from the high-similarity historical cases;

[0031] setting weights and mapping the extracted material parameters, fatigue parameters, and fatigue machine setting parameters through weight allocation rules to obtain new working condition initial preset parameters;

[0032] performing 5% to 10% gradient fine-tuning on the differences between the new working condition initial preset parameters and the material parameters, fatigue parameters, and fatigue machine setting parameters through a gradient adjustment algorithm to obtain adjusted parameters;

[0033] calculating stress root mean square errors and strain correlation coefficients for the adjusted parameters to obtain verification parameters;

[0034] integrating the verification parameters and attaching working condition adaptation instructions to obtain a pre-calibration parameter set;

[0035] When there are data differences, retrieving the data differences through the pre-calibration parameter set to obtain calibration parameters;

[0036] adjusting parameters for the initial CAE model or the initial CAE part model according to the calibration parameters to obtain the target CAE model or the target CAE part model.

[0037] In some embodiments, during the process of adjusting parameters after comparing with the sample fatigue parameters, batch calculation is performed through post-processing compilation.

[0038] In some embodiments, when the data difference or the part data difference exists, the following steps are included:

[0039] When the data difference or the part data difference is stress, Young's modulus, hyperelastic model parameters, contact type or contact stiffness of the initial CAE model or the initial CAE part model are adjusted;

[0040] When the data difference or the part data difference is strain, viscoelastic parameters, plastic model parameters or contact penetration tolerance of the initial CAE model or the initial CAE part model are adjusted;

[0041] When the data difference or the part data difference is fatigue life, fatigue damage parameters, crack propagation parameters, loading spectrum or contact boundary conditions of the initial CAE model or the initial CAE part model are adjusted.

[0042] In some embodiments, when the data difference or the part data difference exists, no more than 2 parameters are adjusted each time, and gradient test is performed according to a 5% step length;

[0043] The convergence criteria include: stress root mean square error is less than 10%, strain correlation coefficient is greater than 0.95, and fatigue life deviation is less than 10%.

[0044] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method described above when executing the computer program.

[0045] To achieve the above object, another aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the method described above when executed by a processor.

[0046] To achieve the above object, another aspect of the embodiments of the present application provides a computer program product, which comprises a computer program, and the computer program implements the method described above when executed by a processor.

[0047] The embodiment of the present application at least has the following beneficial effects: the present application provides a high-precision vehicle fatigue simulation database construction method and related equipment, the scheme obtains the related parameters of the test sample and the parts and models, performs simulation analysis through the fatigue machine setting parameters, repeatedly compares and adjusts the parameters until the simulation and the test data are consistent, and then builds a database, which can greatly improve the accuracy of the structure glue fatigue simulation analysis; a complete simulation data acquisition system is constructed, which provides reliable reference for related research; the method can also accurately find problems in the automobile design stage, reduce physical prototype testing, thereby effectively reducing the research and development cost, shortening the research and development period, and enhancing the market competitiveness of enterprises. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 is a flowchart of a high-precision vehicle fatigue simulation database construction method provided by the embodiment of the present application;

[0049] Figure 2 is a hardware structure schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. When the following description relates to the drawings, the same numerals in different drawings represent the same or similar elements unless otherwise indicated. The implementation described in the following exemplary embodiments does not represent all the implementations consistent with the embodiments of the present application, but is only an example of devices and methods consistent with some aspects of the embodiments of the present application as described in the appended claims.

[0051] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0053] Before the embodiments of the present application are described in detail, first, some nouns and terms involved in the embodiments of the present application are described, and the nouns and terms involved in the embodiments of the present application are applicable to the following explanations.

[0054] CAE model: A virtual model created using computer-aided engineering software, which simulates structural mechanical behavior and predicts product performance through methods such as finite element method.

[0055] Goodman correction factor: A correction factor in fatigue analysis that considers the influence of mean stress, correlates alternating stress with mean stress, and corrects fatigue life prediction.

[0056] Miner's linear cumulative damage rule assumes that fatigue damage accumulates linearly, and failure occurs when the sum of the damage rates at each stress level reaches 1. It is used for life prediction under multiple load conditions.

[0057] Miner coefficient: cumulative damage value, obtained by summing the ratio of the number of cycles to the fatigue life of the material at each stress level. A value ≥1 indicates fatigue failure.

[0058] Paris Law coefficients: Parameters (C and m) describing the relationship between crack propagation rate and stress intensity factor, used to predict crack propagation life, where C is a proportionality constant and m is an exponent.

[0059] Stress RMSE: Root mean square error of stress, a measure of the deviation between simulated stress data and experimental values. The smaller the value, the higher the simulation accuracy.

[0060] Figure 1 This is an optional flowchart of a method for constructing a high-precision vehicle fatigue simulation database provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S110 to S190.

[0061] Step S110: Obtain the parameters of the test specimen, the fatigue machine setting parameters, and the fatigue parameters of the test specimen;

[0062] Step S120: Model the structure of the test specimen according to the parameters of the test specimen to obtain the initial CAE model;

[0063] Step S130: Perform fatigue analysis on the initial CAE model according to the fatigue machine setting parameters to obtain simulation data;

[0064] Step S140: Compare the simulation data and the fatigue parameters of the test specimen; when there are data differences, adjust the parameters of the initial CAE model and then perform fatigue analysis and comparison until there are no data differences; when there are no data differences, the target CAE model is obtained.

[0065] Step S150: Obtain the parameters of the parts in the test sample when there are no data differences, the fatigue machine part setting parameters, and the part fatigue parameters;

[0066] Step S160: Model the structure of the part according to the parameters of the part to obtain the initial CAE part model.

[0067] Step S170: Perform fatigue analysis on the initial CAE part model according to the fatigue machine part setting parameters to obtain part simulation data;

[0068] Step S180: Compare the part simulation data and part fatigue parameters; when there are differences in part data, adjust the parameters of the initial CAE part model and then perform fatigue analysis and comparison until there are no differences in part data; when there are no differences in part data, the target CAE part model is obtained.

[0069] Step S190: Based on the parameters of the target CAE model and the target CAE part model, a database is built to obtain the fatigue simulation database; wherein, the test specimen and the part contain structural adhesive.

[0070] Specifically, the method first involves preparing experimental prototypes and establishing corresponding initial CAE models. Then, simultaneous experiments and simulations are conducted, using fatigue testing machines and simulation software to perform experiments and fatigue simulations on the prototypes and corresponding models. After the experiments and simulations are completed, the data are compared, simulation parameters are adjusted, and recalculations are performed to obtain the target CAE model. Simulation data on the adhesive between real parts in the test prototypes is then collected. Next, a structural adhesive fatigue simulation database with a closed-loop data system and unknown working condition prediction capabilities is constructed. Finally, the fatigue database is applied to the whole vehicle model to improve the accuracy of the whole vehicle simulation. This method can significantly improve the accuracy of simulation analysis by repeatedly comparing and adjusting parameters between experimental and simulation data, constructing a complete simulation data acquisition system, and providing a reference for subsequent research. It can also accurately identify problems during the automotive design stage, reduce physical prototype testing, effectively reduce R&D costs and time, and enhance the company's market competitiveness.

[0071] In some embodiments, in steps S110 to S120, the parameters of the test specimen include plate size, material properties, structural adhesive type, thickness, and connection method. The material properties include elastic modulus and Poisson's ratio. The fatigue test parameters include experimental conditions such as loading force, loading frequency, loading time, loading direction, and ambient temperature. The fatigue parameters of the test specimen are the experimentally measured stress-strain curve of the structural adhesive, fatigue life data (such as the number of cycles), and crack propagation rate. A two-dimensional or three-dimensional model is created using CAE software to accurately replicate the specimen's geometry. Material properties are defined, setting the plate as isotropic or anisotropic, and the structural adhesive as a viscoelastic or elastoplastic constitutive model. Contact conditions are set to simulate the interface behavior between the plate and the structural adhesive. Specifically, test specimens are prepared and corresponding initial CAE models are established. An experiment is conducted by adding structural adhesive between two horizontal plates, and a CAE model with structural adhesive added between the two horizontal plates is established. This model needs to accurately simulate the connection method, dimensional parameters, and material property settings of the plate and structural adhesive in the actual structure, laying the foundation for subsequent simulation and experimental comparisons. The test samples include boards with structural adhesive. The boards do not have to be horizontal and can be of other shapes. The main purpose is to make them easy to manufacture and convenient to test.

[0072] In some embodiments, in step S130, load conditions and boundary conditions consistent with those in the experiment are applied, a fatigue analysis method is selected, such as the stress-life method, strain-life method, or fracture mechanics method, and simulation calculations are run to output the stress distribution, strain history, and predicted fatigue life of the structural adhesive, thus obtaining simulation data. Specifically, experiments and simulations are synchronized. The test specimen with added structural adhesive is placed on a fatigue testing machine. During the test, the loading conditions of the fatigue testing machine are accurately recorded, including key parameters such as the magnitude of the loading force, loading frequency, and loading time. Simultaneously, fatigue analysis is performed in the simulation software according to the loading conditions of the fatigue testing machine to obtain simulation data. The consistency of experimental and simulation conditions is ensured throughout the process.

[0073] In some embodiments, in step S140, the simulation data generated during the simulation process is quantitatively compared with the stress-strain curve morphology, peak stress, and strain value of the experiment. The predicted fatigue life value is compared with the measured number of cycles. The relative error is calculated to obtain the data difference, and the consistency of crack initiation location and propagation path is analyzed. When data differences exist, the parameter adjustment range includes the elastic modulus, yield strength, viscoelastic parameters, contact stiffness, or friction coefficient of the structural adhesive. Iterative optimization methods, such as gradient descent or response surface methodology, are used to adjust 1-2 parameters each time, repeating the simulation and comparison until the root mean square error of stress is less than 10% and the strain correlation coefficient is greater than 0.95. When there are no data differences, the model parameters are confirmed to converge to the experimental results. The final parameter set is saved, a model verification report is generated, and the parameter adjustment process and final error index are recorded to obtain the target CAE model. Specifically, after the experiment and simulation are completed, comprehensive data from both sides are collected for detailed comparison. The comparison includes stress and strain data of the structural adhesive at different stages, fatigue life data, etc. The reasons for the differences between the two are analyzed, and then the simulation parameters, such as the constitutive model parameters and contact parameters of the structural adhesive, are adjusted. After adjustments, perform simulation calculations again, repeating this process continuously until the simulation results match the experimental results.

[0074] In some embodiments, in steps S150 to S180, when there are no data differences in the test specimens during comparison, the parameters of the test specimens include the geometric features, material composition, and heat treatment state of the actual product. The fatigue machine part setting parameters are determined based on the loading spectrum determined by real working conditions such as road excitation or engine vibration. The fatigue parameters of the parts are obtained through actual part fatigue experiments to verify the effectiveness of the model in complex structures. A complete part model including structural adhesive connections is established, considering assembly relationships and geometric nonlinearity. The mesh generation needs to be refined in the structural adhesive region to ensure the calculation accuracy of stress concentration areas. Multiaxial loads and dynamic boundary conditions are applied to simulate the actual service environment, considering mean stress correction and load sequence effects. The predictive ability of the part-level model is evaluated, focusing on the stress state at key locations of the structural adhesive, and the deviation between simulation and experiment in crack initiation location and propagation direction is analyzed. The material parameters of the part, such as the fatigue strength index, and the interface parameters of the structural adhesive are adjusted, and the analysis accuracy of the structural adhesive region is locally refined to obtain the target CAE part model. The reliability of the model at the actual part scale is verified, and a standard process for part-level simulation is established.

[0075] In some embodiments, during step S190, when building the database based on the parameters of the target CAE model and the target CAE part model, all key parameters verified experimentally in both models must first be extracted. These parameters cover the material properties and contact settings of the structural adhesive, the geometric dimensions and material parameters of the test specimen and the real part, as well as fatigue analysis-related parameters. Simultaneously, corresponding operating condition data must be included, including experimental parameters such as the magnitude, frequency, and time of the fatigue test load, actual driving load spectra such as road excitation and start-stop vibration, environmental parameters such as temperature and humidity, and performance data such as stress-strain curves, fatigue life (SN) curves, and crack propagation rates confirmed through experimental and simulation comparisons. Then, this data is categorized and stored according to a hierarchical structure of material parameters - model parameters - operating condition parameters - performance data, establishing multi-dimensional relationships. For example, specific structural adhesive material parameters are bound to corresponding part materials, loading conditions, and fatigue life data, forming a closed-loop data chain of material - model - operating condition - performance. Based on this, data standardization rules and an intelligent retrieval engine are embedded. The data standardization rules include unified parameter units and formats, which ensure that when new working condition parameters are input, highly similar historical data can be quickly matched. Finally, a fatigue simulation database containing a complete parameter system, clear correlation logic and support for intelligent applications is constructed, providing accurate and reusable parameter support for subsequent structural adhesive fatigue simulation under new working conditions.

[0076] In some embodiments, synchronous simulation experiments are not limited to fatigue performance; synchronous experiments can be conducted on various aspects such as stiffness, strength, or hardness, depending on the experimental objective, to obtain accurate data from multiple aspects.

[0077] In some embodiments, structural adhesive fatigue data is acquired, added to fatigue damage parameters and crack propagation parameters, and correlated with actual operating condition load spectra to obtain a closed-loop data system. An intelligent matching engine is then established based on this closed-loop data system. Specifically, this embodiment involves constructing a structural adhesive fatigue simulation parameter database. Fatigue experimental data of structural adhesive under different operating conditions is collected, including stress-strain curves, fatigue life (SN) curves, and crack propagation rates. Fatigue damage parameters such as the Goodman correction factor and Miner's linear cumulative damage law are incorporated, along with crack propagation data such as Paris law coefficients C and m. This is then correlated with actual driving condition load spectra such as road surface excitation and start-stop vibration to form a one-to-one material-operating condition-fatigue life closed-loop data system. An intelligent matching engine is then established. After inputting a new operating condition, historical cases with a similarity greater than 90% are retrieved, and a predicted parameter set is output based on the measured data, achieving standardized data storage and intelligent application. The new operating condition includes component materials, load spectra, and environment.

[0078] More specifically, obtaining fatigue data for structural adhesives requires multi-dimensional testing through experimental design. Fatigue tests are conducted on connectors containing structural adhesives under different loading conditions (such as magnitude, frequency, or waveform of the loading force) and environmental factors (temperature, humidity, corrosive media). The entire process of the structural adhesive, from elastic deformation to plastic accumulation, and finally crack initiation, propagation, and fracture, is precisely recorded. This includes stress-strain dynamic curves at each stage, fatigue life corresponding to different stress levels, and the rate of crack length change with the number of cycles. Adding this fatigue data to fatigue damage parameters and crack propagation parameters involves integrating fatigue life data with fatigue damage parameters such as the Goodman correction factor and Miner's linear cumulative damage rule to clarify the quantitative indicators corresponding to different damage levels. Simultaneously, crack propagation rate data is correlated with crack propagation parameters such as C and m, which describe the relationship between crack propagation rate and stress intensity factor range in Paris's law, forming a complete damage evolution parameter system. Next, these integrated parameters are deeply correlated with the actual operating condition load spectrum. This actual operating condition load spectrum encompasses load characteristics under real-world scenarios such as road surface excitation during vehicle operation, engine start-stop vibration, and component assembly stress. By establishing a correspondence between parameters and the load spectrum, a closed-loop data system is formed, encompassing structural adhesive material properties, actual operating condition loads, fatigue damage levels, crack propagation states, and fatigue life. This ensures that the data comprehensively reflects the fatigue behavior of structural adhesives in practical applications. When building an intelligent matching engine based on this closed-loop data system, the data within the system is first standardized and categorized for storage, constructing a structured database containing material parameters, operating condition parameters, damage and propagation parameters, and life data. The core of the engine is the development of a similarity matching algorithm. When new application scenario parameters are input, the algorithm automatically retrieves historical cases from the database with a matching degree exceeding 90% for each parameter of the new scenario. It then calls upon the fatigue damage parameters and crack propagation parameters from the corresponding cases, quickly outputting a pre-calibrated parameter set adapted to the new scenario, realizing intelligent matching and application of structural adhesive fatigue simulation parameters. This establishes a database with a closed-loop data system and unknown operating condition prediction capabilities, significantly improving simulation accuracy and efficiency.

[0079] In some embodiments, a smart matching engine is used to retrieve parameters for the new working condition to obtain high-similarity historical cases; data is extracted from these high-similarity historical cases to obtain material parameters, fatigue parameters, and fatigue machine setting parameters; weights are assigned and mapped to the extracted material parameters, fatigue parameters, and fatigue machine setting parameters using weight allocation rules to obtain initial preset parameters for the new working condition; a gradient adjustment algorithm is used to fine-tune the differences between the initial preset parameters for the new working condition and the material parameters, fatigue parameters, and fatigue machine setting parameters by 5% to 10% to obtain adjusted parameters; the root mean square error of stress and strain correlation coefficient are calculated for the adjusted parameters to obtain verification parameters; the verification parameters are integrated and working condition adaptation instructions are added to obtain a pre-calibration parameter set; when data differences exist, the pre-calibration parameter set is used to retrieve data differences to obtain calibration parameters; the parameters of the initial CAE model or initial CAE part model are adjusted according to the calibration parameters to obtain the target CAE model or target CAE part model.

[0080] Specifically, when searching for parameters for a new working condition using the intelligent matching engine, the engine accurately compares the new working condition with the existing structural adhesive fatigue simulation parameter database based on its various characteristics. It then selects historical cases with a similarity exceeding 90% to the new working condition. These cases serve as crucial references for subsequent parameter adjustments. The parameters for the new working condition include part materials, load spectrum, and environmental conditions. Data extraction from the selected high-similarity historical cases comprehensively acquires material parameters, fatigue parameters, and fatigue machine setting parameters, providing foundational data for the initial setting of parameters for the new working condition. Material parameters include the elastic modulus, Poisson's ratio, and yield strength of the structural adhesive; fatigue parameters include the Goodman correction factor, parameters related to Miner's linear cumulative damage law, and the Paris law coefficient C / m; and fatigue machine setting parameters include the magnitude of the loading force, loading frequency, and loading time. Then, based on actual needs and priorities, appropriate weights are assigned to the extracted material parameters, fatigue parameters, and fatigue machine setting parameters according to weight allocation rules. These weighted data are then mapped to the new working condition to obtain the initial preset parameters for the new condition, ensuring that the initial parameters closely match the actual situation of the new condition. Next, a gradient adjustment algorithm is used to adjust the parts of the initial preset parameters for the new condition that differ from the material parameters, fatigue parameters, and fatigue machine setting parameters in historical cases. Fine-tuning is done incrementally in gradients of 5% to 10%, adjusting only 1-2 parameters at a time to obtain the adjusted parameters, ensuring the accuracy and controllability of parameter adjustment. During the repeated calculation of parameter adjustments, post-processing compilation is often used for batch calculations to improve computational efficiency. The root mean square error of stress and strain correlation coefficient are calculated for the adjusted parameters. If the root mean square error of stress is less than 10% and the strain correlation coefficient is greater than 0.95, the parameter is determined as a verification parameter to ensure the reliability of the parameters. The calibration parameters are then integrated, along with working condition adaptation instructions, including applicable environmental conditions and load ranges, ultimately forming a pre-calibrated parameter set that can be directly imported into the CAE model. When discrepancies exist between simulation and experimental data under new working conditions, the pre-calibrated parameter set is used to search for these discrepancies and find calibration parameters that can correct them. Finally, based on the calibration parameters, relevant parameters in the initial CAE model or initial CAE part model, such as constitutive model parameters and contact parameters of structural adhesives, are adjusted. After multiple iterations of optimization, the simulation data of the model matches the experimental data, thus obtaining the target CAE model or target CAE part model. Therefore, when facing new working conditions, the calibrated parameters in the database are called upon to quickly generate reliable simulation results, significantly improving prediction efficiency and accuracy.

[0081] More specifically, when predicting parameters for new operating conditions, the intelligent matching engine, after retrieving historical cases with a similarity greater than 90%, first extracts data such as material parameters, fatigue parameters, and contact settings from the cases. It then sets weights according to actual needs and priorities (e.g., weights of 40% for material matching, 35% for load characteristics, and 25% for environmental parameters) and maps them to the new operating conditions. Next, it fine-tunes relevant parameters using a 5%-10% gradient for parameter differences between the new operating conditions and the cases (e.g., temperature, load frequency). It adjusts 1-2 parameters each time and calculates stress RMSE until the deviation is less than 10% and the strain correlation coefficient is greater than 0.95. Finally, it integrates the corrected material constitutive, fatigue damage, and contact parameters, along with operating condition adaptation instructions, to generate a pre-calibrated parameter set that can be directly imported into the CAE model.

[0082] In some embodiments, when data differences or part data differences exist, the following steps are included: when the data difference or part data difference is stress, adjust the Young's modulus, hyperelastic model parameters, contact type, or contact stiffness of the initial CAE model or initial CAE part model; when the data difference or part data difference is strain, adjust the viscoelastic parameters, plastic model parameters, or contact penetration tolerance of the initial CAE model or initial CAE part model; when the data difference or part data difference is fatigue life, adjust the fatigue damage parameters, crack propagation parameters, loading spectrum, or contact boundary conditions of the initial CAE model or initial CAE part model. Specifically, when comparing experimental and simulation data, if discrepancies exist, adjust constitutive parameters or contact settings in the following ways: When stress is inconsistent, fine-tune Young's modulus and hyperelastic model parameters in a 5%-10% gradient, such as changing bonded contact to frictional contact and setting a friction coefficient of 0.3-0.5 to modify the contact type and reduce contact stiffness; when the data difference is due to strain, adjust viscoelastic parameters such as relaxation time or loss factor, plastic model parameters such as yield strength or hardening coefficient, or adjust the contact penetration tolerance from 1% to 0.5% of the mesh size; when the data difference is due to fatigue life, adjust fatigue damage parameters such as Goodman correction factor or Miner factor, crack propagation parameters such as Paris law coefficients C and m, or modify the loading spectrum and contact boundary conditions. Adjust only 1 to 2 parameters at a time, using a 5% step gradient test, with a stress RMSE less than 10%, a strain correlation coefficient greater than 0.95, and a fatigue life deviation less than 10% as convergence criteria.

[0083] In some embodiments, the obtained fatigue simulation database is applied to the whole vehicle model. Specifically, the obtained structural adhesive data is applied to all adhesive bonding points in the whole vehicle model. During the whole vehicle model simulation process, the interactions between various components and actual driving conditions are fully considered. By utilizing the previously acquired accurate adhesive simulation data, more accurate whole vehicle simulation results are obtained, providing strong support for the optimized design of automobiles.

[0084] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0085] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0086] Please see Figure 2 , Figure 2 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0087] The processor 201 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0088] The memory 202 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 202 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 202 and is called and executed by the processor 201 using the methods described in the embodiments of this application.

[0089] Input / output interface 203 is used to implement information input and output;

[0090] The communication interface 204 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0091] Bus 205 transmits information between various components of the device (e.g., processor 201, memory 202, input / output interface 203, and communication interface 204);

[0092] The processor 201, memory 202, input / output interface 203 and communication interface 204 are connected to each other within the device via bus 205.

[0093] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0094] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0095] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0096] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0097] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0099] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0102] It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than those illustrated or described herein. Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0103] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0105] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for constructing a high-precision vehicle fatigue simulation database, characterized in that, The method includes the following steps: Obtain the parameters of the test specimen, the fatigue machine settings, and the fatigue parameters of the test specimen; The structure of the test specimen is modeled based on the parameters of the test specimen to obtain an initial CAE model; The initial CAE model is subjected to fatigue analysis based on the fatigue machine settings to obtain simulation data. The simulation data and the fatigue parameters of the test specimens were compared. When data discrepancies exist, fatigue analysis and comparison are performed on the initial CAE model after parameter adjustment until the data discrepancies no longer exist. When the aforementioned data discrepancies do not exist, the target CAE model is obtained; Obtain the parameters of the parts in the test specimen, the fatigue machine part setting parameters, and the part fatigue parameters when the data difference does not exist; The structure of the part is modeled based on its parameters to obtain an initial CAE part model; Based on the fatigue machine part setting parameters, fatigue analysis is performed on the initial CAE part model to obtain part simulation data; The simulation data of the part and the fatigue parameters of the part are compared; When there are differences in the part data, the initial CAE part model is adjusted in parameters and then subjected to fatigue analysis and comparison until there are no differences in the part data. When there are no discrepancies in the part data, the target CAE part model is obtained; A fatigue simulation database is constructed based on the parameters of the target CAE model and the target CAE part model. Both the test sample and the part contain structural adhesive.

2. The method according to claim 1, characterized in that, The method further includes: Obtain fatigue data for structural adhesives; The structural adhesive fatigue data is added to the fatigue damage parameters and crack propagation parameters, and correlated with the actual working condition load spectrum to obtain a closed-loop data system. An intelligent matching engine is established based on the closed-loop data system.

3. The method according to claim 2, characterized in that, The structural adhesive fatigue data includes stress-strain curves, fatigue life SN curves, and crack propagation rates. The fatigue damage parameters include the Goodman correction factor and the Miner linear cumulative damage rule; The crack propagation parameters include the coefficients of Paris's law.

4. The method according to claim 2, characterized in that, The method further includes: The intelligent matching engine is used to search for new operating condition parameters to obtain historical cases with high similarity. Data was extracted from the highly similar historical cases to obtain material parameters, fatigue parameters, and fatigue machine setting parameters; The extracted material parameters, fatigue parameters, and fatigue machine setting parameters are weighted and mapped using weight allocation rules to obtain the initial preset parameters for the new working condition. The initial preset parameters for the new working condition are finely adjusted by 5% to 10% based on the differences between the material parameters, the fatigue parameters, and the fatigue machine setting parameters, respectively, using a gradient adjustment algorithm to obtain the adjusted parameters. The root mean square error of stress and the strain correlation coefficient are calculated on the adjusted parameters to obtain the verification parameters; The calibration parameters are integrated and an operating condition adaptation description is added to obtain a pre-calibration parameter set; When data discrepancies exist, the calibration parameters are obtained by retrieving the data discrepancies from the pre-calibration parameter set. The initial CAE model or the initial CAE part model is adjusted according to the calibration parameters to obtain the target CAE model or the target CAE part model.

5. The method according to claim 1, characterized in that, During the parameter adjustment process after comparing with the fatigue parameters of the test specimen, batch calculations are performed through post-processing compilation.

6. The method according to claim 1, characterized in that, When the data discrepancy or the part data discrepancy exists, the following steps are included: When the data difference or the part data difference is stress, the Young's modulus, hyperelastic model parameters, contact type or contact stiffness of the initial CAE model or the initial CAE part model are adjusted. When the data difference or the part data difference is strain, the viscoelastic parameters, plastic model parameters or contact penetration tolerance of the initial CAE model or the initial CAE part model are adjusted. When the data difference or the part data difference is fatigue life, the fatigue damage parameters, crack propagation parameters, loading spectrum, or contact boundary conditions of the initial CAE model or the initial CAE part model are adjusted.

7. The method according to claim 5, characterized in that, When there are differences in the data or the part data, no more than two parameters should be adjusted at a time, and gradient testing should be performed in 5% increments. The convergence criteria include: root mean square error of stress less than 10%, strain correlation coefficient greater than 0.95, and fatigue life deviation less than 10%.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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