Instrument panel durability simulation method, device, equipment and medium

Abstract

The embodiment of the invention discloses an instrument board durability simulation method and device, equipment and a medium. The method comprises the following steps: acquiring a target test signal and a corresponding candidate simulation noise signal, and driving a preset target instrument board multi-body model based on the candidate simulation noise signal to obtain a corresponding candidate signal transfer function; determining a current input signal based on the candidate signal transfer function, performing signal iterative compensation on the current input signal to obtain a target input signal, and determining a road section driving signal based on the target input signal under the candidate road surface features; determining a mechanical parameter result according to the road section driving signal and the target instrument board multi-body model, and updating the initial fatigue curve according to the mechanical parameter result to obtain a target fatigue curve; and determining a durability test result according to the mechanical parameter result and the target fatigue curve. The dashboard durability test efficiency is improved, the labor cost is reduced, and the dashboard durability test accuracy is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of vehicle technology, and in particular to a method, device, equipment, and medium for simulating the durability of an instrument panel. Background Art

[0002] Durability is a critical performance requirement for automotive instrument panel structures. Conventional instrument panel durability control and verification relies on bench or road durability testing, which requires prototype vehicles and components. This results in high costs, lengthy development cycles, and often inaccurate test results. Therefore, improving the efficiency and accuracy of instrument panel durability testing while reducing labor costs is crucial. Summary of the Invention

[0003] The present invention provides an instrument panel durability simulation method, device, equipment and medium to improve the efficiency and accuracy of durability testing of the instrument panel and reduce labor costs.

[0004] According to one aspect of the present invention, a method for simulating the durability of an instrument panel is provided, comprising:

[0005] Obtaining a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under candidate road surface characteristics, and driving a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function;

[0006] Determining a current input signal under a corresponding candidate road surface feature based on the candidate signal transfer function, performing signal iterative compensation on the current input signal to obtain a target input signal, and determining a road section driving signal based on the target input signal under the candidate road surface feature;

[0007] Determining mechanical parameter results based on the road section drive signal and the target instrument panel multi-body model, and updating an initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material;

[0008] The durability test result of the instrument panel to be tested is determined according to the mechanical parameter result and the target fatigue curve.

[0009] According to another aspect of the present invention, there is provided an instrument panel durability simulation device, comprising:

[0010] a candidate signal transfer function determination module, configured to obtain a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under candidate road surface characteristics, and drive a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function;

[0011] a road section driving signal determination module, configured to determine a current input signal under a corresponding candidate road surface feature based on the candidate signal transfer function, perform signal iterative compensation on the current input signal to obtain a target input signal, and determine a road section driving signal based on the target input signal under the candidate road surface feature;

[0012] a target fatigue curve determination module, configured to determine mechanical parameter results based on the road section drive signal and the target instrument panel multi-body model, and update the initial fatigue curve of the candidate material based on the mechanical parameter results to obtain a target fatigue curve of the corresponding candidate material;

[0013] The test result determination module is used to determine the durability test result of the instrument panel to be tested according to the mechanical parameter result and the target fatigue curve.

[0014] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0015] one or more processors;

[0016] a memory for storing one or more programs;

[0017] When one or more programs are executed by one or more processors, the one or more processors can execute any one of the instrument panel durability simulation methods provided by the embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, which are used to enable a processor to implement any instrument panel durability simulation method provided by an embodiment of the present invention when executed.

[0019] An embodiment of the present invention provides an instrument panel durability simulation solution, which obtains a target test signal and a corresponding candidate simulation noise signal of the instrument panel to be tested under candidate road surface characteristics, and drives a preset target instrument panel multi-body model based on the candidate simulation noise signal to obtain a corresponding candidate signal transfer function; based on the candidate signal transfer function, determines the current input signal under the corresponding candidate road surface characteristics, performs signal iterative compensation on the current input signal to obtain a target input signal, and determines a section drive signal based on the target input signal under the candidate road surface characteristics; determines mechanical parameter results based on the section drive signal and the target instrument panel multi-body model, and updates the initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material; determines the durability test result of the instrument panel to be tested based on the mechanical parameter results and the target fatigue curve. The above scheme determines a candidate signal transfer function based on a candidate simulated noise signal and a target instrument panel multi-body model, and then determines the current input signal based on the candidate signal transfer function. The target input signal is obtained by performing iterative signal compensation on the current input signal. The initial fatigue curve is updated based on the target input signal and the mechanical parameter results determined by the target instrument panel multi-body model to obtain the target fatigue curve. Finally, the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby realizing automated durability testing of the instrument panel, improving the efficiency of durability testing of the instrument panel, and reducing labor costs. At the same time, the current input signal is determined based on the candidate signal transfer function, and the target input signal is obtained by performing iterative signal compensation on the current input signal, thereby improving the accuracy of the determined target input signal. Subsequently, the mechanical parameter results are determined based on the target input signal, and then the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby improving the accuracy of the determined durability test result, that is, improving the accuracy of the durability test of the instrument panel.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in 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 only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of a method for simulating the durability of an instrument panel provided in the first embodiment of the present invention;

[0023] Figure 2 This is a flow chart of a method for simulating instrument panel durability provided by the second embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a preset actual test position of a dashboard to be tested provided by a third embodiment of the present invention;

[0025] Figure 4 This is a schematic structural diagram of an instrument panel durability simulation device provided in a fourth embodiment of the present invention;

[0026] Figure 5 This is a structural diagram of an electronic device for implementing a method for simulating instrument panel durability, provided in accordance with a fifth embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a dashboard durability simulation method provided in Example 1 of the present invention. This embodiment is applicable to the situation of performing durability testing on a vehicle dashboard. The method can be executed by a dashboard durability simulation device, which can be implemented in software and / or hardware and can be configured in an electronic device that carries the dashboard durability simulation function.

[0030] See also Figure 1 The instrument panel durability simulation method shown includes:

[0031] S110 , obtaining a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under a candidate road surface feature, and driving a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function.

[0032] The instrument panel to be tested refers to the instrument panel that requires durability testing. For example, the instrument panel to be tested can be the instrument panel of a newly developed vehicle model or a reference vehicle model. The reference vehicle model is a developed vehicle model that serves as a reference for instrument panels of undeveloped vehicles. The vibration isolation characteristics of the suspension or mounted assembly should be comparable between the reference vehicle model and the corresponding undeveloped vehicle model.

[0033] Among them, candidate pavement features refer to the pavement features existing on the road section to be tested, that is, the pavement conditions on the road section to be tested. Pavement features refer to the characteristics of the road surface in terms of structure, materials, and performance. The road section to be tested refers to the road section used for durability testing. It should be noted that the road section to be tested can be divided into at least one sub-section based on the candidate pavement features, and each sub-section corresponds to a different candidate pavement feature. The number of candidate pavement features can be at least one.

[0034] The target test signal is the signal obtained by preprocessing the initial test signal. The initial test signal is the response signal of the instrument panel under actual vehicle operating conditions. The actual vehicle operating conditions refer to the actual operation of the vehicle on the road section to be tested.

[0035] Specifically, an initial test signal is obtained from a test sensor provided at a preset actual test position of the vehicle where the instrument panel to be tested is located, and the initial test signal is preprocessed to obtain a target test signal.

[0036] The preset actual test positions are pre-set locations for setting test sensors. Exemplarily, the preset actual test positions may include the fixed restraint end positions on both sides of the instrument panel and the vehicle body, as well as the center of the instrument panel itself. Exemplarily, the preset actual test positions include five positions, each corresponding to a test sensor, resulting in a total of five test sensors.

[0037] The test sensor can be used to collect the initial test signal. For example, the test sensor can be a three-axis acceleration sensor. Preprocessing can include offset correction, trend removal, burr removal, filtering, and road feature segmentation.

[0038] It should be noted that the initial test signal is periodically acquired based on the test sampling frequency. The test sampling frequency of the initial test signal can be determined based on a preset road section frequency range. The test sampling frequency refers to the frequency at which the initial test signal is acquired. The preset road section frequency range refers to a frequency range pre-set based on the road section operating conditions of the road section to be tested. Specifically, the test sampling frequency is greater than ten times the upper limit of the preset road section frequency range. For example, if the upper limit of the preset road section frequency range is 50 Hz, then the test sampling frequency is greater than 500 Hz.

[0039] The candidate simulated noise signal refers to a pre-set simulated noise signal. For example, the candidate simulated noise signal can be set by a technician based on experience or needs. Specifically, white-pink noise, i.e., the candidate simulated noise signal, can be determined based on a pre-set white noise cutoff frequency, pink noise index, and driving standard deviation. The driving standard deviation can be used to measure the magnitude of white noise. There can be multiple candidate simulated noise signals.

[0040] The target instrument panel multibody model, i.e., the multibody dynamics model of the instrument panel system, can be used to simulate the kinematic and dynamic characteristics of the instrument panel under test. The candidate signal transfer function is the correlation function between the candidate response signal output based on the candidate simulated noise signal and the corresponding candidate simulated noise signal. The candidate response signal is the response signal obtained by inputting the candidate simulated noise signal into the target instrument panel multibody model.

[0041] S120. Based on the candidate signal transfer function, determine the current input signal under the corresponding candidate road surface feature, perform signal iterative compensation on the current input signal to obtain a target input signal, and determine the road section driving signal based on the target input signal under the candidate road surface feature.

[0042] The current input signal refers to the signal currently input to the target instrument panel multibody model. The target input signal refers to the final input signal determined after iteration. The road segment drive signal refers to the overall drive signal of the road segment to be tested.

[0043] For example, if the number of candidate road features is at least two, it indicates that the road section to be tested is divided into two sub-sections. Based on the connection relationship between the sub-sections in the road section to be tested, the target input signal of each sub-section is connected to obtain the road section driving signal of the road section to be tested.

[0044] S130 , determining mechanical parameter results based on the road section driving signal and the target instrument panel multi-body model, and updating the initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material.

[0045] Mechanical parameter results refer to the results obtained by inputting the road segment drive signal into the target instrument panel multi-body model. For example, the mechanical parameter results may include stress results and strain results. Stress results refer to the time-varying stress of the instrument panel under test. Strain results refer to the time-varying strain of the instrument panel under test.

[0046] The candidate material refers to the material included in the instrument panel to be tested. For example, the candidate material may include metallic materials and non-metallic materials. Metallic materials may include at least one of carbon steel, aluminum, and gray cast iron. Non-metallic materials may include at least one of polymers and composite materials.

[0047] Among them, the initial fatigue curve refers to the fatigue life curve determined based on the basic properties of the candidate material. Exemplarily, the initial fatigue curve may include an initial stress fatigue curve and an initial strain fatigue curve. The initial stress fatigue curve refers to the relationship curve between the stress amplitude and the fatigue life of the candidate material under cyclic stress. The initial strain fatigue curve refers to the relationship curve between the strain amplitude and the fatigue life of the candidate material under cyclic strain. Exemplarily, the initial strain fatigue curve may include an initial strain life curve and an initial stress strain curve. For example, the initial life curve can be used to show the correspondence between the candidate material and the strain at different numbers of cycles without interference. The initial stress strain curve, that is, the cyclic stress strain curve without interference, can be used to show the correspondence between the stress and strain of the candidate material.

[0048] Among them, the target fatigue curve refers to the fatigue life curve of the candidate material in actual application. Exemplarily, the target fatigue curve may include a target stress fatigue curve and a target strain fatigue curve. The target stress fatigue curve refers to the relationship curve between the stress amplitude and fatigue life of the candidate material in actual application. The target strain fatigue curve refers to the relationship curve between the strain amplitude and fatigue life of the candidate material in actual application. Exemplarily, the target strain fatigue curve may include a target strain life curve and a target stress strain curve. For example, the target life curve can be used to show the corresponding relationship between the candidate material and the strain at different numbers of cycles in actual application. The initial stress strain curve, that is, the cyclic stress strain curve in actual application, can be used to show the corresponding relationship between the stress and strain of the candidate material.

[0049] Exemplarily, the initial stress fatigue curve is updated according to the stress result to obtain the target stress fatigue curve of the corresponding candidate material; the initial strain fatigue curve is updated according to the strain result to obtain the target strain fatigue curve of the corresponding candidate material.

[0050] Specifically, the multi-body model of the target instrument panel is driven based on the road segment drive signal to obtain the modal participation factor. This modal participation factor is then coupled and matched with the modal stress of the instrument panel under test to obtain mechanical parameter results. The modal participation factor can be used to characterize the contribution of the instrument panel under test to the vibration response in a specific mode. Modal stress refers to the stress distribution caused by vibration in each mode of the instrument panel under test.

[0051] S140. Determine the durability test result of the instrument panel to be tested according to the mechanical parameter results and the target fatigue curve.

[0052] The durability test result refers to the result of a durability test on the instrument panel under test. For example, the durability test result can be either a pass or a fail. A pass indicates that the durability performance of the instrument panel under test is normal, meaning it meets the user or enterprise's fatigue life requirements. A fail indicates that the durability performance of the instrument panel under test is abnormal, meaning it does not meet the user or enterprise's fatigue life requirements.

[0053] In an optional embodiment, the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, including: determining the candidate cycle number of the candidate mechanical parameter amplitude based on the mechanical parameter results, and determining the reference cycle number of the corresponding candidate mechanical parameter amplitude based on the target fatigue curve under the candidate material; determining the material damage value corresponding to the corresponding candidate material based on the candidate cycle number and the reference cycle number of the candidate mechanical parameter amplitude; and determining the durability test result of the instrument panel to be tested based on the material damage value corresponding to each candidate material.

[0054] The candidate mechanical parameter amplitude refers to the amplitude in the mechanical parameter results. For example, the candidate mechanical parameter amplitude may include candidate stress amplitudes and candidate strain amplitudes. The candidate stress amplitude refers to the stress amplitude in the stress results. The candidate strain amplitude refers to the strain amplitude in the strain results.

[0055] The candidate cycle count refers to the number of occurrences of any candidate mechanical parameter amplitude in the mechanical parameter results. For example, the candidate cycle count may include candidate stress cycle counts and candidate strain cycle counts. The candidate stress cycle count refers to the number of occurrences of any candidate stress amplitude in the stress results. The candidate strain cycle count refers to the number of occurrences of any candidate strain amplitude in the strain results. The candidate cycle count can be calculated based on rainflow counting.

[0056] The reference number of cycles refers to the number of cycles corresponding to any candidate mechanical parameter amplitude in the target fatigue curve of any candidate material. For example, the reference number of cycles may include a reference stress cycle number and a reference strain cycle number. The reference stress cycle number refers to the number of cycles corresponding to any candidate stress amplitude in the target fatigue curve of any candidate material. The reference strain cycle number refers to the number of cycles corresponding to any candidate strain amplitude in the target fatigue curve of any candidate material.

[0057] The material damage value refers to the structural damage value at the corresponding location of any candidate material in the instrument panel under test. This material damage value may include stress damage value and / or strain damage value. The stress damage value refers to the structural damage value at the corresponding location of any candidate material in the instrument panel under test caused by stress. The strain damage value refers to the structural damage value at the corresponding location of any candidate material in the instrument panel under test caused by strain.

[0058] Exemplarily, the candidate number of cycles corresponding to each candidate mechanical parameter amplitude in the mechanical parameter results is determined; for any candidate material, the reference number of cycles for each candidate mechanical parameter amplitude is determined based on the target fatigue curve of the candidate material; the material damage value of the candidate material is determined based on the candidate number of cycles for each candidate mechanical parameter amplitude and the corresponding reference number of cycles; and the durability test results are determined based on the material damage value of each candidate material.

[0059] Exemplarily, the material damage value of the candidate material is determined based on the candidate cycle number and the corresponding reference cycle number of each candidate mechanical parameter amplitude, including: for any candidate mechanical parameter amplitude, taking the product of the reciprocal of the reference cycle number corresponding to the candidate mechanical parameter amplitude and the candidate cycle number as the local material damage value corresponding to the candidate mechanical parameter amplitude; and taking the sum of the local material damage values ​​of each candidate mechanical parameter amplitude as the material damage value of the candidate material. The local material damage value refers to the local structural loss value of the corresponding position of the candidate material in the instrument panel to be tested, that is, the local material damage value can be understood as the structural damage value of the corresponding position of the candidate material in the instrument panel to be tested under the candidate mechanical parameter amplitude.

[0060] Exemplarily, determining the durability test result based on the material damage values ​​of each candidate material includes: determining the sum of the material damage values ​​of each candidate material; if the sum of the material damage values ​​is less than 1, determining the durability test result as a pass; otherwise, determining the durability test result as a fail. It should be noted that after determining the sum of the material damage values, the product of the sum of the material damage values ​​and the number of vehicle laps can be determined; if the product is less than 1, determining the durability test result as a pass; otherwise, determining the durability test result as a fail. The number of vehicle laps refers to the number of laps that the vehicle containing the instrument panel to be tested has driven on the road section to be tested.

[0061] Exemplarily, the candidate stress cycle number corresponding to each candidate stress amplitude in the stress result is determined; for any candidate material, the reference stress cycle number of each candidate stress amplitude is determined based on the target stress fatigue curve of the candidate material; and the stress damage value of the candidate material is determined based on the candidate stress cycle number of each candidate stress amplitude and the corresponding reference stress cycle number.

[0062] Specifically, the stress damage value of the candidate material is determined based on the candidate stress cycle number and the corresponding reference stress cycle number for each candidate stress amplitude. This includes: for each candidate stress amplitude, multiplying the reciprocal of the reference stress cycle number corresponding to the candidate stress amplitude by the candidate stress cycle number as the local stress damage value corresponding to the candidate stress amplitude; and summing the local stress damage values ​​for each candidate stress amplitude as the stress damage value for the candidate material. The local stress damage value can be understood as the structural damage value of the candidate material at the corresponding location on the instrument panel under test under the candidate stress amplitude.

[0063] Specifically, the sum of the stress damage values ​​of each candidate material is determined. If the sum of the stress damage values ​​is less than 1, the stress test result of the dashboard to be tested is determined to be a stress test pass; if not, the stress test result is determined to be a stress test fail. The stress test result refers to the result of the stress test on the dashboard to be tested. The stress test result can be a stress test pass or a stress test fail. A stress test pass means that the damage caused by the stress to the dashboard to be tested does not exceed the allowable range of stress damage, that is, the sum of the stress damage values ​​is less than 1. A stress test failure means that the damage caused by the stress to the dashboard to be tested exceeds the allowable range of stress damage, that is, the sum of the stress damage values ​​is greater than or equal to 1.

[0064] Exemplarily, the candidate strain cycle number corresponding to each candidate strain amplitude in the strain result is determined; for any candidate material, the reference strain cycle number of each candidate strain amplitude is determined according to the target strain fatigue curve of the candidate material; and the strain damage value of the candidate material is determined according to the candidate strain cycle number of each candidate strain amplitude and the corresponding reference strain cycle number.

[0065] Specifically, the strain damage value of the candidate material is determined based on the candidate strain cycle number and the corresponding reference strain cycle number for each candidate strain amplitude. This includes: for each candidate strain amplitude, multiplying the reciprocal of the reference strain cycle number corresponding to the candidate strain amplitude by the candidate strain cycle number as the local strain damage value corresponding to the candidate strain amplitude; and summing the local strain damage values ​​for each candidate strain amplitude as the strain damage value for the candidate material. The local strain damage value can be understood as the structural damage value of the corresponding location of the candidate material in the instrument panel under test under the candidate strain amplitude.

[0066] Specifically, the sum of the strain damage values ​​of each candidate material is determined. If the sum of the strain damage values ​​is less than 1, the strain test result of the instrument panel to be tested is determined to be a strain test pass; otherwise, the strain test result is determined to be a strain test fail. The strain test result refers to the result of the strain test on the instrument panel to be tested. The strain test result can be a strain test pass or a strain test fail. A strain test pass means that the damage caused by the strain to the instrument panel to be tested does not exceed the allowable range of strain damage, that is, the sum of the strain damage values ​​is less than 1. A strain test failure means that the damage caused by the strain to the instrument panel to be tested exceeds the allowable range of strain damage, that is, the sum of the strain damage values ​​is greater than or equal to 1.

[0067] Further, if the stress test result is stress test passed, and / or the strain test result is strain test passed, the durability test result of the instrument panel to be tested is determined to be test passed; if not, the durability test result of the instrument panel to be tested is determined to be test failed.

[0068] It can be understood that by determining the material damage value of the corresponding candidate material based on the candidate cycle number of the candidate mechanical parameter amplitude and the reference cycle number in the target fatigue curve, and determining the durability test result based on the material damage value, it is achieved that the durability test result is determined based on consideration of each candidate material in the instrument panel to be tested, thereby improving the accuracy of the determined durability test result.

[0069] It should be noted that the candidate mechanical parameter amplitudes can be screened based on the target fatigue curve of each candidate material, so as to delete the candidate mechanical parameter amplitudes that are not related to the durability test results, reduce the amount of data, and obtain the screened candidate mechanical parameter amplitudes. Subsequently, only the candidate cycle number and parameter cycle number of the screened candidate mechanical parameter amplitudes can be determined to determine the material damage value.

[0070] An embodiment of the present invention provides an instrument panel durability simulation solution, which obtains a target test signal and a corresponding candidate simulation noise signal of the instrument panel to be tested under candidate road surface characteristics, and drives a preset target instrument panel multi-body model based on the candidate simulation noise signal to obtain a corresponding candidate signal transfer function; based on the candidate signal transfer function, determines the current input signal under the corresponding candidate road surface characteristics, performs signal iterative compensation on the current input signal to obtain a target input signal, and determines a section drive signal based on the target input signal under the candidate road surface characteristics; determines mechanical parameter results based on the section drive signal and the target instrument panel multi-body model, and updates the initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material; determines the durability test result of the instrument panel to be tested based on the mechanical parameter results and the target fatigue curve. The above scheme determines a candidate signal transfer function based on a candidate simulated noise signal and a target instrument panel multi-body model, and then determines the current input signal based on the candidate signal transfer function. The target input signal is obtained by performing iterative signal compensation on the current input signal. The initial fatigue curve is updated based on the target input signal and the mechanical parameter results determined by the target instrument panel multi-body model to obtain the target fatigue curve. Finally, the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby realizing automated durability testing of the instrument panel, improving the efficiency of durability testing of the instrument panel, and reducing labor costs. At the same time, the current input signal is determined based on the candidate signal transfer function, and the target input signal is obtained by performing iterative signal compensation on the current input signal, thereby improving the accuracy of the determined target input signal. Subsequently, the mechanical parameter results are determined based on the target input signal, and then the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby improving the accuracy of the determined durability test result, that is, improving the accuracy of the durability test of the instrument panel.

[0071] In an optional embodiment, the target dashboard multi-body model is determined based on the following method: obtaining basic attribute data of the dashboard to be tested, component mass of each equipment component and local body data, and determining the dashboard structural model based on the basic attribute data, component mass and local body data; constructing an initial dashboard multi-body model, and obtaining the target dashboard multi-body model based on the initial dashboard multi-body model, the dashboard structural model and a preset modal damping ratio coefficient; wherein the target dashboard multi-body model includes driving points and test points.

[0072] The basic attribute data refers to the attribute data of the structures included in the instrument panel to be tested. For example, the structures included in the instrument panel to be tested may include structural data such as beams, frames, skins, brackets, guard plates, decorative panels, air conditioning ducts, and steering wheels.

[0073] The equipment components refer to the components on the instrument panel to be tested. For example, the equipment components may include a tool box, a display screen, and an air conditioning panel. The component mass refers to the weight of each equipment component.

[0074] The local vehicle body data refers to the local vehicle body data that is fixedly connected to the instrument panel under test, such as the vehicle body data within a 300mm range of the fixed connection between the instrument panel under test and the vehicle body. The instrument panel structure model can be used to simulate the structure of the instrument panel under test.

[0075] It should be noted that the error between the total mass of all equipment components in the instrument panel structure model and the total mass of all equipment components in the instrument panel to be tested must be less than a preset error threshold. This embodiment of the present invention does not impose any restrictions on the value of the preset error threshold; it can be set by technicians based on experience or needs, or determined through extensive testing. For example, the preset error threshold can be 5%.

[0076] The initial instrument panel multibody model refers to a pre-built initial dynamic model of the instrument panel under test. The initial instrument panel multibody model includes a virtual instrument panel, a virtual fixture, and virtual sensors. The virtual instrument panel is a simulated instrument panel of the instrument panel under test. The virtual fixture is a simulated fixture corresponding to the physical fixture on the instrument panel under test. The virtual sensor is a simulated sensor of the test sensor.

[0077] The modal damping ratio coefficient characterizes the vibration attenuation characteristics of the instrument panel under test after excitation. The driving point can be understood as the input point of the target instrument panel multibody model; that is, signals can be input from the driving point to drive the target instrument panel multibody model. The test point can be understood as the output point of the target instrument panel multibody model; that is, response signals can be output from the test point.

[0078] It can be understood that by first constructing the dashboard structure model and then introducing the dashboard structure model into the initial dashboard multi-body model to obtain the target dashboard multi-body model, the comprehensiveness of the information included in the target dashboard multi-body model is improved, so that the results obtained when the target dashboard multi-body model is subsequently driven are as similar as possible to the results of the dashboard to be tested under actual vehicle working conditions, which makes it easier to better simulate the actual vibration conditions of the dashboard to be tested through the target dashboard multi-body model.

[0079] Exemplarily, the driving point is the point between the virtual dashboard and the virtual fixture in the initial dashboard model at the rigid constraint of the truncated body end fracture; the test point is the point between the virtual sensor and the virtual dashboard in the initial dashboard multi-body model that matches the preset actual test position.

[0080] Specifically, the fixed pair established between the virtual instrument panel and the virtual fixture in the initial instrument panel model at the rigid constraint of the truncated body end fracture is used as the driving point, and the local coordinate system of the driving point is formed; the fixed pair established between the virtual sensor and the virtual instrument panel in the initial instrument panel model according to the preset actual test position is used as the test point, and the local coordinate system of the test point is formed.

[0081] It can be understood that by defining the positions of the driving point and the test point, it is convenient to subsequently implement the input and output of signals through the target dashboard multi-body model.

[0082] Example 2

[0083] Figure 2 This is a flowchart of a dashboard durability simulation method provided by the second embodiment of the present invention. Based on the above embodiments, this embodiment further refines the operation of "performing iterative signal compensation on the current input signal to obtain the target input signal" into "for any signal iterative compensation process, determining the current output signal of this iteration based on the current input signal of this iteration, and determining the current signal difference based on the current output signal and the target test signal; if the current signal difference is greater than or equal to the preset difference threshold, determining the input compensation signal of the next iteration based on the current input signal and the current signal difference; determining the current input signal of the next iteration based on the input compensation signal and the current input signal of this iteration; and using the current input signal obtained in the last iteration as the target input signal" to improve the target input signal determination mechanism. It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments.

[0084] See also Figure 2 The instrument panel durability simulation method shown includes:

[0085] S210 , obtaining a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under a candidate road surface feature, and driving a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function.

[0086] S220 : Determine a current input signal corresponding to the candidate road surface feature based on the candidate signal transfer function.

[0087] In an optional embodiment, based on the candidate signal transfer function, the current input signal under the corresponding candidate road surface feature is determined, including: determining the candidate coherence coefficient corresponding to the candidate signal transfer function under the candidate road surface feature, and determining the corresponding target signal transfer function based on the candidate coherence coefficient; according to the target signal transfer function, determining the current input signal under the corresponding candidate road surface feature.

[0088] The candidate coherence coefficient can be used to characterize the degree of linear correlation between the input and output signals in the candidate signal transfer function in the frequency domain. It also characterizes the reliability of the estimated candidate signal transfer function. The target signal transfer function is a candidate signal transfer function whose candidate coherence coefficient is greater than a preset coherence coefficient threshold.

[0089] It should be noted that the candidate signal transfer function can be a transfer function matrix with a 6x15 matrix pattern. The 6 in the transfer function matrix can represent the driving direction of the driving point, such as the displacement in the three linear directions of X, Y, and Z, and the three angular displacements around the X, Y, and Z directions in the vehicle coordinate system; and the 15 in the transfer function matrix can represent the output channels of the virtual sensors (corresponding to the test sensors) corresponding to the five test points.

[0090] Exemplarily, the inverse function of the transfer function matrix is ​​used as the current input signal. It should be noted that the current input signal, determined based on the target signal transfer function, is used as the input signal for the first signal iterative compensation. Subsequent signal iterative compensation is performed based on the previous signal iterative compensation. The candidate simulated noise signal corresponding to the target signal transfer function is introduced as the target simulated noise signal into the target instrument panel multi-body model to simulate the actual noise environment.

[0091] Specifically, for any candidate road surface feature, the corresponding candidate coherence coefficient is determined based on the candidate simulated noise signal and the corresponding candidate signal transfer function under the candidate road surface feature; the target signal transfer function is determined from the candidate signal transfer function based on the candidate coherence coefficients under the candidate road surface feature; and the current input signal under the candidate road surface feature is determined based on the target signal transfer function.

[0092] It can be understood that by introducing candidate coherence coefficients, determining the target signal transfer function, and then determining the current input signal for the first signal iterative compensation based on the target signal transfer function, the accuracy of the determined current input signal is improved.

[0093] In an optional embodiment, a candidate coherence coefficient corresponding to a candidate signal transfer function under a candidate road surface feature is determined, and based on the candidate coherence coefficient, a corresponding target signal transfer function is determined, including: determining the corresponding candidate coherence coefficient based on a candidate simulated noise signal and a candidate signal transfer function under the candidate road surface feature; and determining the target signal transfer function corresponding to the candidate road surface feature based on the candidate coherence coefficient and a preset coherence coefficient threshold.

[0094] Among them, the embodiment of the present invention does not impose any limitation on the size of the preset coherence coefficient threshold, which can be set by technicians based on experience or needs, or determined repeatedly through a large number of experiments. For example, the preset coherence coefficient threshold can be 90%. Specifically, for any candidate coherence coefficient, if the frequency domain corresponding to the candidate coherence coefficient is within the preset road surface start and end frequency interval, and the candidate coherence coefficient is greater than the preset coherence coefficient threshold, the candidate signal transfer function corresponding to the candidate coherence coefficient is used as the target signal transfer function. The preset road surface start and end frequency interval can be [1Hz, 50Hz]. The preset road surface start and end frequency interval refers to a pre-set frequency range for limiting the vibration of the instrument panel to be tested.

[0095] It can be understood that by determining the target signal transfer function based on the candidate coherence coefficients and the preset coherence coefficient threshold, the accuracy of the determined target signal transfer function is improved.

[0096] S230 . For any signal iterative compensation process, determine the current output signal of this iteration according to the current input signal of this iteration, and determine the current signal difference based on the current output signal and the target test signal.

[0097] The signal iterative compensation process refers to the process of iteratively correcting the current input signal. The current signal difference can be used to quantify the difference between the current output signal of an iterative process and the target test signal.

[0098] S240: If the current signal difference is greater than or equal to the preset difference threshold, determine an input compensation signal for the next iteration based on the current input signal and the current signal difference.

[0099] The embodiment of the present invention does not impose any limitation on the value of the preset difference threshold, which can be set by technicians based on experience or needs, or determined repeatedly through a large number of experiments. For example, the preset difference threshold can be 15%.

[0100] The input compensation signal can be used to compensate the current input signal of the next iteration. Specifically, the product value between the current input signal of this iteration and the current signal difference is used as the input compensation signal of the next iteration.

[0101] S250: Determine the current input signal for the next iteration according to the input compensation signal and the current input signal for this iteration.

[0102] Specifically, the sum of the current compensation signal obtained in this iteration and the current input signal in this iteration is used as the current input signal in the next iteration.

[0103] S260: Use the current input signal obtained in the last iteration as the target input signal.

[0104] Exemplarily, for any non-first signal iteration compensation process, the difference of the current signal of the previous iteration is determined based on the current input signal and the current output signal of the previous iteration; if the difference of the current signal of the previous iteration is greater than or equal to the preset difference threshold, the input compensation signal of this iteration is determined based on the current input signal of the previous iteration and the difference of the current signal; the current input signal of this iteration is determined based on the input compensation signal and the current input signal of the previous iteration; and the current input signal obtained in the last iteration is used as the target input signal.

[0105] A non-first signal iterative compensation process refers to a process that is not the first time signal iterative compensation is performed. It should be noted that the current input signal of the first signal iterative compensation process is determined based on the candidate signal transfer function. If the current signal difference of any signal iterative compensation process is less than a preset difference threshold, that signal iterative compensation process is considered the last iteration, and the current input signal obtained from that signal iterative compensation process is used as the target input signal.

[0106] S270: Determine a road section driving signal based on the target input signal under the candidate road surface feature.

[0107] S280 , determining mechanical parameter results based on the road section driving signal and the target instrument panel multi-body model, and updating the initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material.

[0108] S290. Determine a durability test result of the instrument panel to be tested based on the mechanical parameter results and the target fatigue curve.

[0109] An instrument panel durability simulation solution provided by an embodiment of the present invention refines the target input signal determination mechanism by performing iterative signal compensation on the current input signal to obtain the target input signal. The solution further refines the iterative compensation process for any signal. The solution then determines the current output signal for the current iteration based on the current input signal of the current iteration, and determines the current signal difference between the current output signal and the target test signal. If the current signal difference is greater than or equal to a preset difference threshold, the solution determines the input compensation signal for the next iteration based on the current input signal and the current signal difference. The solution also determines the current input signal for the next iteration based on the input compensation signal and the current input signal of the current iteration. The solution stops iterations until the current signal difference from the current iterative compensation is less than the preset difference threshold. The solution then uses the current input signal obtained from the last iterative compensation as the target input signal. Through iterative signal compensation, the current input signal is continuously corrected to obtain the target input signal, thereby improving the accuracy of the determined target input signal.

[0110] Example 3

[0111] The embodiment of the present invention provides an optional example based on the above embodiment. It should be noted that for parts not described in detail in the embodiment of the present invention, reference can be made to the descriptions of other embodiments.

[0112] Currently, the automotive industry uses computers for virtual simulation analysis, which can conduct simulation analysis and prediction of durability performance in the early stages of product development, discover durability risk points of the dashboard structure in advance, and reduce its fatigue safety hazards through structural improvement and optimization, thereby improving the one-time pass rate of subsequent road durability actual vehicle verification, and significantly shortening the product development cycle and cost.

[0113] There are currently three main methods for instrument panel durability analysis in the industry. The first is the static g-load method, which applies a gravity field to the instrument panel at all fixed points and examines its structural stress and safety factor. Since it is a static method, it does not consider the same-frequency resonance effect caused by the random road surface on the instrument panel. At the same time, there is no durability damage superposition factor, and its true durability life cannot be examined. The second is the frequency response vibration intensity method, which applies a gravity field within a certain frequency range at the fixed constraints of the instrument panel to examine the structural stress and safety factor of its structure under resonance. Since its input is a non-real road condition input, there is also no durability damage superposition factor, and its true durability life cannot be examined. The third is the PSD vibration energy method (a vibration energy analysis method based on power spectral density PSD), which applies the PSD (power spectral density) of the measured acceleration signal in three directions at the fixed constraints of the instrument panel, and then uses the frequency domain Dirlik method to estimate the structural life. Because the frequency-domain Dirlik method is based on the Monte Carlo probabilistic method for estimation, it cannot reflect the actual structural dynamic response of the instrument panel over time. It is also only applicable to operating condition analysis under steady-state random inputs, and it underestimates the response under loads that may be subject to impact waveforms (such as triangular waves, sine waves, and trapezoidal waves). In addition, this method uses three-degree-of-freedom input, while the instrument panel system is driven by six-degree-of-freedom excitation under actual operating conditions, resulting in low accuracy in its calculation results.

[0114] In response to the shortcomings of the industry's durability analysis methods for automotive instrument panels and the needs of OEMs for instrument panel product development, the present invention primarily addresses the following issues: The present invention uses the response signals of 15 channels of five acceleration sensors obtained from instrument panel system testing under real road load conditions as the target, and uses the instrument panel system dynamics model (i.e., the target instrument panel multi-body model) as the transfer function to inversely determine the system's true time-domain displacement drive input, thus addressing the industry's limitations of using simplified gravity field loads or frequency-domain power spectral density as inputs in the analysis process. The present invention combines dynamic analysis with finite element analysis, considering the coupling between the instrument panel's modal participation factors and inherent system modal stresses under road excitation, and outputs its time-domain stress and strain under real excitation, thus addressing the issue of insufficient estimation of the instrument panel's true response under resonant conditions. The present invention utilizes a 6-degree-of-freedom system excitation input, addressing the incompleteness of the single-degree-of-freedom or 3-degree-of-freedom excitation inputs used in the industry. Furthermore, the present invention allows for durability analysis of independent instrument panel assemblies, independent of the constraints of the entire vehicle or vehicle model.

[0115] The present invention provides an integrated multidisciplinary automobile instrument panel durability simulation analysis method, which is a semi-analytical method. Through processes such as road load test signal analysis, instrument panel system finite element modeling, instrument panel system multi-body dynamics modeling, virtual load iteration, modal participation factor calculation and durability life prediction, the durability of automobile instrument panels can be accurately predicted, structural durability hazards can be discovered in a timely manner, and they can be optimized and controlled.

[0116] For example, to determine the load-driven behavior of an automotive instrument panel system under real-world operating conditions, road load signal testing is required. While the forces, torques, or absolute displacements acting directly on the instrument panel system's fixed constraints cannot be determined through testing, response signals from key locations on the instrument panel system, such as accelerometer signals, can be measured. These signals serve as input for subsequent virtual load iterations, or inverse drive analysis (i.e., determining the current input signal based on the candidate signal transfer function).

[0117] For example, a finite element model (i.e., the dashboard structural model) is built based on the design geometry of the vehicle dashboard. This model includes the dashboard's crossbars, frame, skin, brackets, guard plates, decorative panels, air conditioning ducts, steering wheel, and other structures. It also includes assemblies connected to the dashboard, such as parts of the body-in-white (BW) structure (fixed area). It also includes concentrated mass elements such as the toolbox load, display screen, and air conditioning panel. The results of the system's constrained modal simulation are then verified and calibrated through experimental modal testing.

[0118] For example, a multibody dynamics model of the instrument panel (i.e., the target instrument panel multibody model) is established. The model must include the instrument panel itself (i.e., the virtual instrument panel), virtual fixtures, sensors (i.e., virtual sensors), and a load-driven coordinate system. The instrument panel itself needs to be flexible, but modal frequency truncation is required to avoid consuming excessive computing resources. To prevent high-frequency distortion caused by modal truncation, CB modal calculations are required to utilize additional static displacement compensation modes to ensure the accuracy of the results. The instrument panel multibody dynamics model is primarily used as the system transfer function for subsequent virtual load iterations.

[0119] For example, using time-domain waveform reproduction technology, with the response signals of key locations of the instrument panel system obtained through testing as the target and the instrument panel's rigid-flexible hybrid multibody dynamics model as the transfer function, a virtual load iteration method is used to reversely solve the six-degree-of-freedom drive of the instrument panel system. This drive includes displacements in three linear directions (X, Y, and Z) in the vehicle coordinate system, as well as three angular displacements around the X, Y, and Z directions. Theoretically, the system drive can be obtained with a single solution. However, because virtual load iteration uses a linear identification method, the obtained response signal will inevitably have errors compared to the result obtained directly through calculation of the multibody dynamics model. Therefore, continuous iteration is required to ensure the consistency between the simulation and test signals. In other words, if the RMS (root mean square) error ratio between the simulation and test signals is less than 15%, it can be considered that the dynamic and kinematic characteristics of the instrument panel system meet the requirements.

[0120] For example, the system dynamics model (i.e., the target instrument panel multibody model) derived from the drive excitation through virtual load iteration is used to output modal participation factors. By coupling these modal participation factors with modal stresses, the stress-strain time history signals (i.e., mechanical parameter results) for all structural units of the instrument panel system can be obtained. Instrument panel materials are grouped by grade, each assigned an accurate fatigue parameter curve (i.e., the initial fatigue curve for each candidate material). Mean stress correction and survival rate parameters are input, and fatigue life calculations based on the critical plane method and Minor damage accumulation criterion are performed. This allows the damage or life of the instrument panel substrate and welded structure under realistic road load conditions to be determined.

[0121] The embodiment of the present invention can solve the system time-domain drive input of the instrument panel under real road conditions, and the model response has a high consistency with the test. By combining finite element and multi-body dynamics, the dynamic characteristics of any position can be observed and analyzed. The embodiment of the present invention couples the modal participation factor of the instrument panel with the modal stress, outputs the full-field time-domain stress and strain, and takes into account the real response under excitation and structural resonance. The embodiment of the present invention uses the strain life analysis method and the structural stress method, combined with plastic deformation, nonlinear correction, average stress correction and damage accumulation law, to analyze the durability of the instrument panel substrate, welds and welds. The method provided by the embodiment of the present invention can break away from the limitations of the whole vehicle conditions or the whole vehicle model, and perform accurate durability analysis on the independent assembly of the instrument panel. The obtained system drive can be used to predict the durability of the instrument panel of newly developed models, and can also be used to solve the cracking problem of the currently produced instrument panel. The method provided by the embodiment of the present invention has been used for durability performance control of multiple instrument panel products.

[0122] For example, the initial test signal acquisition is to conduct a road load spectrum test on a car dashboard. The dashboard to be tested can be the dashboard of a newly developed model or the dashboard of a reference model. The vibration isolation characteristics of the suspension or suspension assembly of the two models should be similar. The test sensor type is a three-axis acceleration sensor, and the measurement point position is as follows: Figure 3 As shown. Due to the differences in the structures of different instrument panels, the positions of the measuring points will not be exactly the same, but the principle followed is that points 1, 2, 3 and 4 are the fixed constraint ends of the instrument panel to be tested and the sides of the vehicle body, and point 5 is the center point of the body of the instrument panel to be tested. The test sample vehicle is required to be in good condition, preferably just after running-in. The road section to be tested is a durability condition section of concern, which can be a durability test road condition at the test site, or a commonly used or special road condition for users. The test sampling frequency needs to be at least 10 times greater than the upper limit of the preset section frequency of concern. For each durability characteristic road surface, at least three cycles of signals need to be tested to avoid or average out the influence of occasional phenomena.

[0123] Exemplary analysis and processing of the initial test signal: The obtained initial test signal is analyzed and processed. Signal processing software is used to perform operations such as offset correction, trend removal, burr removal, filtering, and road feature segmentation on the initial test signal to obtain a target test signal.

[0124] For example, the finite element modeling of the instrument panel (i.e., the instrument panel structural model) is performed: the finite element modeling of the instrument panel is performed, and the instrument panel structural model includes structures such as the instrument panel crossbeam, skeleton, skin, bracket, guard plate, decorative plate, air conditioning duct, and steering wheel; among them, the stamping process type structure in the instrument panel structural model should be meshed with shell elements, and the casting process type structure should be meshed with hexahedron or tetrahedron elements, and the corresponding material property information should be assigned. The weld point structure is simulated using ACM type weld points (Area Contact Method weld points), the weld seam unit is simulated using the common node of the connected structure or SEAM type unit, and the bolt is simulated using rigid + beam unit. At the same time, it is also necessary to establish concentrated mass units including the toolbox load, display screen, air conditioning panel, etc. to ensure that the error between the curb weight of the instrument panel system and the design quality is less than 5%. It should be noted that the instrument panel structural model must include the body portion (within 300mm) that is fixed to the instrument panel under test to ensure the accuracy of its fixed point stiffness. Generally, the front firewall assembly is completely retained, and a rigid unit is fixed at the body fracture to ensure that there is only one principal point (i.e., driving point), and its coordinates are located at the center of the four fixed positions to the body. The results of the system's constrained modal simulation must be verified and calibrated through experimental modal testing, which means that the instrument panel structural model needs to be verified and calibrated.

[0125] For example, a target instrument panel multibody model is established: a multibody dynamics model of the instrument panel system is established. Components are created within the multibody dynamics software environment. These components include the instrument panel itself (i.e., the virtual instrument panel), a virtual fixture, and virtual sensors. A fixed pair is established between the virtual sensor and the virtual instrument panel based on the actual road load spectrum test location (i.e., the preset actual test location), forming a local coordinate system. A fixed pair is established between the virtual instrument panel and the virtual fixture at the rigid constraint of the truncated body end, forming a local coordinate system. The instrument panel itself needs to be made flexible. The instrument panel finite element mesh file is accessed (i.e., the instrument panel structural model is inserted). A Craig-Bampton modal calculation is performed to utilize additional static displacement compensation modes to mitigate high-frequency distortion caused by modal truncation. Element displacements, stresses, restraint reactions, and nodal forces are output. The Craig-Bampton modal calculation is performed using an external solver. The resulting result file is in the OP2 format. Modal damping ratio coefficients are assigned based on experience and written to the multibody dynamics software environment to complete the instrument panel flexibility, thus obtaining the target instrument panel multibody model.

[0126] Exemplarily, determine the road section driving signal: perform virtual load iteration (i.e., signal iteration compensation), use finite element fatigue analysis software, and open the multi-body dynamics software environment at the same time. Set the preset road start and end frequency range according to the preset road section focus frequency range or empirical value. For ground vehicles, it is generally 1-50Hz. The iterative sampling frequency is not higher than the sampling rate of the road load spectrum test signal (i.e., the iterative sampling frequency is less than or equal to the test sampling frequency). Set NFFT to determine the frequency resolution, frequency resolution = iterative sampling rate / NFFT. Define the integral step (i.e., the step used for calculation), and the integral step should be less than the iterative sampling rate. Set the restart condition, calculate a static equilibrium state, and ensure that the iterative process starts after the static equilibrium state. Define the drive channel (i.e., the input channel of the drive point) and the sensor channel (i.e., the output channel of the test point). The drive includes displacements in the XYZ-3 linear directions (i.e., three horizontal directions) in the vehicle coordinate system, and 3 angular displacements around the XYZ direction (i.e., three rotational directions). It needs to be specified to the local coordinate system position of the virtual fixture in the dynamic environment. The sensor channel is such as Figure 3As shown in the figure, the same road load spectrum test scheme uses five tri-directional accelerometers with a total of 15 channels. The white noise cutoff frequency, pink noise exponent, and drive standard deviation are set, and a white-pink noise drive input (i.e., candidate simulated noise signals) is established. This drives the multibody dynamics model to calculate the FRF (Frequency Response Function) transfer function and perform system identification. The transfer function matrix (i.e., candidate signal transfer function) is a 6x15 matrix. After the transfer function calculation is completed, the candidate coherence coefficient is determined. Based on the candidate coherence coefficient and the preset coherence coefficient threshold, the target simulated noise signal is determined and applied to the target instrument panel multibody model. Based on the target test signal, the first drive (i.e., the current input signal for the first iterative signal compensation) is calculated using the inverse function of the transfer function matrix (i.e., the inverse function of the FRF). This drives the target instrument panel multibody model. The RMS (Root Mean Square) error ratio (i.e., the current signal difference) between the candidate signal transfer function obtained by simulation and the target test signal is calculated. If this value is less than 15%, the iteration process is terminated. Otherwise, the next iteration is performed. The drive value is continuously corrected by adjusting the error gain and drive gain. Iterations are repeated until the RMS error ratio of all iterative channels is less than 15%, the iterations converge, and the drive file of the last iteration (i.e., the target input signal) is extracted. Iterative signal compensation is performed on all target test signals segmented by road surface characteristics. All target input signals are then concatenated to form a complete drive file (i.e., the road segment drive signal, with time on the horizontal axis and displacement drive on the vertical axis).

[0127] For example, the target dashboard multi-body model outputs the modal participation factor: the complete drive file is called to drive the multi-body dynamics model (i.e., the target dashboard multi-body model), and the modal participation factor is output after the calculation is completed. The modal participation factor automatically becomes a flexible body event and is directly implanted into the flexible environment of the dashboard, and is automatically coupled and matched with the modal stress of the dashboard to obtain the time domain history stress and strain results (i.e., mechanical parameter results).

[0128] For example, the substrate life calculation is to determine the material damage value of each candidate material: enter the durability analysis environment and perform durability analysis on the instrument panel substrate structure. Insert a durability calculation condition based on the strain method. Group by material and establish a material-based task for each. Assign a cyclic stress-strain curve (the horizontal axis is strain and the vertical axis is stress) to a candidate material, and a strain-life curve (the horizontal axis is the number of cycles and the vertical axis is strain) to a candidate material. According to the reliability, survival rate or credibility requirements, a strain-life curve with a reliability (or survival rate P) of 50%, 90% and 99% can be selected to calculate the durability; that is, the strain-life curve includes multiple curves of changes between strain and cycle number of the candidate material, from which one can be selected as the initial strain fatigue curve of the candidate material according to the reliability. Similarly, one can also be selected from the stress-life curve as the initial stress fatigue curve of the candidate material, or, based on the cyclic stress-strain curve and the initial strain fatigue curve of the candidate material, the initial stress fatigue curve of the candidate material can be determined. It should be noted that cyclic stress-strain curves and strain-life curves (collectively referred to as material fatigue curves) are best determined experimentally. They can also be estimated using the Manson-Coffin equation and the Ramberg-Osgood equation. Simply specifying the material as carbon steel, aluminum, or gray cast iron and inputting the material's tensile strength and elastic modulus allows for material fatigue curve estimation. However, due to the large number of non-metallic materials such as polymers and composites present in the instrument panel to be tested, there is a lack of fatigue parameter curve estimation methods for these materials. Therefore, the actual fatigue curve and parameters obtained by post-test fitting must be input. This means that the initial fatigue curve of non-metallic materials can only be determined experimentally, while the initial fatigue curve of metallic materials can be determined experimentally or estimated using the Manson-Coffin equation and the Ramberg-Osgood equation. Based on the mechanical parameter results, the initial fatigue curves of each candidate material were corrected for average stress using the P-SWT method. Based on the mechanical parameter results, the initial fatigue curves of each candidate material were corrected for local stress state using the critical plane method. Based on the mechanical parameter results, the initial fatigue curves of each candidate material were corrected for nonlinear stress and strain using the Neuber method. The damage accumulation method was determined to use the Minor-elementary criterion, and rain flow counting was performed to calculate the number of stress-strain cycles (i.e., determine the number of candidate cycles) and to delete damage-free signals. The instrument panel substrate durability calculation condition was then executed. A weld joint durability analysis condition was inserted to conduct a weld durability analysis. For welds, a Rupp-based weld identification and calculation method was used, and for welds, a structural stress-based weld identification and calculation method was used. The fatigue curves used were either measured values ​​or empirical values ​​for the weld joints. The instrument panel weld durability analysis condition was executed.

[0129] For example, the durability test results are determined by reviewing the durability results, with the result evaluation value being structural damage (i.e., damage to the structure of each candidate material of the instrument panel under test). The damage threshold evaluation criterion is: if the total damage of each candidate material = ∑ damage to each characteristic road section * number of vehicle laps < 1, then the durability requirement is determined to be met; otherwise, the durability requirement is not met.

[0130] It should be noted that the 6-DOF drive of the instrument panel system (i.e., the target input signal) is obtained through virtual load iteration. The drive can be implemented in the form of a 6-SPS mechanism, a 6-axis orthogonal drive mechanism, etc., but what is ultimately obtained are 3 linear displacement drives and 3 angular displacement drives at the fixed constraints of the instrument panel, which is comprehensive.

[0131] The instrument panel durability analysis method provided by the present invention is characterized by reflecting the instrument panel's true kinematic and dynamic characteristics based on actual road load conditions, and evaluating durability through structural damage or lifespan. The method of the present invention uses virtual load iteration, inversely determining the system's six-degree-of-freedom drive input signals based on the response signals of the instrument panel's five acceleration measurement points and 15 channels and the instrument panel's system dynamic transfer function. Considering the influence of instrument panel resonance, the modal participation factor of the instrument panel under road excitation is coupled with the instrument panel's inherent modal stress to obtain the full-field time-domain stress and strain (i.e., mechanical parameter results) of the instrument panel under road load. The method of calculating the instrument panel substrate's lifespan and damage provided by the present invention is characterized by a strain-based lifespan analysis method in which the material strain-life curve can be based on different reliability or survival rate factors. Cyclic stress-strain curves and strain-life curves can be estimated using the Manson-Coffin equation and the Ramberg-Osgood equation. The mean stress correction method is P-SWT, the local stress state is the critical plane method, the nonlinear stress-strain correction method is the Neuber method, and the damage accumulation method uses the minor-elementary criterion. Only by coupling all methods can the analysis accuracy of the durability life of the instrument panel be ensured; the method for calculating the life and damage of the instrument panel welding joints provided by the embodiment of the present invention is characterized in that, for weld points, a Rupp-based weld point identification and calculation method is adopted, and for welds, a weld identification and calculation method based on the structural stress method is adopted; the scope of the method provided by the embodiment of the present invention may include passenger car or commercial vehicle instrument panels.

[0132] Example 4

[0133] Figure 4This is a schematic diagram of the structure of an instrument panel durability simulation device provided in Example 4 of the present invention. This embodiment is applicable to durability testing of vehicle instrument panels. The method can be performed by the instrument panel durability simulation device, which can be implemented using software and / or hardware and configured in an electronic device that carries the instrument panel durability simulation function.

[0134] like Figure 4 As shown, the device includes: a candidate signal transfer function determination module 410, a road section driving signal determination module 420, a target fatigue curve determination module 430 and a test result determination module 440.

[0135] The candidate signal transfer function determination module 410 is configured to obtain a target test signal and a corresponding candidate simulated noise signal of the instrument panel under test under candidate road surface characteristics, and drive a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function;

[0136] a road segment driving signal determination module 420 for determining a current input signal under a corresponding candidate road surface characteristic based on the candidate signal transfer function, performing signal iterative compensation on the current input signal to obtain a target input signal, and determining a road segment driving signal based on the target input signal under the candidate road surface characteristic;

[0137] a target fatigue curve determination module 430 for determining mechanical parameter results based on the road section drive signal and the target instrument panel multi-body model, and updating the initial fatigue curve of the candidate material based on the mechanical parameter results to obtain a target fatigue curve for the corresponding candidate material;

[0138] The test result determination module 440 is configured to determine a durability test result of the instrument panel to be tested according to the mechanical parameter result and the target fatigue curve.

[0139] An embodiment of the present invention provides an instrument panel durability simulation solution, which obtains a target test signal and a corresponding candidate simulation noise signal of the instrument panel to be tested under candidate road surface characteristics, and drives a preset target instrument panel multi-body model based on the candidate simulation noise signal to obtain a corresponding candidate signal transfer function; based on the candidate signal transfer function, determines the current input signal under the corresponding candidate road surface characteristics, performs signal iterative compensation on the current input signal to obtain a target input signal, and determines a section drive signal based on the target input signal under the candidate road surface characteristics; determines mechanical parameter results based on the section drive signal and the target instrument panel multi-body model, and updates the initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material; determines the durability test result of the instrument panel to be tested based on the mechanical parameter results and the target fatigue curve. The above scheme determines a candidate signal transfer function based on a candidate simulated noise signal and a target instrument panel multi-body model, and then determines the current input signal based on the candidate signal transfer function. The target input signal is obtained by performing iterative signal compensation on the current input signal. The initial fatigue curve is updated based on the target input signal and the mechanical parameter results determined by the target instrument panel multi-body model to obtain the target fatigue curve. Finally, the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby realizing automated durability testing of the instrument panel, improving the efficiency of durability testing of the instrument panel, and reducing labor costs. At the same time, the current input signal is determined based on the candidate signal transfer function, and the target input signal is obtained by performing iterative signal compensation on the current input signal, thereby improving the accuracy of the determined target input signal. Subsequently, the mechanical parameter results are determined based on the target input signal, and then the durability test result of the instrument panel to be tested is determined based on the mechanical parameter results and the target fatigue curve, thereby improving the accuracy of the determined durability test result, that is, improving the accuracy of the durability test of the instrument panel.

[0140] Optionally, the road section driving signal determination module 420 includes:

[0141] a signal difference determination unit, configured to determine, for any signal iterative compensation process, a current output signal of this iteration according to a current input signal of this iteration, and determine a current signal difference based on the current output signal and the target test signal;

[0142] an input compensation signal determining unit, configured to determine an input compensation signal for a next iteration based on the current input signal and the current signal difference if the current signal difference is greater than or equal to a preset difference threshold;

[0143] a current input signal determining unit, configured to determine the current input signal of the next iteration based on the input compensation signal and the current input signal of this iteration;

[0144] The target input signal determination unit is configured to use the current input signal obtained in the last iteration as the target input signal.

[0145] Optionally, the current input signal determining unit includes:

[0146] a target signal transfer function determination subunit, configured to determine candidate coherence coefficients corresponding to the candidate signal transfer functions under the candidate road surface features, and determine a corresponding target signal transfer function based on the candidate coherence coefficients;

[0147] The current input signal determination subunit is used to determine the current input signal under the corresponding candidate road surface feature according to the target signal transfer function.

[0148] Optionally, the target signal transfer function determination subunit is specifically configured to:

[0149] determining corresponding candidate coherence coefficients based on the candidate simulated noise signal and the candidate signal transfer function under the candidate road surface feature;

[0150] A target signal transfer function corresponding to the candidate road surface feature is determined according to the candidate coherence coefficient and a preset coherence coefficient threshold.

[0151] Optionally, the test result determination module 440 includes:

[0152] a cycle number determination unit, configured to determine a candidate cycle number of a candidate mechanical parameter amplitude according to the mechanical parameter result, and determine a reference cycle number of a corresponding candidate mechanical parameter amplitude according to a target fatigue curve under the candidate material;

[0153] a material damage value determining unit, configured to determine a material damage value corresponding to a corresponding candidate material according to a candidate cycle number and a reference cycle number of the candidate mechanical parameter amplitude;

[0154] The test result determination unit is used to determine the durability test result of the instrument panel to be tested according to the material damage value corresponding to each candidate material.

[0155] Optionally, the target instrument panel multi-body model is determined based on the following means:

[0156] an instrument panel structure model determination module, configured to obtain basic attribute data of the instrument panel to be tested, component mass of each device component, and local vehicle body data, and determine an instrument panel structure model based on the basic attribute data, the component mass, and the local vehicle body data;

[0157] The target instrument panel multi-body model determination module is used to construct an initial instrument panel multi-body model, and obtain the target instrument panel multi-body model based on the initial instrument panel multi-body model, the instrument panel structure model and the preset modal damping ratio coefficient; wherein the target instrument panel multi-body model includes driving points and test points.

[0158] Optionally, the driving point is a point between the virtual dashboard and the virtual fixture in the initial dashboard model at the rigid constraint of the truncated body end fracture; the test point is a point between the virtual sensor and the virtual dashboard in the initial dashboard multi-body model that matches the preset actual test position.

[0159] The instrument panel durability simulation device provided in the embodiment of the present invention can execute the instrument panel durability simulation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing each instrument panel durability simulation method.

[0160] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of the target test signals, candidate simulated noise signals and initial fatigue curves involved all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0161] Example 5

[0162] Figure 5 : It is a structural diagram of an electronic device for implementing a dashboard durability simulation method provided by Example 5 of the present invention. The electronic device 510 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0163] like Figure 5As shown, the electronic device 510 includes at least one processor 511, and a memory connected to the at least one processor 511 in communication, such as a read-only memory (ROM) 512, a random access memory (RAM) 513, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 511 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 512 or the computer program loaded from the storage unit 518 into the random access memory (RAM) 513. Various programs and data required for the operation of the electronic device 510 can also be stored in the RAM 513. The processor 511, ROM 512, and RAM 513 are connected to each other via a bus 514. An input / output (I / O) interface 515 is also connected to the bus 514.

[0164] Multiple components in the electronic device 510 are connected to the I / O interface 515, including an input unit 516, such as a keyboard, a mouse, etc.; an output unit 517, such as various types of displays, speakers, etc.; a storage unit 518, such as a magnetic disk, an optical disk, etc.; and a communication unit 519, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 519 allows the electronic device 510 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0165] Processor 511 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 511 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 511 executes the various methods and processes described above, such as the instrument panel durability simulation method.

[0166] In some embodiments, the instrument panel durability simulation method can be implemented as a computer program that is tangibly contained in a computer-readable storage medium, such as the storage unit 518. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 510 via the ROM 512 and / or the communication unit 519. When the computer program is loaded into the RAM 513 and executed by the processor 511, one or more steps of the instrument panel durability simulation method described above can be performed. Alternatively, in other embodiments, the processor 511 can be configured to execute the instrument panel durability simulation method in any other appropriate manner (e.g., by means of firmware).

[0167] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0168] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0169] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0170] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0171] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0172] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0173] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0174] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for simulating instrument panel durability, characterized in that: include: Acquire a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under a candidate road surface feature, and drive a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function; Determining a current input signal under a corresponding candidate road surface feature based on the candidate signal transfer function, performing signal iterative compensation on the current input signal to obtain a target input signal, and determining a road section driving signal based on the target input signal under the candidate road surface feature; Determining mechanical parameter results based on the road section drive signal and the target instrument panel multi-body model, and updating an initial fatigue curve under the candidate material based on the mechanical parameter results to obtain a target fatigue curve under the corresponding candidate material; The durability test result of the instrument panel to be tested is determined according to the mechanical parameter result and the target fatigue curve.

2. The method according to claim 1, characterized in that The performing signal iterative compensation on the current input signal to obtain a target input signal includes: For any signal iterative compensation process, determine the current output signal of this iteration based on the current input signal of this iteration, and determine the current signal difference based on the current output signal and the target test signal; If the current signal difference is greater than or equal to a preset difference threshold, determining an input compensation signal for a next iteration based on the current input signal and the current signal difference; Determining the current input signal for the next iteration according to the input compensation signal and the current input signal for this iteration; The current input signal obtained in the last iteration is used as the target input signal.

3. The method according to claim 1, characterized in that The determining, based on the candidate signal transfer function, a current input signal corresponding to the candidate road surface feature, includes: determining a candidate coherence coefficient corresponding to the candidate signal transfer function under the candidate road surface feature, and determining a corresponding target signal transfer function based on the candidate coherence coefficient; According to the target signal transfer function, a current input signal under the corresponding candidate road surface feature is determined.

4. The method according to claim 3, characterized in that The determining of candidate coherence coefficients corresponding to the candidate signal transfer functions under the candidate road surface features, and determining a corresponding target signal transfer function based on the candidate coherence coefficients, includes: determining corresponding candidate coherence coefficients based on the candidate simulated noise signal and the candidate signal transfer function under the candidate road surface feature; A target signal transfer function corresponding to the candidate road surface feature is determined according to the candidate coherence coefficient and a preset coherence coefficient threshold.

5. The method according to claim 1, wherein Determining the durability test result of the instrument panel to be tested according to the mechanical parameter result and the target fatigue curve includes: Determining candidate cycles of candidate mechanical parameter amplitudes based on the mechanical parameter results, and determining reference cycles of corresponding candidate mechanical parameter amplitudes based on a target fatigue curve under the candidate material; Determining a material damage value corresponding to a corresponding candidate material according to the candidate cycle number and the reference cycle number of the candidate mechanical parameter amplitude; The durability test result of the instrument panel to be tested is determined according to the material damage value corresponding to each of the candidate materials.

6. The method according to any one of claims 1 to 5, characterized in that The target instrument panel multi-body model is determined based on the following method: Obtaining basic attribute data of the instrument panel to be tested, component mass of each device component, and local body data, and determining an instrument panel structure model based on the basic attribute data, the component mass, and the local body data; An initial instrument panel multibody model is constructed, and a target instrument panel multibody model is obtained based on the initial instrument panel multibody model, the instrument panel structure model, and a preset modal damping ratio coefficient; wherein the target instrument panel multibody model includes a driving point and a test point.

7. The method according to claim 6, characterized in that The driving point is the point between the virtual dashboard and the virtual fixture in the initial dashboard model at the rigid constraint of the truncated body end fracture; the test point is the point between the virtual sensor and the virtual dashboard in the initial dashboard multi-body model that matches the preset actual test position.

8. An instrument panel durability simulation device, characterized in that: include: a candidate signal transfer function determination module, configured to obtain a target test signal and a corresponding candidate simulated noise signal of the instrument panel to be tested under candidate road surface characteristics, and drive a preset target instrument panel multi-body model based on the candidate simulated noise signal to obtain a corresponding candidate signal transfer function; a road section driving signal determination module, configured to determine a current input signal under a corresponding candidate road surface feature based on the candidate signal transfer function, perform signal iterative compensation on the current input signal to obtain a target input signal, and determine a road section driving signal based on the target input signal under the candidate road surface feature; a target fatigue curve determination module, configured to determine mechanical parameter results based on the road section drive signal and the target instrument panel multi-body model, and update the initial fatigue curve of the candidate material based on the mechanical parameter results to obtain a target fatigue curve of the corresponding candidate material; The test result determination module is used to determine the durability test result of the instrument panel to be tested according to the mechanical parameter result and the target fatigue curve.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the instrument panel durability simulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the durability simulation method of an instrument panel according to any one of claims 1 to 7 is implemented.

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