A Method and System for Optimizing the Thickness Matching of the Welded Front Axle

Abstract

The present invention discloses a method and system for optimizing the thickness matching of a welded front axle, including obtaining a load spectrum and mechanical characteristic parameters, constructing a multi-body dynamics model through the mechanical characteristic parameters, and based on the multi-body dynamics model, analyzing the load spectrum by a virtual iteration method to obtain the front axle attachment point load; constructing a finite element model of the front axle, and based on the front axle attachment point load and the finite element model of the front axle, obtaining the position of the failed weld; based on the stress state of the position of the failed weld, obtaining the dominant failure load, and based on the dominant failure load, optimizing the thickness of the position of the failed weld by an adaptive response surface method to obtain the optimization result of the thickness matching of the welded front axle. Through the above technical solution, the present invention can improve the fatigue life of the front axle weld by optimizing the matching of the thickness parameters in the weld area.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-fatigue design, and particularly relates to a method and system for optimizing the thickness matching of a welded front axle. Background Art

[0002] Weld fatigue is one of the main forms of vehicle component failure. Most of the existing methods for improving weld life optimize from the perspectives of welding quality, manufacturing process parameters, weld position layout, etc. However, there are no relevant technical solutions for improving the weld life of the front axle by optimizing the matching of thickness parameters in the welding area. Summary of the Invention

[0003] To solve the problem in the above-mentioned prior art of how to improve the weld life of the front axle by optimizing the matching of thickness parameters in the welding area, the present invention provides a method and system for optimizing the thickness matching of a welded front axle, aiming to improve the fatigue life of the front axle weld by optimizing the matching of thickness parameters in the weld area under the condition that the weld quality, process parameters, weld position layout, etc. remain unchanged.

[0004] To achieve the above technical purpose, the present invention provides the following technical solution: A method for optimizing the thickness matching of a welded front axle, comprising:

[0005] Obtain a load spectrum and mechanical characteristic parameters, construct a multi-body dynamics model through the mechanical characteristic parameters, and analyze the load spectrum based on the multi-body dynamics model through a virtual iteration method to obtain the front axle attachment point load;

[0006] Construct a front axle finite element model, obtain the failed weld position based on the front axle attachment point load and the front axle finite element model; obtain the dominant failure load based on the stress state of the failed weld position, and perform matching optimization on the thickness of the failed weld position based on the dominant failure load through the adaptive response surface method to obtain the optimized result of the thickness matching of the welded front axle.

[0007] Optionally, the process of obtaining the mechanical characteristic parameters includes: measuring the front axle to obtain the mechanical characteristic parameters, where the mechanical characteristic parameters include mass characteristic parameters, geometric characteristic parameters, bushing stiffness, and shock absorber damping.

[0008] Optionally, the process of obtaining the dominant failure load includes:

[0009] Perform fatigue simulation analysis on the finite element model to obtain the failed weld position, calculate the pseudo-damage value through the stress state of the failed weld position, judge the pseudo-damage value to obtain the failure-related load, and judge the failure-related load to obtain the dominant failure load;

[0010] The stress state of the failed weld position includes: damage direction analysis, uniaxial damage comparison, and principal stress direction distribution.

[0011] Optionally, the process of matching and optimizing the thickness of the failed weld position includes:

[0012] By taking the failure-dominant load as the load loading condition of the front axle finite element model, analyzing the thickness of the failed weld position to obtain the maximum stress in the failed weld area, taking the thickness of the failed weld position as the design variable, taking the maximum stress and total mass of the failed weld position as the output responses, and based on the design variable and output responses, selecting samples through the Latin hypercube sampling method, constructing a response surface model based on the samples, and based on the response surface model, performing matching optimization on the thickness of the failed weld position through the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle; wherein, the response surface model takes the minimum total mass as the optimization objective.

[0013] Optionally, the process after obtaining the thickness matching optimization result of the welded front axle includes:

[0014] Performing simulation verification on the thickness matching optimization result of the welded front axle through random road loads, conducting tests on the endurance road in the test field, and judging the thickness matching optimization result of the welded front axle based on the simulation verification result and test result to generate a final judgment result, so as to realize the feasibility judgment of the thickness matching optimization result of the welded front axle.

[0015] To better achieve the above technical objectives, the present invention also provides a thickness matching optimization system for a welded front axle, including: an acquisition module and an optimization module;

[0016] The acquisition module is used to acquire the load spectrum and mechanical characteristic parameters, construct a multi-body dynamics model through the mechanical characteristic parameters, and based on the multi-body dynamics model, analyze the load spectrum through the virtual iteration method to obtain the front axle attachment point load;

[0017] The optimization module is used to construct a front axle finite element model, obtain the failed weld position based on the front axle attachment point load and the front axle finite element model; obtain the failure-dominant load based on the stress state of the failed weld position, take the failure-dominant load as the load loading condition during optimization, and perform matching optimization on the thickness of the failed weld position through the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle.

[0018] Optionally, the mechanical characteristic parameters in the acquisition module include mass characteristic parameters, geometric characteristic parameters, bushing stiffness, and shock absorber damping.

[0019] Optionally, the optimization module includes a first optimization module, where the first optimization module is used to obtain the position of the failed weld by performing fatigue simulation analysis on the finite element model, calculate the pseudo-damage value by calculating the stress state of the failed weld position, judge the pseudo-damage value to obtain the failure-related load, and judge the failure-related load to obtain the failure-dominant load; the stress state of the failed weld position includes: damage direction analysis, uniaxial damage comparison, and principal stress direction distribution.

[0020] Optionally, the optimization module includes a second optimization module, and the second optimization module is used to analyze the thickness of the failed weld position by using the failure-dominant load as the load loading condition of the front axle finite element model, obtain the maximum stress in the failed weld area, use the thickness of the failed weld position as the design variable, use the maximum stress and total mass of the failed weld position as the output responses, and select samples by the Latin hypercube sampling method according to the design variable and the output responses. Based on the samples, a response surface model is constructed. Based on the response surface model, the thickness of the failed weld position is matched and optimized by the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle; among them, the response surface model takes the minimum total mass as the optimization goal.

[0021] Optionally, it further includes a verification module, and the verification module is used to perform simulation verification on the thickness matching optimization result of the welded front axle through random road loads, and conduct tests on the endurance road of the test field. Based on the simulation verification result and the test result, the thickness matching optimization result of the welded front axle is judged to generate a final judgment result, so as to realize the feasibility judgment of the thickness matching optimization result of the welded front axle.

[0022] The present invention has the following technical effects:

[0023] A method for optimizing the thickness matching of a welded front axle provided by the present invention can, under the condition that the weld quality, position layout, and welding process parameters remain unchanged, coordinate the stiffness of the weld area by matching and optimizing the thickness parameters of the welding area, so as to increase the anti-fatigue performance of the weld and improve the service life. After the fatigue life simulation result corresponds to the failure characteristics of the road test, the failure-dominant load under the random road of the test field is determined, so that the thickness matching optimization calculation can be quickly completed, making the anti-fatigue design of the weld more targeted. This method is also applicable to the anti-fatigue design of other fillet welds. Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 Flow chart of the thickness matching optimization method for the welded front axle provided by the embodiment of the present invention;

[0026] Figure 2 Flow chart of the virtual iteration to obtain the load of the front axle attachment point provided by the embodiment of the present invention;

[0027] Figure 3 Partial load spectrum diagram of the front axle attachment point provided by the embodiment of the present invention;

[0028] Figure 4 Finite element modeling diagram of the front axle weld provided by the embodiment of the present invention;

[0029] Figure 5 Analysis result diagram of the fatigue life of the front axle weld provided by the embodiment of the present invention;

[0030] Figure 6 Determination diagram of the failure correlation load between the longitudinal force Fx and the lateral force Fy provided by the embodiment of the present invention;

[0031] Figure 7 Determination diagram of the failure correlation load between the longitudinal force Fx and the vertical force Fz provided by the embodiment of the present invention;

[0032] Figure 8 Determination diagram of the failure-dominant load provided by the embodiment of the present invention;

[0033] Figure 9 Distribution diagram of the maximum principal stress direction provided by the embodiment of the present invention. Detailed implementation manner

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] In order to solve the problems in the prior art such as how to improve the life of the front axle weld through the matching optimization of the thickness parameters of the welding area, the present invention provides the following solutions:

[0036] Embodiment 1

[0037] As Figure 1 described, the present invention provides a thickness matching optimization method for a welded front axle, including:

[0038] The overall implementation scheme process of the present invention is as Figure 1As shown, it includes virtual iteration to obtain the front bridge attachment point load, fatigue life analysis of the front bridge weld, determination of the dominant failure load at the front bridge weld under the random load of the test field, using the adaptive response surface method to optimize the weld thickness matching, and verifying the feasibility of the optimization scheme through random load simulation and test field road experiment. Firstly, the test field load is collected, and the front bridge attachment point load spectrum is obtained through virtual iteration. Combining with the inertial release analysis results of the finite element model, the consistency between the fatigue life simulation analysis and the actual road test failure characteristics is determined; the weld failure load analysis is carried out to determine the dominant failure load of the weld; the adaptive response surface method is used to optimize the thickness matching of the weld area to coordinate the stiffness between the weld and the front bridge base material, thereby improving the weld life.

[0039] The specific implementation steps are as follows: Step 1, conduct a test field road durability test on a certain type of light truck equipped with a welded front bridge (early failure occurs at the front bridge weld). According to the test field durability road test specifications, collect the load spectrum, and establish a multi-body dynamics model based on the measured mass characteristic parameters, geometric characteristic parameters, mechanical characteristic parameters such as bushing stiffness and shock absorber damping of the light truck, and obtain the front bridge attachment point load through virtual iteration, as Figure 2 、 Figure 3 shown.

[0040] Step 2, establish a finite element model of the front bridge, as Figure 4 shown. The pshell shell element is used to model the weld, and the seam-weld connection is used; conduct a fatigue simulation analysis on the front bridge, as Figure 5 shown. The failure position and mileage can correspond to the test field road test failure situation (the mileage is 2000 km test field mileage, and the position is the starting and ending arc positions of the weld under the leaf spring seat).

[0041] Step 3, determine the dominant failure load at the front bridge weld under the random road load of the test field through the damage direction distribution, uniaxial damage comparison, and principal stress direction distribution.

[0042] After verifying the accuracy of the model, if the failure location and mileage in the fatigue simulation analysis are consistent with the actual road test failure characteristics, the accuracy of the model establishment in Steps 1 and 2 is verified. The failure load analysis determines the dominant failure load at the front axle weld under the random road load of the test field through the damage direction distribution, uniaxial damage comparison, and principal stress direction distribution. It is characterized in that the pseudo-damage values generated by the load history in each direction are calculated every 10° in the 0-180° direction of the XZ, XY, and YZ planes respectively, so as to determine the failure-related load (when the pseudo-damage value of the force in a certain direction is less than 0.1 times that of the forces in the other two directions, the force in this direction is not the failure-related load, and the forces in the other two directions are identified as the failure-related loads); further combining the effect of the coupling of the inertial release unidirectional force and the random load spectrum, where first the results of the inertial release of the unit load in each direction are obtained, combined with the random load spectrum of the corresponding channel, and according to the Miner linear cumulative damage theory, the damage of the dangerous units of the front axle under the action of the unidirectional load is obtained. At least the first 3 dangerous units at the weld are taken. When the damage in a certain direction is more than 4 times that of the damage values in the other directions, the force in this direction is determined as the dominant failure load.

[0043] Compare the pseudo-damage of the loads in the x, y, and z directions. As Figure 6 shown, the pseudo-damage caused by the longitudinal force Fx in each channel is greater than that of the lateral force Fy; the pseudo-damage caused by the longitudinal force Fx at the attachment points of the shock absorber and the leaf spring is greater than that of the vertical force Fz, and the pseudo-damage of Fx and Fz at the attachment point of the axle head is not much different as Figure 7 shown. The Fx and Fz at each attachment point of the front axle are identified as the failure-related loads. Couple the three-way unit loads in the inertial release condition with the measured load spectrum respectively. As Figure 8 shown, the damage caused by the unidirectional load Fx far exceeds the results of the actions of the forces in the other two directions. Finally, it is determined that the Fx of the front axle is the dominant failure load.

[0044] According to the maximum principal stress direction distribution obtained from the virtual strain rosette (the 0-degree patch is along the Figure 4 y-direction of the coordinate), as Figure 9 shown, the frequency of the maximum principal stress load in the direction forming an angle of 70-80 degrees with the y-direction is the highest, and the direction of crack propagation is the normal direction of the maximum principal stress (approximately the y-direction of the modeling coordinate), which is consistent with the actual failure characteristics.

[0045] Step 4, under the condition that the weld quality, process parameters, weld position layout, etc. of the front axle remain unchanged, in order to improve the fatigue life of the front axle weld, analyze the influence trend of the leaf spring seat, weld, and the thickness of the front axle shaft tube in the failed weld area on the maximum stress of the weld. Among them, 100 groups of samples are taken by Latin hypercube sampling according to the thickness value ranges of the three places, analyze the relationship between the thickness parameters and the maximum stress of the weld in the sample data, and use the adaptive response surface method to match and optimize the three.

[0046] First, analyze the influence trends of the thickness of the leaf spring seat, weld, and front axle tube in the failed weld area on the maximum stress of the weld. Taking the above three thickness parameters as design variables (if this method is used for other fillet welds, the design variables are the thickness parameters at the upper and lower weld toes and in the middle of the weld), the thickness variation range can be -70% to +300% of the initial design value. The maximum stress and total mass of the elements at the weld are the output responses. Use Latin hypercube sampling to select 100 groups of samples and construct a response surface model. Among them, based on the obtained sample distribution data, establish a radial basis function neural network model (rbf), construct the response surface model, and evaluate their accuracy. The coefficient of determination R 2 If it is above 0.9, the accuracy meets the requirements. Use the adaptive response surface method to optimize the matching of the three thicknesses, set the minimum possible mass as the optimization goal, and the stress at the weld being less than 60% of the initial most dangerous element stress as the constraint condition. Calculate the optimal values of the thickness parameters. Under the action of random road loads, simulate and verify the degree of life improvement, and if the early failure problem no longer occurs in the endurance road test on the test field again, it is considered that the optimization scheme is verified to be feasible.

[0047] The design variables are the thicknesses of the three, and the thickness parameter variation range is shown in Table 1, which is the value range of the thickness parameters; the loads for simulation loading are the maximum values of the dominant loads Fx for the failures of each attachment point of the front axle and the vertical load Fz at the attachment point of the axle head; the output responses are the maximum stress and total mass of the elements at the weld; use Latin hypercube sampling to select 100 groups of samples and construct a response surface model. The coefficient of determination for verifying the accuracy of the response surface model is 0.97; in order to take into account the requirements of vehicle lightweighting, set the minimum possible mass as the optimization goal, and the stress at the weld being less than 80 MPa as the constraint condition. The optimal values calculated by the adaptive response surface method are shown in Table 2, which is the thickness matching optimization result calculated by the adaptive response surface method.

[0048] Table 1

[0049]

[0050] Table 2

[0051]

[0052] Step 5, verify the optimization results by random road load simulation. Table 3 is the table showing the improvement of the simulation-verified life under random load. The optimal solution in the 13th group in Table 3 indicates that when the thickness parameters at three locations are increased to 12 mm, 15 mm, and 16 mm respectively, the weld life is greatly improved to nearly 1.6 cycles. This optimal value is consistent with the optimal thickness matching result obtained by the previous adaptive response surface method. When the thicknesses of the three parts of the weld are thickened according to the above optimization scheme, the stiffness at the weld is the most coordinated, and the stress at the weld is most effectively reduced without wasting weld material, enabling the life of the front axle weld to be increased by nearly 8 times compared with that before optimization and reaching 1.5 times the target mileage; when the optimized front axle of the light truck is used for the whole vehicle road test on the test field again, the 10,000 km durability requirement is completed, and no early failure problems occur again.

[0053] Table 3

[0054]

[0055] Aiming at the early failure problem that occurs during the road test of the front axle weld of a light truck, based on the durability test specification of the test field, virtual iteration method, finite element theory, fatigue life simulation analysis, multi-axial damage assessment, multi-axial load dimensionality reduction processing, and adaptive response surface optimization design method, the present invention proposes a thickness matching optimization method for a welded front axle, aiming to improve the fatigue life of the front axle weld by matching and optimizing the thickness parameters in the weld area under the condition that the weld quality, process parameters, weld position layout, etc. remain unchanged, which can provide a reference for the anti-fatigue design of the weld; at the same time, it provides a basis for the grinding thickness target after the weld is processed.

[0056] Embodiment 2

[0057] To better achieve the above technical objectives, the present invention also provides a thickness matching optimization system for a welded front axle, including: an acquisition module and an optimization module;

[0058] The acquisition module is used to acquire the load spectrum and mechanical characteristic parameters, construct a multi-body dynamics model through the mechanical characteristic parameters, and analyze the load spectrum through the virtual iteration method based on the multi-body dynamics model to obtain the front axle attachment point load;

[0059] The optimization module is used to construct a finite element model of the front axle, obtain the failed weld position based on the front axle attachment point load and the finite element model of the front axle; obtain the dominant failure load based on the stress state of the failed weld position, use the dominant failure load as the load loading condition during optimization, and match and optimize the thickness of the failed weld position through the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle.

[0060] Optionally, the mechanical characteristic parameters in the acquisition module include mass characteristic parameters, geometric characteristic parameters, bushing stiffness, and shock absorber damping.

[0061] Optionally, the optimization module includes a first optimization module, where the first optimization module is used to obtain the position of the failed weld by performing fatigue simulation analysis on the finite element model, calculate the pseudo-damage value by calculating the stress state of the failed weld position, judge the pseudo-damage value to obtain the failure-related load, and judge the failure-related load to obtain the failure-dominant load; the stress state of the failed weld position includes: damage direction analysis, uniaxial damage comparison, and principal stress direction distribution.

[0062] Optionally, the optimization module includes a second optimization module, and the second optimization module is used to analyze the thickness of the failed weld position by using the failure-dominant load as the load loading condition of the front axle finite element model to obtain the maximum stress in the failed weld area, use the thickness of the failed weld position as the design variable, use the maximum stress and total mass of the failed weld position as the output responses, and select samples by the Latin hypercube sampling method according to the design variable and the output responses, construct a response surface model based on the samples, and perform matching optimization on the thickness of the failed weld position by the adaptive response surface method based on the response surface model to obtain the thickness matching optimization result of the welded front axle; wherein, the response surface model takes the minimum total mass as the optimization objective.

[0063] Optionally, it further includes a verification module, and the verification module is used to perform simulation verification on the thickness matching optimization result of the welded front axle through random road loads and conduct tests on the endurance road of the test field, judge the thickness matching optimization result of the welded front axle based on the simulation verification result and the test result, and generate a final judgment result to realize the feasibility judgment of the thickness matching optimization result of the welded front axle. The system and method correspond to each other and will not be elaborated.

[0064] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the thickness matching of a welded front axle, characterized in that, it includes: Obtain the load spectrum and mechanical characteristic parameters, construct a multi-body dynamics model through the mechanical characteristic parameters, and based on the multi-body dynamics model, analyze the load spectrum by the virtual iteration method to obtain the front axle attachment point load; Construct a front axle finite element model, and based on the front axle attachment point load and the front axle finite element model, obtain the position of the failed weld; Based on the stress state of the failed weld position, obtain the failure-dominant load. Based on the failure-dominant load, optimize the thickness of the failed weld position by the adaptive response surface method to obtain the optimized result of the thickness matching of the welded front axle; The process of obtaining the failure-dominant load includes: Through fatigue simulation analysis of the finite element model, obtain the position of the failed weld. By calculating the stress state of the failed weld position, obtain the pseudo-damage value. Judge the pseudo-damage value to obtain the failure-related load, and judge the failure-related load to obtain the failure-dominant load; The stress state of the failed weld position includes: damage direction analysis, uniaxial damage comparison, and principal stress direction distribution; The process of optimizing the thickness matching of the failed weld position includes: By using the failure-dominant load as the load loading condition of the front axle finite element model, analyze the thickness of the failed weld position to obtain the maximum stress in the failed weld area. Take the thickness of the failed weld position as the design variable, take the maximum stress and total mass of the failed weld position as the output responses, and select samples by the Latin hypercube sampling method according to the design variable and the output responses. Based on the samples, construct a response surface model. Based on the response surface model, optimize the thickness of the failed weld position by the adaptive response surface method to obtain the optimized result of the thickness matching of the welded front axle; among them, the response surface model takes the minimum total mass as the optimization goal; After verifying the accuracy of the model, if the failure position and mileage of the fatigue simulation analysis are consistent with the actual road test failure characteristics, the accuracy of the model establishment is verified. The failure load analysis determines the failure-dominant load at the front axle weld under the random road load of the test field through the damage direction distribution, uniaxial damage comparison, and principal stress direction distribution; calculate the pseudo-damage values generated by the load history in each direction at intervals of 10° in the 0-180° direction of the XZ, XY, and YZ planes respectively to determine the failure-related load. Among them, when the pseudo-damage value of a certain direction force is less than 0.1 times that of the other two direction forces respectively, this direction force is not the failure-related load, and the other two direction forces are identified as the failure-related load; further combine the effect of the coupling of inertial release of a single direction force and the random load spectrum. First, obtain the results of inertial release of unit loads in each direction, combine with the random load spectrum of the corresponding channel, and according to the Miner linear cumulative damage theory, obtain the damage of the dangerous units of the front axle under the action of the unidirectional load. Take at least the first 3 dangerous units at the weld. When the damage in a certain direction is more than 4 times that of the other directions, determine that the force in this direction is the failure-dominant load; After the fatigue life simulation results are corresponded with the road test failure characteristics, the dominant failure load under the random road of the test field is determined, so that the thickness matching optimization calculation can be quickly completed.

2. The thickness matching optimization method of the welded front axle according to claim 1, characterized in that: the process of obtaining the mechanical characteristic parameters includes: measuring the front axle to obtain the mechanical characteristic parameters, where the mechanical characteristic parameters include mass characteristic parameters, geometric characteristic parameters, bushing stiffness and shock absorber damping.

3. The thickness matching optimization method of the welded front axle according to claim 1, characterized in that: the process after obtaining the thickness matching optimization result of the welded front axle includes: simulating and verifying the thickness matching optimization result of the welded front axle through the random road load, and conducting tests on the endurance road of the test field, judging the thickness matching optimization result of the welded front axle based on the simulation verification result and the test result, and generating a final judgment result to realize the feasibility judgment of the thickness matching optimization result of the welded front axle.

4. The thickness matching optimization system of the welded front axle corresponding to the method according to any one of claims 1-3, characterized in that, comprising: an acquisition module and an optimization module; the acquisition module is used to acquire the load spectrum and mechanical characteristic parameters, construct a multi-body dynamics model through the mechanical characteristic parameters, and analyze the load spectrum based on the multi-body dynamics model through the virtual iteration method to obtain the front axle attachment point load; the optimization module is used to construct a front axle finite element model, and obtain the position of the failed weld based on the front axle attachment point load and the front axle finite element model; Based on the stress state of the position of the failed weld, obtain the dominant failure load, and based on the dominant failure load, perform thickness matching optimization on the thickness of the position of the failed weld through the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle.

5. The thickness matching optimization system of the welded front axle according to claim 4, characterized in that: the mechanical characteristic parameters in the acquisition module include mass characteristic parameters, geometric characteristic parameters, bushing stiffness and shock absorber damping.

6. The thickness matching optimization system of the welded front axle according to claim 4, characterized in that: the optimization module includes a first optimization module, where the first optimization module is used to perform fatigue simulation analysis on the finite element model to obtain the position of the failed weld, calculate the stress state of the position of the failed weld to obtain a pseudo-damage value, judge the pseudo-damage value to obtain a failure-related load, and judge the failure-related load to obtain the dominant failure load; the stress state of the position of the failed weld includes: damage direction analysis, uniaxial damage comparison, and principal stress direction distribution.

7. The thickness matching optimization system of the welded front axle according to claim 4, characterized in that: The optimization module includes a second optimization module. The second optimization module is used to analyze the thickness of the failed weld position by taking the failed dominant load as the load loading condition of the front axle finite element model, obtain the maximum stress in the failed weld area, take the thickness of the failed weld position as the design variable, take the maximum stress and the total mass of the failed weld position as the output responses, select samples by the Latin hypercube sampling method according to the design variable and the output responses, construct a response surface model based on the samples, and perform matching optimization on the thickness of the failed weld position by the adaptive response surface method to obtain the thickness matching optimization result of the welded front axle; wherein, the response surface model takes the minimum total mass as the optimization objective.

8. The thickness matching optimization system for a welded front axle according to claim 4, characterized in that: it further includes a verification module. The verification module is used to simulate and verify the thickness matching optimization result of the welded front axle through random road loads, conduct tests on the endurance road of the test field, judge the thickness matching optimization result of the welded front axle based on the simulation verification result and the test result, and generate a final judgment result to realize the feasibility judgment of the thickness matching optimization result of the welded front axle.

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