Drive axle fatigue life analysis method and apparatus, electronic device, and storage medium

Abstract

The application discloses a kind of drive axle fatigue life analysis method, device, electronic equipment and storage medium.The method comprises: obtaining drive axle assembly grid model;Obtain the random load spectrum of each connection point of drive axle assembly;Based on the random load spectrum of each connection point of drive axle assembly, load is applied to the load-bearing system and the transmission system connection point corresponding to each submodel in drive axle assembly grid model, to obtain the stress result of drive axle assembly;Based on the stress result of drive axle assembly, the random load spectrum of each connection point of drive axle assembly, the material attribute of each component and weld in drive axle assembly grid model determines the fatigue life of drive axle assembly.The above technical solution, drive axle assembly grid model considers the influence of weld on drive axle assembly, and combines measured load spectrum and decomposition load spectrum, while considering bearing system load spectrum and transmission system load spectrum, to predict fatigue life, more comprehensive, improve the accuracy of fatigue life prediction.

Description

Technical Field

[0001] This invention relates to the field of vehicle testing technology, and in particular to a method, apparatus, electronic device, and storage medium for analyzing the fatigue life of a drive axle. Background Technology

[0002] As a core assembly of the vehicle, the reliability of the drive axle directly affects the safety of the entire vehicle.

[0003] Currently, the development and design of drive axle products mainly rely on fatigue strength analysis under test conditions and static strength analysis considering actual ultimate load conditions.

[0004] In the process of realizing this invention, the inventors discovered that the prior art has at least the following technical problems: the test bench condition setting requires separate assessment of the fatigue strength of loads in all directions under a certain amplitude and frequency according to enterprise standards, and the reliability verification of the drive axle assembly relies excessively on the reliability test of the whole vehicle, resulting in long test cycles and poor reliability of drive axle fatigue life prediction. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, and storage medium for analyzing the fatigue life of a drive axle, thereby reducing the testing cycle and improving the reliability of drive axle fatigue life prediction.

[0006] According to one aspect of the present invention, a method for analyzing the fatigue life of a drive axle is provided, comprising:

[0007] Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model;

[0008] Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0009] Based on the random load spectrum of each connection point of the drive axle assembly, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0010] The fatigue life of the drive axle assembly is determined based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly.

[0011] According to another aspect of the present invention, a drive axle fatigue life analysis apparatus is provided, comprising:

[0012] The mesh model acquisition module is used to acquire the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model;

[0013] The random load spectrum acquisition module is used to acquire the random load spectrum of each connection point of the drive axle assembly. The random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0014] The stress result determination module is used to apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, and obtain the stress result of the drive axle assembly.

[0015] The fatigue life determination module is used to determine the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor;

[0018] and a memory communicatively connected to the at least one processor;

[0019] The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the drive axle fatigue life analysis method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the drive bridge fatigue life analysis method according to any embodiment of the present invention.

[0021] The technical solution of this embodiment of the invention considers the influence of welds on the drive axle assembly in the drive axle assembly mesh model; and combines measured load spectrum and decomposed load spectrum, while considering the load spectrum of the load-bearing system and the load spectrum of the transmission system to predict fatigue life. This is more comprehensive and improves the accuracy of fatigue life prediction. Furthermore, the technical solution of this embodiment does not require whole vehicle reliability testing, reducing the number of test verification rounds and shortening the test cycle.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 2 of the present invention;

[0026] Figure 3 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 3 of the present invention;

[0027] Figure 4 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 4 of the present invention;

[0028] Figure 5 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 5 of the present invention;

[0029] Figure 6 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment Six of the present invention;

[0030] Figure 7 This is a schematic diagram of a parameter diagram analysis provided in Embodiment Six of the present invention;

[0031] Figure 8 This is a schematic diagram of the structure of a control factor provided in Embodiment Six of the present invention;

[0032] Figure 9 This is a schematic diagram of the structure of a drive axle fatigue life analysis device according to Embodiment 7 of the present invention;

[0033] Figure 10 This is a schematic diagram of the structure of an electronic device that implements the drive axle fatigue life analysis method according to an embodiment of the present invention. Detailed Implementation

[0034] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] Example 1

[0037] Figure 1 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where drive axle assembly fatigue life is predicted based on a random load spectrum. This method can be executed by a drive axle fatigue life analysis device, which can be implemented in hardware and / or software and can be configured in a computer terminal. Figure 1 As shown, the method includes:

[0038] S110. Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model.

[0039] In this embodiment, the drive axle assembly mesh model refers to the finite element model of the drive axle assembly after mesh generation. Material properties may include, but are not limited to, density, stress-life curves, and other related information.

[0040] Specifically, the drive axle housing, reducer assembly, and brake assembly in the drive axle assembly can be meshed to obtain drive axle housing mesh sub-models, reducer assembly mesh sub-models, and brake assembly mesh sub-models. Weld mesh sub-models can also be determined, and the material properties of each component and weld can be set. Then, the drive axle assembly mesh model can be constructed based on the drive axle housing mesh sub-model, reducer assembly mesh model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld.

[0041] S120. Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0042] In this embodiment, the drive axle assembly may include one or more connection points, which may be located on the component structure of the drive axle assembly. For example, the connection points may be located at the axle head, V-bar, I-bar, or drive shaft. The measured load spectrum of the load-bearing system refers to the random load spectrum at each connection point of the load-bearing system obtained by actual measurement. The decomposed load spectrum at the leaf spring support of the load-bearing system refers to the random load spectrum at the leaf spring support connection point of the load-bearing system obtained by decomposition. The transmission system load spectrum refers to the random load spectrum at each bearing of the reducer obtained by converting the torque load spectrum of the drive shaft.

[0043] S130. Based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0044] For example, finite element analysis software can be used to apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, thereby completing the simulation calculation and obtaining the stress results of the drive axle assembly. Optionally, finite element analysis software such as ABAQUS can be used.

[0045] S140. Based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, determine the fatigue life of the drive axle assembly.

[0046] For example, fatigue life of the drive axle assembly can be obtained by using the ChannelMax module of fatigue strength and optimization analysis software, based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly.

[0047] The technical solution of this embodiment of the invention considers the influence of welds on the drive axle assembly in the drive axle assembly mesh model, and combines measured load spectrum and decomposed load spectrum, while also considering the load spectrum of the load-bearing system and the load spectrum of the transmission system to predict fatigue life. This approach is more comprehensive and improves the accuracy of fatigue life prediction. Furthermore, the technical solution of this embodiment does not require vehicle reliability testing, reducing the number of test verification rounds and shortening the test cycle.

[0048] Example 2

[0049] Figure 2 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 2 of the present invention. The method in this embodiment can be combined with various optional schemes in the drive axle fatigue life analysis methods provided in the above embodiments. The drive axle fatigue life analysis method provided in this embodiment has been further optimized. Optionally, obtaining the drive axle assembly mesh model includes: obtaining a three-dimensional model of the drive axle housing, a three-dimensional model of the reducer assembly, and a three-dimensional model of the brake assembly; based on a preset target size, performing mesh division on the three-dimensional models of the drive axle housing, the reducer assembly, and the brake assembly respectively to obtain drive axle housing mesh sub-models, reducer assembly mesh sub-models, and brake assembly mesh sub-models; generating a weld mesh sub-model based on the weld penetration depth, weld position, weld cross-sectional parameters, and weld direction on the drive axle housing; and constructing a drive axle assembly mesh model based on the drive axle housing mesh sub-model, reducer assembly mesh model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld.

[0050] like Figure 2 As shown, the method includes:

[0051] S210: Obtain the 3D model of the drive axle housing, the 3D model of the reducer assembly, and the 3D model of the brake assembly.

[0052] S220. Based on the preset target size, the three-dimensional models of the drive axle housing, reducer assembly, and brake assembly are meshed to obtain the drive axle housing mesh sub-model, reducer assembly mesh sub-model, and brake assembly mesh sub-model, respectively.

[0053] S230. Generate a weld mesh sub-model based on the weld penetration depth, weld location, weld cross-sectional parameters, and weld direction of the weld structure on the drive axle housing.

[0054] S240. Construct the drive axle assembly mesh model based on the drive axle housing mesh sub-model, reducer assembly mesh sub-model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld.

[0055] S250. Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0056] S260. Based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0057] S270. Based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, determine the fatigue life of the drive axle assembly.

[0058] In this embodiment, the target size refers to the pre-set mesh size, which can be customized according to modeling requirements.

[0059] For example, three-dimensional models of the drive axle housing, reducer assembly, and brake assembly are acquired via electronic devices. Based on preset target dimensions, these three-dimensional models are meshed to obtain drive axle housing mesh sub-models, reducer assembly mesh sub-models, and brake assembly mesh sub-models composed of multiple continuous elements. Further, geometric cleanup is performed, and then welded components are meshed, defining information such as weld location, weld cross-sectional parameters, and weld direction. A weld mesh sub-model can be generated based on the weld penetration depth, weld location, weld cross-sectional parameters, and weld direction on the drive axle housing. Weld node groups can also be defined, including weld toes, weld roots, start ends, or end ends, thus more realistically reflecting the influence of weld penetration depth and weld type on strength. Furthermore, a drive axle assembly mesh model can be constructed based on the drive axle housing mesh sub-model, reducer assembly mesh sub-model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld. The material properties of each component and weld can include density, stress-life curves (SN curves) of each component and weld, and density can be obtained by converting the mass parameters of each component. The assembly relationship between the drive axle housing mesh sub-model, the reducer assembly mesh sub-model, and the brake assembly mesh sub-model is a rigid registration connection.

[0060] The technical solution of this invention involves obtaining a three-dimensional model of the drive axle housing, a three-dimensional model of the reducer assembly, and a three-dimensional model of the brake assembly; based on a preset target size, meshing is performed on the three-dimensional models of the drive axle housing, reducer assembly, and brake assembly to obtain mesh sub-models of the drive axle housing, reducer assembly, and brake assembly; a weld mesh sub-model is generated based on the weld penetration depth, weld position, weld cross-sectional parameters, and weld direction on the drive axle housing; and a drive axle assembly mesh model is constructed based on the mesh sub-models of the drive axle housing, reducer assembly, brake assembly, and weld, as well as the material properties of each component and weld. This drive axle assembly mesh model considers the influence of the weld on the drive axle assembly and more realistically reflects the actual stress state of the weld.

[0061] Example 3

[0062] Figure 3 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 3 of the present invention. The method in this embodiment can be combined with various optional schemes in the drive axle fatigue life analysis methods provided in the above embodiments. The drive axle fatigue life analysis method provided in this embodiment has been further optimized. Optionally, obtaining the random load spectrum of each connection point of the drive axle assembly includes: obtaining the measured load spectrum of the load-bearing system corresponding to the axle six component force position, the measured load spectrum of the load-bearing system corresponding to the V-bar position, the measured load spectrum of the load-bearing system corresponding to the I-bar position, and the measured load spectrum of the load-bearing system corresponding to the drive shaft position through vehicle reliability tests or actual user operating conditions; and / or, performing load iteration through a multibody dynamics model of the drive axle assembly to obtain the decomposed load spectrum at the leaf spring support of the load-bearing system; and / or, obtaining the transmission torque load spectrum, and converting the transmission torque load spectrum to obtain the transmission load spectrum at each bearing of the reducer.

[0063] like Figure 3 As shown, the method includes:

[0064] S310. Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model.

[0065] S320. Obtain the random load spectrum of each connection point of the drive axle assembly, including: obtaining the measured load spectrum of the load-bearing system corresponding to the six-component force position of the axle, the measured load spectrum of the load-bearing system corresponding to the V-bar position, the measured load spectrum of the load-bearing system corresponding to the I-bar position, and the measured load spectrum of the load-bearing system corresponding to the drive shaft position through vehicle reliability testing or actual user operating conditions; and / or, performing load iteration through the multibody dynamics model of the drive axle assembly to obtain the decomposed load spectrum at the leaf spring support of the load-bearing system; and / or, obtaining the transmission torque load spectrum, converting the transmission torque load spectrum to obtain the transmission load spectrum at each bearing of the reducer.

[0066] S330. Based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0067] S340. Based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, determine the fatigue life of the drive axle assembly.

[0068] For example, in some embodiments, the measured load spectra of the load-bearing system corresponding to the six component force positions of the axle, the V-bar position, the I-bar position, and the drive shaft position can be obtained through vehicle reliability testing or actual user operating conditions. In some embodiments, an Automatic Dynamic Analysis of Mechanical Systems (ADAMS) software can be used to construct a multibody dynamics model of the drive axle assembly, and then load iteration can be performed through the multibody dynamics model of the drive axle assembly to obtain the decomposed load spectrum at the leaf spring support of the load-bearing system; in some embodiments, the transmission torque load spectrum can be input into the transmission system analysis software (MASTA), and then MASTA can be used to convert the transmission torque load spectrum to obtain the transmission load spectrum at each bearing of the reducer.

[0069] The technical solution of this invention combines measured load spectrum and decomposed load spectrum, and simultaneously considers the load spectrum of the load-bearing system and the load spectrum of the transmission system to predict fatigue life. This approach is more comprehensive, enabling early prediction of the reliability of the assembly and improving the accuracy of fatigue life prediction.

[0070] Example 4

[0071] Figure 4This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 4 of the present invention. The method in this embodiment can be combined with various optional schemes in the drive axle fatigue life analysis methods provided in the above embodiments. The drive axle fatigue life analysis method provided in this embodiment has been further optimized. Optionally, the step of applying loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly to obtain the drive axle assembly stress result includes: determining the number of drive axle assembly channels; for any drive axle assembly channel, applying loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly to obtain the drive axle assembly stress result under the drive axle assembly channel.

[0072] like Figure 4 As shown, the method includes:

[0073] S410. Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model.

[0074] S420. Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0075] S430. Determine the number of channels in the drive axle assembly.

[0076] S440. For any drive axle assembly channel, based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the drive axle assembly stress results under the drive axle assembly channel.

[0077] S450. Based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, determine the fatigue life of the drive axle assembly.

[0078] For example, by analyzing the internal composition of the drive axle assembly and its interaction with internal and external systems, the loads at each connection point of the drive axle assembly's load-bearing system and transmission system can be determined. Simultaneously, the torque load spectrum of the drive shaft is decomposed to each bearing, yielding the number of drive axle assembly channels. For any given drive axle assembly channel, using ABAQUS software, based on the random load spectrum of each connection point, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model, obtaining the drive axle assembly stress results for that channel. The applied loads can be constant values.

[0079] The technical solution of this invention determines the number of drive axle assembly channels, and then, for any drive axle assembly channel, applies loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, thereby obtaining the drive axle assembly stress results under the drive axle assembly channel and realizing the acquisition of drive axle assembly stress results under load.

[0080] Example 5

[0081] Figure 5 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment 5 of the present invention. The method of this embodiment can be combined with various optional schemes in the drive axle fatigue life analysis methods provided in the above embodiments. The drive axle fatigue life analysis method provided in this embodiment has been further optimized. Optionally, determining the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the drive axle assembly mesh model includes: performing rainflow statistics on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the drive axle assembly mesh model to obtain the number of cycles in the stress range of the load spectrum and the total number of cycles that cause damage within the stress range; determining the fatigue life of the drive axle assembly based on the number of cycles in the stress range of the load spectrum and the total number of cycles that cause damage within the stress range.

[0082] like Figure 5 As shown, the method includes:

[0083] S510. Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model.

[0084] S520. Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0085] S530. Based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0086] S540. Perform rainflow statistics on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly to obtain the number of cycles in the stress range in the load spectrum and the total number of cycles that cause damage when in the stress range.

[0087] S550. The fatigue life of the drive axle assembly is determined based on the number of cycles within the stress range in the load spectrum and the total number of cycles that cause damage within the stress range.

[0088] For example, using the channelMax module in FEMFAT software, rainflow statistics are performed on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the drive axle assembly mesh model. This yields the number of cycles within the stress range in the load spectrum and the total number of cycles leading to damage within that stress range. These numbers are then substituted into the cumulative damage theory formula to obtain the fatigue life of the drive axle assembly. The cumulative damage theory formula is as follows:

[0089]

[0090] Where D represents the fatigue life of the drive axle assembly, and k i The stress range in the load spectrum is σ i The number of loops, N i Indicates the stress range σ i This will result in the total number of corrupted cycles.

[0091] The technical solution of this invention performs rainflow statistics on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly. This yields the number of cycles within the stress range in the load spectrum and the total number of cycles that cause damage within the stress range. Based on the number of cycles within the stress range in the load spectrum and the total number of cycles that cause damage within the stress range, the fatigue life of the drive axle assembly is determined, thus achieving fatigue life prediction.

[0092] Example 6

[0093] Figure 6 This is a flowchart of a drive axle fatigue life analysis method provided in Embodiment Six of the present invention. The method in this embodiment can be combined with various optional schemes in the drive axle fatigue life analysis methods provided in the above embodiments. The drive axle fatigue life analysis method provided in this embodiment has been further optimized. Optionally, after determining the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, the method further includes: when the fatigue life of the drive axle assembly does not meet the fatigue design requirements, determining the input factors, noise factors, control factors, and output factors of the drive axle assembly; and conducting orthogonal experiments based on the input factors, noise factors, control factors, and output factors of the drive axle assembly to determine the target design parameters of the drive axle.

[0094] like Figure 6 As shown, the method includes:

[0095] S610. Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model.

[0096] S620. Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0097] S630. Based on the random load spectrum of each connection point of the drive axle assembly, apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0098] S640. Based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly, determine the fatigue life of the drive axle assembly.

[0099] S650. When the fatigue life of the drive axle assembly does not meet the fatigue design requirements, determine the input factors, noise factors, control factors, and output factors of the drive axle assembly.

[0100] S660. Conduct orthogonal experiments based on the input factors, noise factors, control factors, and output factors of the drive axle assembly to determine the target design parameters of the drive axle.

[0101] In this embodiment, noise factors refer to a set of uncontrollable factors, which may include internal and external interferences and manufacturing errors that can cause fluctuations in product performance. Controllable factors refer to a set of controllable factors in product design, which may be parameters that significantly affect the lifespan of the weld seam in the bridge housing. Input factors may be the load spectrum. Output factors may be the fatigue life of the drive axle assembly.

[0102] Specifically, Figure 7 This is a schematic diagram of a parametric analysis provided in this embodiment. When the fatigue life of the drive axle assembly does not meet the fatigue design requirements, the input factors, noise factors, control factors, and output factors of the drive axle assembly are determined. Then, orthogonal experiments are conducted based on the input factors, noise factors, control factors, and output factors of the drive axle assembly to determine the target design parameters of the drive axle, making the design less sensitive to external influences and improving the reliability of the drive axle.

[0103] Based on the above embodiments, optionally, the input factors include the load spectrum of the preset channel, the output factor is the fatigue life of the weld of the lower bracket of the drive axle assembly, the control factors include the plate thickness parameters and bolt positions of the lower bracket of the reaction rod of the drive axle assembly, and the noise factors are the local thickness of the drive axle housing and the clearance between the reaction rod bracket and the drive axle housing. Orthogonal experiments are conducted based on the input factors, noise factors, control factors, and output factors of the drive axle assembly to determine the target design parameters of the drive axle. This includes: conducting internal and external orthogonal experiments based on the load spectrum of the preset channel, the fatigue life of the weld of the lower bracket of the drive axle assembly, the plate thickness parameters and bolt positions of the lower bracket of the reaction rod of the drive axle assembly, the local thickness of the drive axle housing, and the clearance between the reaction rod bracket and the axle, to obtain an orthogonal experiment table; determining the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments based on the orthogonal experiment table; and determining the target design parameters of the drive axle based on the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments.

[0104] For example, Figure 8 This is a schematic diagram of the structure of a control factor provided in this embodiment. Orthogonal experimental design is used to design samples for the control factor and noise factor. Specifically, orthogonal experimental design is used to obtain the inner table for the control factor and the outer table for the noise factor. In this embodiment, Table 1 is a table of control factors and noise factors for the drive axle assembly. The control factors may include the thickness of the three plates of the lower bracket and the bolt positions, where A represents the thickness of plate 1, B represents the thickness of plate 2, C represents the thickness of plate 3, and D represents the bolt position. Each plate can have two levels, meaning each plate can have two different thickness values. The bolt positions can have four levels. The noise factor includes two factors, N1 and N2. N1 represents the local thickness of the drive axle housing, and N2 represents the clearance between the reaction rod bracket and the drive axle housing. Each factor can have two levels, and the experimental outer table is obtained through orthogonal experiments.

[0105] Table 1 Control Factors and Noise Factors

[0106] Furthermore, based on the experimental design of the internal and external surfaces, an orthogonal experimental table was obtained, as shown in Table 2. The target response value under all combinations of external surface noise factors was calculated for each combination of design variables in the table. In this embodiment, 32 optimization schemes were determined, as shown in Table 2. Simulation analysis was performed on each optimization scheme to obtain the fatigue life y (i.e., y111-y822) corresponding to the experimental design scheme.

[0107] Table 2

[0108]

[0109]

[0110] Furthermore, based on the predicted fatigue life y, the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiment can be calculated. Then, the target design parameters of the drive axle can be determined based on the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiment. Specifically, the process of determining the target design parameters of the drive axle includes: determining the thickness of plate 1 and the bolt position based on the principle of maximizing the signal-to-noise ratio, and determining the thickness of plate 2 and plate 3 based on the principle of minimizing the mean.

[0111] It should be noted that after obtaining the target design parameters of the drive axle, fatigue life analysis was performed on the design scheme corresponding to the target design parameters of the drive axle. The weld damage of the design scheme was reduced from 41.04 in the original scheme to 32.78, which improved the quality of the drive axle assembly.

[0112] The technical solution of this invention, when the fatigue life of the drive axle assembly does not meet the fatigue design requirements, determines the input factors, noise factors, control factors, and output factors of the drive axle assembly, and then conducts orthogonal experiments based on the input factors, noise factors, control factors, and output factors of the drive axle assembly to determine the target design parameters of the drive axle, making the design insensitive to external influences and improving the reliability of the drive axle.

[0113] Example 7

[0114] Figure 9 This is a schematic diagram of a drive axle fatigue life analysis device provided in Embodiment 7 of the present invention. Figure 9 As shown, the device includes:

[0115] The mesh model acquisition module 710 is used to acquire the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model;

[0116] The random load spectrum acquisition module 720 is used to acquire the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0117] The stress result determination module 730 is used to apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, and obtain the stress result of the drive axle assembly.

[0118] The fatigue life determination module 740 is used to determine the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly.

[0119] The technical solution of this embodiment of the invention considers the influence of welds on the drive axle assembly in the drive axle assembly mesh model, and combines measured load spectrum and decomposed load spectrum, while also considering the load spectrum of the load-bearing system and the load spectrum of the transmission system to predict fatigue life. This approach is more comprehensive and improves the accuracy of fatigue life prediction. Furthermore, the technical solution of this embodiment does not require vehicle reliability testing, reducing the number of test verification rounds and shortening the test cycle.

[0120] In some alternative implementations, the mesh model acquisition module 710 is further configured to:

[0121] Obtain the 3D model of the drive axle housing, the 3D model of the reducer assembly, and the 3D model of the brake assembly;

[0122] Based on the preset target size, the three-dimensional models of the drive axle housing, reducer assembly, and brake assembly are meshed to obtain the drive axle housing mesh sub-model, reducer assembly mesh sub-model, and brake assembly mesh sub-model.

[0123] A weld mesh sub-model is generated based on the weld penetration depth, weld location, weld cross-sectional parameters, and weld direction of the weld structure on the drive axle housing.

[0124] The drive axle assembly mesh model is constructed based on the drive axle housing mesh sub-model, reducer assembly mesh sub-model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld.

[0125] In some alternative implementations, the random load spectrum acquisition module 720 is further configured to:

[0126] The measured load spectrum of the load system at the six component force positions of the axle, the V-bar position, the I-bar position, and the drive shaft position are obtained through vehicle reliability tests or actual user operating conditions.

[0127] And / or, by using the multibody dynamics model of the drive axle assembly, load iteration is performed to obtain the decomposed load spectrum at the leaf spring support of the load-bearing system;

[0128] And / or, obtain the transmission torque load spectrum, convert the transmission torque load spectrum to obtain the transmission load spectrum at each bearing of the reducer.

[0129] In some alternative implementations, the stress result determination module 730 is further configured to:

[0130] Determine the number of channels in the drive axle assembly;

[0131] For any drive axle assembly channel, based on the random load spectrum of each connection point of the drive axle assembly, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the drive axle assembly stress results under the drive axle assembly channel.

[0132] In some alternative implementations, the fatigue life determination module 740 is further configured to:

[0133] Rainflow statistics were performed on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly to obtain the number of cycles in the stress range in the load spectrum and the total number of cycles that caused damage in the stress range.

[0134] The fatigue life of the drive axle assembly is determined based on the number of cycles within the stress range in the load spectrum and the total number of cycles that cause damage within the stress range.

[0135] In some alternative embodiments, the apparatus further includes:

[0136] The factor determination module is used to determine the input factors, noise factors, control factors, and output factors of the drive axle assembly when the fatigue life of the drive axle assembly does not meet the fatigue design requirements.

[0137] The target design parameter determination module is used to determine the target design parameters of the drive axle by conducting orthogonal experiments based on the input factors, noise factors, control factors, and output factors of the drive axle assembly.

[0138] In some optional implementations, the input factors include the load spectrum of a preset channel, the output factor is the fatigue life of the weld of the lower bracket of the drive axle assembly, the control factors include the plate thickness parameters and bolt positions of the lower bracket of the reaction rod of the drive axle assembly, and the noise factors are the local thickness of the drive axle housing and the clearance between the reaction rod bracket and the axle.

[0139] Correspondingly, the target design parameter determination module is also used for:

[0140] Based on the load spectrum of the preset channel, the fatigue life of the weld of the lower bracket of the drive axle assembly, the plate thickness parameters of the lower bracket of the reaction rod of the drive axle assembly, the bolt position, the local thickness of the drive axle housing, and the gap between the reaction rod bracket and the drive axle housing, an orthogonal experiment was conducted on the inner and outer surfaces to obtain the orthogonal experiment table.

[0141] Based on the orthogonal experimental table, determine the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments;

[0142] The target design parameters of the drive axle are determined based on the average fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments.

[0143] The drive axle fatigue life analysis device provided in this embodiment of the invention can execute the drive axle fatigue life analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0144] Example 8

[0145] Figure 10 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device 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 assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0146] like Figure 10As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An I / O interface 15 is also connected to the bus 14.

[0147] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0148] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a drive axle fatigue life analysis method, which includes:

[0149] Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model;

[0150] Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system.

[0151] Based on the random load spectrum of each connection point of the drive axle assembly, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly.

[0152] The fatigue life of the drive axle assembly is determined based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly.

[0153] In some embodiments, the drive axle fatigue life analysis method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the drive axle fatigue life analysis method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the drive axle fatigue life analysis method by any other suitable means (e.g., by means of firmware).

[0154] Various embodiments of the systems and techniques described above 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), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0155] Computer programs used to implement 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 executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0156] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0157] 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0158] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0159] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0160] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0161] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for analyzing the fatigue life of a drive axle, characterized in that, include: Obtain the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model; Obtain the random load spectrum of each connection point of the drive axle assembly, wherein the random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system. Based on the random load spectrum of each connection point of the drive axle assembly, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the stress results of the drive axle assembly. The fatigue life of the drive axle assembly is determined based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly. When the fatigue life of the drive axle assembly does not meet the fatigue design requirements, determine the input factors, noise factors, control factors, and output factors of the drive axle assembly. The input factors include the load spectrum of the preset channel, the output factor is the fatigue life of the weld of the lower bracket of the drive axle assembly, the control factors include the plate thickness parameters and bolt positions of the lower bracket of the reaction rod of the drive axle assembly, and the noise factors are the local thickness of the drive axle housing and the gap between the reaction rod bracket and the drive axle housing. Based on the load spectrum of the preset channel, the fatigue life of the weld of the lower bracket of the drive axle assembly, the plate thickness parameters of the lower bracket of the reaction rod of the drive axle assembly, the bolt position, the local thickness of the drive axle housing, and the gap between the reaction rod bracket and the drive axle housing, an orthogonal experiment was conducted on the inner and outer surfaces to obtain the orthogonal experiment table. Based on the orthogonal experimental table, determine the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments; The target design parameters of the drive axle are determined based on the average fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments.

2. The method according to claim 1, characterized in that, The process of obtaining the drive axle assembly mesh model includes: Obtain the 3D model of the drive axle housing, the 3D model of the reducer assembly, and the 3D model of the brake assembly; Based on the preset target size, the three-dimensional models of the drive axle housing, reducer assembly, and brake assembly are meshed to obtain the drive axle housing mesh sub-model, reducer assembly mesh sub-model, and brake assembly mesh sub-model. A weld mesh sub-model is generated based on the weld penetration depth, weld location, weld interface geometry parameters, and weld orientation of the weld structure on the drive axle housing. The drive axle assembly mesh model is constructed based on the drive axle housing mesh sub-model, reducer assembly mesh sub-model, brake assembly mesh model, weld mesh model, and the material properties of each component and weld.

3. The method according to claim 1, characterized in that, The acquisition of the random load spectrum at each connection point of the drive axle assembly includes: The measured load spectrum of the load system at the six component force positions of the axle, the V-bar position, the I-bar position, and the drive shaft position are obtained through vehicle reliability tests or actual user operating conditions. And / or, by using the multibody dynamics model of the drive axle assembly, load iteration is performed to obtain the decomposed load spectrum at the leaf spring support of the load-bearing system; And / or, obtain the transmission torque load spectrum, convert the transmission torque load spectrum to obtain the transmission load spectrum at each bearing of the reducer.

4. The method according to claim 1, characterized in that, The method involves applying loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, thereby obtaining the stress results of the drive axle assembly, including: Determine the number of channels in the drive axle assembly; For any drive axle assembly channel, based on the random load spectrum of each connection point of the drive axle assembly, loads are applied to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model to obtain the drive axle assembly stress results under the drive axle assembly channel.

5. The method according to claim 1, characterized in that, The determination of the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly includes: Rainflow statistics were performed on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly to obtain the number of cycles in the stress range in the load spectrum and the total number of cycles that caused damage in the stress range. The fatigue life of the drive axle assembly is determined based on the number of cycles within the stress range in the load spectrum and the total number of cycles that cause damage within the stress range.

6. A drive axle fatigue life analysis device, characterized in that, include: The mesh model acquisition module is used to acquire the drive axle assembly mesh model, wherein the drive axle assembly mesh model includes a drive axle housing mesh sub-model, a reducer assembly mesh sub-model, a brake assembly mesh model, a weld mesh model, and the material properties of each component and weld in the drive axle assembly mesh model; The random load spectrum acquisition module is used to acquire the random load spectrum of each connection point of the drive axle assembly. The random load spectrum of each connection point includes the measured load spectrum of the load-bearing system, the decomposed load spectrum at the leaf spring support of the load-bearing system, and the load spectrum of the transmission system. The stress result determination module is used to apply loads to the load-bearing system and transmission system connection points corresponding to each sub-model in the drive axle assembly mesh model based on the random load spectrum of each connection point of the drive axle assembly, and obtain the stress result of the drive axle assembly. The fatigue life determination module is used to determine the fatigue life of the drive axle assembly based on the stress results of the drive axle assembly, the random load spectrum of each connection point of the drive axle assembly, and the material properties of each component and weld in the mesh model of the drive axle assembly. The device further includes: The factor determination module is used to determine the input factors, noise factors, control factors, and output factors of the drive axle assembly when the fatigue life of the drive axle assembly does not meet the fatigue design requirements. The target design parameter determination module is used to determine the target design parameters of the drive axle by conducting orthogonal experiments based on the input factors, noise factors, control factors, and output factors of the drive axle assembly. The input factors include the load spectrum of the preset channel, the output factor is the fatigue life of the weld of the lower bracket of the drive axle assembly, the control factors include the plate thickness parameters and bolt positions of the lower bracket of the reaction rod of the drive axle assembly, and the noise factors are the local thickness of the drive axle housing and the gap between the reaction rod bracket and the drive axle housing. The target design parameter determination module is specifically used for: Based on the load spectrum of the preset channel, the fatigue life of the weld of the lower bracket of the drive axle assembly, the plate thickness parameters of the lower bracket of the reaction rod of the drive axle assembly, the bolt position, the local thickness of the drive axle housing, and the gap between the reaction rod bracket and the drive axle housing, an orthogonal experiment was conducted on the inner and outer surfaces to obtain the orthogonal experiment table. Based on the orthogonal experimental table, determine the mean fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments; The target design parameters of the drive axle are determined based on the average fatigue life and signal-to-noise ratio of each experimental group in the internal and external orthogonal experiments.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the drive axle fatigue life analysis method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the drive axle fatigue life analysis method according to any one of claims 1-5.

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