Commercial vehicle drive axle main reducer bevel gear loading dislocation quantity simulation and test calibration method and device

Abstract

The invention provides a commercial vehicle drive axle main reducer bevel gear loading dislocation quantity simulation and test calibration method and equipment, belongs to the technical field of commercial vehicle drive axle simulation, and aims to determine the maximum input torque through theoretical calculation and set 20%-100% multi-gradient loading conditions. Constructing a finite element model of the drive axle assembly, simplifying bearing and gear structures, defining material attributes and boundary conditions, extracting key coordinate points for dislocation quantity simulation, and calculating a theoretical value of axial displacement of the driven wheel. And collecting a measured value. And iteratively correcting simulation model parameters by taking measured data as a reference, so that the error between a calculated value and a measured value is less than or equal to 10%. Based on the calibrated model, the axial displacement delta P, the axial angle displacement delta A and the offset displacement delta E of the driving wheel are output, and a complete meshing performance evaluation system is formed. According to the invention, dislocation quantity prediction precision is improved; the simulation reliability is enhanced through model calibration; multi-dimensional meshing performance quantitative evaluation is realized, and an accurate basis is provided for bevel gear pair design optimization.

Description

Technical Field

[0001] This invention belongs to the field of commercial vehicle drive axle simulation technology, specifically relating to a simulation and test calibration method and equipment for the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle. Background Technology

[0002] For commercial vehicles, the drive axle, as the component that outputs power and drives the vehicle, is characterized by high load and long continuous working time. During the operation of the drive axle under load, each part will deform to varying degrees due to the force. As a key component in the drive axle's main reducer that transmits power between intersecting shafts, the bevel gear will experience varying degrees of misalignment due to the deformation of other parts, thus changing its original meshing state. Moreover, the bevel gear has a complex geometric topology, and its meshing performance is very sensitive to the quality of the meshing state. Therefore, analyzing the generation and variation law of bevel gear misalignment is of great significance for improving the meshing performance of bevel gears.

[0003] The relevant technical tests adopted rated torque or maximum torque conditions to perform simulation tests on the loading misalignment of the bevel gear of the drive axle main reducer. However, due to the failure to reflect the synergistic effects of slope, air, and rolling resistance during actual vehicle operation, the test conditions were out of sync with the actual usage scenario. This resulted in the inability to reflect the deformation pattern of the drive axle under different loads, and the test data could not support the performance evaluation throughout the entire life cycle.

[0004] In relevant technical tests, attention is paid to the axial displacement ΔG of the driven wheel. If the axial displacement ΔP of the driving wheel is too large, it can easily lead to misalignment of the meshing surfaces of the driving and driven wheels. If ΔA is too large, it will reduce the contact area of ​​the tooth surfaces, increase local stress, and cause tooth wear during meshing if the offset displacement ΔE is too large. These parameters together determine the meshing quality. If only one of these indicators is used, it cannot reflect the abnormal noise that occurs in the actual vehicle. If only ΔG is tested and found to be qualified, but ΔA exceeds the standard and causes poor tooth surface contact, it will not be found. The indicators are not correlated with torque conditions, making it impossible to judge the risk changes under different loads, making it difficult to locate the root cause of the fault. Summary of the Invention

[0005] This invention provides a simulation and test calibration method for the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle. By comparing the simulation calculation and loading test results, the error between the theoretical misalignment and the actual misalignment is analyzed, and the simulation model is corrected based on the results to improve the calculation accuracy of the simulation model.

[0006] The methods include: S101: Determine the loading conditions of the drive axle assembly, including calculating the maximum input torque based on the total vehicle mass, tire radius, and reduction ratio parameters, and selecting multiple gradient conditions from 20% to 100% of the maximum torque as simulation and test benchmarks. S102: The drive axle assembly model is simplified, a common node model is used to simulate the bearing assembly, and a thicker mesh is used for the non-meshing gear teeth. S103: Define material properties, displacement constraints, and load conditions determined in the loading conditions of the drive axle assembly in the assembly model, and extract three initial coordinate points on the axial vertical circumference of the main and driven gears. S104: Perform simulation calculation of loading misalignment, perform spatial geometric transformation based on the extracted initial coordinate points, and calculate the theoretical value of the axial displacement ΔG of the passive wheel; S105: Perform drilling on the drive axle housing, install the drive axle assembly onto the static torsion test bench, configure the laser displacement sensor and calibrate it to zero: S106: Load the load on the static torsion test bench according to the set torque condition, and collect the measured value of the axial displacement ΔG of the passive wheel through the laser displacement sensor. S107: Using the measured value of ΔG as the calibration benchmark, iteratively correct the simulation parameters to ensure that the relative error between the calculated and measured values ​​of ΔG under all operating conditions is ≤10%. S108: Based on the calibrated simulation model, the output of the axial displacement ΔP of the driving wheel, the shaft intersection angle displacement ΔA, and the offset distance displacement ΔE form the evaluation index of the meshing performance of the bevel gear pair.

[0007] It should be further noted that, under loading conditions, the maximum torque applied at the input end of the S101 drive axle assembly is:

[0008] In the formula: This is the maximum input torque of the drive axle assembly. The maximum gross vehicle weight. It is the acceleration due to gravity. The tire's rolling radius, For the drive axle reduction ratio, The number of drive axles installed in the vehicle. The enhancement coefficient; With 20% 40% 60% 80% 100% This serves as the loading condition for simulation and testing.

[0009] It should be further explained that the specific steps in S104 include: Using the extracted coordinate values ​​of the three points of the driving gear, a circle O passing through the initial coordinates of these three points is constructed. P1 and the normal L passing through the center of the circle P1 Circle O after loading simulation calculation P2 and the normal L passing through the center of the circleP2 ; Construct circle O using the coordinates of three points on the driven gear. G1 O G2 With normal L G1 L G2 ; For the initial state, passing through the straight line L G1 Draw a line parallel to line L P1 Plane S1 will hold line L P1 With the center O P1 Projecting onto plane S1 yields line L P1a With point O P1a straight line L G1 With line L P1a The intersection point is Q1, and the line segment Q1O G1 The length of line segment Q1O is G1. P1a The length of the line is P1, and the line L is... G1 With line L P1a Angle A1, line L P1 With line L P1a The distance is E1; After loading and simulation calculations, the line segment Q2O is obtained. G2 The length of line segment G2, line segment Q2O P2a The length of line L is P2, and the length of line L is L. G2 With line L P2a Angle A2, line L P2 With line L P2a The distance E2; The geometric model is established, and the method for calculating the misalignment is as follows:

[0010]

[0011]

[0012] .

[0013] It should be further explained that S102 specifically includes the following steps: Identify the bearing assembly geometry in the drive axle assembly model; In finite element modeling software, the inner ring, rollers, and outer ring of the bearing assembly are set to be connected by common nodes through node merging operations to form continuous mesh elements; Based on the analysis of the gear meshing region, the tooth surfaces and volume regions that do not participate in meshing in the driving and driven bevel gears are marked; For the marked non-meshing gear tooth area, use the mesh generation tool to set a larger mesh size parameter to generate tetrahedral or hexahedral mesh elements; The mesh quality check function in the finite element software is used to verify the continuity and integrity of the common node connections and thickened meshes, ensuring that there is no mesh interference or breakage.

[0014] It should be further explained that S103 specifically includes the following steps: Collect the material parameters of each component of the drive axle assembly, and set the material properties for each component in the finite element software; Analyze the actual installation constraints of the drive axle, set fixed displacement constraints for the axle housing mounting part in the model, and set corresponding rotational or displacement constraints for components such as the input shaft; According to the loading condition in step S101, the corresponding torque load is applied to the input end of the drive axle assembly; Determine the circumference perpendicular to the axis on the drive and driven gears, and select three points evenly on the circumference and mark their positions; The initial coordinates of the marked points were obtained and recorded using the coordinate extraction function of the finite element software.

[0015] It should be further explained that S105 specifically includes the following steps: Based on the three-dimensional model of the drive axle assembly, the location and diameter of the opening are determined in the area of ​​the main reducer housing corresponding to the back of the driven wheel. Drilling is performed at the determined location on the housing using CNC machining equipment to form the sensor measurement channel; The drive axle assembly is mounted and fixed on the working platform of the static torsion test bench using a fixture, ensuring that the input end is aligned and connected with the torque actuator. The laser displacement sensor is fixed to the test bench base by a magnetic base or a special bracket. The sensor position is adjusted so that its laser beam passes through the machined hole and directly hits the measurement point on the end face of the passive wheel. Turn on the power to the laser displacement sensor and execute the zero-point calibration command under no-load conditions to return the current reading to zero.

[0016] It should be further explained that S106 specifically includes the following steps: Based on the loading conditions calculated in step S101, a torque condition sequence is created in the digital control system of the static torsion test bench, with each torque value corresponding to an independent loading step and marked with a condition number. Start the hydraulic loading system of the test bench, set the torque loading rate to 0.5MPa / s, apply the torque sequentially according to the working condition sequence, and maintain the torque stable at each loading step before switching to the next torque value; During the torque stabilization phase of each loading step, the laser displacement sensor is triggered to collect real-time data on the axial displacement of the passive wheel, obtaining 100 data points. The original displacement data of each loading step is filtered by moving average to remove high-frequency noise, and the arithmetic mean is calculated as the measured value of the axial displacement ΔG of the passive wheel under the current torque. Save the operating condition number, corresponding torque value, measured ΔG value, acquisition time, and sensor number.

[0017] It should be further explained that S107 specifically includes the following steps: S1071: The calculated values ​​of the passive wheel axial displacement ΔG under each torque condition obtained in step S104 are matched one-to-one with the measured values ​​of ΔG under the corresponding conditions obtained in step S106, and a data comparison table is established. S1072: Select adjustable parameters in the simulation model that affect the axial displacement ΔG of the passive wheel; S1073: Select a torque condition as the initial adjustment target, and adjust the selected adjustable parameters one by one according to the preset range, adjusting only one parameter at a time; S1074: Reload and calculate the simulation model after each adjustment to obtain the new ΔG calculated value under the working condition, and calculate the relative error between the new calculated value and the corresponding measured value; S1075: After completing the initial working condition parameter adjustment, repeat steps S1073~S1074 for other torque working conditions in sequence until the relative error between the calculated value and the measured value of ΔG under all working conditions is no greater than 10%, then stop parameter adjustment.

[0018] It should be further explained that S108 specifically includes the following steps: Load the calibrated simulation model obtained in step S107, check the bearing stiffness coefficient, gear material elastic modulus correction coefficient, and shell constraint stiffness coefficient in the simulation model, and confirm that the parameters are consistent with the final calibration values. In the calibration simulation model, the three initial coordinate points on the axial vertical circumference of the main and driven gears extracted in positioning step S103 are marked as the coordinate point group of the driving gear and the coordinate point group of the driven gear. According to the 20% set in step S101 Up to 100% For torque conditions, the loading calculation is re-executed on the calibrated simulation model to obtain the post-loading coordinates of the active wheel coordinate point group and the passive wheel coordinate point group under each working condition. Compare the initial coordinates and the coordinates after loading of the driving wheel under each working condition to calculate the axial displacement ΔP of the driving wheel; compare the initial coordinates and the coordinates after loading of the driven wheel to calculate the shaft angle displacement ΔA and the offset displacement ΔE. The values ​​of ΔP, ΔG, ΔA, and ΔE under each torque condition are compiled into a table, and the correspondence between each misalignment and the torque condition is marked, forming a set of evaluation indicators for the meshing performance of the bevel gear pair of the main reducer.

[0019] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the simulation and test calibration method for the loading misalignment of the bevel gear in the main reducer of the commercial vehicle drive axle.

[0020] As can be seen from the above technical solutions, the present invention has the following advantages: This invention provides a simulation and experimental calibration method for the loading misalignment of bevel gears in the main reducer of a commercial vehicle drive axle. It iteratively corrects model parameters based on the experimentally measured ΔG value, ensuring that the relative error between the calculated and measured ΔG values ​​under various torque conditions is ≤10%. This solves the problem of simulation models relying solely on theoretical parameters and exhibiting large deviations from reality, ensuring that simulation results accurately reflect the stress and deformation characteristics of the drive axle. The method employs a common-node bearing model and thickened meshing of non-meshing gear teeth, reducing the number of meshes and shortening simulation time while maintaining the accuracy of calculations in the meshing zone and bearings, achieving a balance between accuracy and efficiency. Loading conditions are determined by considering the vehicle's actual resistance and the vehicle's reinforcement coefficient, covering all scenarios from light to full load. Pre-loading, unloading, and secondary zero-adjustment calibration methods eliminate zero-point drift caused by mechanical clearances in the drive axle and initial sensor deviations, keeping the measured ΔG error within a threshold range and providing a reliable benchmark for simulation calibration.

[0021] This invention outputs indices ΔP, ΔA, ΔE, and ΔG based on a calibration model, which can quantify the risk of meshing misalignment and provide a basis for drive axle design optimization and fault diagnosis. Simulation results can directly guide experimental design, and experimental data can be used to inversely optimize the simulation model, thereby improving the reliability of the drive axle. Attached Figure Description

[0022] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description 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.

[0023] Figure 1 Flowchart of simulation and test calibration method for loading misalignment of bevel gear in main reducer of commercial vehicle drive axle; Figure 2 Flowchart of an embodiment of the simulation and experimental calibration method for loading misalignment of bevel gear in the main reducer of a commercial vehicle drive axle; Figure 3 To provide a simplified schematic diagram of the drive axle assembly model, where Figure 3 (a) is a simplified model diagram of the bearing simulation. Figure 3 (b) is a simplified schematic diagram of the gear simulation model; Figure 4 This is a schematic diagram for simulating the theoretical misalignment. Figure 5 This is a schematic diagram of an electronic device. Detailed Implementation

[0024] The simulation and experimental calibration method for the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle provided by this invention adopts a combination of simulation and experiment. Specifically, it establishes a deformation calculation model of the drive axle assembly system, performs loading calculations and analyses based on different torque conditions, and obtains the calculated value of the bevel gear misalignment ΔG under theoretical conditions. Then, it measures the axial displacement of the driven wheel under the simulated torque conditions using a static torsion test bench and displacement sensors, thus obtaining the measured value of the actual misalignment ΔG between the main reducer and driven wheel. Finally, using the measured value of the main reducer and driven wheel misalignment ΔG as an indicator, the simulation model is iteratively corrected to keep the error between the calculated and measured values ​​of ΔG within a small range under different operating conditions. The corrected simulation model calculates all misalignment information, namely the axial displacement ΔP of the driving wheel, the axial displacement ΔG of the driven wheel, the shaft angle displacement ΔA, and the offset displacement ΔE, which are used to evaluate the meshing performance of the bevel gear pair in the main reducer of the drive axle.

[0025] The following describes in detail the simulation and experimental calibration method for the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle, as per this application. Specific details, such as particular system structures and technologies, are presented for illustrative purposes rather than limiting, to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0026] It should be understood that, when used in this specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0029] Please see Figure 1 and Figure 2 The diagram shows a flowchart of a simulation and experimental calibration method for the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle, in a specific embodiment. The method includes: Step S101: Determine the loading conditions of the drive axle assembly, including calculating the maximum input torque based on the total vehicle mass, tire radius, and reduction ratio parameters, and selecting multiple gradient conditions from 20% to 100% of the maximum torque as simulation and test benchmarks.

[0030] In some embodiments, the maximum input torque of the drive axle assembly is calculated by combining the vehicle's maximum gross weight, tire rolling radius, gear ratio of the driving wheel to the driven wheel of the main reducer, the number of drive axles installed on the vehicle, and the reinforcement coefficient of the corresponding vehicle model. Then from 20% Initially, increase the percentage by 20% increments, starting with 20%. 40% 60% 80% 100% Five gradient operating conditions are used as a unified loading benchmark for simulation and testing. This embodiment ensures that the selected operating conditions cover light-load, medium-load, and fully-load scenarios in actual vehicle use, avoiding operating conditions that are out of touch with reality.

[0031] In some specific embodiments, the loading condition is determined such that the maximum torque applied at the input is:

[0032] In the formula: This is the maximum input torque of the drive axle assembly. The maximum gross vehicle weight. It is the acceleration due to gravity. The tire's rolling radius, For the drive axle reduction ratio, The number of drive axles installed in the vehicle. The selected range for the enhancement coefficient is: tractor unit, 1.0~1.5; truck, 1.6~2.2; passenger vehicle, 1.0~2.0. 0.18 is the sum of 16% gradient resistance, 1% air resistance, and 1% rolling resistance.

[0033] Step S102: Simplify the drive axle assembly model, use a common node model to simulate the bearing assembly, and use a thicker mesh for the non-meshing gear teeth.

[0034] In some embodiments, for bearing assemblies, the mesh nodes of the inner ring, rollers, and outer ring are overlapped to simulate the mechanical transmission relationship among them without gaps, without separately defining the contact gap between the rollers and the inner and outer rings. For driving and driven bevel gears, the tooth regions that do not participate in meshing are first determined through meshing analysis. The mesh element size of these regions is adjusted from 0.5 mm in the original meshing region to 1.5~2 mm to achieve a thicker mesh division, while maintaining the original set value for the mesh accuracy in the meshing region.

[0035] Optionally, such as Figure 3 As shown, the drive axle assembly model is simplified. The inner ring, rollers, and outer ring of the bearing adopt a common node model, and the tooth mesh of the active and passive bevel gears that do not participate in meshing adopts a thickened mesh.

[0036] The common-node model in this embodiment can reduce the number of redundant meshes in the bearing assembly without affecting the accuracy of mechanical transmission. Since non-meshing gear teeth do not participate in the critical calculation of force transmission and deformation, thickening the mesh can reduce the computational load in this area, while retaining the high-precision mesh in the meshing area to ensure accurate calculation of meshing deformation.

[0037] In some specific embodiments, S102 specifically includes the following steps: Step S1021: Identify the bearing assembly geometry in the drive axle assembly model, including the contours and positions of the inner ring, rollers, and outer ring.

[0038] Optionally, the three-dimensional model of the drive axle assembly can be imported using CAD software or finite element preprocessing tools, and the boundaries of the inner ring, rollers, and outer ring of the bearing assembly can be selected manually or automatically.

[0039] Step S1022: In the finite element modeling software, the inner ring, rollers and outer ring of the bearing assembly are set to common node connection through node merging operation to form continuous mesh elements.

[0040] Step S1023: Based on the gear meshing region analysis, mark the tooth surfaces and volume regions in the active and passive bevel gears that do not participate in meshing.

[0041] Step S1024: For the marked non-meshing gear tooth area, use the mesh generation tool to set a larger mesh size parameter to generate tetrahedral or hexahedral mesh elements.

[0042] Step S1025: Verify the continuity and integrity of the common node connections and thickened meshes using the mesh quality check function in the finite element software to ensure there is no mesh interference or breakage.

[0043] It should be noted that in step S1022, the node editing function in the finite element software is used to forcibly merge the mesh nodes of the inner ring, rollers, and outer ring of the bearing assembly, eliminating the relative displacement degrees of freedom between the components. In step S1023, based on gear meshing theory, the region of non-meshing gear teeth is defined through geometric analysis or kinematic simulation and assigned a specific identifier. In step S1024, in the mesh generation module, a larger mesh size value is input for the non-meshing gear tooth region, and automatic or manual mesh generation is performed. This shortens the simulation calculation time and ensures that non-critical areas do not affect the accuracy of the overall results. In step S1025, the connection consistency of the shared node region and the element quality index of the thickened mesh are checked to ensure the reliability of the model.

[0044] Step S103: Define material properties, displacement constraints, and load conditions determined in the loading condition of the drive axle assembly in the assembly model, and extract three initial coordinate points on the axial vertical circumference of the main and driven gears.

[0045] In some embodiments, the bridge housing is defined as being made of Q345 steel. Optionally, the gears are made of 20CrMnTi steel, with an elastic modulus of 210 GPa, Poisson's ratio of 0.3, and a density of 7.85 g / cm³. 3 The bearings are made of GCr15 steel, with parameters consistent with the gears, and are assigned to each component one-to-one in the finite element software material library. Displacement constraints can be defined by setting the four mounting points connecting the axle housing and the frame to have zero displacement in the X / Y / Z directions, while constraining rotation about each axis. The input axis is set to release rotational freedom about the Z-axis around its own axis, restricting X / Y / Z displacement and rotation about the X / Y axes.

[0046] Optionally, the load is defined according to the five torque conditions determined in step S101, and the torque is applied to the input end flange surface of the input shaft. Circumferences perpendicular to the gear axis are selected on both the driving and driven gears, and three points are marked at 120° intervals on each circumference. The initial X / Y / Z values ​​of these six points are extracted and saved using the software's coordinate query function. This ensures that the material properties, constraints, and load parameters of the simulation model are consistent with the actual drive axle, guaranteeing that the model's mechanical behavior closely approximates reality.

[0047] S103 of this embodiment further includes the following steps: S1031: Collect material parameters of each component of the drive axle assembly, such as elastic modulus, Poisson's ratio, and density, and set material properties for each component in the finite element software.

[0048] S1032: Analyze the actual installation constraints of the drive axle, set fixed displacement constraints for the axle housing mounting location in the model, and set corresponding rotational or displacement constraints for components such as the input shaft.

[0049] S1033: According to the loading condition in step S101, apply the corresponding torque load to the input end of the drive axle assembly to ensure that the load application position is consistent with the actual working condition.

[0050] S1034: Determine the circumference perpendicular to the axis on the drive and driven gears, and select three points evenly on the circumference and mark their positions.

[0051] S1035: Use the coordinate extraction function of the finite element software to obtain and record the initial coordinates of the marked points in step S1034.

[0052] In step S1031 of this embodiment, the material properties determine the deformation and stress characteristics of the component under stress, which is the basis for the accurate simulation of the mechanical behavior of the finite element simulation. S1032 clarifies the installation method of the drive axle in the actual vehicle, such as the connection between the axle housing and the frame being a fixed constraint, and the input shaft being rotatable. Constraints are then set at corresponding positions in the model to ensure the realism of the constraints in the simulation model. S1033 The load is crucial for simulating the stress on the component, ensuring that the model bears the same load as the actual working conditions, guaranteeing complete consistency between the simulation and experimental loading conditions. S1034 Three points determine the position and orientation of a circle, used for subsequent analysis of the gear's axial displacement and circumferential orientation changes. The selected points accurately reflect the gear's axial position and circumferential state, ensuring the accuracy of the misalignment calculation. S1035 The initial coordinates of the marked points in step S1034 are found and recorded using the node coordinate query function. This ensures the accuracy of the misalignment calculation, such as ΔG, thereby guaranteeing the reliability of model correction and subsequent misalignment extraction.

[0053] Step S104: Perform simulation calculation of loading misalignment, perform spatial geometric transformation based on the extracted initial coordinate points, and calculate the theoretical value of the axial displacement ΔG of the passive wheel.

[0054] In some embodiments, the model defined in step S103 is called in the finite element software, and the result is calculated at 20%. 40% 60% 80% 100% The loading calculations are performed sequentially, and the calculations stop after convergence for each working condition. Using the software's result extraction function, the three coordinate points of the driven wheel marked in step S103 are located, and the three-dimensional coordinates of each point after loading are extracted. The coordinate differences along the gear axis before and after loading are compared, and the arithmetic mean of the three point differences is taken as the theoretical value of the axial displacement ΔG of the driven wheel under this torque condition. Five theoretical ΔG values ​​are recorded in the order of the working conditions to form a simulation dataset.

[0055] Optionally, such as Figure 4As shown, a simulation calculation of the loading misalignment is performed, the coordinates of the loading simulation results of the above points are extracted, spatial geometric calculations are performed on the extracted coordinate values, and the calculated value of the axial displacement ΔG of the passive wheel of the main reducer bevel gear under the corresponding torque state in step S101 is statistically analyzed.

[0056] It should be further explained that the specific steps in S104 include: Using the extracted coordinate values ​​of the three points of the driving gear, a circle O passing through the initial coordinates of these three points is constructed. P1 and the normal L passing through the center of the circle P1 Circle O after loading simulation calculation P2 and the normal L passing through the center of the circle P2 .

[0057] Construct circle O using the coordinates of three points on the driven gear. G1 O G2 With normal L G1 L G2 .

[0058] For the initial state, passing through the straight line L G1 Draw a line parallel to line L P1 Plane S1 will hold line L P1 With the center O P1 Projecting onto plane S1 yields line L P1a With point O P1a straight line L G1 With line L P1a The intersection point is Q1, and the line segment Q1O G1 The length of line segment Q1O is G1. P1a The length of the line is P1, and the line L is... G1 With line L P1a Angle A1, line L P1 With line L P1a The distance is E1.

[0059] For the state after loading and simulation calculation, the line segment Q2O is obtained. G2 The length of line segment G2, line segment Q2O P2a The length of line L is P2, and the length of line L is L. G2 With line L P2a Angle A2, line L P2 With line L P2a The distance is E2.

[0060] The geometric model is established, and the method for calculating the misalignment is as follows:

[0061]

[0062]

[0063] .

[0064] S104 obtains the simulated theoretical values ​​of ΔG under various working conditions, which are compared with the measured values ​​in step S106 to provide data support. Multi-point averaging calculations reduce errors and improve the accuracy of the theoretical values.

[0065] Step S105: Perform hole machining on the drive axle housing, install the drive axle assembly onto the static torsion test bench, configure the laser displacement sensor and calibrate it to zero.

[0066] In some embodiments, according to the measurement requirements of the laser displacement sensor in step S106, a circular hole is machined using a CNC drilling machine at a position near the end face of the driven wheel on the outer axle housing of the drive axle. The axis of the hole is ensured to be parallel to the axis of the driven wheel. The machined drive axle assembly is then mounted on a static torsion test bench using a fixture. The drive axle attitude is adjusted so that the input shaft axis is coaxial with the loading shaft axis of the test bench. The fixture is then fixed to the test bench with bolts to ensure the drive axle is not loose. A laser displacement sensor is selected and fixed to the test bench using an adjustable bracket, aligning the sensor probe with the measurement point on the end face of the driven wheel, with the probe axis parallel to the axis of the driven wheel. The sensor power supply and data acquisition line are connected. With the test bench unloaded, the sensor zeroing button is pressed to set the reading to 0. A small torque preload is then applied to the input shaft. After the sensor reading stabilizes, the load is completely unloaded, and the zeroing button is pressed again to reset the reading to 0. This preload, unload, and zeroing process is repeated twice to ensure the sensor zero position is stable.

[0067] Step S106: Load the static torsion test bench according to the set torque condition, and collect the measured value of the axial displacement ΔG of the passive wheel through the laser displacement sensor.

[0068] In some embodiments, the torque is applied sequentially according to the five torque conditions set in step S101. After each condition reaches the target torque, the torque is kept stable. The readings of the laser displacement sensor are collected in real time using data acquisition software, with 10 data points collected for each condition, and 30 data points collected continuously for each condition. After the data acquisition is completed, the data files are saved in the order of the conditions. Multiple data acquisitions can reduce random errors caused by environmental interference and ensure data reliability.

[0069] Step S107: Using the measured value of ΔG as the calibration benchmark, iteratively correct the simulation parameters so that the relative error between the calculated value and the measured value of ΔG under all working conditions is ≤10%.

[0070] In some embodiments, the theoretical value of ΔG in step S104 and the measured value of ΔG in step S106 are matched one-to-one according to the working conditions to form a comparison table, and the relative error for each working condition is calculated. Relative error = |theoretical value - measured value| / measured value × 100%. Simulation model parameters that significantly affect ΔG are selected—through parameter sensitivity testing, the bearing stiffness coefficient and the gear material elastic modulus correction coefficient are determined as adjustable parameters. The working condition with the largest error is selected as the initial correction target, and the adjustable parameters are adjusted one by one in increments of ±5%. After adjustment, the simulation calculation in step S104 is re-executed to obtain a new theoretical value of ΔG, and the relative error with the measured value is calculated; if the error decreases, fine-tuning continues in that direction.

[0071] If the error increases, the adjustment is reversed. After correcting the initial working condition to an error ≤10%, the above correction process is repeated for the other four working conditions. If adjusting a parameter causes the error of the corrected working condition to rise again, the parameter is fine-tuned until the error of all working conditions is ≤10%, then the correction is stopped, and the final model parameters are saved. This ensures that the calculation accuracy of the axial displacement ΔG of the driven wheel in the calibrated simulation model meets the requirements under all working conditions, thus improving the reliability of the model.

[0072] Step S108: Based on the calibrated simulation model, output the axial displacement ΔP of the driving wheel, the shaft intersection angle displacement ΔA, and the offset distance displacement ΔE to form the evaluation index of the bevel gear pair meshing performance.

[0073] S108 specifically includes the following steps: S1081: Load the calibrated simulation model obtained in step S107, check the bearing stiffness coefficient, gear material elastic modulus correction coefficient, and shell constraint stiffness coefficient in the simulation model, and confirm that the parameters are consistent with the final calibration values.

[0074] S1082: In the calibration simulation model, the three initial coordinate points on the vertical circumference of the main and driven gears extracted in step S103 are marked as the driving gear coordinate point group and the driven gear coordinate point group.

[0075] S1083: 20% as set in step S101 Up to 100% Under torque conditions, the calibration simulation model is re-loaded to obtain the post-load coordinates of the active wheel coordinate point group and the passive wheel coordinate point group under each working condition.

[0076] S1084: Compare the initial coordinates and the coordinates after loading of the driving wheel under each working condition, and calculate the axial displacement ΔP of the driving wheel; compare the initial coordinates and the coordinates after loading of the driven wheel, and calculate the shaft angle displacement ΔA and the offset displacement ΔE.

[0077] S1085: Compile ΔP, ΔG, ΔA, and ΔE under each torque condition into a table, and mark the correspondence between each misalignment and the torque condition to form a set of evaluation indicators for the meshing performance of the bevel gear pair of the main reducer.

[0078] In this embodiment, S1081 uses the model parameter query function of the finite element software to verify the values ​​of bearing stiffness coefficient, gear material elastic modulus correction coefficient, and shell constraint stiffness coefficient in the calibrated model one by one, ensuring consistency with the calibration parameters finally recorded in S107. S1082 locates the coordinate points of the active and passive gears marked in S103 by coordinate point numbering or spatial position, clarifying the distribution of the three points of the active gear around the active gear axis and the three points of the passive gear around the passive gear axis, ensuring that the selected positions of the two sets of points are consistent with the initial modeling. S1083 The deformation of the gear after loading will cause changes in the coordinate point positions; stable loading and calculation convergence ensure that the coordinate data reflects the true deformation state. S1084 The spatial position changes of the coordinate points directly reflect the axial, angular, and distance displacements of the gears; displacement parameters can be quantified by comparing points in the same group. S1085 Constructs a table with torque conditions as rows and misalignment types as columns, fills in the specific values ​​for each condition, labels the units, and explains in the table remarks that the data source is the calibrated simulation model, forming an evaluation index document. This makes it easier for users to query misalignment data under different working conditions.

[0079] It can be seen that the calibrated model's mechanical behavior is consistent with reality; therefore, the calculated coordinate changes can accurately reflect the multi-dimensional displacement of the gears. ΔP reflects the axial offset of the driving wheel, ΔA reflects the deviation of the axial angle, and ΔE reflects the deviation of the axial distance. These three, together with ΔG, constitute the key misalignment parameters of the bevel gear meshing, which can comprehensively evaluate the meshing state. The index set can be directly used for the design optimization and fault diagnosis of the drive axle main reducer.

[0080] In one embodiment of the present invention, based on step S105, the following is a possible embodiment and its specific implementation will be described in a non-limiting manner. S105 specifically includes the following steps: Step S1051: Based on the three-dimensional model of the drive axle assembly, determine the opening position and diameter in the area corresponding to the back of the driven wheel in the main reducer housing.

[0081] Step S1052: Use CNC machining equipment to drill holes at the determined housing position to form the sensor measurement channel.

[0082] Step S1053: Fix the drive axle assembly on the working platform of the static torsion test bench using a fixture, ensuring that the input end is aligned and connected with the torque actuator.

[0083] Step S1054: Fix the laser displacement sensor to the test bench base using a magnetic base or a special bracket, and adjust the sensor position so that its laser beam passes through the machined hole and directly hits the measurement point on the end face of the passive wheel.

[0084] Step S1055: Turn on the power to the laser displacement sensor and execute the zero-point calibration command under no-load conditions to reset the current reading to zero.

[0085] It should be noted that step S1051 determines the optimal measurement path through model analysis to ensure that the laser beam reaches the target measurement surface without obstruction. Step S1052 forms a physical channel on the housing through precision machining to ensure that the laser beam can be projected onto the measured surface without interference. Step S1053 reproduces the actual installation state of the assembly through precise positioning and firm clamping. This ensures that the test boundary conditions are consistent with the simulation model and actual operating conditions, improving the validity of the test data. Step S1054 utilizes the linear propagation characteristics of laser to establish a non-contact measurement path, enabling precise measurement of the axial displacement of the rotating component. Step S1055 defines the current voltage output as the zero-point reference for displacement through an electronic zero-adjustment circuit. This eliminates reference deviations caused by initial system installation errors and environmental factors, ensuring the accuracy and reliability of subsequent displacement measurement data.

[0086] In one embodiment of the present invention, based on step S106, the following is a possible embodiment and its specific implementation will be described in a non-limiting manner. S106 specifically includes the following steps: Step S1061: Based on the loading conditions calculated in step S101, create a torque condition sequence in the digital control system of the static torsion test bench, where each torque value corresponds to an independent loading step and is marked with a condition number. Step S1062: Start the hydraulic loading system of the test bench, set the torque loading rate to 0.5MPa / s, apply the torque sequentially according to the working condition sequence, and keep the torque stable in each loading step before switching to the next torque value; Step S1063: During the torque stabilization phase of each loading step, trigger the laser displacement sensor to collect real-time data on the axial displacement of the passive wheel and obtain 100 data points. Step S1064: Perform a moving average filter on the original displacement data of each loading step, remove high-frequency noise, and calculate the arithmetic mean as the measured value of the axial displacement ΔG of the passive wheel under the current torque. Step S1065: Save the working condition number, corresponding torque value, measured ΔG value, acquisition time, and sensor number to establish a one-to-one correspondence between the working condition and the measured data.

[0087] It should be noted that step S1061, the static torsion test, must reproduce the gradient torque boundary conditions in the simulation. Step S1062, with its constant loading rate, avoids impact loads, and the settling time allows the assembly's elastic deformation to reach equilibrium. The obtained measured ΔG value reflects the true static misalignment, rather than the temporary deformation during the dynamic transition process, thus improving data reliability. Step S1063 uses high-frequency sampling to capture subtle displacement changes, providing sufficient data points for subsequent statistical processing and reducing the impact of random noise. Step S1064 uses a low-pass filter to suppress high-frequency noise and retain the low-frequency components of the true deformation. Step S1065 stores the data for easy retrieval and comparison, ensuring that each measured ΔG value can be traced back to the corresponding simulation condition.

[0088] In one embodiment of the present invention, based on step S107, the following is a possible embodiment and its specific implementation will be described in a non-limiting manner. S107 specifically includes the following steps: Step S1071: The calculated values ​​of the axial displacement ΔG of the driven wheel under each torque condition obtained in step S104 are matched one-to-one with the measured values ​​of ΔG under the corresponding conditions obtained in step S106, and a data comparison table is established.

[0089] Step S1072: Select adjustable parameters in the simulation model that affect the axial displacement ΔG of the passive wheel, including the bearing stiffness coefficient, the gear material elastic modulus correction coefficient, and the shell constraint stiffness coefficient.

[0090] Step S1073: Select a torque condition as the initial adjustment target, and adjust the selected adjustable parameters one by one according to the preset range, adjusting only one parameter at a time.

[0091] Step S1074: Reload and calculate the simulation model after each adjustment to obtain the new ΔG calculated value under the working condition, and calculate the relative error between the new calculated value and the corresponding measured value.

[0092] Step S1075: After completing the initial working condition parameter adjustment, repeat steps S1073~S1074 for other torque working conditions in sequence until the relative error between the calculated value and the measured value of ΔG under all working conditions is no greater than 10%, and then stop parameter adjustment.

[0093] It should be noted that step S1071 presents the differences between simulation results and experimental results under various working conditions through comparison. Step S1072 focuses on key parameters based on the degree of parameter influence on simulation results, shortening the calibration cycle and ensuring that adjustments effectively change the calculated ΔG value, thus improving calibration efficiency. Step S1073 uses the controlled variable method to eliminate multi-parameter interference and accurately identify the direction and degree of influence of a single parameter on the calculated ΔG value. Step S1074 evaluates the improvement effect of a single adjustment through repeated simulation and error quantification, providing a basis for whether to retain the adjustment. Step S1075 ensures the model's effectiveness under all working conditions, rather than just a single working condition, through working condition-by-working-condition verification and cross-fine-tuning. This guarantees that the calibrated model can accurately calculate ΔG under all working conditions, improving the model's versatility.

[0094] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0095] like Figure 5 As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, a communication module 104, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of simulating and calibrating the loading misalignment of the bevel gear in the main reducer of a commercial vehicle drive axle.

[0096] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0097] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0098] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0099] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0100] The communication module 104 transmits radio signals to and / or receives radio signals from at least one of a base station, an external terminal, and a server. Such radio signals may include voice call signals, video call signals, or various types of data sent and / or received according to text and / or multimedia messages.

[0101] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating and testing calibration of bevel gear loading misalignment amount of a commercial vehicle drive axle main reducer, characterized in that, The method comprises: S101: Determine the drive axle assembly loading condition, and select a plurality of gradient conditions of 20% to 100% maximum torque as simulation and test benchmarks; S102: Simplify the drive axle assembly model, and simulate the bearing assembly by using a common node model, and divide the non-engaged gear teeth by using a mesh division tool; S103: Define material properties, displacement constraints, and load conditions in the assembly model, and extract three initial coordinate points on the axial vertical circumferences of the driving and driven gears; S104: Perform loading misalignment simulation calculation, perform spatial geometric transformation based on the extracted initial coordinate points, and calculate the theoretical value of the axial displacement ΔG of the driven gear; S105: Perform hole processing on the drive axle housing, install the drive axle assembly on the static torque test bench, configure the laser displacement sensor, and calibrate to zero; S106: Load the torque condition on the static torque test bench, and collect the measured value of the axial displacement ΔG of the driven gear by using the laser displacement sensor; S107: Take the measured value of ΔG as a calibration benchmark, iteratively correct the simulation parameters, and make the relative error between the calculated value and the measured value of ΔG in the full condition less than or equal to 10%; S108: Output the axial displacement ΔP of the driving gear, the axial displacement ΔA of the shaft intersection angle, and the displacement ΔE of the offset distance based on the calibrated simulation model, and form the evaluation index of the bevel gear pair engagement performance.

2. The method of claim 1, wherein, In the drive axle assembly loading condition of S101, the maximum torque applied to the input end is: wherein: is the maximum input torque of the drive axle assembly, is the maximum total mass of the vehicle, is the acceleration of gravity, is the rolling radius of the tire, is the reduction ratio of the drive axle, is the number of drive axles installed on the vehicle, is the reinforcement factor; 20% , 40% , 60% , 80% , 100% as the loading conditions of simulation and test.

3. The method of claim 1, wherein, The specific steps of S104 include: By the coordinate values of the three points of the extracted driving gear, a circle O passing the initial coordinates of the three points is established P1 and the normal line L passing the center of the circle P1 , the circle O after loading the simulation calculation P2 and the normal line L passing the center of the circle P2 ; By the three points on the passive gear coordinates, establish circle O G1 , O G2 With the normal L G1 , L G2 ; For the initial state, passing through the straight line L G1 Draw a line parallel to line L P1 Plane S1 will hold line L P1 With the center O P1 Projecting onto plane S1 yields line L P1a With point O P1a straight line L G1 With line L P1a The intersection point is Q1, and the line segment Q1O G1 The length of line segment Q1O is G1. P1a The length of the line is P1, and the line L is... G1 With line L P1a Angle A1, line L P1 With line L P1a The distance is E1; For the loaded post-simulation calculated state, obtain the length G2 of the line segment Q2O G2 , the length P2 of the line segment Q2O P2a , the angle A2 of the line L G2 to the line L P2a , the distance E2 of the line L P2 to the line L P2a ; A geometric model is established, and the misalignment calculation method is as follows: 。 4. The method of claim 1, wherein, S102 specifically includes the following steps: Identify the bearing assembly geometry in the drive axle assembly model; In the finite element modeling software, the inner ring, rollers and outer ring of the bearing assembly are connected by node merging operation to form a continuous grid element; Based on the gear engagement area analysis, mark the tooth surface and volume area of the driving and driven bevel gears that do not participate in engagement; For the marked non-engaged gear teeth area, use the mesh division tool to set a larger mesh size parameter to generate tetrahedral or hexahedral grid elements; Verify the continuity and integrity of the common node connection and thickened mesh by using the mesh quality checking function in the finite element software, and ensure that there is no mesh interference or fracture.

5. The method of simulating and testifying the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to claim 1, characterized in that, S103 specifically includes the following steps: Collect the material parameters of each component of the drive axle assembly, and set the material properties for each component in the finite element software; Analyze the actual installation constraints of the drive axle, set fixed displacement constraints at the installation position of the axle housing in the model, and set corresponding rotation or displacement constraints for the input shaft and other components; According to the loading condition of step S101, apply the corresponding torque load to the input end of the drive axle assembly; Determine the circumferences perpendicular to the axis on the driving and driven gears, and uniformly select three points on the circumferences and mark the positions; Use the coordinate extraction function of the finite element software to obtain the initial coordinates of the marked points and record them.

6. The method of simulating and testifying the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to claim 1, characterized in that, S105 specifically includes the following steps: Based on the three-dimensional model of the drive axle assembly, determine the hole position and aperture size in the area corresponding to the driven gear back of the main reducer housing; Use a numerical control machining device to drill at the determined housing position to form a sensor measurement channel; The drive axle assembly is fixed on the working platform of the static torque test bench through the clamp, and the input end is connected with the torque actuator in the center; The laser displacement sensor is fixed on the base of the test bench through the magnetic base or special support, and the position of the sensor is adjusted so that the laser beam passes through the processing hole and directly irradiates the measurement point on the end surface of the driven wheel. Turn on the power supply of the laser displacement sensor, and execute zero point calibration instruction in the no-load state, and return the current reading to zero.

7. The method of simulating and testifying the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to claim 1, characterized in that, S106 specifically includes the following steps: According to the loading condition calculated in step S101, create a torque condition sequence in the digital control system of the static torque test bench, and each torque value corresponds to an independent loading step and is marked with a condition number; Start the hydraulic loading system of the test bench, set the torque loading rate to 0.5 MPa / s, and apply the torque according to the condition sequence, and after each loading step is stable, switch to the next torque value; In the torque stable stage of each loading step, trigger the laser displacement sensor to collect, collect the real-time data of the driven wheel axial displacement, and obtain 100 data points; After the original displacement data of each loading step is subjected to sliding average filtering, the arithmetic mean value is calculated after removing the high-frequency noise, which is the measured value of the driven wheel axial displacement ΔG under the current torque; Save the condition number, corresponding torque value, ΔG measured value, collection time and sensor number.

8. The method of simulating and testifying the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to claim 1, characterized in that, S107 specifically includes the following steps: S1071: Calculate the driven wheel axial displacement ΔG of each torque condition obtained in step S104, and establish a data comparison relationship table corresponding to the measured value of the corresponding condition ΔG obtained in step S106; S1072: Screen the adjustable parameters in the simulation model that affect the driven wheel axial displacement ΔG; S1073: Select one torque condition as the initial adjustment object, and adjust the selected adjustable parameters one by one according to the preset amplitude, and adjust only one parameter each time; S1074: Re-calculate the simulation model after each adjustment, obtain the new ΔG calculation value under the condition, and calculate the relative error between the new calculation value and the corresponding measured value; S1075: After completing the initial condition parameter adjustment, repeat steps S1073-S1074 for other torque conditions, until the relative error between the ΔG calculation value and the measured value under all conditions is not greater than 10%, and stop parameter adjustment.

9. The method of simulating and testifying the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to claim 1, characterized in that, S108 specifically includes the following steps: Load the calibrated simulation model obtained in step S107, check the bearing stiffness coefficient, gear material elastic modulus correction coefficient and shell constraint stiffness coefficient in the simulation model, and confirm that the parameters are consistent with the final calibration value; In the calibrated simulation model, locate the three initial coordinate points on the axial vertical circle of the main driven gear extracted in step S103, and mark them as the driven wheel coordinate point group and the driven wheel coordinate point group; 20% according to step S101 up to 100% Torque conditions, after calibration simulation model re-execution of loading calculation, get each condition under the main wheel coordinate point group, passive wheel coordinate point group of loading after the coordinates; Compare the initial coordinates of the driving wheel and the loaded coordinates under each condition, calculate the axial displacement ΔP of the driving wheel; compare the initial coordinates of the driven wheel and the loaded coordinates, calculate the axial angle displacement ΔA and the offset distance displacement ΔE; Arrange ΔP, ΔG, ΔA and ΔE under each torque condition into a table, mark the corresponding relationship between each displacement and the torque condition, and form the main reducer bevel gear pair meshing performance evaluation index set.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the simulation and test calibration method of the bevel gear loading misalignment amount of the commercial vehicle drive axle main reducer according to any one of claims 1 to 9 when executing the program.

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