A cargo box stiffness analysis method based on multi-sample simulation test

Abstract

The present application relates to the technical field of truck box structure performance detection, and specifically discloses a box stiffness analysis method based on multi-sample simulation test; comprising the following steps: step 1, determining samples and determining the design parameters of each sample; step 2, constructing a simulation model, then carrying out static stiffness simulation calculation and extracting the simulation deformation of key measuring points to form a simulation data set; step 3, carrying out a bench test, collecting measured data of the samples, and building a test database; step 4, determining a simulation model correction coefficient; using the correction coefficient to correct the simulation model parameters to obtain correction data; step 5, establishing a quantitative evaluation system; step 6, determining the weak position of the box in the quantitative evaluation system; the box stiffness analysis method based on multi-sample simulation test in the present application solves the problems of single evaluation sample, disconnection between simulation and test, and disconnection between the evaluation system and the actual application scenario.

Description

Technical Field

[0001] This invention relates to the field of cargo box structural performance testing technology for trucks, specifically to a cargo box stiffness analysis method based on multi-sample simulation tests. Background Technology

[0002] As a core load-bearing component of a truck, the cargo box's stiffness directly determines the vehicle's load-bearing capacity, lifespan, and user experience, making it a key indicator for cargo box structural design and optimization. Current industry methods for assessing cargo box stiffness suffer from the following technical shortcomings: First, the evaluation samples are limited and lack universality. Existing technologies mostly conduct simulation analysis or bench tests on individual cargo boxes, which can only reflect the stiffness characteristics of cargo boxes with a single structure. Without the support of multi-sample data, the evaluation conclusions cannot cover cargo boxes with different structures, different loads, and different longitudinal and transverse beam forms, and therefore lack general guiding significance.

[0003] Secondly, simulation and testing are disconnected, resulting in insufficient accuracy. Traditional simulation modeling is overly idealistic, neglecting actual engineering details and failing to perform quantitative correction through physical testing. This leads to a large deviation between the simulated stiffness results and the actual stiffness of the vehicle, making it difficult to accurately guide structural design.

[0004] Third, the evaluation system is detached from actual application scenarios. Existing methods rely solely on pure data from simulations or experiments to determine stiffness performance, without incorporating cargo box design experience, real-vehicle road test data under complex road conditions, and customer feedback on issues encountered in actual use. This easily leads to the problem of "stiffness meeting standards in the laboratory but failing in actual use," and fails to identify weak points in stiffness under real working conditions.

[0005] While methods for analyzing the stiffness of heavy-duty truck frames exist, such as the one disclosed in patent number CN118313201A, which involves applying constraints and loads for stiffness calculation to the finite element model of the heavy-duty truck frame assembly after obtaining the finite element model, and then obtaining the stiffness value of the heavy-duty truck frame through the solver's calculation formula, this method can calculate the bending and torsional stiffness of the frame, improving the accuracy of frame stiffness calculation to some extent and shortening the product development cycle. However, it still fails to address the aforementioned shortcomings: a single evaluation sample, a disconnect between simulation and testing, and an evaluation system detached from actual application scenarios. Summary of the Invention

[0006] The present invention aims to provide a cargo box stiffness analysis method based on multi-sample simulation test to solve the problems of single evaluation sample, disconnect between simulation and test, and evaluation system deviating from actual application scenario.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments, comprising the following steps: Step 1: Determine the sample size. The sample size is greater than 1. Determine the design parameters for each sample. Step 2: Construct simulation models for all samples according to the design parameters, then perform static stiffness simulation calculations and extract the simulation deformation of key measuring points. Convert the simulation deformation into stiffness data using stiffness calculation formulas to form a simulation dataset. Step 3: Conduct bench tests on all samples, including bending and torsional stiffness bench tests, collect measured data of the samples, and establish a test database; Step 4: Compare the simulation dataset of the same sample with the same parameter in the experimental database, calculate the absolute error and relative error of the result data, fit the error law of multiple samples, and determine the correction coefficient of the simulation model; use the correction coefficient to correct the simulation model parameters until the deviation between the corrected simulation stiffness data and the experimental data is less than or equal to 5%, and obtain the corrected data. Step 5: Introduce dynamic performance data from real vehicle road tests, and combine it with test database and calibration data to determine the stiffness level and weak points of the samples, and establish a quantitative evaluation system. Step 6: Determine the type and design parameters of the cargo box to be analyzed, and identify the weak points of the cargo box in the quantitative evaluation system.

[0008] The key testing points include locations prone to damage during road testing, weak points in the cargo box reported by drivers or other users or maintenance personnel during actual vehicle use, and weak points identified based on design experience.

[0009] Weak points refer to locations in the cargo box with a high probability of damage or failure, specifically including: 1) weak points identified by structural engineers based on experience; and 2) locations where dents, weld cracks, or poor panel sealing have been observed during actual vehicle road tests or customer feedback. Furthermore, the actual vehicle road test dynamic performance data in step 5 includes road test feedback and customer feedback.

[0010] Furthermore, customer feedback includes at least one of the following: whether there are local dents, whether the welds are cracked, and whether the railings are not properly closed.

[0011] Furthermore, the dynamic performance data from the actual vehicle road test also includes the dynamic deformation of the cargo box.

[0012] Furthermore, after determining the stiffness level in step 5, the quantitative evaluation system will simultaneously link the determination result with the dynamic deformation of the cargo box and customer feedback.

[0013] The beneficial effects of this plan are: 1. The quantitative evaluation system established in step 5 encompasses design parameters, bench test results, and dynamic performance data from real vehicle road tests for multiple samples. The multiple samples break the limitation of a single evaluation sample. At the same time, combining bench test results with dynamic performance data from real vehicle road tests makes the evaluation no longer solely dependent on simulation and bench testing, but can also be analyzed in conjunction with real vehicle road tests. The evaluation results are no longer divorced from the actual application scenario, making the evaluation results more accurate.

[0014] 2. During the design and development phase, after determining the type and design parameters of the cargo box to be developed, a quantitative evaluation system can be used to quickly identify weak points before bench tests and real vehicle road tests are conducted. Based on this, designers can make targeted improvements, reduce the number of physical prototype tests, significantly shorten the development cycle, and reduce testing costs.

[0015] Secondly, the weak points identified by the quantitative evaluation system can directly pinpoint the shortcomings in the stiffness of the cargo box, making it easier to propose targeted solutions such as crossbeam layout, longitudinal beam section optimization, and connection structure reinforcement. This avoids blind design and also provides guidance for targeted testing and parameter collection in subsequent bench tests and real vehicle road tests.

[0016] Furthermore, the absolute error = |simulated value - experimental measured value|, the relative error = |simulated value - experimental measured value| / experimental measured value × 100%, and the simulation model correction coefficient is determined by fitting the average relative error of all samples in step 1.

[0017] The beneficial effects of this scheme are as follows: by fitting the average relative error of all samples to determine the model correction coefficients, it is possible to uniformly correct the systematic deviations between simulation and experiment under different structural positions and loading conditions, thereby comprehensively improving the global calculation accuracy of the cargo box simulation model. The specific gains are as follows: 1. Eliminate systematic errors: Based on the overall error fitting correction of multi-measurement points and multi-working-condition samples, avoid local overcorrection caused by single-set data calibration, and make the simulation results of each position of the longitudinal beam and transverse beam synchronously fit the actual test data of the test bench; 2. Expand the scope of model application: The corrected coefficients are suitable for both bending and torsion loading conditions. There is no need to adjust the parameters separately for each loading condition. One model can cover all the forces on the cargo box, reducing redundant modeling. 3. Improve the reliability of structural optimization guidance: Accurate simulation output can provide reliable data support for plate thickness, beam layout, and cross-section optimization schemes, reducing the number of iterations and testing costs in subsequent bench and real vehicle road tests; 4. Ensure uniformity and reliability of results: The mean of all samples is used for fitting to balance the errors of different parts of the cargo box (both ends / middle of the longitudinal beams, front and rear crossbeams), so that there will be no problem of high local structural simulation accuracy and large deviation in other parts.

[0018] Further, the design parameters in step 1 include plate thickness, crossbeam layout, crossbeam section form, and rated load.

[0019] Further, the test positions for the measured data in step 3 include both ends of the longitudinal beam of the cargo box, the middle of the longitudinal beam, the front crossbeam, the middle crossbeam and the tail crossbeam.

[0020] Further, the bench test in step 3 also includes tests under different loading conditions, and the loading conditions include bending load and torsional load.

[0021] Further, in step 5, based on the corrected simulation stiffness data and the bench multi-condition test data, the sample stiffness level is determined in combination with road test feedback and customer feedback: 1) If both the simulation result and the test result meet the requirements and there are no problems from customer and road test feedback, the level is excellent; 2) If the simulation result and the test result fail to meet the requirements in some cases, the level is qualified; 3) If there are problems from customer and road test feedback and it is determined that the problems are caused by the structure of the cargo box itself, the level is determined as unqualified; if the problems are not caused by the cargo box itself, the level is determined as qualified.

[0022] The beneficial effects of this solution are as follows: The analysis method of this solution covers the test results of different positions under different loading conditions, can fully cover the test data of each part of the cargo box of the truck under different working conditions, so as to evaluate the stiffness performance of each part of the cargo box more fully and accurately, and further improve the reliability of the cargo box. Brief Description of the Drawings

[0023] Figure 1 is the flowchart of step 1 to step 5 in Embodiment 1 of the present invention. Detailed Description of the Embodiments

[0024] The following further describes in detail through specific embodiments: Embodiment 1 Embodiment 1 discloses a cargo box stiffness analysis method based on multi-sample simulation tests, as shown in Figure 1 , including the following steps: In step 1, 8 different types of truck cargo boxes are used as samples, the 8 samples are numbered respectively, and the design parameters of each sample are determined. The design parameters include cargo box length, cargo box width, cargo box height, longitudinal beam spacing, bottom plate type, bottom plate thickness, crossbeam layout, crossbeam section form, longitudinal beam type, crossbeam type, number of crossbeams and rated load; In step 2, the simulation models of the 8 samples are constructed respectively according to the design parameters, then the static stiffness simulation calculation is carried out for each simulation model respectively, the simulation deformation of the key measuring points is extracted, and the simulation deformation is converted into stiffness data through the stiffness calculation formula to form a simulation data set: The torsional stiffness is calculated using the following formula (1):

[0025] Formula (1); Where: K1 is the torsional stiffness, unit: Nm / rad; M is the torque = 2000 N / m; Z1 and Z2 are the displacements of the two loading points in the Z direction (vehicle coordinate system) obtained after simulation calculation, in mm; L is the distance between the two loading points in the Y direction (vehicle coordinate system), in mm; F1 is the loading force at one loading point of the cargo box, in the +Z direction (vehicle coordinate system), in N; The bending stiffness is calculated using the following formula (2): Formula (2); Where: K2 is the bending stiffness, in N / mm; F1 and F2 are the loading forces at the two loading points of the cargo box, with a value of 1000, in the -Z direction (vehicle coordinate system), in N; Z3 and Z4 are the displacements in the Z direction (vehicle coordinate system) of the displacement measurement points corresponding to the two loading points, obtained after simulation calculation, in mm. Error analysis is performed using the following formulas (3) to (6): Formula (3); Formula (4); Formula (5); Formula (6); in, This is the absolute error; x: Stiffness value obtained from simulation; x0: Stiffness value obtained from the experiment; Relative error; x2: Corrected simulation stiffness value; C: Correction coefficient; Step 3: Conduct bench tests on all samples under different loading conditions, including bending load and torsional load. The test content includes bending and torsional stiffness bench tests. Collect measured data of the samples and establish a test database. The test locations for the measured data include both ends of the longitudinal beam of the cargo box, the middle of the longitudinal beam, the front crossbeam, the middle crossbeam and the tail crossbeam. Step 4: Compare the simulation dataset of the same sample with the experimental database for the same parameters, and calculate the absolute error and relative error of the data, where the absolute error = |simulated value - experimental measured value|, and the relative error = |simulated value - experimental measured value| / experimental measured value × 100%; Fitting and determining the correction coefficient of the simulation model according to the average value of the relative error of the same parameter in all samples; using the correction coefficient to modify the parameters of the simulation model until the deviation between the corrected simulated stiffness data and the test data is less than or equal to 5%, so as to obtain corrected data; Step 5: Introduce the dynamic performance data of the real vehicle road test. The dynamic performance data of the real vehicle road test includes the dynamic deformation of the cargo box, road test feedback and customer feedback, where the customer feedback includes whether there is local sag, whether the weld is cracked, and whether the coaming is not closed tightly; the road test feedback includes the cargo box deformation data measured under the real vehicle road driving condition, the structural deformation performance of the cargo box under the whole vehicle bumping and torsion road condition, and the weld and local deformation failure phenomena of the box body occurring during the road test.

[0026] Meanwhile, combining the test database and the corrected data, the stiffness grade and weak position of the sample are determined, and the criteria for determining the stiffness grade of the sample are as follows: 1) If both the simulation result and the test result meet the requirements and there are no problems from customer and road test feedback, the grade is excellent; 2) If the simulation result and the test result do not meet the requirements in some cases, the grade is qualified; 3) If there are problems from customer and road test feedback, and it is judged that the problems are caused by the structure of the cargo box itself, the grade is determined as unqualified; if the problems are not caused by the cargo box itself, the grade is determined as qualified; The weak positions include: 1) Weak positions judged by structural engineers based on experience; 2) Positions where failure problems such as sag, weld cracking, and loose sealing of the coaming occur in real vehicle road tests or customer feedback; A quantitative evaluation system is established by combining the stiffness grade and the weak positions, and the judgment result is associated with both the dynamic deformation of the cargo box and the customer feedback in the quantitative evaluation system; Step 6: When it is necessary to analyze the stiffness of a certain cargo box, first determine the cargo box type and design parameters of the cargo box, and determine the weak position of the cargo box in the quantitative evaluation system.

[0027] What has been described above is only the embodiment of the present invention, and common knowledge such as well-known specific technical solutions and / or features in the solution are not described in detail herein. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicability of the patent. The protection scope claimed by the present application shall be subject to the content of its claims, and the records such as the specific embodiments in the specification can be used to interpret the content of the claims.

Claims

1. A method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments, characterized in that: comprising the following steps: Step 1: determining samples, where the number of samples is greater than 1, and determining the design parameter of each sample; Step 2: respectively constructing a simulation model for all samples according to the design parameters, then performing static stiffness simulation calculation and extracting the simulated deformation of key measuring points, converting the simulated deformation into stiffness data through a stiffness calculation formula, so as to form a simulation dataset; Step 3: carrying out bench tests on all samples, where the test content includes bending and torsional stiffness bench tests, collecting the measured data of the samples, and establishing a test database; Step 4: comparing the same parameter of the simulation dataset and the test database of the same sample, calculating the absolute error and relative error of the result data, fitting the error rule of multiple samples, and determining a correction coefficient of the simulation model; correcting the parameters of the simulation model by using the correction coefficient until the deviation between the corrected simulated stiffness data and the test data is less than or equal to 5%, so as to obtain corrected data; Step 5: introducing dynamic performance data of actual vehicle road test, and combining the test database and the corrected data at the same time, determining the stiffness grade and weak position of the sample, and establishing a quantitative evaluation system; Step 6: determining the container type and design parameters of the to-be-analyzed container, and determining the weak position of the container in the quantitative evaluation system.

2. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 1, characterized in that: The dynamic performance data of actual vehicle road test in Step 5 includes road test feedback and customer feedback.

3. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 2, characterized in that: The customer feedback includes at least one of whether the local part is dented, whether the weld seam is cracked, and whether the coaming is not tightly closed.

4. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 3, characterized in that: The dynamic performance data of actual vehicle road test further includes the dynamic deformation of the container.

5. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 4, characterized in that: After the stiffness grade is determined in Step 5, the determination result in the quantitative evaluation system is associated with both the dynamic deformation of the container and the customer feedback at the same time.

6. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 1, characterized in that: Absolute error = |simulated value - test measured value|, relative error = |simulated value - test measured value| / test measured value × 100%, and the correction coefficient of the simulation model is determined by fitting the average value of the relative errors of all samples in Step 1.

7. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 1, characterized in that: The design parameters in Step 1 include plate thickness, crossbeam layout, crossbeam section form, and rated load.

8. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 7, characterized in that: The test positions of the measured data in Step 3 include both ends of the container longitudinal beam, the middle of the longitudinal beam, the front crossbeam, the middle crossbeam and the rear crossbeam.

9. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 8, characterized in that: The bench test in Step 3 further includes tests under different loading conditions, where the loading conditions include bending load and torsional load.

10. The method for analyzing the stiffness of a cargo box based on multi-sample simulation experiments according to claim 1, characterized in that: In Step 5, based on the corrected simulated stiffness data and the bench multi-working condition test data, the stiffness grade of the sample is determined by combining the road test feedback and the customer feedback: 1) If both the simulation result and the test result meet the requirements and there are no customer and road test feedback problems, the grade is excellent; 2) If the simulation result and the test result fail to meet the requirements in some cases, but there are no customer and road test feedback problems, the grade is qualified; 3) If there are customer and road test feedback problems, and it is determined that the problems are caused by the structural reason of the container itself, the grade is determined as unqualified; if the problems are not caused by the reason of the container itself, the grade is determined as qualified.

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