CN114792060A - Accelerated fatigue test method for front auxiliary frame - Google Patents
Accelerated fatigue test method for front auxiliary frame Download PDFInfo
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- CN114792060A CN114792060A CN202210301290.8A CN202210301290A CN114792060A CN 114792060 A CN114792060 A CN 114792060A CN 202210301290 A CN202210301290 A CN 202210301290A CN 114792060 A CN114792060 A CN 114792060A
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- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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Abstract
The invention discloses a method for testing the accelerated fatigue of a front auxiliary frame, which comprises the following steps: step one, establishing a simulation analysis model of a bench fatigue test of a front auxiliary frame, and performing quasi-static analysis; step two, building a simulation optimization process based on a load scaling factor as a design variable, outputting a fatigue damage value with response as a critical position, and performing DOE sampling calculation; fitting the calculation result to obtain a mathematical response surface model meeting the precision requirement; according to the obtained mathematical response surface model, optimizing by taking the load scaling factor as a variable, taking the fatigue damage value of the critical position as a constraint and taking the minimum damage target error as a target to obtain the optimal solution of the load scaling factor; and fifthly, calculating to obtain the optimized load, loading the front auxiliary frame, setting the cycle times as the optimized cycle target times, and performing a bench fatigue test. The method can reduce the cycle times of the fatigue test, shorten the fatigue test period and improve the development efficiency.
Description
Technical Field
The invention relates to a bench fatigue test of automobile parts, in particular to an accelerated fatigue test method of a front auxiliary frame.
Background
The fatigue test of the part-level rack is generally arranged to be carried out before the test verification of the whole vehicle and the system, and aims to check the fatigue endurance problem of parts of each product in advance, so that the test verification of the whole vehicle and the system can pass through the test verification as one time as possible, the development period can be shortened, and the development cost can be reduced.
The single-piece rack fatigue test period of the front set of auxiliary frame is about more than one hundred hours (the time for setting up the test tooling is not considered), and general rack fatigue test verification needs at least three sets of parts, so that at least fifteen days of test period is needed, and the project development period can be greatly influenced. In the process of test verification, if a failure problem occurs, the optimization scheme cannot be quickly verified, so that the development period and the cost of a product are increased.
Disclosure of Invention
The invention aims to provide a front subframe accelerated fatigue test method which can reduce the cycle times of fatigue tests, shorten the period of the fatigue tests and improve the development efficiency.
The invention relates to a method for testing accelerated fatigue of a front auxiliary frame, which comprises the following steps:
step one, establishing a simulation analysis model of a bench fatigue test of a front auxiliary frame, performing quasi-static analysis, wherein the cycle number is the same as the actual cycle number before optimization, performing fatigue calculation on an analysis result, screening out a critical weight position and obtaining a corresponding fatigue damage value;
step two, building a simulation optimization flow taking a load scaling factor as a design variable, outputting a fatigue damage value taking a response as a critical position, performing DOE sampling calculation on the design variable, wherein the cycle times are the same as the optimized cycle target times;
step three, fitting the DOE sampling calculation result of the step two to obtain a mathematical response surface model meeting the precision requirement;
step four, optimizing by taking the load scaling factor as a variable, the fatigue damage value of the critical position obtained in the step one as a constraint and the minimum damage target error as a target according to the mathematical response surface model obtained in the step three to obtain an optimal solution of the load scaling factor;
and fifthly, calculating to obtain the optimized load according to the load scaling factor obtained in the fourth step, loading the front auxiliary frame by the optimized load, setting the cycle times as the optimized cycle target times, and performing a bench fatigue test on the front auxiliary frame.
Further, the load scaling factor is 1-2.
Further, the second step is specifically: in simulation optimization software Optimus, a simulation optimization process based on a load scaling factor as a design variable is set up, an output response is a fatigue damage value of a critical position, the cycle times are the same as the optimized cycle target times, and DOE sampling calculation based on the design variable is carried out by adopting an optimal Latin hyper-square test design method.
And further, verifying whether the mathematical response surface model meets the precision requirement by adopting a finite element benchmarking model in the third step, if not, increasing sample points of DOE sampling calculation, and updating the mathematical response surface model until the precision meets the requirement.
Further, the damage target error in the fourth step is the root mean square of the fatigue damage value of the corresponding critical position before optimization minus the fatigue damage value of the corresponding critical position after optimization.
And further substituting the optimized load obtained in the fifth step into the simulation analysis model in the first step for quasi-static analysis to verify whether the optimization precision meets the requirement, and if not, repeating the second step to the fifth step until the optimization precision meets the requirement.
According to the method, a load scaling factor is used as a design variable, a fatigue damage value of a critical weight position is used as an output response to carry out DOE sampling calculation, a DOE sampling calculation result is fitted to obtain a mathematical response surface model meeting precision requirements, then the load scaling factor is used as a variable according to the mathematical response surface model, the fatigue damage value of the critical weight position obtained through simulation analysis is used as a constraint, optimization is carried out by taking the minimum damage target error as a target to obtain an optimal solution of the load scaling factor, the optimized load is obtained through calculation according to the optimal solution of the load scaling factor, the optimized load is used for loading a front auxiliary frame, the cycle number is set as the optimized cycle target number, and a rack fatigue test is carried out on the front auxiliary frame. The loading load of the front subframe is increased, so that the fatigue damage value of the close-weight position of the front subframe can reach the corresponding fatigue damage value before optimization under fewer cycle times, the cycle times of a fatigue test are reduced, the fatigue test period is shortened, and the development efficiency is improved.
Drawings
FIG. 1 is a schematic flow diagram of a method for testing the accelerated fatigue of a front subframe according to the present invention;
FIG. 2 is a schematic view of the front subframe in a loaded or restrained position;
FIG. 3 is a schematic of the rack load of the front subframe before optimization;
FIG. 4 is a graph illustrating the results of a quasi-static analysis of the front subframe prior to optimization;
FIG. 5 is a schematic illustration of a DOE test design matrix;
FIG. 6 is a graph illustrating the results of a residual analysis of a mathematical response surface model;
FIG. 7 is a schematic representation of the rack load of the optimized front subframe;
FIG. 8 is a graph showing the results of a quasi-static analysis of the optimized front subframe.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, the method for testing the accelerated fatigue of the front subframe includes the following steps:
step one, referring to fig. 2 and fig. 3, establishing a corresponding simulation analysis model according to the actual loading and constraint states of the bench fatigue test of the front subframe. Under the left transverse working condition, the loading force F1 of the first loading position is 6575N, and the loading force F2 of the second loading position is 3995N; under the right transverse working condition, the loading force of the first loading position is 3995N, and the loading force of the second loading position is 6575N. When the fatigue test is carried out, the loading of the left transverse working condition and the loading of the right transverse working condition are respectively recorded as 1 cycle.
Quasi-static analysis is carried out on the simulation analysis model through Abaqus software, the cycle number is the same as the actual cycle number before optimization, namely the cycle number is set to be C1=27 ten thousand, and fatigue calculation is carried out on the analysis result. According to the principle of Miner linear criterion, the fatigue accumulated damage Di = Di × C1, wherein Di is damage loaded in a single cycle, and Di is accumulated damage after C1 cycles. According to the analysis result, referring to fig. 4, five critical weight positions with large damage are screened out and corresponding fatigue damage values are obtained.
And step two, in simulation optimization software Optimus, a simulation optimization process based on load scaling factors as design variables is built, fatigue damage values with responses of five critical positions are output, the cycle times are the same as the optimized cycle target times, and the optimal Latin hyper-square test design method is adopted to carry out DOE sampling calculation based on the design variables.
The load scaling factors comprise a first load factor S1 of a loading force F1 at a first loading position and a first load factor S2 of a loading force F1 at a second loading position, and the condition ranges of the first load factor S1 and the second load factor S2 are both 1-2 under the left transverse working condition.
In this embodiment, the optimized cycle target number is set to 8.5 ten thousand, and it should be noted that the cycle target number may be reasonably set according to the self test condition.
Step three, fitting the DOE sampling calculation result of the step two to obtain a mathematical response surface model meeting the precision requirement; referring to fig. 5, the DOE design test is 60 sets in total, the first 50 sets are used for constructing the mathematical response surface model, and the last 10 sets are used for verifying whether the accuracy of the constructed mathematical response surface model meets the requirement.
After the fitting is finished, the maximum precision residual error of the mathematical response surface analysis model of optimus software is 2.2% and is less than the set standard of 5%, so that the precision of the mathematical response surface model meets the requirement and can be used for subsequent optimization iterative calculation, and the specific residual error analysis result is shown in fig. 6.
And step four, according to the mathematical response surface model obtained in the step three, optimizing by taking the first load scaling factor S1 and the second scaling factor S2 as variables, the fatigue damage value of the critical position obtained in the step one as a constraint and the damage target error as a target, so that the optimal solution of the first load scaling factor S1 is 1.463, and the optimal solution of the second load scaling factor S2 is 1.482. And the damage target error is the root mean square of the fatigue damage value of the corresponding critical position before optimization minus the fatigue damage value of the corresponding critical position after optimization.
And step five, calculating to obtain the optimized load according to the load scaling factor obtained in the step four, namely the loading force F1=6575 × 1.463N =9620N in the first loading position and the loading force F2=3995 × 1.482N =5922N in the second loading position under the left transverse working condition.
Referring to fig. 7, the obtained optimized load is substituted into the simulation analysis model in the step one to perform quasi-static analysis, the cycle number is set to 8.5 ten thousand, and the calculation result is shown in fig. 8. Compared with the fatigue damage values corresponding to five critical positions in the graph 8 and the graph 4, the fatigue damage values are close, and the precision requirement is met.
And loading the front auxiliary frame by the optimized load, setting the cycle times as the optimized cycle target times, and performing a rack fatigue test on the front auxiliary frame.
According to the method, a load scaling factor is used as a design variable, a fatigue damage value of a critical weight position is used as an output response to carry out DOE sampling calculation, the DOE sampling calculation result is fitted to obtain a mathematical response surface model meeting the precision requirement, then the fatigue damage value of the critical weight position obtained through simulation analysis is used as a constraint according to the mathematical response surface model, the load scaling factor is used as a variable, the objective of minimum damage target error is used as an objective to carry out optimization, an optimal solution of the load scaling factor is obtained, and the optimized load is obtained through calculation according to the optimal solution of the load scaling factor. Therefore, the fatigue damage value of the close-weight position of the front subframe can reach the corresponding fatigue damage value before optimization under fewer circulation times by increasing the load of the loading position of the front subframe. On the premise of ensuring that the weight-close position reaches a similar fatigue damage value, the cycle number is reduced from 27 ten thousand times to 8.5 times, the fatigue test period is reduced from the original fifteen days to five days, and the development efficiency is greatly improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. A front auxiliary frame accelerated fatigue test method is characterized by comprising the following steps:
establishing a simulation analysis model of a rack fatigue test of a front auxiliary frame and performing quasi-static analysis, wherein the cycle times are the same as the actual cycle times before optimization, performing fatigue calculation on an analysis result, screening out a critical position and obtaining a corresponding fatigue damage value;
step two, building a simulation optimization flow taking a load scaling factor as a design variable, outputting a fatigue damage value taking a response as a critical position, performing DOE sampling calculation on the design variable, wherein the cycle times are the same as the optimized cycle target times;
step three, fitting the DOE sampling calculation result of the step two to obtain a mathematical response surface model meeting the precision requirement;
step four, optimizing by taking the load scaling factor as a variable, the fatigue damage value of the critical position obtained in the step one as a constraint and the minimum damage target error as a target according to the mathematical response surface model obtained in the step three to obtain an optimal solution of the load scaling factor;
and fifthly, calculating to obtain the optimized load according to the load scaling factor obtained in the fourth step, loading the front auxiliary frame by the optimized load, setting the cycle times as the optimized cycle target times, and performing a bench fatigue test on the front auxiliary frame.
2. The front subframe accelerated fatigue test method of claim 1, wherein: the load scaling factor is 1-2.
3. The front subframe accelerated fatigue test method according to claim 1 or 2, wherein the second step is specifically: in simulation optimization software Optimus, a simulation optimization process based on a load scaling factor as a design variable is built, a response is output as a fatigue damage value of a critical position, the cycle times are the same as the optimized cycle target times, and DOE sampling calculation based on the design variable is carried out by adopting an optimal Latin hyper-square test design method.
4. The front subframe accelerated fatigue test method of claim 1 or 2, wherein: and in the third step, verifying whether the mathematical response surface model meets the precision requirement by adopting a finite element benchmarking model, if not, increasing DOE sampling calculation sample points, and updating the mathematical response surface model until the precision meets the requirement.
5. The front subframe accelerated fatigue test method of claim 1 or 2, wherein: and the damage target error in the fourth step is the root mean square of the fatigue damage value of the corresponding critical position before optimization minus the fatigue damage value of the corresponding critical position after optimization.
6. The front subframe accelerated fatigue test method according to claim 1 or 2, characterized in that: and substituting the optimized load obtained in the fifth step into the simulation analysis model in the first step to perform quasi-static analysis so as to verify whether the optimization precision meets the requirement, and if not, repeating the second step to the fifth step until the optimization precision meets the requirement.
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