CN110955933A - Mechanical structure fuzzy fatigue reliability calculation method based on response surface method - Google Patents
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Abstract
The invention discloses a mechanical structure fuzzy fatigue reliability calculation method based on a response surface method, and belongs to the field of fatigue reliability calculation. The method aims to solve the problem of solving the fuzzy fatigue reliability under limited sample data. The principle is that the parameters of the mechanical structure material are obtained through tests; acquiring load information of a hinge position of a mechanical structure by utilizing multi-body dynamics analysis; further determining input variables, output variables and design variables of the mechanical structure; randomly sampling by using a Latin hypercube method, and acquiring a corresponding output variable through elastoplastic finite element analysis; constructing a fuzzy fatigue reliability function of the mechanical structure based on a response surface method; and calculating the fuzzy fatigue reliability and the failure probability according to the Monte Carlo theory and the distribution form of the design variables. The invention can fully consider the uncertain factors of the mechanical structure in design, manufacture and use, accurately calculate the fuzzy fatigue reliability, needs less sample data and greatly reduces the time and resource cost.
Description
Technical Field
The invention relates to a method for calculating the fuzzy fatigue reliability of a mechanical structure based on a response surface method, and belongs to the field of fuzzy fatigue reliability calculation.
Background
At present, mechanical equipment gradually develops to high speed, large scale and intellectualization, the working environment and load condition of a mechanical structure are increasingly harsh, and how to ensure the fatigue reliability of the mechanical structure under extremely severe conditions is a main task in the design stage. The fatigue reliability of the mechanical structure mainly has two evaluation indexes, namely service life and fatigue reliability. Therefore, accurate calculation of the fatigue reliability of the mechanical structure is important work of designers, and has important theoretical significance and engineering application value.
The exact solution of the fatigue reliability of the mechanical structure requires a large amount of sample data, however, most methods combine theoretical formulas and statistical sample data to perform the solution of the fatigue reliability, limited to the cost of resources and time. The fatigue life reliability evaluation method and device (publication number: 106874639A) of the mechanical structure under constant amplitude loading of Liwei of Beijing physical engineering university establish a reliability grade factor model under certain reliability by estimating the minimum life parameter, scale parameter and shape parameter of the fatigue life of the mechanical structure, simultaneously assume that amplitude loading is a set of multi-stage constant amplitude loading, and calculate the fatigue life reliability of the mechanical structure under amplitude loading based on the equivalent principle. Although this method takes into account randomness in structure, material parameters, and the like, the calculation of the fatigue life reliability is performed under a given load, and randomness in load is not taken into account. The invention relates to a shore bridge structure wind vibration fatigue reliability forecasting method (publication number: 102567633B) based on probability accumulated damage, which is invented by the board construction of Shanghai traffic university, calculates a stress response time course by means of finite element analysis, and forecasts the wind vibration fatigue reliability of the shore bridge structure in a certain service period by adopting a probability theory method. The method fully considers uncertainty factors in the design, manufacture and use processes, has certain accuracy, but is difficult to obtain a mechanical structure probability accumulation damage model. The invention relates to a steel bridge fatigue reliability assessment method (publication number: 102384856A) based on a probability finite element method, which is invented by Gutong university in southeast, and is characterized in that a probability distribution model of vehicle type, lane distribution, axle weight and axle distance is established by combining actually measured dynamic weighing data, and a probability finite element program is compiled to calculate the fatigue reliability. Obviously, a large amount of resources and time are consumed for actually measuring sample data, and the method cannot directly provide a function for calculating the fatigue reliability. A prediction method (publication number: 106021839B) for the fatigue reliability of a subway tunnel cable support invented by Jiangsu Power-saving company Nanjing Power supply company considers the uncertainty of model parameters, fits a response surface model according to parameter samples and fatigue life values and calculates a reliability index. Although the method gives a fatigue reliability function, the ambiguity of the fatigue strength is not considered, and the calculation accuracy of the fatigue reliability is yet to be researched. In summary, it is still an urgent need to solve the problem of how to accurately solve the fatigue reliability of the mechanical structure under the condition of limited sample data and by fully considering the uncertainty factors of the material, structure and load in the design, manufacture and use processes.
Disclosure of Invention
In order to solve the problems of low prediction precision and calculation efficiency and the like of the existing mechanical structure fatigue reliability calculation method and overcome the defects of the background technology, the invention provides a mechanical structure fuzzy fatigue reliability calculation method based on a response surface method, which comprises the following steps:
(1) obtaining mechanical property parameters and fatigue characteristic parameters of the mechanical structure material through tests;
(2) simulating the actual working process of the mechanical structure by utilizing multi-body dynamics analysis to obtain load information of the hinge position of the mechanical structure;
(3) defining input variables, output variables and design variables;
(4) randomly sampling input variables by using a Latin hypercube method, and calculating corresponding output variables by performing mechanical structure elastoplasticity finite element analysis according to mechanical structure material mechanical property parameters, mechanical structure material fatigue characteristic parameters and mechanical structure hinge position load information;
(5) combining an input variable and an output variable, and constructing a fuzzy fatigue reliability function of the mechanical structure based on a response surface method;
(6) and calculating the fuzzy fatigue reliability and the failure probability of the mechanical structure according to the design variables and the membership functions.
Further, in the step (1), the mechanical property parameters of the mechanical structure material comprise an elastic modulus E and a Poisson ratio mu; the fatigue characteristic parameters of the mechanical structure material comprise a cyclic strengthening coefficient K ', a cyclic strain hardening index n' and a fatigue strength sigmalim。
Further, in the step (2), the multi-body dynamics analysis includes establishment of a mechanical structure multi-body dynamics model, definition of driving conditions, definition of load information output of a mechanical structure hinge position, and definition of a solver; the driving conditions are determined according to the actual working process of the mechanical structure; the mechanical structure hinge position load information comprises a force-time course curve and a maximum force; commercial software used for the analysis of the polytomodynamics was msc.
Further, in the step (3), a variable x is inputiThe method is characterized in that the method comprises the following steps of (1) obtaining a geometric dimension parameter, a material parameter and a load parameter related to the fuzzy fatigue reliability of a mechanical structure, belonging to random variables, wherein i is the number of input variables; according to the stress intensity interference theory, the output variable y is the maximum equivalent stress under the maximum force action; design variable Y is fatigue strength σ 'after blurring'limAnd belongs to fuzzy variables.
Further, in the step (4), the minimum number of sample points of the input variable is 2 i-1; the constraint information of the hinge position of the mechanical structure required by the elastoplastic finite element analysis is determined according to the actual working process of the mechanical structure; the commercial software used for elastoplastic finite element analysis was ABAQUS or ANSYS or HYPERWHORKS or PATRAN or MARC or ADINA.
Further, in the step (5), the fuzzy fatigue reliability function of the mechanical structure is established according to the following steps:
the method comprises the following steps: will input variable xiAnd the output variable y is stored as a txt file in a list form;
step two: importing the txt file into commercial software ISIGHT, and selecting a response surface method as a construction input variable xiAnd a method of outputting a functional relationship of the variable y;
step three: fitting a functional relation, acquiring coefficients and constant terms in front of each functional term, and establishing a fuzzy fatigue reliability function of the mechanical structure;
further, in the step (6), the solution of the fuzzy fatigue reliability of the mechanical structure is established according to the following steps:
the method comprises the following steps: writing a solving program of a mechanical structure fuzzy fatigue reliability function in commercial software MATLAB;
step two: according to the Monte Carlo theory and the distribution form of the design variable Y, compiling a solving program of the mechanical structure fuzzy fatigue reliability and the mechanical structure fuzzy fatigue failure probability;
step three: defining the variation range of the input variable and the cycle sampling times N of the input variable; and executing a program to obtain the fuzzy fatigue reliability and the failure probability of the mechanical structure.
Further, the functional relationship is a first order function or a second order function or a third order function or a fourth order function.
Further, the distribution form of the design variable Y is a linear distribution or a normal type distribution or a parabolic distribution or a cauchy distribution.
Further, the variation range of the input variable does not exceed 30%; the input variable cycle sampling times N meet the following conditions: n is more than or equal to 1; the sum of the fuzzy fatigue reliability and the failure probability of the mechanical structure is equal to one.
The method has the beneficial effects that: uncertainty factors of the mechanical structure in design, manufacture and use and fuzziness of fatigue strength can be fully considered, fatigue reliability of the mechanical structure is accurately calculated, sample data needed is few, and time and resource cost are greatly reduced.
Drawings
FIG. 1 is a flow chart of a method for calculating fuzzy fatigue reliability of a mechanical structure based on a response surface method;
FIG. 2 is a schematic size diagram of a mechanical structure material test specimen;
FIG. 3 is a graph showing mechanical properties of a mechanical structure material;
FIG. 4 is a graph illustrating fatigue characteristics of a mechanical structure material;
FIG. 5 is a schematic view of a welding structure of an A-shaped frame of the electric wheel dumper;
FIG. 6 is a schematic diagram of force time history for a certain hinge position of the mechanical structure;
FIG. 7 is a schematic diagram of equivalent cyclic loading at a certain hinge position of a mechanical structure;
FIG. 8 is a diagram illustrating the results of random sampling of input variables and the results of calculation of output variables for a mechanical structure;
FIG. 9 is a graph showing the results of E cycle sampling of the elastic modulus of a mechanical structure;
FIG. 10 is a schematic diagram showing the cyclic sampling result of the cyclic reinforcement coefficient K' of the mechanical structure;
FIG. 11 is a graph showing fuzzy fatigue reliability results for mechanical structures;
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
An example of fuzzy fatigue reliability calculation of a certain electric wheel dumper A-type frame welding mechanical structure is given below, but the protection scope of the invention is not limited to the following implementation examples.
The method comprises the following steps: and carrying out a monotonous tensile test, a fatigue test and a Poisson ratio test to obtain the mechanical property parameters of a certain mechanical structure material and the fatigue characteristic parameters of the mechanical structure material.
For a certain electric motorA mechanical structure is welded on the A-shaped frame of the wheel dumper, and the size of a mechanical structure material test specimen is shown in figure 2. The monotonous tensile test equipment adopts an MTS810 universal material testing machine, the dynamic load capacity of the monotonous tensile test equipment is +/-100 KN, and the loading weighing precision range is +/-0.5%. The mechanical structure material test specimen monotonous tensile test adopts displacement control, and the test is carried out by applying axial monotonous tensile load on the specimen until the specimen is broken. Because the test piece material of the mechanical structure material test is high strain steel, the extensometer for the test is protected from being damaged by sudden conditions of equipment, and the test is stopped when the material load obviously drops. The applied cyclic fatigue loading of the fatigue test is as follows: all fatigue test load types are sine tension and compression loads, the loading frequency is controlled within a range of 30Hz, and the strain ratio is-1; the test temperature was room temperature. Under the action of the fatigue load cycle, the final failure of the test piece is judged according to the load peak value change of 50%, namely when the load change reaches 50%, the test piece is failed. The mechanical property curve of the mechanical structure material obtained by data processing is shown in fig. 3, the fatigue characteristic curve of the mechanical structure material is shown in fig. 4, the mechanical property parameters of the mechanical structure are shown in table 1, the fatigue characteristic parameters of the mechanical structure material are shown in table 2, the Poisson ratio mu of the mechanical structure material is 0.3, the fatigue strength sigma of the material islimIs 132 MPa.
TABLE 1 mechanical Property parameters of mechanical Structure materials
| Material properties | Parameter value |
| Modulus of elasticity E (MPa) | 2.01×105 |
| Yield strength sigmas(MPa) | 456 |
| Tensile Strength σb(MPa) | 615.9 |
| Elongation delta (%) | 18.1 |
| Reduction of area psi (%) | - |
| True fracture stress sigmaF(MPa) | 24 |
| True strain at break epsilonF(10mm/mm) | 0.279 |
TABLE 2 fatigue characteristics parameters of mechanical structural materials
| Circulation strengthening system K' (MPa) | Cyclic strain hardening index n' | |
| Parameters of the material | 860.99 | 0.1792 |
Step two: and carrying out multi-body dynamics analysis on the electric wheel dumper, simulating the actual working process of the mechanical structure, and acquiring load information of the hinged position of the mechanical structure.
For a welding mechanical structure of an A-shaped frame of a certain electric wheel dumper, as shown in fig. 5, multi-body dynamics simulation analysis on severe working conditions of downhill turning braking is carried out based on commercial software MSC. Considering that the A-shaped frame and the frame are structural bearing parts with certain deformation, the A-shaped frame and the frame are defined as flexible bodies, and then corresponding kinematic pairs are established at connecting points of the parts; introducing a stiffness and damping force curve of the suspension in a SPLINE curve form, and establishing the stiffness and the damping force by using an AKISPL and BISTOP function; the tire model of the electric wheel dumper adopts a common UA modeling method, and the rigidity parameters of the tire are obtained by fitting experimental data provided by Michelin company; a sine wave superposition method is utilized to establish an E-level two-dimensional road surface unevenness mathematical model in MATLAB, a two-dimensional random road surface spectrum is generated, a two-dimensional road surface unevenness data file is converted into an rdf road surface which can be identified by a dynamic model, and the road surface model required by the dynamic simulation can be obtained. The whole vehicle multi-body dynamic model comprises 41 rigid body parts and 2 flexible body parts which are connected with each other through 63 kinematic pairs and 4 contacts. The force-time history of the positions of the A-shaped frame and the front traction joint of the frame of the electric wheel dump truck is obtained by simulating the downhill turning braking working condition of the electric dump truck as shown in fig. 6, and the load information of other hinged positions is not redundant. The method provides input load for elastic-plastic finite element analysis of the subsequent A-shaped frame of the electric wheel dump truck, and also needs to convert the force time history of the A-shaped frame of the electric wheel dump truck and the position of the front traction joint of the frame into equivalent cyclic load, as shown in fig. 7.
Step three: and defining input variables, output variables and design variables required by the fuzzy fatigue reliability calculation of the A-type frame of the electric wheel dumper.
The parameters related to the calculation of the fuzzy fatigue reliability of the A-shaped frame of the electric wheel dumper are more, such as material parameters of the A-shaped frame, structural dimension parameters, loads and the like. However, the a-frame has a complex structure and a large size, and it is difficult to implement parameterization of the model, so the structural size parameters of the a-frame are regarded as deterministic variables. By the stress condition of the A-shaped frame of the electric wheel dumperThe front tie rod is mainly used for bearing the lateral load of a front tie rod, the vertical load borne by a front traction joint of an A-shaped frame and the lateral steering load borne by a steering power rod on two sides. For this reason, in the A-type frame fuzzy fatigue reliability calculation, the four main loads are defined as the input variable xiAnd belongs to a random variable. The A-shaped frame of the electric wheel dumper connects plates with different thicknesses together in a welding mode, and fatigue cracking mostly occurs at the weld toe part of a weld seam, so that the material parameters of the weld seam are the other key for calculating the fatigue reliability of the A-shaped frame. The calculation of the elastoplasticity mechanical response of the welding seam of the A-shaped frame is mainly divided into two stages, firstly, the stress strain response of the welding seam of the A-shaped frame of the electric wheel dumper in the elastic stage is realized, and the parameters related to the welding seam material of the A-shaped frame of the electric wheel dumper in the stage are mainly the elastic modulus E and the Poisson ratio mu; secondly, the stress strain response of the welding seam of the A-shaped frame of the electric wheel dumper at the plastic stage is realized, and the material parameters playing a determining role at the stage are a cyclic strengthening coefficient K 'and a cyclic strain hardening index n'. Therefore, the elastic modulus E and the Poisson ratio mu of the welding seam material of the A-shaped frame of the electric wheel dumper as well as the cyclic reinforcement coefficient K 'and the cyclic strain hardening index n' for representing the performance of the welding seam material of the A-shaped frame of the electric wheel dumper are defined as input variables xiAnd belongs to a random variable. In addition, the invention establishes the fatigue reliability function of the A-type frame of the electric wheel dumper based on the stress intensity interference theory, thereby taking the maximum equivalent stress under the action of load as an output variable. Meanwhile, the fatigue strength of the material of the A-shaped frame of the electric wheel dumper is a threshold value for measuring fatigue damage of the material or the structure, so that the fatigue strength sigma of the welding seam of the A-shaped frame of the electric wheel dumper is measuredlimAs a design variable Y. However, the fatigue strength of the welding seam of the A-shaped frame of the electric wheel dumper depends on the quality of the welding seam, and in the welding process, the working experience of technicians is not completely the same, so that the quality of the welding seam of the A-shaped frame is different, and the fatigue strength of the welding seam of the A-shaped frame has certain ambiguity, so that the fatigue strength sigma of the material of the A-shaped frame of the electric wheel dumper is differentlimBelongs to fuzzy variables, and the fatigue strength of the fuzzy material is recorded as sigma'lim。
Step four: and C, carrying out random sampling on the input variable determined in the step three by using a Latin hypercube method, carrying out mechanical structure elastoplasticity finite element analysis according to a random sampling result, and calculating a corresponding output variable.
Partial results of the eight input variables randomly sampled by the latin hypercube method are shown in fig. 8, a finite element model of the electric wheel dumper a-type frame is established based on commercial software ABAQUS by combining the geometric structure of the electric wheel dumper a-type frame and boundary conditions consistent with actual work, material parameters, loads and constraint conditions of the finite element model are defined, wherein the material parameters are defined according to the parameters in the first step, the loads are defined according to equivalent cyclic loads in the second step, and the constraints are defined according to actual working states of the electric wheel dumper a-type frame. And further carrying out elastic-plastic finite element analysis on the A-shaped frame of the electric wheel dumper and outputting an equivalent stress result, and determining the maximum equivalent stress corresponding to each group of input variables, namely the output variables.
Step five: and fitting a functional relation between the input variable and the output variable determined in the step four based on a response surface method, and constructing a fuzzy fatigue reliability functional function of the mechanical structure.
According to the random sampling result of the mechanical structure input variables and the calculation result of the output variables shown in fig. 8, the random sampling result and the calculation result of the output variables are manufactured into an axtxt format file in a list form, then the axtxt format file is put into commercial software ISIGHT, a second-order function response surface model is selected to fit the functional relationship between the input variables and the output variables, and finally the obtained fuzzy fatigue reliability function of the a-frame of the electric wheel dumper is shown as the following formula:
y＝90.6808+0.0021x1-168.7607x2-0.05x3-2997.9914x4
+0.001x5-0.0008x6+0.0022x7+0.00028x8-8.7072e-9x1 2+
803.6045x2 2-2.2164e-5x3 2+5044.2594x4 2+9.6219e-10x5 2
-1.8729e-9x6 2-1.8545e-8x7 2-1.5586e-9x8 2-0.0028x1x2
+1.9523x1x3+0.0087x1x4+8.3739e-10x1x5+2.4638e-8x1x6
-8.1652e-9x1x7+4.9362e-9x1x8+0.2348x2x3-471.623x2x4
+0.007x2x5+0.00076x2x6-0.001x2x7+0.00088x2x8+0.1054x3x4
-1.1317e-7x3x5-1.0085e-6x3x6+3.0944e-7x3x7-2.7844e-7x3x8
+0.0006x4x5-0.01484x4x6+0.0045x4x7-0.0052x4x8+1.6174e-9x5x6
-2.2463e-9x5x7-1.3421e-10x5x8+9.9652e-10x6x7-1.0516x6x8+
3.0322x7x8
in the above formula x1、x2、x3、x4、x5、x6、x7、x8The system comprises an electric wheel dumper A-type frame material elastic modulus E, a Poisson ratio mu, a cyclic strengthening coefficient K ', a cyclic strain hardening index n', a front traction joint load F1, a right steering joint equivalent load F2, a steering joint equivalent load F3 and a front tie rod equivalent load F4, wherein the eight variables are input variables, and an output variable y is the maximum equivalent stress.
Step six: and calculating the fuzzy fatigue reliability and the failure probability of the mechanical structure according to the design variables and the membership functions.
According to the third step, the design variable Y is the material fatigue strength sigmalimThe blurred and blurred material must have a fatigue strength σ'limThe fuzzy fatigue reliability and the failure probability of the A-type frame of the electric wheel dump truck can be calculated by taking the distribution form of the design variable Y as an example, wherein the distribution form of the design variable Y is parabolic distribution, and the failure probability of the design variable Y is calculated according to the following formula:
in the above formula, l (y) and R (y) are reference functions, F is a failure probability, and the fuzzy fatigue reliability R is 1-F. And defining the variation range of the input variable and the cycle sampling times N of the input variable, namely compiling a program to calculate the fuzzy fatigue reliability and the failure probability of the A-type frame of the electric wheel dump truck. The cyclic sampling times N of the invention are 100000 times, wherein the elastic modulus E is cyclically sampled for 1000 times, and the cyclic reinforcement coefficient K' is cyclically sampled for 1000 times, which are respectively shown in FIG. 9 and FIG. 10. The following program for a parabolic distribution written in the commercial software MATLAB is shown below:
after 100000 times of cyclic sampling, the calculated fuzzy fatigue reliability and failure probability of the a-shaped frame of the electric wheel dump truck under the parabolic distribution are shown in fig. 11. According to the same method, the calculation results of the fuzzy fatigue reliability and the failure probability of the a-shaped frame of the electric wheel dump truck under normal distribution and linear distribution are shown in fig. 11.
Claims (10)
1. A method for calculating the fuzzy fatigue reliability of a mechanical structure based on a response surface method is characterized by comprising the following steps:
the method comprises the following steps: obtaining mechanical property parameters and fatigue characteristic parameters of the mechanical structure material through tests;
step two: simulating the actual working process of the mechanical structure by utilizing multi-body dynamics analysis to obtain load information of the hinge position of the mechanical structure;
step three: defining input variables, output variables and design variables;
step four: randomly sampling input variables by using a Latin hypercube method, and calculating corresponding output variables by performing mechanical structure elastoplasticity finite element analysis according to mechanical structure material mechanical property parameters, mechanical structure material fatigue characteristic parameters and mechanical structure hinge position load information;
step five: combining an input variable and an output variable, and constructing a fuzzy fatigue reliability function of the mechanical structure based on a response surface method;
step six: and calculating the fuzzy fatigue reliability and the failure probability of the mechanical structure according to the design variables and the membership functions.
2. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the step one, the mechanical property parameters of the mechanical structure material comprise an elastic modulus E and a Poisson ratio mu; the fatigue characteristic parameters of the mechanical structure material comprise a cyclic strengthening coefficient K ', a cyclic strain hardening index n' and a fatigue strength sigmalim。
3. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the second step, the multi-body dynamics analysis includes establishment of a multi-body dynamics model of the mechanical structure, definition of driving conditions, definition of load information output of a hinge position of the mechanical structure, and definition of a solver; the driving conditions are determined according to the actual working process of the mechanical structure; the mechanical structure hinge position load information comprises a force-time course curve and a maximum force; commercial software used for the analysis of the polytomodynamics was msc.
4. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the third step, the variable x is inputiThe method is characterized in that the method comprises the following steps of (1) obtaining a geometric dimension parameter, a material parameter and a load parameter related to the fuzzy fatigue reliability of a mechanical structure, belonging to random variables, wherein i is the number of input variables; according to the stress intensity interference theory, the output variable y is the maximum equivalent stress under the maximum force action; design variable Y is fatigue strength σ 'after blurring'limAnd belongs to fuzzy variables.
5. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the fourth step, the minimum number of sample points of the input variables is 2 i-1; the constraint information of the hinge position of the mechanical structure required by the elastoplastic finite element analysis is determined according to the actual working process of the mechanical structure; the commercial software used for elastoplastic finite element analysis was ABAQUS or ANSYS or HYPERWHORKS or PATRAN or MARC or ADINA.
6. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the fifth step, the fuzzy fatigue reliability function of the mechanical structure is established according to the following steps:
the method comprises the following steps: will input variable xiAnd the output variable y is stored as a txt file in a list form;
step two: importing the txt file into commercial software ISIGHT, and selecting a response surface method as a construction input variable xiAnd a method of outputting a functional relationship of the variable y;
step three: and fitting the functional relation, acquiring coefficients and constant terms in front of each functional term, and establishing a fuzzy fatigue reliability functional function of the mechanical structure.
7. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 1, wherein in the sixth step, the solution of the fuzzy fatigue reliability of the mechanical structure is established as follows:
the method comprises the following steps: writing a solving program of a mechanical structure fuzzy fatigue reliability function in commercial software MATLAB;
step two: according to the Monte Carlo theory and the distribution form of the design variable Y, compiling a solving program of the mechanical structure fuzzy fatigue reliability and the mechanical structure fuzzy fatigue failure probability;
step three: defining the variation range of the input variable and the cycle sampling times N of the input variable; and executing a program to obtain the fuzzy fatigue reliability and the failure probability of the mechanical structure.
8. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 6, wherein: the functional relationship is a first order function or a second order function or a third order function or a fourth order function.
9. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 7, wherein: the distribution form of the design variable Y is linear distribution or normal distribution or parabolic distribution or Cauchy distribution.
10. The method for calculating the fuzzy fatigue reliability of the mechanical structure based on the response surface method as claimed in claim 7, wherein: the variation range of the input variable does not exceed 30 percent; the input variable cycle sampling times N meet the following conditions: n is more than or equal to 1000; the sum of the fuzzy fatigue reliability and the failure probability of the mechanical structure is equal to one.
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| CN112036661A (en) * | 2020-09-10 | 2020-12-04 | 南京工程学院 | Ceramic cutter reliability prediction method based on random distribution of mechanical properties of cutter |
| CN112036661B (en) * | 2020-09-10 | 2023-11-21 | 南京工程学院 | Ceramic cutter reliability prediction method based on cutter mechanical property random distribution |
| CN113515810A (en) * | 2021-05-17 | 2021-10-19 | 中车长春轨道客车股份有限公司 | Motor train unit bogie design and development method based on reliability and safety analysis |
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