CN113111310A - Normalization processing method for testing data of fatigue life of welding spot under multi-stress working condition - Google Patents

Normalization processing method for testing data of fatigue life of welding spot under multi-stress working condition Download PDF

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CN113111310A
CN113111310A CN202110330375.4A CN202110330375A CN113111310A CN 113111310 A CN113111310 A CN 113111310A CN 202110330375 A CN202110330375 A CN 202110330375A CN 113111310 A CN113111310 A CN 113111310A
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王腾腾
赵岩
姜发同
梁宾
范吉富
蒋松蔚
王扬卫
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Abstract

The invention discloses a method for normalizing data of a welding spot fatigue life test under multiple stress working conditions, which relates to the technical field of a material mechanics curve normalization processing method and comprises 4 steps. After the initial values of the 9 parameters are obtained, the secondary values of the 9 parameters are obtained according to the calculation of the target function, the 9 parameters do not change after the initial values are obtained, but change according to subsequent calculation and change, so that the whole normalization process does not depend on the selection of the initial values of the 9 parameters completely, namely, the method has small dependence on the initial values of the 9 parameters, and in the parameter identification process, if the initial values of the 9 parameters are set unreasonably, the subsequent adjustment can be carried out, and the obtained result is closer to a global optimal solution rather than a local optimal solution. And the subsequent value change of the 9 parameters is determined according to calculation and fitting change, and does not depend on the experience of engineering personnel.

Description

Normalization processing method for testing data of fatigue life of welding spot under multi-stress working condition
Technical Field
The invention relates to the technical field of material mechanics curve normalization processing methods, in particular to a normalization processing method for welding spot fatigue life test data under multiple stress working conditions.
Background
Fatigue is a process of mechanical damage in which repeated changes in load will cause failure even if the nominal stress is below the yield strength of the material. The analysis of the fatigue of the welding spot aims to predict the fatigue life of the welding spot, so that related enterprises can reduce the development and test cost, shorten the time for putting on the market and improve the service life of products.
The problem of normalization of welding spot fatigue test data under the existing multi-stress working condition is solved by adopting a nonlinear GRG method. The method is an optimization method, converts the constraint problem into a nonlinear programming problem, adopts a steepest descent method, and has the advantage of high optimization speed.
However, the method has a large dependency on the initial parameter values, and in the parameter identification process, if the initial parameter values are set unreasonably, the method is easy to fall into a local optimal solution, and the method has a large dependency on the experience of engineering personnel.
Disclosure of Invention
The invention aims to: aiming at the existing problems, the normalization processing method for the test data of the fatigue life of the welding spot under the multi-stress working condition that the normalization result is not completely dependent on the initial value of the parameter and is possibly trapped in the local optimal solution is relatively reduced is provided.
The technical scheme adopted by the invention is as follows:
a normalization processing method for testing data of fatigue life of a welding spot under multiple stress working conditions comprises the following steps:
(1) using a structural stress formula for welding spot force and moment data obtained by a welding spot fatigue test, wherein 9 parameters are involved in the structural stress formula, and respectively taking initial values m for the 9 parameters1、m2、m3、m4、m5、m6、m7、m8、m9；
(2) Taking the fitted curve function y as axbAnd calculating to obtain the values of a and b;
(3) calculating to obtain an objective function, calculating partial derivatives of the objective function, and taking negative values of partial derivatives to obtain 9 parameter values n1、n2、n3、n4、n5、n6、n7、n8、n9；
(4) Judging whether the fitting curve function meets the convergence condition, if so, calculating the fitting degree of the fitting curve function, and judging whether the fitting curve equation meets the requirement; if not, adjusting the step size factor and returning to the step (1).
Preferably, the step (5) of determining whether the fitted curve equation meets the requirement specifically includes: if the fitting degree is larger than 0.7, updating a and b, updating the confidence interval of the independent variable, and returning to the step (1); if the fitting degree is larger than 0.65, updating the independent variable confidence interval, and returning to the step (1); and (5) if the fitting degree is less than or equal to 0.65, randomly selecting seeds, and returning to the step (1).
Preferably, step (1) is preceded by the steps of: taking a welding spot fatigue test standard part, and carrying out a welding spot fatigue test on the welding spot fatigue test standard part to obtain F-N test data, wherein F is a load range loaded by the fatigue test, and N is a fatigue life, namely a cycle number; carrying out finite element static analysis on the stress condition of the welding spot under the F-N test data to obtain the data of welding spot force and moment of the welding spot fatigue test standard component, wherein the data comprises forces in x, y and z directions in space: fx、Fy、FzX, y, in space,z three-directional moment Mx、My、Mz，FxAnd FyThe angle theta of the perpendicular vector.
Preferably, the structural stress formula in step (1) is as follows:
σstru＝-σmax(Fx)cosθ-σmax(Fy)sinθ+σ(Fz)+σmax(Mx)sinθ-σmax(My)cosθ
Figure BDA0002996146230000021
Figure BDA0002996146230000022
Figure BDA0002996146230000023
wherein: d is the weld nugget diameter of the standard part for the welding spot fatigue test, and t is the thickness of the standard part for the welding spot fatigue test;
the 9 parameters are respectively 3 scaling coefficients in a structural stress formula: SFFXY, SFFZ, SFMXY; 3 thickness indices: DEFXY, DEFZ, DEFXY; 3 diameter indices: TEFXY, TEFZ, TEMXY.
Preferably, structural stress data are obtained after a structural stress formula is used for data of welding spot force and moment obtained in the welding spot fatigue test in the step (1); the method also comprises the following steps between the steps (1) and (2): and multiplying the structural stress data by the F-N test data to obtain S-N data, wherein S is the stress range, and N is the fatigue life, namely the cycle number.
Preferably, the objective function in step (3) is S-N data and the fitting curve function y ═ axbThe sum of the residuals of (a).
Preferably, after taking a negative value for the partial derivative result in step (3), the direction of change of the independent variable is determined.
Preferably, the values of a and b in step (2) are calculated by a least square method.
Preferably, m in step (1)1、m2、m3、m4、m5、m6、m7、m8、m9All within the range of 0-1.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that: after the initial values of the 9 parameters are obtained, the secondary values of the 9 parameters are obtained according to the calculation of the target function, the 9 parameters do not change after the initial values are obtained, but change according to subsequent calculation and change, so that the whole normalization process does not depend on the selection of the initial values of the 9 parameters completely, namely, the method has small dependence on the initial values of the 9 parameters, and in the parameter identification process, if the initial values of the 9 parameters are set unreasonably, the subsequent adjustment can be carried out, and the obtained result is closer to a global optimal solution rather than a local optimal solution. And the subsequent value change of the 9 parameters is determined according to calculation and fitting change, and does not depend on the experience of engineering personnel.
Drawings
FIG. 1 is a flow chart of a normalization processing method of solder joint fatigue life test data under a multi-stress working condition.
FIG. 2 is a set of data points from a weld fatigue test standard test prior to normalization.
Fig. 3 is the result of the normalization process performed on fig. 1 using the nonlinear GRG method.
FIG. 4 is a result of normalization processing performed on FIG. 1 using a normalization processing method for solder joint fatigue life test data under multiple stress conditions.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, a method for normalizing data of a solder joint fatigue life test under multiple stress conditions includes the following steps:
s01: taking a welding spot fatigue test standard part, and carrying out a welding spot fatigue test on the welding spot fatigue test standard part to obtain F-N test data, wherein F is a load range loaded by the fatigue test, and N is a fatigue life, namely a cycle number;
s02: finite element static analysis is carried out on the stress condition of the welding spot under the F-N test data, and two finite element static analysis models are created: pulling and shearing the sample and stripping the sample to obtain the data of the welding spot force and the moment of the welding spot fatigue test standard part, wherein the data comprises the force F in the x direction, the y direction and the z direction in the spacex、Fy、FzMoments M in three directions x, y and z in spacex、My、Mz，FxAnd FyThe angle theta of the vertical vector; in the finite element static force analysis, the stress of the pulling and shearing sample and the peeling sample in a certain direction is set to be 1N respectively, and the numerical values of other forces, torque and theta are calculated to obtain the data of the welding spot force and torque of the welding spot fatigue test standard component.
S03: and obtaining structural stress data by using a structural stress formula for the data of the welding spot force and the moment. The structural stress formula is:
σstru＝-σmax(Fx)cosθ-σmax(Fy)sinθ+σ(Fz)+σmax(Mx)sinθ-σmax(My)cosθ
Figure BDA0002996146230000041
Figure BDA0002996146230000051
Figure BDA0002996146230000052
wherein: d is the weld nugget diameter of the standard part for the welding spot fatigue test, and t is the thickness of the standard part for the welding spot fatigue test;
for the 9 parameters involved in the structural stress formula: 3 scaling factors: SFFXY, SFFZ, SFMXY; 3 thickness indices: DEFXY, DEFZ, DEFXY; 3 diameter indices: respectively taking initial values of m for TEFXY, TEFZ and TEMXY1、m2、m3、m4、m5、m6、m7、m8、m9The 9 initial values are all within the range of 0-1;
s04: multiplying the structural stress data by F-N test data to obtain S-N data, wherein S is a stress range, and N is fatigue life, namely cycle number;
s05: taking the fitted curve function y as axbAnd calculating the values of a and b by using a least square method;
s06: calculating the function y ═ ax of the S-N data and the fitting curvebObtaining the target function, calculating the partial derivative, taking the negative value of the partial derivative result, namely adding a negative sign on the partial derivative result, determining the change direction of the independent variable, namely a steepest descent method, and obtaining 9 parameter values n1、n2、n3、n4、n5、n6、n7、n8、n9；
S07: judging whether the fitting curve function meets the convergence condition, if so, continuing to the step S08; if not, adjusting the step factor, and returning to the step S03;
s08: calculating the fitting degree of the fitting curve function, and if the fitting degree is greater than 0.75, judging that the fitting curve function meets the requirement and applying; if the fitting degree is more than 0.7 and more than 0.75, updating the values of a and b by using a least square method, updating the argument confidence interval by using a dichotomy method, and returning to the step S03; if the fitting degree is more than 0.65 and more than 0.7, updating the independent variable confidence interval by using a dichotomy, and returning to the step S03; and if the fitting degree is less than or equal to 0.65, randomly selecting seeds which are the values of the independent variables, namely randomly selecting a group of independent variables in the independent variable confidence interval, and returning to the step S03.
Further, the fitting degree in step S08 is calculated by: setting tolerance, taking the argument x1And independent variable x2Will bex1And x2Respectively substituting into the objective function, respectively calculating to obtain dependent variable y1And dependent variable y2Then, y is recalculated1And y2If the difference is less than the tolerance, increasing the step factor and taking the independent variable again; if the difference is greater than the tolerance, then the argument x is accepted1And independent variable x2. Wherein the step factor is an independent variable x1To the independent variable x2And increasing the increment, i.e., increasing the step factor.
Referring to fig. 2-4, the non-linear GRG method and method were used for normalization experiments:
1. the experimental method comprises the following steps:
the method comprises the following steps:
s1: performing steps S01-S05 to obtain S-N data points as shown in FIG. 2;
s2: normalizing the S-N data points by using a nonlinear GRG method to obtain a normalization processing result shown in FIG. 3;
s3: the S-N data points are normalized by using the method, and the normalization processing result shown in figure 4 is obtained;
2. the experimental results are as follows:
the normalization processing result obtained in the experiment step S2 contains local data points, and part of the data points are far away from the fitting curve to form a local optimal solution;
the result of the normalization process using the experimental step S3 contains almost all data points, forming a globally optimal solution. That is, the result of normalization processing by using the method almost contains all data points, and a global optimal solution is formed.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to aid in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.

Claims (9)

1. A normalization processing method for testing data of fatigue life of a welding spot under multiple stress working conditions is characterized by comprising the following steps:
(1) using a structural stress formula for welding spot force and moment data obtained by a welding spot fatigue test, wherein 9 parameters are involved in the structural stress formula, and respectively taking initial values m for the 9 parameters1、m2、m3、m4、m5、m6、m7、m8、m9；
(2) Taking the fitted curve function y as axbAnd calculating to obtain the values of a and b;
(3) calculating to obtain an objective function, calculating partial derivatives of the objective function, and taking negative values of partial derivatives to obtain 9 parameter values n1、n2、n3、n4、n5、n6、n7、n8、n9；
(4) Judging whether the fitting curve function meets the convergence condition, if so, calculating the fitting degree of the fitting curve function, and judging whether the fitting curve equation meets the requirement; if not, adjusting the step size factor and returning to the step (1).
2. The method for normalizing the data of the fatigue life test of the welding spot under the multi-stress working condition according to claim 1, wherein the step (5) of determining whether the fitted curve equation meets the requirement specifically comprises the following steps: if the fitting degree is larger than 0.7, updating a and b, updating the confidence interval of the independent variable, and returning to the step (1); if the fitting degree is larger than 0.65, updating the independent variable confidence interval, and returning to the step (1); and (5) if the fitting degree is less than or equal to 0.65, randomly selecting seeds, and returning to the step (1).
3. The method for normalizing the data of the fatigue life test of the welding spot under the multiple stress working conditions according to claim 1, wherein the step (1) is preceded by the steps of: taking a welding spot fatigue test standard part, and carrying out a welding spot fatigue test on the welding spot fatigue test standard part to obtain F-N test data, wherein F is a load range loaded by the fatigue test, and N is a fatigue life, namely a cycle number; for F-N measurementAnd (3) carrying out finite element static analysis on the stress condition of the welding spot under the test data to obtain the data of welding spot force and moment of the welding spot fatigue test standard part, wherein the data comprises forces in x, y and z directions in space: fx、Fy、FzMoments M in three directions x, y and z in spacex、My、Mz，FxAnd FyThe angle theta of the perpendicular vector.
4. The method for normalizing the test data of the fatigue life of the welding spot under the multi-stress working condition according to claim 1, wherein the structural stress formula in the step (1) is as follows:
σstru＝-σmax(Fx)cosθ-σmax(Fy)sinθ+σ(Fz)+σmax(Mx)sinθ-σmax(My)cosθ
Figure FDA0002996146220000021
Figure FDA0002996146220000022
Figure FDA0002996146220000023
wherein: d is the weld nugget diameter of the standard part for the welding spot fatigue test, and t is the thickness of the standard part for the welding spot fatigue test;
the 9 parameters are respectively 3 scaling coefficients in a structural stress formula: SFFXY, SFFZ, SFMXY; 3 thickness indices: DEFXY, DEFZ, DEFXY; 3 diameter indices: TEFXY, TEFZ, TEMXY.
5. The method for normalizing the test data of the fatigue life of the welding spot under the multi-stress working condition according to claim 1, wherein structural stress data is obtained after a structural stress formula is used for the data of the welding spot force and the moment obtained by the welding spot fatigue test in the step (1); the method also comprises the following steps between the steps (1) and (2): and multiplying the structural stress data by the F-N test data to obtain S-N data, wherein S is the stress range, and N is the fatigue life, namely the cycle number.
6. The method for normalizing the data of the fatigue life test of the welding spot under the multi-stress working condition according to claim 1, wherein the objective function in the step (3) is S-N data and a fitting curve function y ═ axbThe sum of the residuals of (a).
7. The method for normalizing the data of the fatigue life test of the welding spot under the multiple stress working conditions according to claim 1, wherein the variation direction of the independent variable is determined after taking a negative value for the partial derivative result in the step (3).
8. The method for normalizing the data of the fatigue life test of the welding spot under the multi-stress working condition according to claim 1, wherein the values of a and b in the step (2) are calculated by a least square method.
9. The method for normalizing the fatigue life test data of the welding spot under the multiple stress working conditions according to claim 1, wherein m in the step (1) is1、m2、m3、m4、m5、m6、m7、m8、m9All within the range of 0-1.
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