Method for judging failure risk of metal material based on simulation result

Abstract

The invention discloses a method for judging a failure risk of a metal material based on a simulation result. The method comprises the following steps: S1, obtaining a finite element simulation analysis result of a structural performance class of a test object; s2, selecting a corresponding failure standard; s3, performing first-stage correction on the finite element simulation analysis result of the test object according to the selected failure standard to obtain a corresponding first-stage correction coefficient; s4, judging whether the test object is a main force-bearing part or not, and if yes, entering the step S5; if not, entering the step S7; s5, whether stress concentration exists in the test object or not is judged, if yes, the finite element simulation analysis result of the test object is subjected to second-stage correction, a corresponding second-stage correction coefficient is obtained, and then the step S6 is executed; if not, directly entering the step S6; s6, performing failure risk calculation according to the first-stage correction coefficient and the second-stage correction coefficient to obtain a corrected first simulation result, and then entering the step S8; s7, performing failure risk calculation according to the first-stage correction coefficient to obtain a corrected second simulation result; and S8, judging the failure risk probability of the test object according to the corrected first simulation result or the corrected second simulation result. According to the method, the failure risk probability of the metal material in the physical test can be accurately judged.

Description

Technical Field

[0001] The present invention belongs to the field of metal material failure risk judgment method, and specifically relates to a method for judging the failure risk of metal materials based on simulation results. Background Art

[0002] The development of machinery, automotive products and corresponding parts requires a large number of rigorous physical tests and verifications of structural performance such as static strength, impact and collision. However, metal materials are prone to fracture and failure in such tests, resulting in test failure.

[0003] Based on this, the existing development process usually uses structural performance finite element simulation analysis technology to identify the risk of fracture failure of metal materials in physical tests in advance, and conduct targeted optimization design based on the simulation results, which greatly reduces the risk of test NG and reduces the test cost and cycle; however, the structural performance finite element simulation models in the industry are basically established based on ideal design conditions, and under the influence of simulation accuracy, structural factors, test factors, and sample processing and manufacturing factors, there is often a large difference between the actual physical test state and the ideal finite element simulation model, which makes it impossible to accurately assess the risk of fracture failure of metal materials. Summary of the invention

[0004] The purpose of the present invention is to provide a method for judging the failure risk of metal materials applied to the development process of machinery, automobile products and corresponding parts. The method corrects the finite element simulation analysis results of the structural performance of the test object, and then judges the failure risk probability of the test object in the physical test based on the correction results.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is as follows:

[0006] A method for judging the failure risk of a metal material based on simulation results comprises the following steps:

[0007] Step S1. Obtaining finite element simulation analysis results of the structural performance of the test object;

[0008] Step S2. Select the corresponding failure standard according to the material type of the test object;

[0009] Step S3. Performing a first-stage correction on the finite element simulation analysis results of the test object according to the selected failure standard to obtain a corresponding first-stage correction coefficient;

[0010] Step S4. Determine whether the test object is a main load-bearing part. If so, proceed to step S5; if not, proceed to step S7;

[0011] Step S5. Determine whether the test object has stress concentration. If yes, first perform the second-stage correction on the finite element simulation analysis results of the test object to obtain the corresponding second-stage correction coefficient, and then proceed to step S6; if no, directly proceed to step S6;

[0012] Step S6. Calculate the failure risk according to the first-stage correction coefficient and the second-stage correction coefficient to obtain a corrected first simulation result, and then proceed to step S8;

[0013] Step S7. Calculate the failure risk according to the correction coefficient of the first stage to obtain a corrected second simulation result;

[0014] Step S8: Determine the failure risk probability of the test object according to the corrected first simulation result or the corrected second simulation result.

[0015] The present invention provides a method for judging the failure risk of metal materials based on simulation results. The method first performs finite element simulation analysis on the structural performance of the metal material, then performs a first-stage correction on the simulation results according to the material type and failure standard of the metal material, and then performs a second-stage correction on the simulation results according to the load-bearing conditions of the metal material and whether stress concentration exists to obtain a corrected simulation result. Finally, the failure risk probability of the metal material in a physical test can be accurately judged based on the corrected simulation result.

[0016] Preferably, the finite element simulation analysis results of the structural performance of the test object obtained in step S1 include simulation results of static strength and simulation results of impact and collision.

[0017] Preferably, the material types of the test object in step S2 include brittle materials and plastic materials.

[0018] Preferably, the failure criterion of the brittle material is the tensile strength of the material; and the failure criterion of the plastic material is the elongation of the material.

[0019] Preferably, the first stage correction in step S3 includes sequentially performing correction on the finite element simulation analysis results of the test object, namely, correction on simulation accuracy fluctuation, correction on raw material mechanical property fluctuation, correction on forming influencing factors, correction on welding processing influence, correction on test type factors, and correction on deformation degree.

[0020] Preferably, the first-stage correction coefficients in step S3 include simulation accuracy correction coefficient ±α, raw material mechanical property fluctuation correction coefficient ±β, forming influencing factor correction coefficient λ, welding process influence correction coefficient ω, test type factor correction coefficient η, and deformation degree correction coefficient θ.

[0021] Preferably, step S5 comprises:

[0022] Step S5.1. Determine whether there is stress concentration in the test object. If so, proceed to Step S5.2; if not, directly proceed to Step S6;

[0023] Step S5.2. Correct the stress concentration effect on the finite element simulation analysis result of the test object to obtain the corresponding stress concentration effect correction coefficient ρ, and then proceed to Step S6.

[0024] Preferably, the simulation accuracy correction coefficient ±α includes the simulation accuracy fluctuation tolerance +α and -α; the raw material mechanical property fluctuation correction coefficient ±β includes the raw material mechanical property fluctuation tolerance +β and -β.

[0025] Preferably, Step S6 includes obtaining the simulation stress or strain value of the test object; Step S6 also includes calculating the upper difference A of the corrected strain value and the lower difference B of the corrected strain value as follows:

[0026] A = simulation stress / strain value × [(+α) × (+β)] × η × θ × ρ × λ × ω;

[0027] B = simulation stress / strain value × [(-α) × (-β)] × η × θ × ρ × λ × ω.

[0028] Preferably, Step S8 includes obtaining the critical failure criterion C of the test object, and comparing the magnitudes of A, B, and C. If B < A < C, directly determine that the failure risk probability D of the test object is 0%; if C < B < A, directly determine that the failure risk probability D of the test object is 100%; if B < C < A, calculate the failure risk probability D as follows:

[0029]

[0030] Beneficial effects:

[0031] A method for judging the failure risk of metal materials based on simulation results according to the present invention corrects the influencing factors of the simulation results under the ideal design state from four aspects: simulation accuracy, structural factors, test factors, and sample processing and manufacturing, accumulatively calculates the correction coefficients, and then determines the fracture failure risk of the metal materials, and can accurately judge the failure risk probability of the metal materials in physical tests. Description of the Drawings

[0032] Figure 1 Shown is the overall flowchart of a method for judging the failure risk of metal materials based on simulation results in an embodiment;

[0033] Figure 2 Shown is the sub-flowchart of a method for judging the failure risk of metal materials based on simulation results in an embodiment;

[0034] Figure 3 It is a timing diagram of a method for judging the failure risk of a metal material based on simulation results according to an embodiment;

[0035] Figure 4 The figure shows a stress change gradient diagram for judging the failure risk of metal materials based on simulation results according to an embodiment. DETAILED DESCRIPTION

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0037] The technical solution of the present invention is described in detail below with specific embodiments.

[0038] Embodiment 1

[0039] like Figure 1 and 2 As shown, a method for judging the failure risk of a metal material based on simulation results in this embodiment includes the following steps:

[0040] Step S1. Obtaining finite element simulation analysis results of the structural performance of the test object;

[0041] Step S2. Select the corresponding failure standard according to the material type of the test object;

[0042] Step S3. Performing a first-stage correction on the finite element simulation analysis results of the test object according to the selected failure standard to obtain a corresponding first-stage correction coefficient;

[0043] Step S4. Determine whether the test object is a main load-bearing part. If so, proceed to step S5; if not, proceed to step S7;

[0044] Step S5. Determine whether the test object has stress concentration. If yes, first perform the second-stage correction on the finite element simulation analysis results of the test object to obtain the corresponding second-stage correction coefficient, and then proceed to step S6; if no, directly proceed to step S6;

[0045] Step S6. Calculate the failure risk according to the first-stage correction coefficient and the second-stage correction coefficient to obtain a corrected first simulation result, and then proceed to step S8;

[0046] Step S7. Calculate the failure risk according to the correction coefficient of the first stage to obtain a corrected second simulation result;

[0047] Step S8: Determine the failure risk probability of the test object according to the corrected first simulation result or the corrected second simulation result.

[0048] A method for judging the failure risk of metal materials based on simulation results in this embodiment first performs finite element simulation analysis on the structural performance of the metal material, then performs a first-stage correction on the simulation results according to the material type and failure standard of the metal material, and then performs a second-stage correction on the simulation results according to the load-bearing conditions of the metal material and whether there is stress concentration to obtain a corrected simulation result. Finally, based on the corrected simulation result, the failure risk probability of the metal material in the physical test can be accurately judged.

[0049] Preferably, the test object in steps S1 to S5 is a metal material; the finite element simulation analysis results of the structural performance of the test object obtained in step S1 include simulation results of static strength and simulation results of impact collision.

[0050] Preferably, the material types of the test object in step S2 include brittle materials and plastic materials.

[0051] Preferably, the failure criterion of the brittle material is the tensile strength of the material; and the failure criterion of the plastic material is the elongation of the material.

[0052] Preferably, the first stage correction in step S3 includes sequentially performing correction on the finite element simulation analysis results of the test object, namely, correction on simulation accuracy fluctuation, correction on raw material mechanical property fluctuation, correction on forming influencing factors, correction on welding processing influence, correction on test type factors, and correction on deformation degree.

[0053] Preferably, the first-stage correction coefficients in step S3 include simulation accuracy correction coefficient ±α, raw material mechanical property fluctuation correction coefficient ±β, forming influencing factor correction coefficient λ, welding process influence correction coefficient ω, test type factor correction coefficient η, and deformation degree correction coefficient θ.

[0054] Preferably, step S5 comprises:

[0055] Step S5.1. Determine whether the test object has stress concentration. If yes, proceed to step S5.2; if no, proceed directly to step S6;

[0056] Step S5.2. Perform stress concentration correction on the finite element simulation analysis results of the test object to obtain the corresponding stress concentration correction coefficient ρ, and then proceed to step S6.

[0057] Preferably, the simulation accuracy correction coefficient ±α includes simulation accuracy fluctuation tolerances +α and -α; and the raw material mechanical property fluctuation correction coefficient ±β includes raw material mechanical property fluctuation tolerances +β and -β.

[0058] Preferably, step S6 includes obtaining the simulated stress or strain value of the test object; step S6 also includes calculating the upper difference A and the lower difference B of the corrected strain value as follows:

[0059] A = simulated stress / strain value × [(+α) × (+β)] × η × θ × ρ × λ × ω;

[0060] B = simulated stress / strain value × [(-α) × (-β)] × η × θ × ρ × λ × ω.

[0061] Preferably, step S8 includes obtaining the critical failure criterion C of the test object, and comparing the magnitudes of A, B, and C. If B < A < C, then directly determine that the failure risk probability D of the test object is 0%; if C < B < A, then directly determine that the failure risk probability D of the test object is 100%; if B < C < A, then calculate the failure risk probability D as follows:

[0062]

[0063] Specifically, as Figure 3 shown is the timing diagram when the method of this embodiment is applied to automotive parts made of metal materials. When the automotive part of the test object is a main load-bearing part, it is necessary to determine whether a large deformation test has been carried out on it. If so, it is necessary to first correct the deformation mode of its simulation results; if not, directly perform the correction of the mechanical property fluctuation of the part raw material, and then, according to whether it is die-casting or stamping, correspondingly perform the correction of die-casting influencing factors or stamping influencing factors, and finally perform the correction of welding processing influence.

[0064] Specifically, in this embodiment, when the metal material type is brittle material, the first principal stress and shear stress are used as the judgment basis, and the judgment criterion is the material's tensile strength; when the metal material type is brittle-plastic material, the Von-mises strain is used as the judgment basis, and the judgment criterion is the material's elongation.

[0065] Further, when the test object of this embodiment is a brittle material, assuming that the corrected simulated first principal stress result is [200 Mpa, 300 Mpa], and the critical failure criterion of the metal material is 220 Mpa. Since 200 Mpa < 220 Mpa < 300 Mpa, then calculate the failure risk probability D as follows:

[0066]

[0067] It means that after correcting each influencing factor of its simulation results, the risk of fracture failure of the corresponding metal material during its physical test is 80%.

[0068] Specifically, the correction of simulation accuracy fluctuation factors in this embodiment is: based on the past projects, the one-time pass rate of the same test is used as the correction coefficient of the simulation accuracy influencing factors, and the form of tolerance is adopted. The specific method is as follows:

[0069] [1-(1-test one-time pass rate), 1+(1-test one-time pass rate)].

[0070] Specifically, the test type factor correction coefficient η of this embodiment is calculated according to the static test or the dynamic test, and the specific value is as follows:

[0071] Correction factor for static test: 1.2,

[0072] Correction factor for dynamic test: 1.3.

[0073] Specifically, the deformation degree correction coefficient θ of this embodiment is calculated according to whether it is a large deformation test, and the specific value is as follows:

[0074] Large deformation test correction factor: 1.2,

[0075] Correction factor for non-large deformation test: 1.0.

[0076] Specifically, the stress concentration correction coefficient ρ of this embodiment is calculated according to the following formula: Figure 4 The specific calculation method is as follows:

[0077]

[0078] Where: L1 is the minimum distance between the 1st to 3rd stress gradients measured in the simulation results,

[0079] L2 is the minimum distance between the 4th to 8th stress gradients measured in the simulation results.

[0080] Based on the stress concentration factor, the correction coefficient of the stress concentration influencing factor is converted through the following Table 1, where the stress concentration factor is between 1 and 10. According to the upper and lower limits of the interval, a linear conversion is performed to obtain the correction coefficient of the stress concentration influencing factor.

[0081]

[0082] Table 1 Conversion table of correction coefficients of stress concentration influencing factors

[0083] Specifically, the raw material mechanical property fluctuation correction coefficient ±β of this embodiment is in the form of tolerance, and is corrected according to different material types and grades of material mechanical properties. The specific correction method and coefficient are shown in Table 2 below:

[0084]

[0085] Table 2 Correction coefficients for fluctuations in mechanical properties of incoming raw materials

[0086] Specifically, the molding influencing factor correction coefficient λ of this embodiment is used to correct the influencing factors according to the two molding processes of die casting and stamping as follows:

[0087] (1) For die casting, the correction is based on whether the maximum stress / strain value of the simulation result is located at the high-risk position of three quality problems: air holes, sand holes, and cold shuts. The specific correction method is as follows:

[0088] Correction factor for locations with high quality risk: 1.2;

[0089] Correction factor for non-quality high-risk locations: 1.0.

[0090] (2) For stamping, the thickness change rate of the material after forming (drawing thinning and stacking thickening) is used as the basis for correction. The correction coefficient is calculated as follows:

[0091]

[0092] Specifically, the welding process influence correction coefficient ω of this embodiment is determined by whether the maximum simulated stress / strain value is located at the welding position. The specific correction method is as follows:

[0093] Correction factor for non-welding position: 1.0;

[0094] Welding position correction coefficient: 1.2. Furthermore, a method for judging the failure risk of metal materials based on simulation results in this embodiment can adopt different solutions for different failure risk coefficient probabilities of metal materials, as follows:

[0095] If the failure risk coefficient is between 0% and 30%, the main influencing factors are controlled in a targeted manner. For example, if the fluctuation of raw material supply is the main influencing factor, the material fracture failure can be avoided by controlling the fluctuation range of the tensile strength of the incoming materials.

[0096] If the failure risk factor is between 30% and 60%, targeted optimization design is performed to reduce the failure risk factor;

[0097] If the failure risk factor is greater than 60%, both targeted optimization design and influencing factor control methods are applied simultaneously to improve work efficiency.

[0098] A method for judging the failure risk of metal materials based on simulation results in this embodiment corrects the influencing factors from four aspects, namely, simulation accuracy fluctuation, structural factors, test factors, and sample processing and manufacturing, on the finite element simulation results under ideal design conditions, converts the results into tolerance forms, calculates the probability distribution with the critical failure standard, and obtains the failure risk coefficient, thereby realizing quantitative assessment of the fracture failure risk of metal materials.

[0099] The present embodiment provides a method for determining the failure risk of metal materials based on simulation results. Different solutions are provided based on the different fracture failure risks of metal materials, thereby reducing the product development cycle and cost, improving the one-time pass rate of the test, and reducing the test cost.

[0100] Embodiment 2

[0101] This embodiment takes the simulation result of the strength of the rear seat belt fixing point under ideal conditions as an example: the yield strength of the skeleton material is 532Mpa, the elongation at break is 0.42, and in the simulation result, the maximum strain value is 0.209, and the maximum deformation is 212mm, which belongs to a large deformation test, wherein:

[0102] Stress concentration factor γ=L1 / L2=0.6( / 0.65×2)=0.46.

[0103] According to a method for judging the failure risk of metal materials based on simulation results in Example 1, the risk of material failure in the test is evaluated as follows:

[0104] Calculation of simulation accuracy fluctuation tolerance: ±α = [0.95, 1.05] - the one-time pass rate of past project tests after simulation analysis is 95%;

[0105] Calculation of fluctuation tolerance of mechanical properties of raw materials: ±β=[0.8, 1.2]-material yield strength 532Mpa;

[0106] Calculation of test type factor correction coefficient: η = 1.2;

[0107] Calculation of deformation correction coefficient: θ = 1.2, the maximum deformation is 212mm, which belongs to the category of large deformation;

[0108] Calculation of stress concentration correction factor: ρ = 1 + γ = 1 + 0.46 = 1.46;

[0109] Calculation of the correction coefficient of forming influence factors: λ = 1, standard pipe, no forming influence;

[0110] Calculation of correction factor for welding process effect: ω = 1, non-welding area;

[0111] Therefore, the calculation of the corrected strain value is as follows:

[0112] Simulation strain value × [(-α) × (-β), (+α) × (+β)] × η × θ × ρ × λ × ω

[0113] =0.209×[0.95×0.8,1.05×1.2]×1.2×1.2×1.46×1×1

[0114] =[0.33, 0.55];

[0115] The corrected strain range distribution is [0.33, 0.55], That is, if the incoming material yield strength decreases by 20%, the failure risk will reach 82% probability.

[0116] Verification of the accuracy of the above correction method: By reducing the yield strength of the skeleton material of the finite element model by 20%, and keeping other conditions unchanged, in the recalculated simulation results, the strain here increases from 0.209 to 0.486, exceeding the critical strain of failure.

[0117] Among them, through the correction method of the present invention, without considering the influence factors of simulation accuracy fluctuation, when the yield strength of the known material is clearly reduced by 20%, the strain value corrected by the influence factors of large deformation test, stress concentration, and physical sample processing and manufacturing is:

[0118] Simulation strain value ×η×θ×ρ×λ×ω

[0119] =0.209×1.2×1.2×1.46×1×1

[0120] =0.439;

[0121] The consistency with the result calculated by simulation reached 90.3% (0.439 / 0.486), which proved that the method of the present invention has high accuracy.

[0122] The above is a detailed description of an embodiment of a method for judging the failure risk of metal materials based on simulation results provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for judging the failure risk of metal materials based on simulation results, characterized in that: The following steps are involved: Step S1. Obtaining finite element simulation analysis results of the structural performance of the test object; Step S2. Select the corresponding failure standard according to the material type of the test object; Step S3. Performing a first-stage correction on the finite element simulation analysis results of the test object according to the selected failure standard to obtain a corresponding first-stage correction coefficient; Step S4. Determine whether the test object is a main load-bearing part. If so, proceed to step S5; if not, proceed to step S7; Step S5. Determine whether the test object has stress concentration. If yes, first perform the second-stage correction on the finite element simulation analysis results of the test object to obtain the corresponding second-stage correction coefficient, and then proceed to step S6; if no, directly proceed to step S6; Step S6. Calculate the failure risk according to the first-stage correction coefficient and the second-stage correction coefficient to obtain a corrected first simulation result, and then proceed to step S8; Step S7. Calculate the failure risk according to the correction coefficient of the first stage to obtain a corrected second simulation result; Step S8: Determine the failure risk probability of the test object according to the corrected first simulation result or the corrected second simulation result.

2. The method for judging the failure risk of metal materials based on simulation results according to claim 1, characterized in that: The finite element simulation analysis results of the structural performance of the test object obtained in step S1 include simulation results of static strength and simulation results of impact and collision.

3. The method for judging the failure risk of metal materials based on simulation results according to claim 1, characterized in that: The material types of the test object in step S2 include brittle materials and plastic materials.

4. The method for judging the failure risk of metal materials based on simulation results according to claim 3, characterized in that: The failure criterion of the brittle material is the tensile strength of the material; the failure criterion of the plastic material is the elongation of the material.

5. The method for judging the failure risk of metal materials based on simulation results according to claim 1, characterized in that: The first stage correction in step S3 includes correcting the simulation accuracy fluctuation, raw material mechanical property fluctuation, forming influencing factor, welding processing influence, test type factor and deformation degree of the finite element simulation analysis results of the test object in sequence.

6. The method for judging the failure risk of metal materials based on simulation results according to claim 5, characterized in that: The first-stage correction coefficients in step S3 include simulation accuracy correction coefficient ±α, raw material mechanical property fluctuation correction coefficient ±β, forming influence factor correction coefficient λ, welding process influence correction coefficient ω, test type factor correction coefficient η, and deformation degree correction coefficient θ.

7. The method for determining the failure risk of metal materials based on simulation results according to claim 6, characterized in that: The step S5 comprises: Step S5.

1. Determine whether the test object has stress concentration. If yes, proceed to step S5.2; if no, proceed directly to step S6; Step S5.

2. Perform stress concentration correction on the finite element simulation analysis results of the test object to obtain the corresponding stress concentration correction coefficient ρ, and then proceed to step S6.

8. The method for judging the failure risk of metal materials based on simulation results according to claim 7, characterized in that: The simulation accuracy correction coefficient ±α includes simulation accuracy fluctuation tolerances +α and −α; the raw material mechanical property fluctuation correction coefficient ±β includes raw material mechanical property fluctuation tolerances +β and −β.

9. The method for judging the failure risk of metal materials based on simulation results according to claim 8, characterized in that: The step S6 includes obtaining the simulated stress or strain value of the test object; the step S6 also includes calculating the corrected strain value upper difference A and the corrected strain value lower difference B as follows: A = simulated stress / strain value × [(+α) × (+β)] × η × θ × ρ × λ × ω; B = simulated stress / strain value × [(-α) × (-β)] × η × θ × ρ × λ × ω.

10. The method for judging the failure risk of metal materials based on simulation results according to claim 9, characterized in that: The step S8 includes obtaining the critical failure criterion C of the test object, comparing the magnitudes of A, B, and C. If B < A < C, then directly determine that the failure risk probability D of the test object is 0%; If C < B < A, then directly determine that the failure risk probability D of the test object is 100%; if B < C < A, then calculate the failure risk probability D as follows:

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