Creep deformation statistical analysis method

Abstract

The invention discloses a statistical analysis method for creep deformation. The statistical analysis method comprises the following specific steps: step 001, acquiring a relationship between creep deformation and time of not less than three samples of the same batch and the same size or parts of the same batch under the same condition; step 002, selecting some typical times and creep strains corresponding to the typical times, selecting a proper statistical distribution function to perform statistical analysis on the creep strains under the typical times, and fitting distribution parameters; step 003, calculating the survival rate of the tested sample or part not exceeding a certain creep strain under typical time through the selected statistical distribution function, or the creep strain of the tested sample or part under a certain survival rate, and drawing a P-C-T curve; by means of the method, statistical analysis can be conducted on creep deformation of the material or the part, then the survival rate of the material or the part not exceeding a certain creep strain under the given condition is calculated, and the method has important significance on accurate evaluation of the creep performance of the material or the part.

Description

Technical Field

[0001] The invention relates to the technical field of material analysis, and in particular to a creep deformation statistical analysis method. Background Art

[0002] Many materials (such as metals, rocks, etc.) exhibit creep properties under certain conditions. Accurately evaluating the creep properties of materials is crucial for the safe and reliable service of engineering structures.

[0003] However, due to the differences between individual samples or parts, even samples of the same size and from the same batch of materials may show significant differences in creep deformation under the same conditions (such as stress, temperature and time).

[0004] Therefore, it is urgent to establish a creep deformation analysis method that takes into account individual differences of samples. Summary of the invention

[0005] The purpose of the present invention is to provide a creep deformation statistical analysis method, which takes into account the creep deformation under the condition of individual differences of samples, so as to solve the technical problems existing in the prior art.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] A creep deformation statistical analysis method comprises the following steps:

[0008] Step 001, performing creep experiments on multiple samples under the same experimental conditions, wherein the materials of the multiple samples are from the same batch of materials and the sizes of the multiple samples are consistent; recording the creep strain values ​​of the multiple samples at typical time respectively, and making a curve graph of creep strain value versus time;

[0009] Step 002, selecting a suitable distribution function, and fitting the creep strain value using the selected distribution function, and obtaining distribution parameters;

[0010] Step 003, using the selected distribution function, calculate the survival rate of the sample not exceeding a certain creep strain value under typical time, or the creep strain value of the sample under a certain survival rate, and draw a PCT curve;

[0011] Step 004, if the time point at which the creep strain of the sample needs to be calculated is not within the typical time range, obtain the creep strain value of the sample at that time point from the creep strain value versus time curve graph prepared in step 001, or obtain the creep strain value of the sample at that time point according to step 001; repeat steps 002 and 003 to obtain the survival rate of the sample that does not exceed a certain creep strain value at a specific time, or the creep strain value of the sample at a certain survival rate.

[0012] Furthermore, in step 001, the experimental conditions include creep temperature, creep stress, and creep time.

[0013] Furthermore, in step 002, based on the cumulative probability, calculation is performed according to formula (1):

[0014]

[0015] Where n is the total number of samples, P i For the estimated probability of being less than or equal to the i-th level, each data is numbered from small to large, that is, x1≤x2≤x3≤x4≤x n ;

[0016] Then plot the data points (x1, P1), (x2, P2), ..., (x n , P n ), if the data points corresponding to the creep strain values ​​at these typical times are approximately straight lines, it means that the distribution function can better describe the statistical distribution characteristics of the creep strain of these samples.

[0017] Furthermore, in step 002, the mean μ and standard deviation σ of the normal distribution function are calculated according to equations (2) and (3):

[0018]

[0019]

[0020] Where n is the total number of test specimens, x i is the creep strain data of sample i at a typical time.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] The present invention provides a creep deformation statistical analysis method, which proposes the PCT (Probabilistic-Creep strain-Time) curve concept that takes into account the dispersion of creep deformation of samples or components, and then provides a creep deformation statistical analysis method based on this; through this method, the creep deformation of materials or components can be statistically analyzed, and then the probability (survival rate) of the material or component not exceeding a certain creep strain value under given conditions can be calculated, which is of great significance for accurately evaluating the creep performance of materials or components. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the drawings required for the implementation methods or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0024] Figure 1 Schematic diagram of the shape and size of the Ti-6Al-4V alloy compression creep specimen;

[0025] Figure 2 This is a curve of creep strain value of Ti-6Al-4V alloy sample under 912MPa compressive stress versus time;

[0026] Figure 3 Schematic diagram of the normal distribution fitting results of the creep strain of Ti-6Al-4V alloy specimens at different creep times;

[0027] Figure 4 Schematic diagram of creep test results and PCT curve of Ti-6Al-4V alloy sample under 912MPa compressive stress. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Below Figure 1 The compression creep deformation of the Ti-6Al-4V alloy sample at room temperature is taken as an example for specific description.

[0030] Step 001, use MTS Landmark test machine to Figure 1 The Ti-6Al-4V alloy specimen shown was subjected to a compression creep test at room temperature for 50 h, with a stress level of compressive yield strength of 912 MPa.

[0031] A total of three parallel experiments were conducted, all of which were completed on the same machine; some typical times and their corresponding creep strains were selected.

[0032] In this embodiment, the typical time is 10h, 20h, 30h, 40h, 50h, and the creep strain value of the Ti-6Al-4V alloy sample obtained is changed with time. Figure 2As shown; it can be seen that the creep strain of the Ti-6Al-4V alloy sample shows a certain dispersion.

[0033] Step 002: Select a suitable distribution function, and use the selected distribution function to fit the creep strain value to obtain distribution parameters.

[0034] In this embodiment, the distribution function is selected based on the cumulative probability, and the cumulative probability is calculated according to formula (1):

[0035]

[0036] Where n is the total number of samples, P i For the estimated probability of being less than or equal to the i-th level, each data is numbered from small to large, that is, x1≤x2≤x3≤x4≤x n ;

[0037] Then plot the data points (x1, P1), (x2, P2), ..., (x n , P n ), if the data points corresponding to the creep strain values ​​at these typical times are approximately straight lines, it means that the distribution function can better describe the statistical distribution characteristics of the creep strain of these samples.

[0038] In this embodiment, the creep strain values ​​of Ti-6Al-4V alloy samples at three time points of 10h, 20h, and 40h are selected to calculate the cumulative probability, as shown in FIG. Figure 3 As shown in the figure, the creep strains of the three Ti-6Al-4V alloy specimens at different times follow the normal distribution well, and the determination coefficient R 2 All exceeded 0.98.

[0039] Therefore, this embodiment uses normal distribution to perform statistical analysis on the creep strain of the Ti-6Al-4V alloy sample at different times.

[0040] Using normal distribution Figure 2 The creep strains of different samples at different creep times are fitted to obtain normal distribution parameters; the mean μ and standard deviation σ of the normal distribution function are calculated according to formulas (2) and (3):

[0041]

[0042] Where n is the total number of test specimens, x i is the creep strain data of sample i at a typical time.

[0043] Step 003, calculate the creep strain of the Ti-6Al-4V alloy sample at different survival rates (such as 50%, 90%, 95% and 99%) according to the normal distribution function, connect the creep strains of the same survival rate at different times, and obtain the PCT curve of the Ti-6Al-4V alloy sample, as shown in FIG. Figure 4 shown.

[0044] Step 004, taking the calculation of the survival rate of the Ti-6Al-4V alloy sample whose room temperature compressive creep strain does not exceed 2.5% at 50 hours as an example.

[0045] First, follow step 001 and use the MTS Landmark test machine to test Figure 1 The Ti-6Al-4V alloy sample shown in the figure is subjected to a compression creep test at room temperature for 50 hours, and the compression creep strain values ​​of different Ti-6Al-4V alloy samples at 50 hours are obtained; or by Figure 2 The compressive creep strain values ​​of different Ti-6Al-4V alloy samples at 50h were directly obtained.

[0046] Then, according to step 002, the parameters of the normal distribution function are fitted according to the creep strain values ​​of multiple different Ti-6Al-4V alloy samples; and according to step 003, the survival rate of the Ti-6Al-4V alloy sample with room temperature compressive creep strain not exceeding 2.5% is calculated by the normal distribution function to be 92%.

[0047] Similarly, the compressive creep strain value of Ti-6Al-4V alloy specimen at a given survival rate was calculated by the normal distribution function.

[0048] In the present application, PCT of the PCT curve refers to three elements: probability (Probabilistic), creep strain (Creepstrain) and time (Time), where probability refers to the probability of occurrence (survival rate) not exceeding a certain creep strain value.

[0049] The PCT curve can be used to evaluate and study the creep performance of materials or components.

[0050] Considering uncertainty factors: This curve can take into account the influence of some uncertain factors of materials or components in the creep process, such as the heterogeneity of the microstructure of materials or components, and more truly reflects the creep performance of materials or components than the traditional deterministic creep strain-time curve.

[0051] Determine material characteristic parameters: Through probabilistic analysis and curve fitting of a large amount of test data, the creep characteristic parameters of the material can be determined more accurately, providing a more reliable basis for the establishment of the creep constitutive model and performance evaluation of the material.

[0052] PCT curves can also be used to predict the reliability and life of structures.

[0053] In engineering applications, understanding the creep strain of materials after long-term use is crucial to assessing the reliability and life of the structure. The PCT curve can more accurately predict the creep life of a component, that is, the time during which the creep strain of a component does not exceed a certain critical value, so that a reasonable maintenance and replacement plan can be made to ensure engineering safety.

[0054] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A creep deformation statistical analysis method, characterized in that: The following steps are involved: Step 001, performing creep tests on a plurality of samples under the same experimental conditions, wherein the materials of the plurality of samples are materials from the same batch and the sizes of the plurality of samples are consistent; The creep strain values ​​of multiple samples at typical times are recorded respectively, and a curve diagram of creep strain value versus time is prepared; Step 002, selecting a suitable distribution function, and fitting the creep strain value using the selected distribution function, and obtaining distribution parameters; Step 003, using the selected distribution function, calculate the survival rate of the sample not exceeding a certain creep strain value under typical time, or the creep strain value of the sample under a certain survival rate, and draw a PCT curve; Step 004, if the time point at which the creep strain of the sample needs to be calculated is not within the typical time range, obtain the creep strain value of the sample at that time point from the creep strain value versus time curve prepared in step 001, or obtain the creep strain value of the sample at that time point according to step 001; repeat steps 002 and 003 to obtain the survival rate of the sample that does not exceed a certain creep strain value at a specific time, or the creep strain value of the sample at a certain survival rate.

2. The creep deformation statistical analysis method according to claim 1, characterized in that: In step 001, the experimental conditions include creep temperature, creep stress, and creep time.

3. The creep deformation statistical analysis method according to claim 1, characterized in that: In step 002, based on the cumulative probability, calculation is performed according to formula (1): Where n is the total number of samples, P i For the estimated probability of being less than or equal to the i-th level, each data is numbered from small to large, that is, x1≤x2≤x3≤x4≤x n ; Then plot the data points (x1, P1), (x2, P2), ..., (x n , P n ), if the data points corresponding to the creep strain values ​​at these typical times are approximately straight lines, it means that the distribution function can better describe the statistical distribution characteristics of the creep strain of these samples.

4. The creep deformation statistical analysis method according to claim 1, characterized in that: In step 002, the mean μ and standard deviation σ of the normal distribution function are calculated according to equations (2) and (3): Where n is the total number of test specimens, x i is the creep strain data of sample i at a typical time.

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