A calibration method of a cleavage fracture model parameter of ferrite steel

Abstract

The application relates to the technical field of fatigue test of engineering machinery, in particular to a calibration method of cleavage fracture model parameters of ferrite steel, data acquisition, establishment of an equivalent elastic-plastic stress intensity factor K J and a fitting function relationship of Weibull stress sigma W ; Weibull stress sigma W is sorted in ascending order, and each cumulative failure probability P f corresponding to each Weibull stress sigma W is calculated by using a cumulative failure probability calculation formula; a linear relationship between Weibull stress sigma W and the cumulative failure probability P f is established, a new group of m and sigma u values are obtained; whether |m-M|<=0.01 is achieved is verified, if yes, corresponding (m, sigma u ) is output, otherwise, the method is repeatedly executed until |m-M|<=0.01 is achieved; the application can obtain a group of model parameters with good prediction curves by using less data.

Description

Technical Field

[0001] This invention relates to the field of fatigue testing technology for engineering machinery, specifically a method for calibrating parameters of a cleavage fracture model for ferritic steel. Background Technology

[0002] During the service life of steel materials and structures, a series of fracture problems typically occur, resulting in incalculable losses. Fracture toughness is one of the important indicators for assessing the integrity of materials or structures. The fracture toughness of a material is not only related to the material's intrinsic properties, but also depends on other factors such as the loading method or constraint level of the object under study.

[0003] The commonly used research method is to calibrate the fracture toughness parameters of the research object. However, the parameters calibrated by this method generally exhibit strong dispersion. Even when selecting a subset of data from the same set for calibration, the calibrated parameters often differ significantly. This dispersion decreases considerably with a large number of samples, meaning that only with a sufficiently large number of samples can the accuracy and uniqueness of the calibrated parameters be guaranteed. However, in reality, it is difficult to obtain a large amount of data, leading to significant errors in the calibration results and consequently, low accuracy, failing to meet the calibration requirements. Summary of the Invention

[0004] To avoid and overcome the technical problems existing in the prior art, this invention provides a method for calibrating the parameters of a cleavage fracture model for ferritic steel. This invention can accurately calculate fracture toughness parameters.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for calibrating parameters of a cleavage fracture model for ferritic steel includes the following steps:

[0007] S1. Obtain data, including the equivalent elastic-plastic stress intensity factor K. J and the Weibull stress σ corresponding to the equivalent elastoplastic stress intensity factor W ;

[0008] S2. Establish a neural network model and use it to analyze the equivalent elastic-plastic stress intensity factor K. J and Weibull stress σ W The dataset is trained and augmented using a neural network model to form the original dataset.

[0009] S3. Establish the equivalent elastic-plastic stress intensity factor K J and Weibull stress σ W The fitting function relationship;

[0010] S4. Assign each Weibull stress σ in ascending order. W The stresses are sorted and the Weibull stress σ is calculated using the cumulative failure probability formula. W The corresponding cumulative failure probabilities P f ;

[0011] S5. Establish Weibull stress σ W and cumulative failure probability P f If the slope of the linear relationship between the two values ​​falls within a set range, then the cumulative failure probability P is calculated using the fracture toughness probability model formula of the Weibull distribution. f The corresponding Weibull scale parameter σ u Conversely, repeat steps S2-S5 until the corresponding Weibull scale parameter σ is obtained. u .

[0012] As a further aspect of the present invention, the specific steps of step S1 are as follows:

[0013] S11. Use Abaqus software to establish a finite element sample model of ferritic steel.

[0014] S12. Mesh the finite element specimen model to divide it into a predetermined number of stress elements, and derive the element stress σ1 of each stress element.

[0015] S13. An external force is applied to the finite element specimen model. This external force causes the stress point of the finite element specimen model to displace downward, forming a plastic deformation zone at the crack tip. This downward displacement is denoted as the equivalent elastic-plastic stress intensity factor K. J ;

[0016] S14. Obtain the stress elements in the plastic deformation zone at the crack tip where the element stress σ1 is greater than the screening stress reference value σ0. These stress elements constitute the fracture process region, and calculate the Weibull stress σ in this fracture process region using the Weibull stress calculation formula. W .

[0017] As a further aspect of the present invention, the specific steps of step S2 are as follows:

[0018] S21. Obtain the equivalent elastic-plastic stress intensity factor K in the fracture process region. J The corresponding Weibull stresses σ below W ;

[0019] S22. Import the data from step S21 into the neural network for training to obtain a trained neural network;

[0020] S23, The new equivalent elastic-plastic stress intensity factor K J The input is fed into the trained neural network to predict the corresponding Weibull stress σ. W ;

[0021] S24. The already obtained equivalent elastic-plastic stress intensity factor K J and the corresponding Weibull stress σ W It is stored in the dataset, which constitutes the original dataset.

[0022] Construct a fitting model, substitute the existing data into the fitting model, and obtain the equivalent elastoplastic stress intensity factor K. J and Weibull stress σ W The fitting function relationship.

[0023] As a further aspect of the present invention, the specific steps of step S3 are as follows:

[0024] S31. Using linear interpolation, find the adjacent equivalent elastoplastic stress intensity factors K in the original dataset. J Randomly insert multiple new equivalent elastoplastic stress intensity factors K between them J And obtain each new equivalent elastoplastic stress intensity factor K. J The corresponding Weibull stresses σ W ;

[0025] S32. Construct a fitting model, substitute the existing data into the fitting model, and obtain the equivalent elastic-plastic stress intensity factor K. J and Weibull stress σ W The fitting function relationship.

[0026] As a further aspect of the present invention: each Weibull stress σ W The sequences are sorted in ascending order to form a sequence library; the cumulative failure probability is calculated using the following formula:

[0027]

[0028] Among them, P f This represents the cumulative failure probability value; N represents the Weibull stress σ in the sequence library. W The total number of sequences; i represents the corresponding Weibull stress σ in the sequence library. W The serial number.

[0029] As a further aspect of the present invention, the specific operation process of step S5 is as follows:

[0030] S51. Construct a fitting model, incorporating the existing Weibull stress σW and cumulative failure probability P f Substituting into the fitting model, the Weibull stress σ is obtained. W and cumulative failure probability P f The linear relationship between them, with the slope of the linear relationship being m;

[0031] S52. The set range is: |Mm|≤0.01;

[0032] Where M is the initial value;

[0033] S53. If the calculated m is within the set range, then use the Weibull distribution fracture toughness probability model formula to calculate the cumulative failure probability P. f The corresponding Weibull scale parameter σ u Conversely, repeat steps S21-S24, S31-S32, S4, and S51-S53 until the corresponding Weibull scale parameter σ is obtained. u .

[0034] As a further aspect of the present invention, the fracture toughness probability model formula of the Weibull distribution is as follows:

[0035]

[0036] Where, σ u For Weibull scale parameters.

[0037] As a further aspect of the present invention, the formula for calculating the screening stress reference value σ0 is as follows:

[0038] σ0=λσ ys

[0039] Where σ0 represents the screening stress reference value; λ represents a constant; σ ys This indicates the yield strength of the material.

[0040] As a further aspect of the present invention, the formula for calculating the Weibull stress is as follows:

[0041]

[0042] Where, σ W Weibull stress in the fracture process region; σ 1,i V represents the element stress of the i-th stress element within the fracture process region; n represents the total number of stress elements within the fracture process region; V i Vi represents the volume of the i-th stress-bearing element within the fracture process region; V0 represents the standard volume of the stress-bearing element within the fracture process region; and d represents the exponent.

[0043] As a further aspect of the present invention: the fracture toughness K JC The calculation formula is as follows:

[0044]

[0045] Among them, K JC E represents fracture toughness; V represents Young's modulus; ν represents Poisson's ratio; J represents the critical value; J C This represents the integral value of J.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. The calibration of parameters in the Beremin finite element model requires a large amount of data to obtain a set of relatively stable and reliable values. However, this invention can simplify the calculation process and obtain a set of model parameters with good prediction curves using less data. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the operation process structure of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 As shown, a finite element model of ferritic steel was created using Abaqus software. The finite element model is rectangular, and the horizontally placed rectangular finite element model has support points at both ends of its bottom, forming a two-point support structure. A downward vertical force is applied to the middle of the finite element model. This force causes a downward displacement in the stress area, forming a plastic deformation zone at the crack tip. This downward displacement is denoted as the equivalent elastoplastic stress intensity factor K. J .

[0051] The model is meshed using conventional methods to divide the finite element specimen model into multiple stress elements. Due to the different mesh sizes, the sizes of each stress element also vary, and each stress element has a corresponding element stress σ1.

[0052] For each stress element in the plastic deformation zone at the crack tip where the element stress σ1 is greater than the screening stress reference value σ0, we obtain the stress elements that constitute the fracture process region. We then use the Weibull stress calculation formula to calculate the Weibull stress σ in this fracture process region. W .

[0053] The formula for calculating the screening stress reference value σ0 is as follows:

[0054] σ0=λσ ys

[0055] Where σ0 represents the screening stress reference value; λ represents a constant; σ ys This indicates the yield strength of the material.

[0056] Weibull stress σ W The calculation formula is as follows:

[0057]

[0058] Where, σ W Weibull stress in the fracture process region; σ 1,i σ represents the element stress of the i-th stress element within the fracture process region; n represents the total number of stress elements within the fracture process region; i V represents the element stress of the i-th stressed element within the fracture process region; i Vi represents the volume of the i-th stress-bearing element within the fracture process region; V0 represents the standard volume of the stress-bearing element within the fracture process region; and d represents the exponent.

[0059] Obtain the fracture process region at each equivalent elastoplastic stress intensity factor K J The corresponding Weibull stresses σ below W The data is imported into the neural network for training to obtain a trained neural network. The new equivalent elastoplastic stress intensity factor K is then applied. J The input is fed into the trained neural network to predict the corresponding Weibull stress σ. W The equivalent elastoplastic stress intensity factor K that has already been obtained. J and the corresponding Weibull stress σ W It is stored in the dataset, which constitutes the original dataset.

[0060] The adjacent equivalent elastoplastic stress intensity factor K, known in the original dataset, is used by linear interpolation. J Randomly insert multiple new equivalent elastoplastic stress intensity factors K between them J And obtain each new equivalent elastoplastic stress intensity factor K. J The corresponding Weibull stresses σW A fitting model is constructed, and the existing data is substituted into the fitting model to obtain the equivalent elastoplastic stress intensity factor K. J and Weibull stress σ W The fitting function relationship.

[0061] Each Weibull stress σ W The sequences are sorted in ascending order to form a sequence library; the cumulative failure probability is calculated using the following formula:

[0062]

[0063] Among them, P f This represents the cumulative failure probability value; N represents the Weibull stress σ in the sequence library. W The total number of sequences; i represents the corresponding Weibull stress σ in the sequence library. W The serial number.

[0064] Construct a fitting model and incorporate the existing Weibull stress σ W and cumulative failure probability P f Substituting into the fitting model, the Weibull stress σ is obtained. W and cumulative failure probability P f The linear relationship between them is given by the expression m.

[0065] The set range is: |Mm|≤0.01; where M is the initial value;

[0066] If the calculated m is within the set range, then the cumulative failure probability P is calculated using the fracture toughness probability model formula of the Weibull distribution. f The corresponding Weibull scale parameter σ u Conversely, repeat the above steps until the corresponding Weibull scaling parameter σ is obtained. u .

[0067] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for calibrating parameters of a cleavage fracture model for ferritic steel, characterized in that, Includes the following steps: S1. Obtain data, including the equivalent elastic-plastic stress intensity factor. K J and the Weibull stress corresponding to the equivalent elastoplastic stress intensity factor σ W ; S2. Establish a neural network model and use it to analyze the equivalent elastic-plastic stress intensity factor. K J and Weibull stress σ W The dataset is trained and augmented using a neural network model to form the original dataset. S3. Establish the equivalent elastic-plastic stress intensity factor K J and Weibull stress σ W The fitting function relationship; S31. Using linear interpolation, the adjacent equivalent elastoplastic stress intensity factors known in the original dataset are... K J Randomly insert multiple new equivalent elastoplastic stress intensity factors between them K J And obtain each new equivalent elastic-plastic stress intensity factor. K J Corresponding Weibull stresses σ W ; S32. Construct a fitting model, substitute the existing data into the fitting model, and obtain the equivalent elastic-plastic stress intensity factor. K J and Weibull stress σ W The fitting function relationship; S4. Apply Weibull stresses in ascending order. σ W The stresses were sorted and each Weibull stress was calculated using the cumulative failure probability formula. σ W Corresponding cumulative failure probabilities P f ; S5. Establish Weibull stress σ W and cumulative failure probability P f If the slope of the linear relationship between the two values ​​falls within a set range, the cumulative failure probability is calculated using the fracture toughness probability model formula of the Weibull distribution. P f Corresponding Weibull scale parameters σ u Conversely, repeat steps S2-S5 until the corresponding Weibull scale parameter is obtained. σ u ; S51. Construct a fitting model, incorporating existing Weibull stresses. σ W and cumulative failure probability P f Substituting the values ​​into the fitting model, the Weibull stress is obtained. σ W and cumulative failure probability P f The linear relationship between them, the slope of which is m ; S52, The set range is: .01; in, M Initial value; S53, if the result is found m If the value is within the set range, the cumulative failure probability is calculated using the fracture toughness probability model formula of the Weibull distribution. P f Corresponding Weibull scale parameters σ u Conversely, repeat steps S21-S24, S31-S32, S4, and S51-S53 until the corresponding Weibull scale parameter is obtained. σ u ; Various Weibull stresses σ W The sequences are sorted in ascending order to form a sequence library; the cumulative failure probability is calculated using the following formula: in, P f This represents the cumulative failure probability value. N Represents Weibull stress in the sequence library σ W The total number; i Represents the corresponding Weibull stress in the sequence library. σ W The serial number.

2. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Use Abaqus software to establish a finite element sample model of ferritic steel. S12. Mesh the finite element specimen model to divide it into a predetermined number of stress elements, and derive the element stress of each stress element. σ 1; S13. An external force is applied to the finite element specimen model. This external force causes the stress point of the finite element specimen model to displace downward, forming a plastic deformation zone at the crack tip. This downward displacement is denoted as the equivalent elastic-plastic stress intensity factor. K J ; S14. Obtain the element stress within the plastic deformation zone at the crack tip. σ 1 is greater than the screening stress reference value σ Each stress element of 0 constitutes a fracture process region, and the Weibull stress in this fracture process region is calculated using the Weibull stress calculation formula. σ W .

3. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 2, characterized in that, The specific steps of step S2 are as follows: S21. Obtain the equivalent elastic-plastic stress intensity factor of the fracture process region. K J The corresponding Weibull stresses σ W ; S22. Import the data from step S21 into the neural network for training to obtain a trained neural network; S23, The new equivalent elastic-plastic stress intensity factor K J The input is fed into the trained neural network to predict the corresponding Weibull stress. σ W ; S24. The obtained equivalent elastic-plastic stress intensity factor K J and the corresponding Weibull stress σ W It is stored in the dataset, which constitutes the original dataset.

4. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 1, characterized in that, The formula for the fracture toughness probability model of the Weibull distribution is as follows: in, σ u For Weibull scale parameters.

5. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 2, characterized in that, Screening stress reference values σ 0 The calculation formula is as follows: in, σ 0 Indicates the reference value for screening stress; λ Represents a constant; σ ys This indicates the yield strength of the material.

6. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 5, characterized in that, The Weibull stress calculation formula is as follows: in, σ W This represents the Weibull stress in the fracture process region; σ 1,i Indicates the first region within the fracture process area i The element stress of each stressed element; n This indicates the total number of stress-bearing elements within the fracture process region; V i Indicates the first region within the fracture process area i The volume of each force-bearing unit; V 0 This represents the standard volume of a stress-bearing element within the fracture process region; d Indicates an index.

7. The method for calibrating parameters of a cleavage fracture model for ferritic steel according to claim 6, characterized in that, fracture toughness K JC The calculation formula is as follows: in, K JC Indicates fracture toughness; E Indicates Young's modulus; v Indicates Poisson's ratio; J Indicates the critical value; J C express J The integral value.

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