A method and system for identifying performance parameters of ceramic matrix composite materials

Abstract

The present invention provides a method and system for identifying performance parameters of ceramic matrix composite materials, wherein the method reads the raw data file of a testing machine and processes the data into stress-strain curve data according to a method for processing ceramic matrix composite data, so that when faced with a large amount of test data, the data processing efficiency can be greatly improved, and the processed stress-strain curve data is more accurate. The present invention divides the entire data set into multiple groups according to the number of data points. This improves the accuracy of data fitting and reduces the complexity of the calculation process. In the present invention, the first linear segment, the second linear segment and the nonlinear segment of the entire stress-strain curve are obtained by observing the law of the modulus drop of the ceramic matrix composite material in the nonlinear segment, effectively reflecting the strength properties of the ceramic matrix material and obtaining reliable performance parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of ceramic matrix composite material parameter identification, and in particular relates to a ceramic matrix composite material performance parameter identification method and system. Background Art

[0002] Ceramic matrix composites (CMCs) have broad application prospects in aerospace, automotive, and shipbuilding due to their high specific modulus, high specific strength, and high-temperature resistance. However, due to factors such as processing and weaving, CMCs exhibit a distinct mix of linear and nonlinear characteristics. Furthermore, the parameters of CMCs are typically determined by researchers based on experimental data and their own experience, resulting in a significant dispersion in their performance parameters. Furthermore, for these reasons, only a subset of these parameters is used when applying CMC performance parameters.

[0003] Therefore, there is an urgent need for a method that can accurately identify the performance parameters of ceramic matrix composites based on experimental data. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for identifying performance parameters of ceramic matrix composite materials.

[0005] In a first aspect, the present invention provides a method for identifying performance parameters of a ceramic matrix composite material, comprising:

[0006] Carry out strength tests on two-dimensional woven parts of ceramic matrix composites, and read the deformation and load test data in the original file in the target format according to the type of testing machine, and convert the test data into the strength stress-strain curve of the ceramic matrix composite;

[0007] The strength stress-strain curve of ceramic matrix composites is divided into multiple segments according to the number of test data;

[0008] Fit each stress-strain curve to obtain the modulus of each segment in the stress-strain curve;

[0009] The part of the stress-strain curve where the modulus change exceeds the modulus threshold is regarded as the nonlinear segment of the material, and the midpoint of the nonlinear segment is taken as the nonlinear strength value;

[0010] Perform linear regression fitting on the front part of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve;

[0011] Perform linear regression fitting on the latter part of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve;

[0012] The maximum value of the stress-strain curve in the stress direction is taken as the ultimate strength of the material, and the maximum value of the stress-strain curve in the strain direction is taken as the failure strain of the material.

[0013] Furthermore, fitting each stress-strain curve to obtain the modulus of each segment in the stress-strain curve includes:

[0014] Construct a linear regression model for experimental data:

[0015] Y i =β1+β2·X i +μ i ;

[0016] Among them, Y i is the stress in the i-th test data; X i is the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the i-th experimental data;

[0017] Calculate β1 and β2 according to the following formula:

[0018]

[0019] Where n is the total number of test data.

[0020] In a second aspect, the present invention provides a ceramic matrix composite material performance parameter identification system, comprising:

[0021] A test data conversion module is used to read the deformation and load test data in the original file in a target format according to the type of testing machine and convert the test data into a strength stress-strain curve of the ceramic matrix composite material;

[0022] A curve division module is used to divide the strength stress-strain curve of ceramic matrix composite materials into multiple segments according to the amount of test data;

[0023] The first curve fitting module is used to fit each stress-strain curve to obtain the modulus of each segment in the stress-strain curve;

[0024] A nonlinear segment confirmation module is used to identify the portion of the stress-strain curve where the modulus change exceeds the modulus threshold as the nonlinear segment of the material, and to take the midpoint of the nonlinear segment as the nonlinear strength value;

[0025] The second curve fitting module is used to perform linear regression fitting on the front part of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve;

[0026] The third curve fitting module is used to perform linear regression fitting on the latter part of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve;

[0027] The stress-strain confirmation module is used to take the maximum value of the stress-strain curve in the stress direction as the ultimate strength of the material, and take the maximum value of the stress-strain curve in the strain direction as the failure strain of the material.

[0028] Furthermore, the first curve fitting module includes:

[0029] Building blocks for constructing linear regression models for experimental data:

[0030] Y i =β1+β2·X i +μ i ;

[0031] Among them, Y i is the stress in the i-th test data; X i is the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the i-th experimental data;

[0032] A calculation unit for calculating β1 and β2 according to the following formula:

[0033]

[0034] Where n is the total number of test data.

[0035] In a third aspect, the present invention provides a computer device comprising a processor and a memory.

[0036] In a fourth aspect, the present invention provides a computer-readable storage medium for storing a computer program.

[0037] The present invention provides a method and system for identifying performance parameters of ceramic-based composite materials. The method reads the original data file of a testing machine and processes the data into stress-strain curve data according to a method for processing ceramic-based composite material data. This greatly improves the data processing efficiency when faced with a large amount of test data, and makes the processed stress-strain curve data more accurate. The present invention divides the entire data set into multiple groups according to the number of data points. This improves the accuracy of data fitting and reduces the complexity of the calculation process. The present invention obtains the first linear segment, the second linear segment and the nonlinear segment of the entire stress-strain curve by observing the law of modulus drop of ceramic-based composite materials in nonlinear segments, effectively reflects the strength properties of ceramic-based materials, and obtains reliable performance parameters. The present invention fits the data using a linear regression method multiple times and uses the residual square sum criterion to judge the goodness of fit curve, thereby obtaining more accurate performance parameter fitting results. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flow chart of a method for identifying performance parameters of a ceramic matrix composite material provided by an embodiment of the present invention;

[0040] Figure 2 A strength stress-strain curve diagram of a ceramic matrix composite material provided by an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of automatic performance parameter recognition provided by an embodiment of the present invention;

[0042] Figure 4 Schematic diagram of linear regression observations and predictions provided by an embodiment of the present invention;

[0043] Figure 5 This is a structural diagram of a ceramic matrix composite material performance parameter identification system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying performance parameters of a ceramic matrix composite material, comprising:

[0046] Step 101 , conduct a strength test of a two-dimensional woven component of a ceramic matrix composite material, and read the deformation and load test data in the original file in a target format according to the type of the testing machine, and convert the test data into a strength stress-strain curve of the ceramic matrix composite material.

[0047] A high-temperature strength test of 2.5D woven ceramic-based composite materials was carried out. The specimen was clamped onto the testing machine, and the control mode was adjusted to load control in the testing machine control system. Then the high-temperature environment system was turned on to increase the temperature. When the temperature reached 1000°C, the control mode was changed to displacement control, and the parameter was set to 0.5mm / min. After loading was started, the test was terminated until the specimen broke.

[0048] Obtain the original test data after the test is completed. The file includes the specimen number, test date, tester, time, force, deformation, displacement, etc. The force and deformation in the file are extracted through the algorithm and converted into stress and strain at the same time.

[0049] Step 102: Divide the strength stress-strain curve of the ceramic matrix composite material into multiple segments according to the amount of test data.

[0050] like Figure 2 As shown in Figure 1, the strength stress-strain curve of ceramic matrix composites is divided into the first linear segment, the nonlinear segment, and the second linear segment. Since the ceramic matrix composite is initially supported by the matrix during the load loading process, no damage occurs inside the material, and the stress-strain curve shows a linear change. The first linear segment corresponds to the elastic behavior of the matrix until the stress reaches σ mc , cracks begin to appear in the matrix, and the matrix does not suffer damage in this section. The nonlinear section corresponds to the process of matrix crack generation. As the stress increases, the cracks in the matrix continue to increase. When the cracks in the matrix reach saturation, the stress is σ sa , and the second linear segment begins. The second linear segment corresponds to the interface debonding stage, at which time the fiber bears the main load and continues to break until the stress reaches σ ult , all the remaining fibers are broken, and the test piece fails under the load.

[0051] As shown in Figure 3, analysis of the stress-strain curve shows that the modulus of the first linear segment is relatively high, with minimal fluctuations within this range. The modulus gradually decreases in the nonlinear segment until it reaches the second linear segment, where the modulus also fluctuates slightly within this range. The key to identifying the nonlinear segment is finding it in the test data, based on the principle of a significant drop in modulus within this segment.

[0052] Step 103 : Fit each stress-strain curve to obtain the modulus of each segment in the stress-strain curve.

[0053] The entire curve is divided into at least 10 segments. When the data volume is large, further segmentation is performed based on the number of data points to improve the accuracy of parameter calculation. When the number of data points is large, more segments can be used to improve the accuracy of the final linear and nonlinear segment division. Linear regression analysis is performed on each segment to obtain the modulus of each segment. Then, segment by segment comparison is performed to identify the nonlinear segments.

[0054] In this embodiment, in order to obtain the best fitting curve, the minimum total residual of the fitting result can be used for judgment. The selection can be made according to the following three criteria: (a) the minimum sum of residuals; (b) the minimum sum of the absolute values ​​of the residuals; (c) the minimum sum of the squared residuals. Among them, (a) there will be a cancellation of positive and negative values ​​in the calculation process, resulting in a large calculation error; (b) the calculation of the absolute value is more troublesome; (c) is the principle of the least squares method, which is not only convenient for calculation, but also has excellent characteristics for the estimated value obtained, and is also very sensitive to outliers in the observations. In the embodiment of the present invention, the minimum sum of squared residuals is used as the criterion to judge the fitting curve.

[0055] like Figure 4 As shown, a linear regression model of experimental data is constructed:

[0056] Y=β1+β2·X.

[0057] Y i =β1+β2·X i +μ i .

[0058] Among them, Y is the predicted value of the dependent variable; Y i is the observed value of the dependent variable, that is, the stress in the i-th test data; X is the independent variable, that is, the strain in the data point; X i is the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the ith trial data.

[0059] Residual sum of squares:

[0060]

[0061] in,

[0062] μ i =Y i -β1-β2X i .

[0063] therefore,

[0064]

[0065] The regression line is determined by minimizing Q, which requires β1 and β2. Taking β1 and β2 as variables and treating them as functions of Q, we can differentiate the function by finding the extreme value. The partial derivatives of the two variables are as follows:

[0066]

[0067] Calculate β1 and β2 according to the following formula:

[0068]

[0069] Where n is the total number of test data.

[0070] Step 104 : The portion of the stress-strain curve where the modulus change exceeds the modulus threshold is regarded as the nonlinear segment of the material, and the midpoint of the nonlinear segment is taken as the nonlinear strength value.

[0071] Step 105 : Perform linear regression fitting on the front portion of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve.

[0072] Step 106 : Perform linear regression fitting on the rear portion of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve.

[0073] Step 107 : taking the maximum value of the stress-strain curve in the stress direction as the ultimate strength of the material, and taking the maximum value of the stress-strain curve in the strain direction as the failure strain of the material.

[0074] The embodiment of the present invention reads the original data file of the testing machine and automatically processes the data into stress-strain curve data according to the method of processing data of ceramic-based composite materials. When faced with a large amount of test data, the data processing efficiency can be greatly improved, and the processed stress-strain curve data can be more accurate. The embodiment of the present invention divides the entire data set into a minimum of 10 groups according to the number of data points or automatically calculates the number of groups by methods such as square root. This can improve the accuracy of data fitting and reduce the complexity of the calculation process. The embodiment of the present invention obtains the first linear segment, the second linear segment and the nonlinear segment of the entire stress-strain curve by the law of the decrease in the modulus of the ceramic-based composite material in the nonlinear segment. This method can effectively reflect the strength properties of the ceramic-based material and obtain reliable performance parameters. The embodiment of the present invention fits the data using the linear regression method multiple times and uses the standard of the sum of squared residuals to judge the goodness of the fitting curve, so as to obtain more accurate performance parameter fitting results.

[0075] Based on the same inventive concept, an embodiment of the present invention also provides a ceramic-based composite material performance parameter identification system. Since the principle of solving the problem by this system is similar to that of the ceramic-based composite material performance parameter identification method, the implementation of this system can refer to the implementation of the ceramic-based composite material performance parameter identification method, and the repeated parts will not be repeated.

[0076] In another embodiment, the ceramic matrix composite material performance parameter identification system provided by the embodiment of the present invention is as follows: Figure 5 Shown, including:

[0077] The test data conversion module 10 is used to read the test data of deformation and load in the original file in a target format according to the type of the testing machine, and convert the test data into a strength stress-strain curve of the ceramic matrix composite material.

[0078] The curve division module 20 is used to divide the strength stress-strain curve of the ceramic matrix composite material into multiple segments according to the amount of test data.

[0079] The first curve fitting module 30 is used to fit each segment of the stress-strain curve to obtain the modulus of each segment in the stress-strain curve.

[0080] The nonlinear segment confirmation module 40 is used to take the portion of the stress-strain curve where the modulus change exceeds the modulus threshold as the nonlinear segment of the material, and take the midpoint of the nonlinear segment as the nonlinear strength value.

[0081] The second curve fitting module 50 is used to perform linear regression fitting on the front part of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve.

[0082] The third curve fitting module 60 is used to perform linear regression fitting on the rear portion of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve.

[0083] The stress-strain confirmation module 70 is configured to take the maximum value of the stress-strain curve in the stress direction as the ultimate strength of the material, and take the maximum value of the stress-strain curve in the strain direction as the failure strain of the material.

[0084] Exemplarily, the first curve fitting module includes:

[0085] Building blocks for constructing linear regression models for experimental data:

[0086] Y i =β1+β2·X i +μ i .

[0087] Among them, Y i is the stress in the i-th test data; X iis the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the i-th experimental data;

[0088] A calculation unit for calculating β1 and β2 according to the following formula:

[0089]

[0090] Where n is the total number of test data.

[0091] For more specific working processes of the above modules, please refer to the corresponding contents disclosed in the above embodiments, which will not be repeated here.

[0092] In another embodiment, the present invention provides a computer device comprising a processor and a memory; wherein the processor implements the steps of the above-mentioned method for identifying performance parameters of ceramic matrix composite materials when executing a computer program stored in the memory.

[0093] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0094] In another embodiment, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the above-mentioned method for identifying performance parameters of ceramic matrix composite materials are implemented.

[0095] For more specific details about the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be described again here.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments will be sufficient. The systems, devices, and storage media disclosed in the embodiments are described briefly because they correspond to the methods disclosed in the embodiments. For relevant details, refer to the method description.

[0097] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0098] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. However, these descriptions should not be construed as limiting the present invention. Those skilled in the art will appreciate that various equivalent substitutions, modifications, or improvements may be made to the technical solutions and implementations of the present invention without departing from the spirit and scope of the present invention, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for identifying performance parameters of ceramic matrix composite materials, characterized in that: include: Carry out strength tests on two-dimensional woven parts of ceramic matrix composites, and read the deformation and load test data in the original file in the target format according to the type of testing machine, and convert the test data into the strength stress-strain curve of the ceramic matrix composite; The strength stress-strain curve of ceramic matrix composites is divided into multiple segments according to the number of test data; Fit each stress-strain curve to obtain the modulus of each segment in the stress-strain curve; The part of the stress-strain curve where the modulus change exceeds the modulus threshold is regarded as the nonlinear segment of the material, and the midpoint of the nonlinear segment is taken as the nonlinear strength value; Perform linear regression fitting on the front part of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve; Perform linear regression fitting on the latter part of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve; The maximum value of the stress-strain curve in the stress direction is taken as the ultimate strength of the material, and the maximum value of the stress-strain curve in the strain direction is taken as the failure strain of the material.

2. The method for identifying performance parameters of ceramic matrix composite materials according to claim 1, characterized in that: The fitting of each stress-strain curve to obtain the modulus of each segment in the stress-strain curve includes: Construct a linear regression model for experimental data: Y i =β1+β2·X i +m i ; Among them, Y i is the stress in the i-th test data; X i is the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the i-th experimental data; Calculate β1 and β2 according to the following formula: Where n is the total number of test data.

3. A ceramic matrix composite material performance parameter identification system, characterized in that: include: A test data conversion module is used to read the deformation and load test data in the original file in a target format according to the type of testing machine and convert the test data into a strength stress-strain curve of the ceramic matrix composite material; A curve division module is used to divide the strength stress-strain curve of ceramic matrix composite materials into multiple segments according to the amount of test data; The first curve fitting module is used to fit each stress-strain curve to obtain the modulus of each segment in the stress-strain curve; A nonlinear segment confirmation module is used to identify the portion of the stress-strain curve where the modulus change exceeds the modulus threshold as the nonlinear segment of the material, and to take the midpoint of the nonlinear segment as the nonlinear strength value; The second curve fitting module is used to perform linear regression fitting on the front part of the nonlinear segment to obtain the modulus of the first linear segment of the stress-strain curve; The third curve fitting module is used to perform linear regression fitting on the latter part of the nonlinear segment to obtain the modulus of the second linear segment of the stress-strain curve; The stress-strain confirmation module is used to take the maximum value of the stress-strain curve in the stress direction as the ultimate strength of the material, and take the maximum value of the stress-strain curve in the strain direction as the failure strain of the material.

4. The ceramic matrix composite material performance parameter identification system according to claim 3, characterized in that: The first curve fitting module includes: Building blocks for constructing linear regression models for experimental data: Y i =β1+β2·X i +m i ; Among them, Y i is the stress in the i-th test data; X i is the strain in the i-th test data; β1 is the stress regression value corresponding to each segment; β2 is the modulus corresponding to each segment; μ i is the random error in the i-th experimental data; A calculation unit for calculating β1 and β2 according to the following formula: Where n is the total number of test data.

5. A computer device, characterized in that: The method comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the method for identifying performance parameters of ceramic matrix composite materials according to any one of claims 1 to 2 are realized.

6. A computer-readable storage medium, characterized in that Used to store computer programs; when the computer programs are executed by the processor, the steps of the method for identifying performance parameters of ceramic matrix composite materials according to any one of claims 1 to 2 are implemented.

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