Impact resistance evaluation method and device for biphase structure

Abstract

The invention discloses an impact resistance evaluation method and device for a double-phase structure. The method comprises the following steps: obtaining a performance index set of the double-phase structure; the performance index set comprises a plurality of performance index subsets; each performance index subset comprises acquisition sequence values of a plurality of parameters; utilizing the performance index set to construct an anti-impact index numerical matrix; and carrying out comprehensive evaluation processing on the impact resistance index numerical value matrix to obtain an impact resistance evaluation value. According to the method, the problem that the impact resistance of the material in practical application is difficult to accurately predict due to the fact that existing evaluation methods mostly depend on empirical formulas or qualitative analysis is solved.

Description

Technical Field

[0001] The invention relates to the field of material design, and in particular to an impact resistance evaluation method and device for a dual-phase structure. Background Art

[0002] With the development of modern industrial technology, the demand for the impact resistance of materials is increasing. In the fields of aerospace, building structures, and high-speed transportation vehicles, the ability of materials to protect against impact loads is crucial. Traditional single material structures, such as homogeneous metals or ceramics, although they exhibit good mechanical properties in some applications, often cannot maintain their original shape under extreme impact loads and are prone to deformation or even fracture. In order to improve the impact resistance of the structure, researchers began to explore dual-phase structural materials, which combine two different phases of materials and use their complementary properties to improve the overall impact resistance.

[0003] In terms of impact resistance assessment of dual-phase structures, existing assessment methods mostly rely on empirical formulas or qualitative analysis, which are often not precise enough to accurately predict the impact resistance of materials in practical applications. Therefore, developing a new dual-phase structure impact resistance assessment method has important application value for improving the accuracy of assessment and guiding the design and preparation of impact-resistant dual-phase materials. Summary of the invention

[0004] The present invention mainly solves the problem that existing evaluation methods mostly rely on empirical formulas or qualitative analysis and are difficult to accurately predict the impact resistance of materials in practical applications. The present invention discloses an impact resistance evaluation method and device for a dual-phase structure.

[0005] In a first aspect of the embodiments of the present application, a method for evaluating the impact resistance of a dual-phase structure is disclosed, comprising:

[0006] S1, obtaining a performance indicator set of a dual-phase structure; the performance indicator set includes several performance indicator subsets; each performance indicator subset includes a collection sequence value of several parameters;

[0007] S2, using the performance index set, constructs a numerical matrix of impact resistance index;

[0008] S3, performing comprehensive evaluation processing on the impact resistance index numerical matrix to obtain an impact resistance performance evaluation value.

[0009] The performance index set is used to construct a shock resistance index value matrix, including:

[0010] S21, performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset;

[0011] S22, constructing a shock resistance index numerical matrix using the first-level parameter time series corresponding to all performance index subsets; the row vector of the shock resistance index numerical matrix is ​​the first-level parameter time series corresponding to each performance index subset.

[0012] The performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset includes:

[0013] S211, setting the collection sequence value of each parameter of the performance indicator subset to be the first data sequence of the parameter;

[0014] S212, performing data smoothing processing on each first data sequence to obtain a second data sequence; each parameter of a performance indicator subset has a corresponding first data sequence and a second data sequence;

[0015] S213, for each parameter of the performance indicator subset, respectively performing high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to the parameter to obtain a high-order cumulant sequence corresponding to the parameter;

[0016] S214, performing weight calculation processing on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter;

[0017] S215, normalizing the first weight values ​​corresponding to all parameters included in the performance indicator subset to obtain a normalized weight vector;

[0018] S216, using the normalized weight vector, respectively perform weighted sum processing on the first data series of all parameters included in each performance indicator subset to obtain the primary parameter time series corresponding to the performance indicator subset.

[0019] The step of performing high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to each parameter of the performance indicator subset to obtain a high-order cumulant sequence corresponding to the parameter includes:

[0020] S2131, represent the first parameter sequence and the second parameter sequence corresponding to each parameter as x(n) and y(n), respectively, where n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence;

[0021] S2132, performing high-order self-cumulants calculation on the first parameter sequence and the second parameter sequence respectively to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6, and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5, and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes:

[0022]

[0023] Wherein, FFT means Fourier transform;

[0024] S2133, performing high-order cross-cumulant calculations on the first parameter sequence and the second parameter sequence respectively to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second cross-cumulant RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as:

[0025]

[0026] S2134: Determine the set of high-order cross-cumulants and the set of high-order self-cumulants as a sequence of high-order cumulants corresponding to the parameters.

[0027] The step of performing weight calculation processing on the high-order cumulant sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter includes:

[0028] A weight calculation process is performed on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value a1 corresponding to the parameter; the expression of the weight calculation process is:

[0029]

[0030] Among them, α is the weight coefficient.

[0031] The comprehensive evaluation process of the impact resistance index value matrix to obtain the impact resistance performance evaluation value includes:

[0032] S31, performing weight calculation processing on the anti-shock index numerical matrix to obtain a standard feature vector and a weight factor vector;

[0033] S32, performing correlation vector calculation processing on the anti-shock index numerical matrix and the standard eigenvector to obtain a correlation vector;

[0034] S33, performing weighted summation on the associated vectors using the weight factor vector to obtain an impact resistance performance evaluation value.

[0035] The weight calculation process of the anti-shock index numerical matrix is ​​performed to obtain a standard feature vector and a weight factor vector, including:

[0036] The cross-correlation matrix of the shock resistance index numerical matrix is ​​calculated; the elements of the i-th row and j-th column of the cross-correlation matrix are the cross-correlation values ​​of the primary parameter time series corresponding to the i-th performance index subset and the primary parameter time series corresponding to the j-th performance index subset;

[0037] Decomposing the cross-correlation matrix to obtain its eigenvalues ​​and eigenvectors;

[0038] Determine the eigenvector corresponding to the maximum eigenvalue as the standard eigenvector;

[0039] The standard feature vector is normalized to obtain a weight factor vector.

[0040] In a second aspect of the embodiments of the present application, a dual-phase structure impact resistance evaluation device is disclosed, the device comprising:

[0041] A memory storing executable program code;

[0042] a processor coupled to the memory;

[0043] The processor calls the executable program code stored in the memory to execute the impact resistance evaluation method of the dual-phase structure.

[0044] In a third aspect of the embodiments of the present application, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the impact resistance assessment method of the dual-phase structure.

[0045] In a fourth aspect of the embodiments of the present application, an information data processing terminal is disclosed, wherein the information data processing terminal is used to implement the impact resistance evaluation method of the dual-phase structure.

[0046] The beneficial effects of the present invention are:

[0047] The present invention performs feature extraction processing on the collected performance indicator set to obtain a primary parameter time series, thereby filtering irrelevant data and noise in the original performance indicator set and ensuring the purity of the original collected data.

[0048] The present invention performs high-order cumulant calculation processing on each parameter of a performance index subset to obtain a high-order cumulant sequence, determines the weight value corresponding to each type of parameter based on importance analysis in the high-order cumulant sequence, performs comprehensive evaluation processing on the impact resistance index numerical matrix to obtain an impact resistance performance evaluation value, and realizes accurate evaluation of the impact resistance of dual-phase materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0050] In order to better understand the content of the present invention, an embodiment is given here.

[0051] Figure 1 It is a flow chart for implementing the method of the present invention.

[0052] In order to accurately predict the impact resistance of materials in practical applications, the present invention discloses an impact resistance evaluation method and device for a dual-phase structure.

[0053] The dual-phase structure is a structure constructed using materials of two different phases.

[0054] In a first aspect of the embodiments of the present application, a method for evaluating the impact resistance of a dual-phase structure is disclosed, comprising:

[0055] S1, obtaining a performance indicator set of a dual-phase structure; the performance indicator set includes several performance indicator subsets; each performance indicator subset includes a collection sequence value of several parameters;

[0056] S2, using the performance index set, constructs a numerical matrix of impact resistance index;

[0057] S3, performing comprehensive evaluation processing on the impact resistance index numerical matrix to obtain an impact resistance performance evaluation value.

[0058] The performance index set is used to construct a shock resistance index value matrix, including:

[0059] S21, performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset;

[0060] S22, constructing a shock resistance index numerical matrix using the first-level parameter time series corresponding to all performance index subsets; the row vector of the shock resistance index numerical matrix is ​​the first-level parameter time series corresponding to each performance index subset.

[0061] The performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset includes:

[0062] S211, setting the collection sequence value of each parameter of the performance indicator subset to be the first data sequence of the parameter;

[0063] S212, performing data smoothing processing on each first data sequence to obtain a second data sequence; each parameter of a performance indicator subset has a corresponding first data sequence and a second data sequence;

[0064] S213, for each parameter of the performance indicator subset, respectively perform high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to the parameter to obtain a high-order cumulant sequence corresponding to the parameter;

[0065] S214, performing weight calculation processing on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter;

[0066] S215, normalizing the first weight values ​​corresponding to all parameters included in the performance indicator subset to obtain a normalized weight vector;

[0067] S216, using the normalized weight vector, respectively performing weighted sum processing on the first data series of all parameters included in each performance indicator subset to obtain a primary parameter time series corresponding to the performance indicator subset;

[0068] The step of performing high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to each parameter of the performance indicator subset to obtain a high-order cumulant sequence corresponding to the parameter includes:

[0069] S2131, represent a first parameter sequence and a second parameter sequence corresponding to a certain parameter as x(n) and y(n), respectively, where n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​included in the parameter sequence;

[0070] S2132, performing high-order self-cumulants calculation on the first parameter sequence and the second parameter sequence respectively to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6, and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5, and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes:

[0071]

[0072] Wherein, FFT means Fourier transform;

[0073] S2133, performing high-order cross-cumulant calculations on the first parameter sequence and the second parameter sequence respectively to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second cross-cumulant RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as:

[0074]

[0075] S2134: Determine the set of high-order cross-cumulants and the set of high-order self-cumulants as a sequence of high-order cumulants corresponding to the parameters.

[0076] The step of performing weight calculation processing on the high-order cumulant sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter includes:

[0077] A weight calculation process is performed on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value a1 corresponding to the parameter; the expression of the weight calculation process is:

[0078]

[0079] Among them, α is the weight coefficient.

[0080] The comprehensive evaluation process of the impact resistance index value matrix to obtain the impact resistance performance evaluation value includes:

[0081] S31, performing weight calculation processing on the anti-shock index numerical matrix to obtain a standard feature vector and a weight factor vector;

[0082] S32, performing correlation vector calculation processing on the anti-shock index numerical matrix and the standard eigenvector to obtain a correlation vector;

[0083] S33, performing weighted summation on the associated vectors using the weight factor vector to obtain an impact resistance performance evaluation value.

[0084] The weight calculation process of the anti-shock index numerical matrix is ​​performed to obtain a standard feature vector and a weight factor vector, including:

[0085] The cross-correlation matrix of the shock resistance index numerical matrix is ​​calculated; the elements of the i-th row and j-th column of the cross-correlation matrix are the cross-correlation values ​​of the primary parameter time series corresponding to the i-th performance index subset and the primary parameter time series corresponding to the j-th performance index subset;

[0086] Decomposing the cross-correlation matrix to obtain its eigenvalues ​​and eigenvectors;

[0087] Determine the eigenvector corresponding to the maximum eigenvalue as the standard eigenvector;

[0088] The standard feature vector is normalized to obtain a weight factor vector.

[0089] The calculation process of the correlation vector of the anti-shock index numerical matrix and the standard eigenvector is performed to obtain the correlation vector, including:

[0090] Perform correlation calculation on the anti-shock index numerical matrix S and the standard eigenvector r to obtain the correlation matrix V;

[0091] The calculation expression of the associated calculation process is:

[0092]

[0093] Among them, v ki represents the element in the kth row and ith column of the incidence matrix, s ki is the element of the kth row and ith column of the impact resistance index matrix S, r i is the i-th element of the standard eigenvector r, m and n are the row and column dimensions of the anti-shock index numerical matrix S, and ρ is the correlation calculation factor, which can be 0.5.

[0094] Performing correlation normalization calculation processing on the correlation matrix V to obtain an initial correlation vector y;

[0095] The expression of the association normalization calculation process is:

[0096]

[0097] Among them, y k represents the kth element of the initial correlation vector y;

[0098] The initial correlation vector y is normalized to obtain the correlation vector

[0099] The decomposition process may adopt a matrix eigenvalue decomposition algorithm.

[0100] The weight coefficient α is a preset value, which can be 10.

[0101] The elements of the high-order cumulant sequence include a first high-order self-cumulant, a second high-order self-cumulant, a first mutual cumulant RXY 34 , the second cross-cumulant RXY 56 and the third cross-cumulant RXY 78 .

[0102] The data smoothing process can be implemented by using a second-order polynomial interpolation method.

[0103] The acquisition of the performance indicator set of the dual-phase structure is specifically as follows: within an acquisition time range, according to a preset sampling rate, the time series value of each parameter of each performance indicator subset is acquired.

[0104] The performance index set includes a mechanical performance index subset, a microstructure index subset, a heat treatment and preparation process index subset, and an environmental adaptability index subset.

[0105] The mechanical performance index subset includes:

[0106] Elastic modulus parameter: measures the material's ability to resist elastic deformation.

[0107] Yield strength parameter: The stress value at which a material changes from elastic deformation to plastic deformation under the action of external force.

[0108] Hardness parameter: the ability of a material to resist local plastic deformation.

[0109] Toughness parameter: the ability of a material to absorb energy during plastic deformation and fracture.

[0110] Impact absorption energy parameter: The energy that a material can absorb during an impact, usually obtained through an impact test.

[0111] The microstructure indicator subset includes:

[0112] Phase ratio parameter: The ratio of ferrite phase to austenite phase in the dual-phase structure affects the mechanical properties and corrosion resistance of the material.

[0113] Grain size parameters: The size of the grains affects the yield strength and toughness of the material.

[0114] Phase interface characteristic parameters: The interface characteristics between different phases, such as the type, shape and distribution of the interface, have an important influence on the impact toughness of the material.

[0115] The heat treatment and preparation process indicator subset includes:

[0116] Solution treatment temperature parameters: The temperature of solution treatment of materials such as duplex stainless steel affects the phase ratio and grain size of the material.

[0117] Rolling process parameters: By changing the rolling process, the phase morphology of dual-phase steel can be improved and the impact toughness can be improved.

[0118] The environmental adaptability indicator subset includes:

[0119] Low temperature impact performance parameters: the impact absorption energy of the material under low temperature conditions.

[0120] High temperature performance parameters: the impact resistance of materials in high temperature environments.

[0121] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for evaluating the impact resistance of a dual-phase structure, characterized in that: include: S1, obtaining a set of performance indicators of the dual-phase structure; The performance indicator set includes several performance indicator subsets; each performance indicator subset includes collection sequence values ​​of several parameters; S2, using the performance index set, constructs a numerical matrix of impact resistance index; S3, performing comprehensive evaluation processing on the impact resistance index numerical matrix to obtain an impact resistance performance evaluation value.

2. The impact resistance evaluation method of the dual-phase structure according to claim 1, characterized in that: The performance index set is used to construct a shock resistance index value matrix, including: S21, performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset; S22, constructing a shock resistance index numerical matrix using the first-level parameter time series corresponding to all performance index subsets; the row vector of the shock resistance index numerical matrix is ​​the first-level parameter time series corresponding to each performance index subset.

3. The impact resistance evaluation method of the dual-phase structure according to claim 2, characterized in that: The performing feature extraction processing on each performance indicator subset of the performance indicator set to obtain a primary parameter time series corresponding to each performance indicator subset includes: S211, setting the collection sequence value of each parameter of the performance indicator subset to be the first data sequence of the parameter; S212, performing data smoothing processing on each first data sequence to obtain a second data sequence; each parameter of a performance indicator subset has a corresponding first data sequence and a second data sequence; S213, for each parameter of the performance indicator subset, respectively performing high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to the parameter to obtain a high-order cumulant sequence corresponding to the parameter; S214, performing weight calculation processing on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter; S215, normalizing the first weight values ​​corresponding to all parameters included in the performance indicator subset to obtain a normalized weight vector; S216, using the normalized weight vector, respectively perform weighted sum processing on the first data series of all parameters included in each performance indicator subset to obtain the primary parameter time series corresponding to the performance indicator subset.

4. The impact resistance evaluation method of the dual-phase structure according to claim 3, characterized in that: The step of performing high-order cumulant calculation processing on the first data sequence and the second data sequence corresponding to each parameter of the performance indicator subset to obtain a high-order cumulant sequence corresponding to the parameter includes: S2131, represent the first parameter sequence and the second parameter sequence corresponding to each parameter as x(n) and y(n), respectively, where n represents the number of numerical values ​​in the sequence, n=1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence; S2132, performing high-order self-cumulants calculation on the first parameter sequence and the second parameter sequence respectively to obtain a high-order self-cumulants set; the high-order self-cumulants set includes a first high-order self-cumulants and a second high-order self-cumulants; the first high-order self-cumulants include a first parameter fourth-order cumulant RX4, a first parameter sixth-order cumulant RX6, and a first parameter eighth-order cumulant RX8; the second high-order self-cumulants include a second parameter third-order cumulant RY3, a second parameter fifth-order cumulant RY5, and a second parameter seventh-order cumulant RY7; the calculation process of the high-order self-cumulants includes: Wherein, FFT means Fourier transform; S2133, performing high-order cross-cumulant calculations on the first parameter sequence and the second parameter sequence respectively to obtain a high-order cross-cumulant set; the high-order cross-cumulant set includes a first cross-cumulant RXY 34 , the second cross-cumulant RXY 56 and the third cross-cumulant RXY 78 ; The calculation process of the high-order cross-cumulant is expressed as: S2134: Determine the set of high-order cross-cumulants and the set of high-order self-cumulants as a sequence of high-order cumulants corresponding to the parameters.

5. The impact resistance evaluation method of the dual-phase structure according to claim 4, characterized in that: The step of performing weight calculation processing on the high-order cumulant sequence corresponding to each parameter to obtain a first weight value corresponding to the parameter includes: A weight calculation process is performed on the high-order cumulative amount sequence corresponding to each parameter to obtain a first weight value a1 corresponding to the parameter; the expression of the weight calculation process is: Among them, α is the weight coefficient.

6. The impact resistance evaluation method of the dual-phase structure according to claim 3, characterized in that: The comprehensive evaluation process of the impact resistance index value matrix to obtain the impact resistance performance evaluation value includes: S31, performing weight calculation processing on the anti-shock index numerical matrix to obtain a standard feature vector and a weight factor vector; S32, performing correlation vector calculation processing on the anti-shock index numerical matrix and the standard eigenvector to obtain a correlation vector; S33, performing weighted summation on the associated vectors using the weight factor vector to obtain an impact resistance performance evaluation value.

7. The impact resistance evaluation method of the dual-phase structure according to claim 6, characterized in that: The weight calculation process of the anti-shock index numerical matrix is ​​performed to obtain a standard feature vector and a weight factor vector, including: The cross-correlation matrix of the shock resistance index numerical matrix is ​​calculated; the elements of the i-th row and j-th column of the cross-correlation matrix are the cross-correlation values ​​of the primary parameter time series corresponding to the i-th performance index subset and the primary parameter time series corresponding to the j-th performance index subset; Decomposing the cross-correlation matrix to obtain its eigenvalues ​​and eigenvectors; Determine the eigenvector corresponding to the maximum eigenvalue as the standard eigenvector; The standard feature vector is normalized to obtain a weight factor vector.

8. A dual-phase structure impact resistance evaluation device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the impact resistance evaluation method for a dual-phase structure according to any one of claims 1 to 7.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the impact resistance evaluation method for a dual-phase structure according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the impact resistance evaluation method of the dual-phase structure according to any one of claims 1 to 7.

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