A method and apparatus for impact evaluation of a dual phase structure

Abstract

The application discloses a kind of dual-phase structure's impact evaluation method and device, the method comprises: obtaining the performance index set of dual-phase structure;The performance index set includes several performance index subsets;Each performance index subset includes the acquisition sequence value of several parameters;Impact resistance index numerical matrix is constructed using performance index set;Comprehensive evaluation processing is carried out to the impact resistance index numerical matrix, and impact resistance performance evaluation value is obtained.The application solves the problem that existing evaluation method is mostly dependent on empirical formula or qualitative analysis, and it is difficult to accurately predict the impact resistance performance of material in actual application.

Description

Technical Field

[0001] This invention relates to the field of materials design, and specifically to a method and apparatus for evaluating the impact resistance of a two-phase structure. Background Technology

[0002] With the development of modern industrial technology, the demand for impact resistance of materials is increasing. In aerospace, building structures, and high-speed transportation, the protective ability of materials under impact loads is crucial. Traditional single-material structures, such as homogeneous metals or ceramics, while exhibiting good mechanical properties in some applications, often fail to maintain their original shape under extreme impact loads, easily deforming or even fracturing. To improve the impact resistance of structures, researchers have begun to explore two-phase structural materials. These materials combine two different phases, utilizing their complementary properties to enhance overall impact resistance.

[0003] In the assessment of the impact resistance of two-phase structures, existing methods mostly rely on empirical formulas or qualitative analysis. These methods are often imprecise and struggle to accurately predict the impact resistance performance of materials in practical applications. Therefore, developing a new method for assessing the impact resistance of two-phase structures is of significant practical value for improving the accuracy of assessments and guiding the design and fabrication of impact-resistant two-phase materials. Summary of the Invention

[0004] This invention addresses the problem that existing evaluation methods rely heavily on empirical formulas or qualitative analysis, making it difficult to accurately predict the impact resistance of materials in practical applications. This invention discloses a method and apparatus for evaluating the impact resistance of a two-phase structure.

[0005] In a first aspect, this application discloses a method for evaluating the impact resistance of a two-phase structure, comprising:

[0006] S1, obtain the performance index set of the two-phase structure; the performance index set includes several performance index subsets; each performance index subset includes the collected sequence values ​​of several parameters;

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

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

[0009] The method of constructing a numerical matrix of impact resistance indicators using a set of performance indicators includes:

[0010] S21, Perform feature extraction processing on each subset of the performance indicators in the performance indicator set to obtain the time series of first-level parameters corresponding to each subset of performance indicators;

[0011] S22, using the time series of first-level parameters corresponding to all performance index subsets, a numerical matrix of shock resistance index is constructed; the row vectors of the numerical matrix of shock resistance index are the time series of first-level parameters corresponding to each performance index subset.

[0012] The step of performing feature extraction processing on each subset of the performance index set to obtain the time series of first-level parameters corresponding to each subset of performance indexes includes:

[0013] S211, Set the collection sequence value of each parameter in the performance index subset as the first data sequence of the parameter;

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

[0015] S213, For each parameter of the performance index subset, perform high-order cumulative calculation on the first data sequence and the second data sequence corresponding to the parameter respectively to obtain the high-order cumulative sequence corresponding to the parameter;

[0016] S214, perform weight calculation on the higher-order cumulative sequence corresponding to each parameter to obtain the first weight value corresponding to the parameter;

[0017] S215, normalize the first weight values ​​corresponding to all parameters contained in the performance index subset to obtain a normalized weight vector;

[0018] S216, using the normalized weight vector, perform weighted summation on the first data sequence of all parameters contained in each performance index subset to obtain the first-level parameter time series corresponding to the performance index subset.

[0019] For each parameter in the subset of performance indicators, higher-order cumulative calculations are performed on its corresponding first and second data sequences to obtain the higher-order cumulative sequence corresponding to the parameter, including:

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

[0021] S2132, perform higher-order self-accumulator calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order self-accumulators; the set of higher-order self-accumulators includes a first higher-order self-accumulator and a second higher-order self-accumulator; the first higher-order self-accumulator includes a fourth-order accumulator RX4, a sixth-order accumulator RX6, and an eighth-order accumulator RX8; the second higher-order self-accumulator includes a third-order accumulator RY3, a fifth-order accumulator RY5, and a seventh-order accumulator RY7; the calculation process of the higher-order self-accumulators includes:

[0022]

[0023] Where FFT stands for Fourier Transform;

[0024] S2133, perform higher-order cross-cumulative calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order cross-cumulatives; the set of higher-order cross-cumulatives includes the first cross-cumulative RXY. 34 Second cross-cumulative quantity RXY 56 and the third mutual accumulator RXY 78 The calculation process of the higher-order cross-cumulants is expressed as follows:

[0025]

[0026] S2134, determine the set of higher-order cross-cumulants and the set of higher-order self-cumulants as the higher-order cumulant sequence corresponding to the parameter.

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

[0028] For each parameter, the higher-order cumulant sequence is weighted to obtain the first weight value a1 corresponding to the parameter; the expression for the weighting is:

[0029]

[0030] Where α is the weighting coefficient.

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

[0032] S31, perform weight calculation on the numerical matrix of the anti-impact index to obtain the standard eigenvector and weight factor vector;

[0033] S32, perform correlation vector calculation on the numerical matrix of the anti-impact index and the standard eigenvector to obtain the correlation vector;

[0034] S33, using the weight factor vector to perform a weighted summation of the associated vectors, obtains the impact resistance performance evaluation value.

[0035] The numerical matrix of the resistance to impact index is weighted to obtain a standard feature vector and a weight factor vector, including:

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

[0037] The cross-correlation matrix is ​​decomposed to obtain its eigenvalues ​​and eigenvectors;

[0038] The eigenvector corresponding to the largest eigenvalue is determined as the standard eigenvector;

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

[0040] A second aspect of this application discloses a dual-phase structure impact resistance assessment device, the device comprising:

[0041] Memory containing 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 shock resistance assessment method for the biphase structure.

[0044] A third aspect of this application discloses a computer-storeable medium storing computer instructions, which, when invoked by a computer, are used to execute the shock resistance assessment method for the biphase structure.

[0045] A fourth aspect of this application discloses an information data processing terminal, which is used to implement the shock resistance assessment method for the biphase structure.

[0046] The beneficial effects of this invention are as follows:

[0047] This invention extracts features from the collected performance index set to obtain a first-level parameter time series, thereby filtering out irrelevant data and noise from the original performance index set and ensuring the purity of the original collected data.

[0048] This invention calculates higher-order cumulative quantities for each parameter in a subset of performance indicators to obtain a higher-order cumulative quantity sequence. Based on the importance analysis of the higher-order cumulative quantity sequence, the weight value corresponding to each type of parameter is determined. The impact resistance index numerical matrix is ​​then comprehensively evaluated to obtain the impact resistance performance evaluation value, thus achieving an accurate evaluation of the impact resistance performance of dual-phase materials. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention. Detailed Implementation

[0050] To better understand the content of this invention, an embodiment is provided here.

[0051] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention.

[0052] To address the problem of accurately predicting the impact resistance of materials in practical applications, this invention discloses a method and apparatus for evaluating the impact resistance of a two-phase structure.

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

[0054] In a first aspect, this application discloses a method for evaluating the impact resistance of a two-phase structure, comprising:

[0055] S1, obtain the performance index set of the two-phase structure; the performance index set includes several performance index subsets; each performance index subset includes the collected sequence values ​​of several parameters;

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

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

[0058] The method of constructing a numerical matrix of impact resistance indicators using a set of performance indicators includes:

[0059] S21, Perform feature extraction processing on each subset of the performance indicators in the performance indicator set to obtain the time series of first-level parameters corresponding to each subset of performance indicators;

[0060] S22, using the time series of first-level parameters corresponding to all performance index subsets, a numerical matrix of shock resistance index is constructed; the row vectors of the numerical matrix of shock resistance index are the time series of first-level parameters corresponding to each performance index subset.

[0061] The step of performing feature extraction processing on each subset of the performance index set to obtain the time series of first-level parameters corresponding to each subset of performance indexes includes:

[0062] S211, Set the collection sequence value of each parameter in the performance index subset as the first data sequence of the parameter;

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

[0064] S213, For each parameter of the performance index subset, perform high-order cumulative calculation processing on its corresponding first data sequence and second data sequence to obtain the high-order cumulative sequence corresponding to the parameter.

[0065] S214, perform weight calculation on the higher-order cumulative sequence corresponding to each parameter to obtain the first weight value corresponding to the parameter;

[0066] S215, normalize the first weight values ​​corresponding to all parameters contained in the performance index subset to obtain a normalized weight vector;

[0067] S216, using the normalized weight vector, perform weighted summation on the first data sequence of all parameters contained in each performance index subset to obtain the first-level parameter time series corresponding to the performance index subset;

[0068] For each parameter in the subset of performance indicators, higher-order cumulative calculations are performed on its corresponding first and second data sequences to obtain the higher-order cumulative sequence corresponding to the parameter, including:

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

[0070] S2132, perform higher-order self-accumulator calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order self-accumulators; the set of higher-order self-accumulators includes a first higher-order self-accumulator and a second higher-order self-accumulator; the first higher-order self-accumulator includes a fourth-order accumulator RX4, a sixth-order accumulator RX6, and an eighth-order accumulator RX8; the second higher-order self-accumulator includes a third-order accumulator RY3, a fifth-order accumulator RY5, and a seventh-order accumulator RY7; the calculation process of the higher-order self-accumulators includes:

[0071]

[0072] Where FFT stands for Fourier Transform;

[0073] S2133, perform higher-order cross-cumulative calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order cross-cumulatives; the set of higher-order cross-cumulatives includes the first cross-cumulative RXY. 34 Second cross-cumulative quantity RXY 56 and the third mutual accumulator RXY 78 The calculation process of the higher-order cross-cumulants is expressed as follows:

[0074]

[0075] S2134, determine the set of higher-order cross-cumulants and the set of higher-order self-cumulants as the higher-order cumulant sequence corresponding to the parameter.

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

[0077] For each parameter, the higher-order cumulant sequence is weighted to obtain the first weight value a1 corresponding to the parameter; the expression for the weight calculation process is:

[0078]

[0079] Where α is the weighting coefficient.

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

[0081] S31, perform weight calculation on the numerical matrix of the anti-impact index to obtain the standard eigenvector and weight factor vector;

[0082] S32, perform correlation vector calculation on the numerical matrix of the anti-impact index and the standard eigenvector to obtain the correlation vector;

[0083] S33, using the weight factor vector to perform a weighted summation of the associated vectors, obtains the impact resistance performance evaluation value.

[0084] The numerical matrix of the resistance to impact index is weighted to obtain a standard feature vector and a weight factor vector, including:

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

[0086] The cross-correlation matrix is ​​decomposed to obtain its eigenvalues ​​and eigenvectors;

[0087] The eigenvector corresponding to the largest eigenvalue is determined as the standard eigenvector;

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

[0089] The numerical matrix of the resistance to impact index and the standard eigenvector are subjected to correlation vector calculation to obtain the correlation vector, including:

[0090] The correlation matrix V is obtained by performing correlation calculations on the numerical matrix S of the anti-impact index and the standard eigenvector r.

[0091] The calculation expression for the association calculation process is:

[0092]

[0093] Among them, v ki s represents the element in the k-th row and i-th column of the correlation matrix. ki Let r be the element in the k-th row and i-th column of the shock resistance index numerical matrix S. i ρ is the i-th element of the standard feature vector r, m and n are the row and column dimensions of the shock resistance index numerical matrix S, and ρ is the correlation calculation factor, which can take the value of 0.5.

[0094] The correlation matrix V is subjected to correlation normalization calculation to obtain the initial correlation vector y;

[0095] The expression for the correlation normalization calculation is:

[0096]

[0097] Among them, y k This represents the k-th element of the initial association vector y;

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

[0099] The decomposition process can employ a matrix eigenvalue decomposition algorithm.

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

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

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

[0103] The acquisition of the performance index set of the two-phase structure specifically involves: within a certain acquisition time range, acquiring the time series value of each parameter of each performance index subset according to a preset sampling rate.

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

[0105] The subset of mechanical performance indicators includes:

[0106] Elastic modulus parameter: measures a 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 localized 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 impact testing.

[0111] The subset of microstructure indicators includes:

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

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

[0114] Phase interface characteristic parameters: The interfacial 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 subset of heat treatment and preparation process parameters includes:

[0116] Solution treatment temperature parameters: The temperature at which duplex stainless steel and other materials are solution treated affects the phase ratio and grain size of the material.

[0117] Rolling process parameters: By changing the rolling process, the phase morphology of duplex steel can be improved, thereby increasing its impact toughness.

[0118] The subset of environmental adaptability indicators includes:

[0119] Low-temperature impact performance parameters: the impact energy absorbed by a material under low-temperature conditions.

[0120] High-temperature performance parameters: the impact resistance of the material under high-temperature conditions.

[0121] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for evaluating the impact resistance of a two-phase structure, characterized in that, include: S1, obtain the performance index set of the two-phase structure; the performance index set includes several performance index subsets; each performance index subset includes the collected sequence values ​​of several parameters; The set of performance indicators includes a subset of mechanical performance indicators, a subset of microstructure indicators, a subset of heat treatment and preparation process indicators, and a subset of environmental adaptability indicators. S2, using the set of performance indicators, constructs a numerical matrix of shock resistance indicators, including: S21, perform feature extraction processing on each subset of the performance indicators in the performance indicator set to obtain the time series of first-level parameters corresponding to each subset of performance indicators, including: S211, Set the collection sequence value of each parameter in the performance index subset as the first data sequence of the parameter; S212, perform data smoothing on each first data sequence to obtain a second data sequence; each parameter of a subset of performance indicators has a corresponding first data sequence and a second data sequence; S213, for each parameter of the performance index subset, perform higher-order cumulative calculation processing on the first and second data sequences corresponding to the parameter respectively, to obtain the higher-order cumulative sequence corresponding to the parameter, including: S2131, the first parameter sequence and the second parameter sequence corresponding to each parameter are represented as x(n) and y(n) respectively, where n represents the number of numerical indexes in the sequence, n = 1, 2, ..., N, and N represents the number of numerical values ​​contained in the parameter sequence; S2132, perform higher-order self-accumulator calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order self-accumulators; the set of higher-order self-accumulators includes a first higher-order self-accumulator and a second higher-order self-accumulator; the first higher-order self-accumulator includes a fourth-order accumulator RX4, a sixth-order accumulator RX6, and an eighth-order accumulator RX8; the second higher-order self-accumulator includes a third-order accumulator RY3, a fifth-order accumulator RY5, and a seventh-order accumulator RY7; the calculation process of the higher-order self-accumulators includes: Where FFT stands for Fourier Transform; S2133, perform higher-order cross-cumulative calculations on the first parameter sequence and the second parameter sequence respectively to obtain a set of higher-order cross-cumulatives; the set of higher-order cross-cumulatives includes the first cross-cumulative RXY. 34 Second cross-cumulative quantity RXY 56 and the third mutual accumulator RXY 78 The calculation process of the higher-order cross-cumulants is expressed as follows: S2134, determine the set of higher-order cross-cumulants and the set of higher-order self-cumulants as the higher-order cumulant sequence corresponding to the parameter; S214, perform weight calculation on the higher-order cumulative sequence corresponding to each parameter to obtain the first weight value corresponding to the parameter; S215, normalize the first weight values ​​corresponding to all parameters contained in the performance index subset to obtain a normalized weight vector; S216, using the normalized weight vector, perform weighted summation on the first data sequence of all parameters contained in each performance index subset to obtain the first-level parameter time series corresponding to the performance index subset; S22, using the time series of first-level parameters corresponding to all performance index subsets, a numerical matrix of shock resistance index is constructed; the row vectors of the numerical matrix of shock resistance index are the time series of first-level parameters corresponding to each performance index subset. S3, perform comprehensive evaluation processing on the numerical matrix of the impact resistance index to obtain the impact resistance performance evaluation value.

2. The impact resistance assessment method for a two-phase structure as described in claim 1, characterized in that, The step of performing weight calculation on the higher-order cumulant sequence corresponding to each parameter to obtain the first weight value corresponding to the parameter includes: For each parameter, the higher-order cumulant sequence is weighted to obtain the first weight value a1 corresponding to the parameter; the expression for the weighting is: Where α is the weighting coefficient.

3. The impact resistance assessment method for a two-phase structure as described in claim 1, characterized in that, The comprehensive evaluation and processing of the impact resistance index numerical matrix to obtain the impact resistance performance evaluation value includes: S31, perform weight calculation on the numerical matrix of the anti-impact index to obtain the standard eigenvector and weight factor vector; S32, perform correlation vector calculation on the numerical matrix of the anti-impact index and the standard eigenvector to obtain the correlation vector; S33, using the weight factor vector to perform a weighted summation of the associated vectors, obtains the impact resistance performance evaluation value.

4. The impact resistance assessment method for a two-phase structure as described in claim 3, characterized in that, The numerical matrix of the resistance to impact index is weighted to obtain a standard feature vector and a weight factor vector, including: The cross-correlation matrix of the numerical matrix of the shock resistance index is calculated; the elements in the i-th row and j-th column of the cross-correlation matrix are the cross-correlation values ​​of the time series of the first-level parameters corresponding to the i-th subset of performance indicators and the time series of the first-level parameters corresponding to the j-th subset of performance indicators. The cross-correlation matrix is ​​decomposed to obtain its eigenvalues ​​and eigenvectors; The eigenvector corresponding to the largest eigenvalue is determined as the standard eigenvector; The standard feature vector is normalized to obtain the weight factor vector.

5. A two-phase structure impact resistance assessment device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the shock resistance assessment method for the biphase structure as described in any one of claims 1 to 4.

6. A computer-storable medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked by the computer, are used to execute the shock resistance assessment method for the biphase structure as described in any one of claims 1 to 4.

7. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the shock resistance assessment method for biphase structures as described in any one of claims 1 to 4.

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