A metal fatigue micro-damage detection method, device, medium and equipment

Abstract

The application belongs to the technical field of material engineering and failure analysis, and particularly relates to a metal fatigue micro-damage detection method, device, medium and equipment, the method comprising the following steps: acquiring a longitudinal wave sound velocity of a metal material to be detected; calculating a normalized damage index of the metal material to be detected based on the longitudinal wave sound velocity; taking the normalized damage index as an input of a fatigue micro-damage evaluation model that has been constructed, and obtaining a plastic strain of the metal material to be detected, so as to realize fatigue micro-damage detection of the metal material to be detected. The application can improve the sensitivity of metal fatigue micro-damage detection.

Description

Technical Field

[0001] This application belongs to the field of materials engineering and failure analysis technology, specifically relating to a method, device, medium and equipment for detecting micro-damage in metal fatigue. Background Technology

[0002] In the aerospace industry, key components made of lightweight and high-strength advanced metal materials are prone to micro-damage in stress concentration areas due to fatigue under long-term cyclic loading, leading to performance degradation and seriously threatening flight safety. Therefore, it is crucial to develop highly sensitive early micro-damage non-destructive testing technology.

[0003] In existing technologies, the Longitudinal Critical Refraction (LCR) wave has been used for damage detection due to its sensitivity to surface and near-surface damage. However, relying solely on the characteristics of a single signal domain makes it difficult to achieve reliable and highly sensitive micro-damage characterization and early warning. How to comprehensively extract damage features and establish a mapping relationship between them and the degree of damage remains a key problem that needs to be solved. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this application is to provide a method, device, medium and equipment for detecting micro-damage in metal fatigue, and the purpose of this application is to improve the sensitivity of metal fatigue micro-damage detection.

[0005] To achieve the above objectives, this application provides the following technical solution: A method for detecting micro-damage in metal fatigue includes: acquiring the longitudinal wave velocity of the metal material to be tested; calculating a normalized damage index of the metal material to be tested based on the longitudinal wave velocity; and using the normalized damage index as input to a pre-constructed fatigue micro-damage assessment model to obtain the plastic strain of the metal material to be tested, thereby achieving fatigue micro-damage detection of the metal material to be tested.

[0006] Optionally, the step of calculating the normalized damage index of the metal material under test based on the longitudinal wave velocity includes: calculating the critical refractive longitudinal wave critical angle of the metal material under test based on the longitudinal wave velocity; and calculating the critical refractive longitudinal wave critical angle based on the longitudinal wave velocity. The incident angle and geometric dimensions of the critical refraction longitudinal wave wedge are designed to form a probe-wedge detection fixture. Based on the probe-wedge detection fixture, for metallic materials with different degrees of micro-damage, the critical refraction longitudinal wave signal and the vertical incident pulse reflection signal are repeatedly acquired at the same position. Multi-domain feature extraction is performed on the critical refraction longitudinal wave signal and the vertical incident pulse reflection signal. Based on the extracted multi-domain features, the change in characteristic parameters corresponding to different degrees of damage is calculated to obtain the rate of change of characteristic parameters. The rate of change of characteristic parameters is normalized to obtain the normalized damage index for each stage of fatigue damage.

[0007] Optionally, the step of calculating the critical refractive longitudinal wave critical angle of the metal material under test based on the longitudinal wave velocity includes: calculating the ratio of the longitudinal wave velocity to the wedge velocity; and calculating the arcsine of the ratio.

[0008] Optionally, the multi-domain features include time-domain features, frequency-domain features, and time-frequency-domain features.

[0009] Optionally, the time-domain features include acoustic time. Real-time longitudinal wave velocity and attenuation coefficient ,in, Real-time longitudinal wave velocity The following formula is used to calculate:

[0010] Attenuation coefficient The following formula is used to calculate:

[0011] in, When indicating sound; Indicates the thickness of the plate-shaped sample; This represents the highest amplitude of the first bottom-reflected wave of a vertically incident pulse reflection signal; This represents the highest amplitude of the secondary bottom-reflected wave of a vertically incident pulse reflection signal; Indicates the attenuation coefficient; Indicates the real-time longitudinal wave velocity; The frequency domain features include, for example, the frequency domain energy of the critically refracted longitudinal wave signal. Frequency domain energy of the reflected signal from the vertically incident pulse Specifically, it is obtained through the following calculation:

[0012] in, This represents the spectral representation of an ultrasonic signal.

[0013] Optionally, the time-frequency domain features include the Shannon entropy of the critically refracted longitudinal wave signal. Shannon entropy of the reflected signal from a vertically incident pulse Specifically, it is obtained through the following calculation:

[0014] in, Indicates the total number of time-frequency points; Describes the th probability in the normalized probability distribution. Each element.

[0015] Optionally, the fatigue micro-damage assessment model is expressed as:

[0016] in, Indicates the normalized damage index; Indicates the characteristic parameters of fatigue micro-damage; , , Denotes the fitting coefficient, where, The upper limit of the damage index is determined, corresponding to the damage state when the material is close to fracture. It reflects the rate at which the damage index increases with plastic strain. The larger the value, the steeper the curve rises, indicating that the material is more sensitive to stomach damage. It is a constant close to 0, representing the intrinsic damage index caused by the initial microstructure of the material or the noise of the measurement system when the plastic strain is theoretically 0.

[0017] This application also provides a metal fatigue micro-damage detection device, the device comprising: an acquisition module for acquiring the longitudinal wave velocity of the metal material to be tested; a calculation module for calculating the normalized damage index of the metal material to be tested based on the longitudinal wave velocity; and a detection module for obtaining the plastic strain of the metal material to be tested by using the normalized damage index as input to a constructed fatigue micro-damage assessment model, so as to realize fatigue micro-damage detection of the metal material to be tested.

[0018] This application also provides a storage medium including instructions that, when executed on a computer, cause the computer to perform the method as described in the preceding claim.

[0019] This application also provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any of the preceding claims.

[0020] Compared with the prior art, the beneficial effects of this application are as follows: This application comprehensively extracts the time-domain, frequency-domain, and time-frequency-domain multi-domain features of critically refracted longitudinal waves and vertically incident pulse reflection signals, calculates the changes in characteristic parameters and performs normalization processing to obtain a normalized damage index, and then constructs a quantitative mapping relationship with fatigue micro-damage characteristic parameters. This significantly improves the sensitivity and reliability of metal fatigue micro-damage detection, enabling early and accurate identification and assessment of micro-damage states, and providing an effective non-destructive assessment method for the safety monitoring and life prediction of metal components in aerospace and other fields. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a metal fatigue micro-damage detection method provided in one embodiment of this application; Figure 2 This is a schematic diagram of the aluminum alloy sample. Figure 3 These are the hysteresis curves of the aluminum alloy specimens after 1 week and 500 weeks of fatigue loading. Figure 4 These are waveforms of two types of ultrasonic signals before fatigue loading of an aluminum alloy sample. Figure 5 These are waveforms of two types of ultrasonic signals after fatigue loading of an aluminum alloy sample. Figure 6 This is a comparison chart of two types of ultrasonic signals before and after fatigue loading of an aluminum alloy sample. Figure 7 It is the normalized result of the changes in ultrasonic characteristic parameters of the aluminum alloy sample before and after fatigue loading. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0023] It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0024] In this application, unless otherwise expressly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0025] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0026] Figure 1 This is a schematic flowchart of a metal fatigue micro-damage detection method provided in one embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps: S100: The longitudinal wave velocity of a metallic material is measured using an ultrasonic testing system consisting of a digital ultrasonic pulse transmitter and receiver, a digital fluorescence oscilloscope, a piezoelectric ultrasonic probe, and a wedge. The critical refractive longitudinal wave angle of the metallic material is calculated based on the longitudinal wave velocity. The critical angle is expressed as follows:

[0027] in, The longitudinal wave velocity of metallic materials; The velocity of sound of the wedge.

[0028] It should be noted that, for the purposes of this application, any solid metal material with uniform texture that can conduct ultrasonic waves (i.e., transmit sound) is applicable to the method of this application, such as including but not limited to aluminum alloys, titanium alloys, steel, copper, iron, etc.

[0029] S200: Based on the critical refractive longitudinal wave critical angle Design the incident angle (i.e. the angle between the ultrasonic probe and the bottom surface of the wedge) and geometric dimensions of the critical refractive longitudinal wave wedge to form a probe-wedge detection fixture; In this step, based on the critical refractive longitudinal wave critical angle The wedge incident angle needs to be designed to be slightly greater than (Typically, an increase of 1°~3°) is made to effectively excite and receive critically refracted longitudinal waves near the surface of the metal being tested using the principle of total internal reflection. The geometry of the wedge is optimized through ray tracing and acoustic field simulation to ensure that the wedge height is sufficient to accommodate the complete mode conversion of the ultrasonic wave, and that its bottom length is greater than the width of the ultrasonic beam to avoid edge diffraction interference. At the same time, the thickness of the wedge front end must meet the pulse echo timing requirements. Finally, through precision machining, the angle between the ultrasonic wave emission direction generated by the probe-wedge fixture system and the normal of the detection plane is stably controlled within the range of 20°~30°. Within this angle range, the critically refracted longitudinal wave is most sensitive to surface lattice distortion and dislocation aggregation, thereby obtaining a micro-damage response signal with the best signal-to-noise ratio.

[0030] S300: Based on the probe-wedge detection fixture, for metal materials with different degrees of micro-damage, the critical refraction longitudinal wave signal and the vertical incident pulse reflection signal are collected multiple times at the same position; In this step, critically refracted longitudinal wave signals and vertically incident pulse reflection signals are repeatedly acquired at the same location. The aim is to eliminate random measurement errors caused by the inherent inhomogeneity of the metal material's microstructure, ensuring that changes in the acquired signal characteristics accurately and reliably reflect the micro-damage evolution introduced by the fatigue loading process. By fixing the measurement location, the metal material itself is used as a reference frame for its initial state. This allows subsequent differences in signals acquired at the same point at different damage stages (such as acoustic duration extension, amplitude attenuation, energy changes, or entropy changes) to be directly and accurately attributed to the accumulation of fatigue micro-damage, rather than interference from the material's spatial variability. Simultaneously, multiple acquisitions followed by averaging or statistical analysis effectively suppress random noise, improve data stability and repeatability, and lay a solid foundation for the subsequent accurate extraction of micro-damage-sensitive features and the establishment of a quantitative evaluation model.

[0031] S400: Perform multi-domain feature extraction on the critically refracted longitudinal wave signal and the vertically incident pulse reflection signal; In this step, the multi-domain features include time-domain features, frequency-domain features, and time-frequency-domain features, wherein the time-domain features include, for example, acoustic time. Real-time longitudinal wave velocity and attenuation coefficient Among them, sound time The real-time longitudinal wave velocity can be directly displayed through an ultrasonic testing system. and attenuation coefficient The following formula is used to calculate:

[0032]

[0033] in, Indicates the thickness of the plate-shaped sample; This represents the highest amplitude of the first bottom-reflected wave of a vertically incident pulse reflection signal; This represents the highest amplitude of the secondary bottom-reflected wave of a vertically incident pulse reflected signal.

[0034] Frequency domain characteristics, such as the frequency domain energy of the critically refracted longitudinal wave signal. Frequency domain energy of the reflected signal from the vertically incident pulse Specifically, it is obtained through the following calculation:

[0035] in, This represents the spectral representation of an ultrasonic signal.

[0036] Time-frequency domain characteristics include the Shannon entropy of the critically refracted longitudinal wave signal. Shannon entropy of the reflected signal from the vertically incident pulse Specifically, it is obtained through the following calculation:

[0037] in, Indicates the total number of time-frequency points; Describes the th probability in the normalized probability distribution. Each element.

[0038] S500: Based on the extracted multi-domain features, calculate the change in feature parameters corresponding to different degrees of damage, obtain the change rate of feature parameters, normalize the change rate of feature parameters, and obtain the normalized damage index for each stage of fatigue damage. In this step, the changes in characteristic parameters corresponding to different degrees of damage include, for example, the following: , , , , , , That is, the acoustic time obtained by ultrasonic testing and subsequent calculations at different fatigue loading damage stages relative to the initial metallic material. Longitudinal wave speed attenuation coefficient Frequency domain energy of critically refracted longitudinal wave signal Frequency domain energy of the reflected signal from a vertically incident pulse Shannon entropy of critically refracted longitudinal wave signal Shannon entropy of the reflected signal from a vertically incident pulse The change in the characteristic parameter. The specific change in the characteristic parameter and its calculation process are as follows: First, the measured values ​​of each characteristic parameter of the undamaged sample were used as a benchmark. Then, the characteristic parameter values ​​measured at the same location after each predetermined fatigue loading cycle (representing different degrees of damage) are recorded as follows: ,in, Indicates the first One feature parameter, Indicates the first Each stage of damage. Changes in characteristic parameters. (Right now , , , , , , ) through calculation and The absolute difference is obtained, that is .

[0039] Furthermore, when normalizing the rate of change of characteristic parameters, the rate of change of each characteristic parameter relative to its initial baseline value is first calculated at each damage stage. Then, a minimum-maximum normalization method is used to linearly map the rate of change of each characteristic parameter across all damage stages to the [0,1] interval. This normalization process effectively eliminates the influence of differences in dimensions and orders of magnitude between different characteristic parameters, allowing heterogeneous features such as acoustic time, amplitude, sound velocity, attenuation coefficient, frequency domain energy, and Shannon entropy to be placed on a unified and comparable scale. This lays the foundation for subsequent comprehensive weighted calculation of the normalized damage index.

[0040] After normalization, the normalized damage index is expressed as follows:

[0041] in, Indicates the first Weight coefficients of each feature parameter; 7 indicates the amount of change in the characteristic parameter; 7 indicates the number of changes in the characteristic parameter.

[0042] S600: Normalized damage index and fatigue micro-damage characteristic parameters (such as plastic strain) for each stage of fatigue damage. Fitting was performed to construct a model based on the normalized damage index and fatigue micro-damage characteristic parameters (such as plastic strain). A fatigue micro-damage assessment model is developed to achieve quantitative assessment of fatigue micro-damage in metallic materials.

[0043] In this step, the normalized damage index and fatigue micro-damage characteristic parameters can be represented by the following functions:

[0044] in, Indicates the normalized damage index; Indicates plastic strain; , , Denotes the fitting coefficient, where, The upper limit of the damage index is determined, corresponding to the damage state when the material is close to fracture. It reflects the rate at which the damage index increases with plastic strain. The larger the value, the steeper the curve rises, indicating that the material is more sensitive to micro-damage. A constant close to 0 represents the intrinsic damage index caused by the initial microstructure of the material or noise in the measurement system, when the plastic strain is theoretically zero. It can be determined by fitting experimental data, and its value can be, for example, 0.001. 0.01.

[0045] The specific process for implementing quantitative evaluation is as follows: First, experimental calibration is performed, that is, through fatigue testing, a set of plastic strains under different cycles (i.e. different damage degrees) are obtained. and the calculated normalized damage index Secondly, curve fitting is performed, that is, using the above data points and algorithms such as nonlinear least squares, the curve fitting is determined. , , The optimal values ​​of the three parameters are obtained, thus yielding the "exponential-" value specifically for this type of material. The curve (i.e., the quantitative assessment model) is used. Finally, when testing samples of the same material with unknown damage states, it is only necessary to collect ultrasonic signals on-site, calculate the normalized damage index, and then substitute it into the above-mentioned calibrated relationship to solve for the corresponding plastic strain, thereby achieving a quantitative assessment of the degree of fatigue micro-damage.

[0046] The method described above will now be illustrated with specific examples.

[0047] (1) This application uses the following: Figure 2 The aluminum alloy specimen shown has the following geometric specifications: the parallel section has a width of 12 mm, a length of 15 mm, a thickness of 5 mm, the non-parallel section has a width of 24 mm, and the radius of the transition arc is 60 mm.

[0048] (2) The MTS Landmark hydraulic servo testing system was used to test... Figure 2 The aluminum alloy specimens of the specifications shown were subjected to fatigue loading. During the loading process, a stress control mode was adopted, the loading waveform was a sine wave, the loading frequency was 10Hz, the R ratio was 0.1, and the maximum stress was 450 MPa.

[0049] To characterize the evolution of aluminum alloy samples from their initial state to the formation of micro-damage, this application selected two key nodes—cycle 1 (considered the initial reference state) and cycle 500—for comparative analysis (e.g., Figure 3 As shown in the figure. Analysis of the hysteresis curves of the aluminum alloy specimens after 500 loading cycles revealed a plastic strain of 2.61%. After each loading cycle, an ultrasonic testing system was used to simultaneously acquire critically refracted longitudinal wave (LCR wave) signals and vertically incident pulse reflection signals (e.g., ...) at the same location on the specimen. Figure 4 and Figure 5 As shown in the figure, this provides a data foundation for subsequent multi-domain feature extraction and damage assessment.

[0050] Two conventional ultrasonic probes of identical specifications, one transmitting and one receiving, were used. The center frequency was 5MHz, and the wedge material was plexiglass. The longitudinal wave velocities of the plexiglass and aluminum alloy were measured to be 2730m / s and 6300m / s, respectively. The critical angle of the critical refracted longitudinal wave was calculated using formula (1). θ CR Prepare wedges at the appropriate angles to generate critical refracted longitudinal waves; (1) (3) Extract time-domain features from the two types of signals in (2), and extract the acoustic time from the critical refracted longitudinal wave signal. t 1. A 1. Taking the vertically incident pulse reflection signal of the initial state of the sample as an example, the longitudinal wave velocity is calculated based on formulas (2) and (3). and attenuation coefficient ,like Figure 6 As shown.

[0051] (2) (3) (4) Perform Fast Fourier Transform on the two types of signals in (2) respectively, and extract the following: Figure 7 The frequency domain characteristics are shown; taking the vertically incident pulse reflection signal collected in the initial state of the sample as an example, the frequency domain energy of the signal is calculated based on formula (4). .

[0052] (4) (5) Based on the wavelet basis function "Daubechies", wavelet transforms are performed on the two types of signals respectively to extract time-frequency domain features. Taking the vertically incident pulse reflection signal collected in the initial state of the sample as an example, Shannon entropy is calculated based on formula (5). .

[0053] (5) (6) Calculate the change in characteristic parameters Δ corresponding to different degrees of micro-damage for the time domain, frequency domain, and time-frequency domain features in (3) to (5). t 1. Δ A 1. Δ v 2. Δ α 2. Δ E 1. Δ E 2. Δ H 1. Δ H 2. Then, normalization is performed to obtain the rate of change of the characteristic parameters. S i Optimize the weight ratio of feature parameters and calculate the normalized damage index based on formula (6); (6) (7) By substituting the normalized damage index of 0.1751 into the fatigue micro-damage assessment model, the plastic strain of the metal material can be detected as 2.61%, thereby realizing the non-destructive assessment of the fatigue micro-damage state of the metal material.

[0054] In another exemplary embodiment, this application also provides a metal fatigue micro-damage detection device, the device comprising: an acquisition module for acquiring the longitudinal wave velocity of the metal material to be tested; a calculation module for calculating a normalized damage index of the metal material to be tested based on the longitudinal wave velocity; and a detection module for obtaining the plastic strain of the metal material to be tested by using the normalized damage index as input to a constructed fatigue micro-damage assessment model, so as to realize fatigue micro-damage detection of the metal material to be tested.

[0055] This application also provides a storage medium including instructions that, when executed on a computer, cause the computer to perform the metal fatigue micro-damage detection method as described in any of the preceding embodiments.

[0056] This application also provides an electronic device, the electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the metal fatigue micro-damage detection method as described in any of the preceding embodiments.

[0057] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for detecting micro-damage in metal fatigue, characterized in that, The method includes: Obtain the longitudinal wave velocity of the metallic material under test; The normalized damage index of the metal material under test is calculated based on the longitudinal wave velocity. Using the normalized damage index as input to the constructed fatigue micro-damage assessment model, the plastic strain of the metal material under test is obtained, thereby realizing the fatigue micro-damage detection of the metal material under test.

2. The method according to claim 1, characterized in that, The normalized damage index of the metal material under test, calculated based on the longitudinal wave velocity, includes: The critical refractive longitudinal wave critical angle of the tested metallic material is calculated based on the longitudinal wave velocity. Based on the critical angle of critical refraction longitudinal wave, the incident angle and geometric dimensions of the critical refraction longitudinal wave wedge are designed to form a probe-wedge detection fixture; Based on the probe-wedge detection fixture, for metal materials with different degrees of micro-damage, the critical refraction longitudinal wave signal and the vertical incident pulse reflection signal are collected multiple times at the same position. Multi-domain feature extraction is performed on critically refracted longitudinal wave signals and vertically incident pulse reflection signals; Based on the extracted multi-domain features, the change in feature parameters corresponding to different degrees of damage is calculated to obtain the change rate of feature parameters. The change rate of feature parameters is then normalized to obtain the normalized damage index for each stage of fatigue damage.

3. The method according to claim 2, characterized in that, The calculation of the critical refractive longitudinal wave critical angle of the tested metallic material based on the longitudinal wave velocity includes: Calculate the ratio of the longitudinal wave velocity to the wedge velocity; Calculate the arcsine of the proportional value.

4. The method according to claim 2, characterized in that, The multi-domain features include time-domain features, frequency-domain features, and time-frequency-domain features.

5. The method according to claim 4, characterized in that, The time-domain features include sound time, amplitude, real-time longitudinal wave velocity, and attenuation coefficient. in, The real-time longitudinal wave velocity is calculated using the following formula: The attenuation coefficient is calculated using the following formula: in, When indicating sound; Indicates the thickness of the plate-shaped sample; This represents the highest amplitude of the first bottom-reflected wave of a vertically incident pulse reflection signal; This represents the highest amplitude of the secondary bottom-reflected wave of a vertically incident pulse reflection signal; Indicates the attenuation coefficient; Indicates the real-time longitudinal wave velocity; The frequency domain features include, for example, the frequency domain energy of the critically refracted longitudinal wave signal. Frequency domain energy of the reflected signal from the vertically incident pulse Specifically, it is obtained through the following calculation: in, This represents the spectral representation of an ultrasonic signal.

6. The method according to claim 4, characterized in that, The time-frequency domain features include the Shannon entropy of the critically refracted longitudinal wave signal. Shannon entropy of the reflected signal from a vertically incident pulse Specifically, it is obtained through the following calculation: in, Indicates the total number of time-frequency points; Describes the th probability in the normalized probability distribution. Each element.

7. The method according to claim 7, characterized in that, The fatigue micro-damage assessment model is expressed as follows: in, Indicates the normalized damage index; Indicates the characteristic parameters of fatigue micro-damage; , , Denotes the fitting coefficient, where, The upper limit of the damage index is determined, corresponding to the damage state when the material is close to fracture. It reflects the rate at which the damage index increases with plastic strain. The larger the value, the steeper the curve rises, indicating that the material is more sensitive to stomach damage. It is a constant close to 0, representing the intrinsic damage index caused by the initial microstructure of the material or the noise of the measurement system when the plastic strain is theoretically 0.

8. A metal fatigue micro-damage detection device, characterized in that, The device includes: The acquisition module is used to acquire the longitudinal wave velocity of the metal material under test; The calculation module is used to calculate the normalized damage index of the metal material under test based on the longitudinal wave velocity. The detection module is used to obtain the plastic strain of the metal material under test by using the normalized damage index as input to the constructed fatigue micro-damage assessment model, so as to realize the fatigue micro-damage detection of the metal material under test.

9. A storage medium, characterized in that, It includes instructions that, when executed on a computer, cause the computer to perform the method described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes: Memory, processor, and computer programs stored in memory and executable on the processor, wherein, When the processor executes the program, it implements the method as described in any one of claims 1 to 7.

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