Prediction method of maximum deflection angle of micro fatigue crack based on acoustic emission signals

Abstract

The present invention relates to the technical field of online monitoring of acoustic emission signals, and specifically to a method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals, comprising: S1, conducting an in-situ scanning electron microscope fatigue crack growth experiment and simultaneously conducting in-situ acoustic emission monitoring, collecting a SEM image of the fatigue crack growth process and a corresponding acoustic emission signal segment; S2, measuring the maximum deflection angle of the fatigue crack based on the SEM image of the fatigue crack growth process; S3, filtering the acoustic emission signal segment and calculating a single characteristic parameter SPC-I based on the filtered acoustic emission signal; S4, establishing a relationship model between the single characteristic parameter SPC-I and the maximum deflection angle of the fatigue crack based on the maximum deflection angle of the fatigue crack and the characteristic parameter value SPC-I. The method obtains a crack growth SEM image and a corresponding acoustic emission signal segment through an in-situ scanning electron microscope fatigue experiment system, establishes a quantitative relationship between SPC-I and the maximum deflection angle of the fatigue crack, and can predict the maximum deflection angle of the crack.

Description

Technical Field

[0001] The invention belongs to the technical field of alloy material life prediction, and in particular relates to a method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals. Background Art

[0002] As the requirements for equipment reliability in fields such as aerospace gradually increase, acoustic emission signal acquisition and online monitoring technology with good online monitoring performance has received widespread attention.

[0003] For critical components subjected to alternating cyclic loading, fatigue failure is the most prevalent failure mode, resulting in the greatest economic losses. Traditional research on acoustic emission signal acquisition and processing remains at the macroscopic level, lacking understanding of the microscopic crack propagation process. Macroscopic mechanical properties are determined by the microstructure, so research on acoustic emission signal acquisition and processing at the microscopic level is essential. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals. Acoustic emission is used to monitor the fatigue crack propagation process at the micro level in situ. An in-situ scanning electron microscope fatigue test system is used to obtain crack propagation SEM images and corresponding acoustic emission signal segments. A quantitative relationship between the acoustic emission signal and the maximum deflection angle of the fatigue crack is established through the SPC-I parameters. The validity of the quantitative relationship is verified through other test specimens and applied in actual fatigue monitoring.

[0005] To achieve the above objectives, the present invention discloses the following technical solutions:

[0006] A method for predicting the maximum deflection angle of a micro fatigue crack based on an acoustic emission signal comprises the following steps:

[0007] S1. Prepare a mild steel test block for arc additive manufacturing. Cut BMT and WAAM specimens at different locations on the test block and machine initial cracks. Then conduct in-situ scanning electron microscopy fatigue crack growth experiments and synchronous in-situ acoustic emission monitoring. Collect SEM images and corresponding acoustic emission signal segments during the fatigue crack growth process.

[0008] S2, measuring the maximum deflection angle of fatigue crack based on SEM images during fatigue crack growth;

[0009] S3, filtering the acoustic emission signal segment, and calculating a single characteristic parameter SPC-I based on the filtered acoustic emission signal, specifically including the following sub-steps:

[0010] S31. Based on the calculation formula of the characteristic parameter SPC, the characteristic parameter SPC of the acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack is obtained. The calculation formula of the characteristic parameter SPC is:

[0011]

[0012] Where UL is the upper limit of the threshold, LL is the lower limit of the threshold, T is the threshold, and N(X) is the number of signal peaks exceeding x;

[0013] S32, extracting a single characteristic parameter SPC-I, by taking the average value of the number of signal peaks from the SPC curve to obtain the single characteristic parameter SPC-I;

[0014] S4. Based on the maximum deflection angle of fatigue crack and the single characteristic parameter SPC-I, a relationship model between the single characteristic parameter SPC-I and the maximum deflection angle of fatigue crack is established:

[0015] SPC-I(α)=a+b[α+(d-d0) / k]

[0016] Wherein, SPC-I(α) is the single characteristic parameter corresponding to the maximum deflection angle α of the fatigue crack, α is the maximum deflection angle of the fatigue crack, d is the grain size of ferrite in the BMT specimen, d0 is the grain size of ferrite in the WAAM specimen, and a, b, and k are all intermediate parameters;

[0017] S5. During fatigue monitoring, a single characteristic parameter SPC-I of the structure to be monitored and the grain size of the substrate of the structure to be detected are obtained, and the maximum deflection angle of the fatigue crack is solved based on the relationship model of step S4.

[0018] Preferably, the intermediate parameters a, b, and k in step S4 are obtained by fitting through the maximum likelihood method, and a, b, and k are 37.1771, -0.3893, and 0.0949, respectively.

[0019] Preferably, the formula of SPC-I in step S32 can be expressed as:

[0020] SPC-I=Average(SPC(T)), (LL≤T≤UL).

[0021] Preferably, step S1 specifically includes the following sub-steps:

[0022] S11. Prepare a low-carbon steel test block and machine a pre-crack on the test block to serve as a specimen for an in-situ scanning electron microscope fatigue crack growth test;

[0023] S12. Build an in-situ scanning electron microscope fatigue crack growth test bench with in-situ acoustic emission monitoring function;

[0024] S13. Perform an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and collect SEM images of the fatigue crack growth process and corresponding acoustic emission signal segments.

[0025] Preferably, in step S11, a low-carbon steel test block is prepared by an arc additive manufacturing method, and in step S12, the in-situ scanning electron microscope fatigue crack propagation test bench includes a fatigue test bench, welding leads, a sensor fixture and an acoustic emission sensor, and the acoustic emission sensor is fixed on the fatigue test bench by means of the sensor fixture.

[0026] Preferably, the BMT specimens and WAAM specimens are prepared in step S11 as follows: a forged low-carbon steel plate is used as the base material of the low-carbon steel specimen, a cold metal transfer arc welding mode is adopted, a six-axis robotic arm is used to drive the welding gun, and low-carbon steel wire is stacked layer by layer on the forged low-carbon steel plate to prepare an additive low-carbon steel material specimen, each layer is stacked by oscillation to construct a low-carbon steel specimen with a target thickness, a BMT specimen is cut from the base material part of the low-carbon steel specimen, and a WAAM specimen is cut from the filling part of the low-carbon steel specimen, and then initial cracks are processed respectively.

[0027] Preferably, the fatigue crack growth experiment in step S13 is specifically as follows: connecting the acoustic emission sensor to the surface of the specimen, setting the acquisition parameters and performing a lead breaking experiment to test the quality of acoustic emission signal acquisition, performing an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and synchronously obtaining the SEM image and the corresponding acoustic emission signal segment during the fatigue crack growth process.

[0028] Preferably, the filtering process of the acoustic emission signal in step S3 is specifically as follows: performing Fourier transform on the original acoustic emission signal to obtain a spectrum, performing bandpass filtering on the spectrum, selecting a 100-700kHz frequency band, and performing inverse Fourier transform after filtering to obtain a time domain signal.

[0029] Preferably, in step S2, the maximum deflection angle of the fatigue crack is measured by image processing based on the fatigue crack propagation SEM image, specifically: the deflection angle of the fatigue crack propagation trajectory is measured, and the angle between the extension line of the path before the crack deflects and the path after the crack deflects is measured as the fatigue crack deflection angle, and the maximum deflection angle of the propagating crack in the SEM image observed from the current fatigue cycle number to the previous fatigue cycle number is taken as the maximum deflection angle of the fatigue crack.

[0030] Preferably, in step S3, the filtered acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack is selected, all signals in the segment are connected into one signal segment, Fourier transform is performed, and the SPC characteristic parameters are calculated after the spectrum is obtained.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) This invention achieves real-time capture of the microscopic fatigue crack growth process by simultaneously conducting in-situ scanning electron microscopy fatigue crack growth experiments and in-situ acoustic emission monitoring. This innovative method not only ensures the clarity and accuracy of crack growth images, but also significantly improves the precision and real-time performance of fatigue crack maximum deflection angle characterization through the immediate feedback of acoustic emission signals, providing more reliable data support for the evaluation of material fatigue properties.

[0033] (2) By performing noise reduction on the acoustic emission signal and introducing the characteristic parameter SPC-I and the maximum deflection angle of the fatigue crack, this paper successfully establishes a quantitative relationship between the characteristic parameter SPC-I and the maximum deflection angle of the fatigue crack. The establishment of this relationship model provides a new perspective and tool for in-depth analysis of crack growth behavior and can be used for rapid prediction of the maximum deflection angle of fatigue cracks in fatigue monitoring.

[0034] (3) During the verification phase, the present invention employed multiple sets of specimens to validate the quantitative relationship between acoustic emission SPC-I characteristic parameters and the maximum deflection angle of fatigue cracks, ensuring the broad applicability and high repeatability of the experimental results. This feature makes the present method applicable not only to the characterization of the maximum deflection angle of fatigue cracks under specific conditions, but also to its wider application in the field of material fatigue research. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals of the present invention;

[0036] Figure 2 It is a schematic diagram of the sample sampling scheme of the present invention;

[0037] Figure 3 This is a structural diagram of a specimen for an in-situ scanning electron microscope fatigue crack growth experiment of the present invention;

[0038] Figure 4 The corresponding relationship between the maximum deflection angle of fatigue crack in ferrite of the specimen of the present invention and the SPC-I parameter;

[0039] Figure 5 Schematic diagram of SPC sideband peak counting of the present invention;

[0040] Figure 6 Schematic diagram of the number of N(T) peaks of the present invention;

[0041] Figure 7 The corresponding relationship between the maximum deflection angle of fatigue crack in ferrite of WAAM specimen and SPC-I parameters;

[0042] Figure 8This is the correspondence between the maximum deflection angle of fatigue crack in the ferrite of the BMT specimen of the present invention and the SPC-I parameter. DETAILED DESCRIPTION

[0043] The exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0044] The present invention provides a method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals. The method uses acoustic emission to in-situ monitor the fatigue crack growth process at the micro level; obtains crack growth SEM images and corresponding acoustic emission signal segments through an in-situ scanning electron microscope fatigue test system; establishes a quantitative relationship between the acoustic emission signal and the maximum deflection angle of the crack through SPC-I parameters; and verifies the validity of the quantitative relationship through other test pieces. The specific steps include:

[0045] S1. Prepare low-carbon steel test blocks for arc additive manufacturing. Cut BMT specimens and WAAM specimens at different locations of the test blocks and machine initial cracks in each. Then, conduct in-situ scanning electron microscopy fatigue crack growth experiments and synchronously perform in-situ acoustic emission monitoring. Collect SEM images and corresponding acoustic emission signal segments during the fatigue crack growth process as experimental data for fatigue crack growth in arc additive manufacturing of low-carbon steel materials.

[0046] In the embodiment of the present invention, a low-carbon steel wire with a diameter of 1.2 mm is used to manufacture the arc additive test block. The chemical composition of the wire is shown in Table 1.

[0047] Table 1 Chemical composition of low carbon steel wire (wt.%)

[0048]

[0049] The preparation of BMT specimens and WAAM specimens is specifically as follows: forged low-carbon steel plates are used as the substrate of the low-carbon steel test blocks, cold metal transfer arc welding mode is adopted, a six-axis robot arm is used to drive the welding gun, and low-carbon steel wire is stacked layer by layer on the forged low-carbon steel plates to prepare additive low-carbon steel material test blocks. Each layer is stacked by oscillation to construct a low-carbon steel test block with a target thickness. BMT specimens are cut from the substrate part of the low-carbon steel test block, and WAAM specimens are cut from the filling part of the low-carbon steel test block. Then, the initial cracks are processed separately. In this embodiment, five in-situ scanning electron microscope fatigue crack growth test specimens were processed and prepared from different positions of the arc additively manufactured low-carbon steel material. The sampling scheme diagram is shown in FIG. Figure 2 As shown, the dimensions of the in situ SEM specimens are as follows: Figure 3The pre-crack length is 0.625 mm, and the pre-crack is offset from the center line by 3 mm to reserve space for the acoustic emission sensor to be loaded.

[0050] An in-situ scanning electron microscope fatigue crack growth test system with in-situ acoustic emission monitoring was constructed. In-situ scanning electron microscope fatigue crack growth experiments must be conducted within a vacuum chamber. To extract the signals collected by the acoustic emission sensor from the chamber electrically, the chamber requires processing. Iron wires were welded to the inner and outer ends of the vacuum chamber's sealing cover, and sealant was applied to fill any gaps to ensure the chamber's tightness. Analog and digital filters were used for filtering, with a bandwidth of 90-700 kHz. Other parameters included the Peak Definition Time (PDT), Hit Definition Time (HDT), and Hit Lock Time (HLT), set to 300 μs, 600 μs, and 1000 μs, respectively. A lead-breaking experiment was conducted to verify the effectiveness of the acoustic emission signal acquisition. Specifically, a pencil lead of a specified hardness and diameter was extended approximately 2.5 mm, placed at a 30° angle to the specimen, and then broken on the specimen to serve as a simulated acoustic emission signal source. If the amplitude difference in the test results after three consecutive breaks was less than 4 dB, the system's accuracy was confirmed. If the signal amplitude received by the acoustic emission probe each time it is broken is greater than 90dB, it means that the coupling between the acoustic emission probe and the material is good.

[0051] Then, a fatigue crack growth experiment was conducted to obtain SEM images of the fatigue crack growth process and the corresponding acoustic emission signal segments. A sine wave with a frequency of 10 Hz was applied as the fatigue load. The stress ratio of the cyclic load was kept constant at R = 0.1. During the stable crack growth stage, a constant amplitude load control experiment was conducted, setting the maximum force F of the applied load. max = 1kN until the in-situ scanning electron microscope fatigue specimen breaks. During the fatigue test, the experiment and acoustic emission data acquisition are suspended at regular intervals to record the fatigue crack SEM image, acoustic emission signal segment and the current cyclic stress loading cycle N.

[0052] S2. Measure the maximum deflection angle of the fatigue crack based on the SEM image of the fatigue crack growth process. In a specific application, the maximum deflection angle of the fatigue crack is measured through image processing based on the fatigue crack growth SEM image. Specifically, the deflection angle of the fatigue crack growth trajectory is measured. The angle between the extension line of the path before the crack deflection and the path after the crack deflection is measured as the fatigue crack deflection angle. The maximum deflection angle of the fatigue crack observed in the SEM image between the current fatigue cycle number and the previous fatigue cycle number is the maximum deflection angle of the fatigue crack.

[0053] In this embodiment, the maximum deflection angle of the fatigue crack is measured based on the fatigue crack propagation SEM image using image processing software. Specifically, the deflection angle of the fatigue crack propagation trajectory is measured using ImageJ software, and the trajectory segments on both sides of the crack path closest to the inflection point (i.e., the point where the curvature changes significantly) are selected. The angle between the extension line of the original crack path and the subsequent crack path is the deflection angle. The original crack path refers to the crack path before the crack deflects; the subsequent crack path refers to the crack path after the crack deflects. The maximum deflection angle of the fatigue crack is the largest deflection angle observed in the SEM image from the current fatigue cycle number to the previous fatigue cycle number.

[0054] S3, filtering the acoustic emission signal segment, and calculating a single characteristic parameter SPC-I based on the filtered acoustic emission signal, specifically including the following sub-steps:

[0055] S31. Based on the calculation formula of the characteristic parameter SPC, the characteristic parameter SPC of the acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack is obtained. The calculation formula of the characteristic parameter SPC is:

[0056]

[0057] Wherein, UL is the upper limit of the threshold, LL is the lower limit of the threshold, T is the threshold, and N(X) is the number of signal peaks exceeding x.

[0058] S32. Extract a single characteristic parameter SPC-I by taking the average value of the number of signal peaks from the SPC curve to obtain the single characteristic parameter SPC-I. The formula of SPC-I can be expressed as:

[0059] SPC-I=Average(SPC(T)),(LL≤T≤UL)

[0060] Among them, UL is usually 100% or the highest peak, and LL is usually set above the noise level of the received signal. All parameters need to be carefully selected taking into account the characteristics of the received signal in order to accurately analyze the signal.

[0061] In this embodiment, the acoustic emission signal is filtered and the frequency band of 100-700kHz is selected. The maximum deflection angle of the fatigue crack is measured based on the fatigue crack growth SEM image using image processing software;

[0062] The original acoustic emission signal is Fourier transformed to obtain the spectrum, the spectrum is band-pass filtered, the 100-700 kHz frequency band is selected, and after filtering, an inverse Fourier transform is performed to obtain the time domain signal.

[0063] Select the filtered acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack, connect all the signals in the segment into one signal, perform Fourier transform, and obtain the spectrum. The Fourier transform formula is:

[0064]

[0065] The spectrum is normalized based on the maximum peak value, and the SPC parameter of the acoustic emission signal segment corresponding to the maximum deflection angle of each fatigue crack is calculated according to the formula of the characteristic parameter SPC. The formula is:

[0066]

[0067] Among them, such as Figure 5 As shown, UL is the upper limit of the threshold, LL is the lower limit of the threshold, T is the threshold, and N(X) is the number of signal peaks exceeding x.

[0068] like Figure 6 As shown, the SPC curve is obtained from the N(X) curve, and the nonlinearity is evaluated by taking the average of the number of peaks from the SPC curve to produce a single value. SPC-I can be expressed as:

[0069] SPC-I=Average(SPC(T)),(LL≤T≤UL)

[0070] Among them, UL is usually 100% or the highest peak, and LL is usually set above the noise level of the received signal. All parameters need to be carefully selected taking into account the characteristics of the received signal in order to accurately analyze the signal.

[0071] The acoustic emission signal contains a lot of noise, so the value of LL needs to be explored. In this embodiment, multiple different LLs are set. When LL = 0.035, there is a quantitative relationship between the SPC-I parameter and the maximum deflection angle of the fatigue crack. The results are as follows: Figure 4 As shown, the SPC-I characteristic parameters exhibit a decreasing relationship with the maximum deflection angle of the fatigue crack. UL is typically set to 100% or the highest peak value, while LL is typically set above the noise level of the received signal. All parameters must be carefully selected to take into account the characteristics of the received signal in order to accurately analyze it.

[0072] Low-carbon steel is primarily composed of ferrite and pearlite. There are multiple mechanisms that cause fatigue crack deflection, and different mechanisms produce different acoustic emission signals. This example only analyzes deflection occurring within pure ferrite.

[0073] S4. Based on the maximum deflection angle of fatigue crack and the single characteristic parameter SPC-I, a relationship model between the single characteristic parameter SPC-I and the maximum deflection angle of fatigue crack is established:

[0074] SPC-I(α)=a+b[α+(d-d0) / k]

[0075] Wherein, SPC-I(α) is the single characteristic parameter corresponding to the maximum deflection angle α of the fatigue crack, α is the maximum deflection angle of the fatigue crack, d is the grain size of ferrite in the BMT specimen, d0 is the grain size of ferrite in the WAAM specimen, and a, b, and k are all intermediate parameters;

[0076] The intermediate parameters a, b, and k in step S4 are coded in MATLAB software to construct a relationship model:

[0077] SPC-I(α)=a+b[α+Ax0]

[0078] x0=(d-d0) / k

[0079] α=[α1, α2, α3, α4, β1, β2, β3, β4,] T , α i is the maximum deflection angle of fatigue crack in BMT specimen, β j is the maximum deflection angle of fatigue crack in WAAM specimen, x0 is the angular translation related to the grain size of specimen, d is the grain size of BMT, d0 is the grain size of WAAM, set 0-1 matrix A = [1 1 1 1 0 0 0 0] T To facilitate vector group calculations, enter the initial parameter ranges and sample data for a, b, and k:

[0080]

[0081] α=[79.474, 67.786, 87.931, 88.128, 27.615, 49.222, 54.189, 45.000]

[0082] SPC-I(α)=[20.792, 23.349, 15.250, 12.659, 25.299, 17.998, 16.648, 20.235]

[0083] The maximum likelihood estimation function and the built-in fminsearch function of MATLAB software are used to iterate continuously until the SPC-I(α) estimate converges to the SPC-I(α) sample value, and the fitting is achieved. a, b, and k are 37.1771, -0.3893, and 0.0949, respectively.

[0084] Afterwards, the validity of the quantitative relationship between the acoustic emission SPC-I characteristic parameters and the maximum deflection angle of fatigue cracks was verified by other specimens. The specific method is as follows:

[0085] Randomly select other specimens with the same microstructure characteristics, and also select only the maximum deflection angle of fatigue cracks in pure ferrite and the corresponding acoustic emission segments for fatigue crack maximum deflection angle measurement and acoustic emission SPC-I parameter calculation. LL is also selected as 0.035 to verify the validity of the quantitative relationship between characteristic parameters and fatigue crack maximum deflection angle. Figure 4 As shown in FIG, when LL=0.035, the trends of the relationship between SPC-I and the maximum deflection angle of fatigue cracks for different specimens are similar, which proves the effectiveness of the method and parameters proposed in the present invention.

[0086] S5. During the fatigue monitoring process, a single characteristic parameter SPC-I of the structure to be monitored and the grain size of the ferrite in the low-carbon steel material to be tested are obtained, and the maximum deflection angle of the fatigue crack is solved based on the relationship model of step S4, and the grain boundary angle information of the ferrite in the low-carbon steel material can be obtained based on the maximum deflection angle of the fatigue crack. The grain boundary is the interface between grains with the same structure but different orientations. In a polycrystalline material, due to the different orientations of the grains, there is an interface between the grains, and this interface is called a grain boundary. The atomic arrangement at the grain boundary is in a transitional state, gradually transitioning from one orientation to another. Therefore, the atomic arrangement on the grain boundary is often more irregular than the atomic arrangement within the grain and has higher energy. Grain boundaries are an important factor affecting fatigue crack propagation. The propagation of fatigue cracks at grain boundaries is affected by the synergistic effect of multiple mechanisms, among which the grain boundary angle is a more important link in the grain boundary influence mechanism. High-angle grain boundaries (HAGBs) and low-angle grain boundaries (LAGBs) are concepts used in crystallography to describe boundaries between different crystallographic orientations within polycrystalline materials. They are primarily distinguished by the orientation difference between the grains on either side of the boundary. High-angle grain boundaries (HAGBs) typically refer to grain boundaries where the orientation difference between the grains on either side of the boundary is greater than 10 to 15 degrees. These grain boundaries are structurally complex and have high energy, significantly impacting the mechanical properties of the material. HAGBs can inhibit dislocation motion, thereby enhancing the material's strength and hardness. During deformation, HAGBs facilitate grain reorientation and are therefore closely related to the material's plastic deformation capacity. In contrast, low-angle grain boundaries (LAGBs) refer to grain boundaries where the orientation difference between the grains on either side of the boundary is less than 10 to 15 degrees. These grain boundaries are structurally simpler and have lower energy, significantly affecting the material's electronic structure and physical properties, but less so its mechanical properties. In the actual fatigue monitoring of arc additively manufactured low-carbon steel, the solved maximum deflection angle of fatigue cracks can be used for auxiliary monitoring of crack growth in fatigue monitoring. By obtaining information on the number of high grain boundary angles of ferrite in low-carbon steel materials, the strength and hardness of low-carbon steel materials can be quickly determined.

[0087] The present invention achieves real-time capture of the microscopic fatigue crack growth process by simultaneously conducting in-situ scanning electron microscopy fatigue crack growth experiments and in-situ acoustic emission monitoring. This innovative method not only ensures the clarity and accuracy of crack growth images, but also significantly improves the precision and real-time performance of fatigue crack maximum deflection angle characterization through instant feedback of acoustic emission signals, providing more reliable data support for the evaluation of material fatigue properties. By performing noise reduction processing on the acoustic emission signal and introducing the characteristic parameter SPC-I, a quantitative relationship between this parameter and the maximum deflection angle of the fatigue crack is established. The establishment of this relationship model provides a new perspective and tool for in-depth analysis of crack growth behavior. During the verification phase, the present invention used multiple groups of specimens to verify the validity of the quantitative relationship between the acoustic emission SPC-I characteristic parameter and the maximum deflection angle of the fatigue crack, ensuring the wide applicability and high repeatability of the experimental results. This feature makes the present method not only suitable for the characterization of the maximum deflection angle of fatigue cracks under specific conditions, but also can be promoted and applied in the broader field of material fatigue research.

[0088] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals, characterized by: It includes the following steps: S1. Prepare a mild steel test block for arc additive manufacturing. Cut BMT and WAAM specimens at different locations on the test block and machine initial cracks. Then conduct in-situ scanning electron microscopy fatigue crack growth experiments and synchronous in-situ acoustic emission monitoring. Collect SEM images and corresponding acoustic emission signal segments during the fatigue crack growth process. S2, measuring the maximum deflection angle of fatigue crack based on SEM images during fatigue crack growth; S3, filtering the acoustic emission signal segment, and calculating a single characteristic parameter SPC-I based on the filtered acoustic emission signal, specifically including the following sub-steps: S31. Based on the calculation formula of the characteristic parameter SPC, the characteristic parameter SPC of the acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack is obtained. The calculation formula of the characteristic parameter SPC is: ； in, UL is the upper limit of the threshold, LL is the lower limit of the threshold, T is the threshold, N (x) is the number of signal peaks exceeding x; S32, extracting a single characteristic parameter SPC-I, by taking the average value of the number of signal peaks from the SPC curve to obtain the single characteristic parameter SPC-I; S4. Based on the maximum deflection angle of fatigue crack and the single characteristic parameter SPC-I, a relationship model between the single characteristic parameter SPC-I and the maximum deflection angle of fatigue crack is established: ； in, is the maximum deflection angle of fatigue crack The corresponding single feature parameter, is the maximum deflection angle of fatigue crack, d is the grain size of ferrite in BMT specimen, d0 is the grain size of ferrite in WAAM specimen, a, b, k are all intermediate parameters; S5. During fatigue monitoring, a single characteristic parameter SPC-I of the structure to be monitored and the grain size of ferrite in the substrate of the structure to be monitored are obtained, and the maximum deflection angle of the fatigue crack is solved based on the relationship model of step S4.

2. The method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals according to claim 1, characterized in that: The intermediate parameters a, b, and k in step S4 are obtained by maximum likelihood fitting, and a, b, and k are 37.1771, -0.3893, and 0.0949, respectively.

3. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1, characterized in that: The formula of SPC-I in step S32 can be expressed as: 。 4. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1, characterized in that: Step S1 specifically includes the following sub-steps: S11. Prepare a low-carbon steel test block and machine a pre-crack on the test block to serve as a specimen for an in-situ scanning electron microscope fatigue crack growth test; S12. Build an in-situ scanning electron microscope fatigue crack growth test bench with in-situ acoustic emission monitoring function; S13. Perform an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and collect SEM images of the fatigue crack growth process and corresponding acoustic emission signal segments.

5. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 4, characterized in that: In step S11, a low-carbon steel test block is prepared using an arc additive manufacturing method. In step S12, an in-situ scanning electron microscope fatigue crack propagation test bench includes a fatigue test bench, welding leads, a sensor fixture, and an acoustic emission sensor. The acoustic emission sensor is fixed on the fatigue test bench by means of the sensor fixture.

6. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 4, characterized in that: The preparation of BMT specimens and WAAM specimens in step S1 is specifically as follows: forged low-carbon steel plates are used as the base material of the low-carbon steel test blocks, cold metal transfer arc welding mode is adopted, a six-axis robotic arm is used to drive the welding gun, and low-carbon steel wire is stacked layer by layer on the forged low-carbon steel plates to prepare additive low-carbon steel material test blocks. Each layer is stacked by oscillation to construct a low-carbon steel test block with a target thickness. BMT specimens are cut from the base material part of the low-carbon steel test block, and WAAM specimens are cut from the filling part of the low-carbon steel test block. Then, initial cracks are processed separately.

7. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 4, characterized in that: The fatigue crack growth experiment in step S13 is specifically as follows: connecting the acoustic emission sensor to the surface of the specimen, setting the acquisition parameters and performing a lead breaking experiment to test the quality of the acoustic emission signal acquisition, performing an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and synchronously obtaining the SEM image and the corresponding acoustic emission signal segment during the fatigue crack growth process.

8. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1, characterized in that: In step S3, the acoustic emission signal is filtered by performing Fourier transform on the original acoustic emission signal to obtain a spectrum, performing bandpass filtering on the spectrum, selecting a 100-700 kHz frequency band, and performing inverse Fourier transform after filtering to obtain a time domain signal.

9. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1, characterized in that: In step S2, the maximum deflection angle of the fatigue crack is measured by image processing based on the fatigue crack propagation SEM image, specifically: the deflection angle of the fatigue crack propagation trajectory is measured, and the angle between the extension line of the path before the crack deflection and the path after the crack deflection is measured as the fatigue crack deflection angle. The maximum deflection angle of the propagating crack in the SEM image observed from the current fatigue cycle number to the previous fatigue cycle number is the maximum deflection angle of the fatigue crack.

10. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1, characterized in that: In step S3, the filtered acoustic emission signal segment corresponding to the maximum deflection angle of the fatigue crack is selected, all signals in the segment are connected into one signal segment, Fourier transform is performed, and the spectrum is obtained to calculate the SPC characteristic parameters.

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