Microscopic fatigue crack maximum deflection angle prediction method based on acoustic emission signal

Abstract

The invention relates to the technical field of acoustic emission signal online monitoring, in particular to a microscopic fatigue crack maximum deflection angle prediction method based on acoustic emission signals, which comprises the following steps: S1, carrying out an in-situ scanning electron microscope fatigue crack propagation experiment and synchronously carrying out acoustic emission in-situ monitoring, acquiring a fatigue crack growth process SEM image 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; and S4, based on the maximum deflection angle of the fatigue crack and the characteristic parameter value SPC-I, establishing a relation model between the single characteristic parameter SPC-I and the maximum deflection angle of the fatigue crack. A crack propagation SEM image and a corresponding acoustic emission signal segment are obtained through an in-situ scanning electron microscope fatigue experiment system, a quantitative relation between SPC-I and the maximum deflection angle of the fatigue crack is established, and the maximum deflection angle of the crack can be predicted.

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 an acoustic emission signal. 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 key components subjected to alternating cyclic loads, fatigue failure is the most important failure mode that causes the greatest economic losses. Traditional research on the acquisition and processing of acoustic emission signals remains at the macro level, and is not familiar with the crack propagation process at the micro level. Macroscopic mechanical properties are determined by microstructures, so research on the acquisition and processing of acoustic emission signals at the micro level is very necessary. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for predicting the maximum deflection angle of a micro fatigue crack based on acoustic emission signals, which adopts acoustic emission to in-situ monitor the fatigue crack propagation process at the micro level; obtains crack propagation 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 fatigue crack through SPC-I parameters; verifies the validity of the quantitative relationship through other specimens, and applies it in actual fatigue monitoring.

[0005] To achieve the above object, the present invention discloses the following technical solution:

[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 low-carbon steel test blocks for arc additive manufacturing, cut BMT test pieces and WAAM test pieces at different parts of the test blocks and process initial cracks respectively, then conduct in-situ scanning electron microscope fatigue crack growth experiments and synchronously perform in-situ acoustic emission monitoring, collect SEM images during fatigue crack growth and corresponding acoustic emission signal segments;

[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+(dd 0 ) / k]

[0016] Among them, SPC-I(α) is the single characteristic parameter corresponding to the maximum deflection angle α of fatigue crack, α is the maximum deflection angle of fatigue crack, d is the grain size of ferrite in BMT specimen, d 0 is the grain size of ferrite in the WAAM specimen, 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 a 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, k in step S4 are obtained by fitting through the maximum likelihood method, and a, b, 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, preparing a low carbon steel test block and machining a pre-crack on the test block as a test piece 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 preparation of BMT specimens and WAAM specimens in step S11 is specifically as follows: using a forged low-carbon steel plate as a substrate of a low-carbon steel test block, adopting a cold metal transfer arc welding mode, using a six-axis robotic arm to drive a welding gun, and stacking low-carbon steel wires layer by layer on the forged low-carbon steel plate to prepare an additive low-carbon steel material test block, each layer is stacked by oscillation to construct a low-carbon steel test block with a target thickness, cutting a BMT specimen from the substrate part of the low-carbon steel test block, and cutting a WAAM specimen from the filling part of the low-carbon steel test block, and then processing initial cracks 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 acquisition parameters and performing a lead breaking experiment to verify the quality of acoustic emission signal acquisition, performing an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and synchronously acquiring 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 frequency band of 100-700kHz, 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 extension SEM image, specifically: the deflection angle of the fatigue crack extension 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 extended crack observed in the SEM image 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, a 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 frequency spectrum is obtained.

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

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

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

[0034] (3) In the verification stage, the present invention uses multiple groups of test pieces to verify the validity of the quantitative relationship between the acoustic emission SPC-I characteristic parameters and the maximum deflection angle of fatigue cracks, ensuring the wide applicability and high repeatability of the experimental results. This feature makes the method of the present invention 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 a wider range of material fatigue research fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a flow chart of the method for predicting the maximum deflection angle of micro fatigue cracks 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 the ferrite of the specimen of the present invention and the SPC-I parameter;

[0039] Figure 5 It is a schematic diagram of SPC sideband peak counting of the present invention;

[0040] Figure 6 It is a 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 the ferrite of the WAAM specimen of the present invention and the SPC-I parameter;

[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 specified.

[0044] The present invention provides a method for predicting the maximum deflection angle of a micro fatigue crack based on an acoustic emission signal. The method adopts acoustic emission to in-situ monitor the fatigue crack propagation process at the micro level; obtains a crack propagation SEM image and a corresponding acoustic emission signal segment 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 parts of the test blocks and process initial cracks respectively. Then, perform in-situ scanning electron microscope fatigue crack growth experiments and synchronously perform in-situ acoustic emission monitoring. Collect SEM images and corresponding acoustic emission signal segments during fatigue crack growth as fatigue crack growth experimental data of arc additive manufacturing 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 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 wires are 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, and then the initial cracks are processed respectively. In this embodiment, 5 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 schematic 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, which is a reserved position 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 built. The in-situ scanning electron microscope fatigue crack growth test needs to be carried out in a vacuum box. In order to export the signal collected by the acoustic emission sensor from the vacuum box in the form of an electrical signal, the vacuum box needs to be processed. Iron wires are welded at both ends of the vacuum box sealing cover, and the sealant fills the pores to ensure the sealing of the vacuum box. Filtering is performed by setting analog filters and digital filters, with a bandwidth of 90-700kHz. Other parameters are the peak definition time (PDT), hit definition time (HDT) and hit lock time (HLT), which are set to 300μs, 600μs and 1000μs respectively. A lead breaking experiment is carried out to verify the effectiveness of acoustic emission signal collection. Specifically, a pencil lead of a certain hardness and diameter is extended by about 2.5mm, formed an angle of 30° with the specimen and broken on the specimen as the acoustic emission signal simulation source. If the amplitude difference of the test results is less than 4dB after three consecutive breaks, it proves that the accuracy of the system is good. 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 the SEM image of the fatigue crack growth process and the corresponding acoustic emission signal segment. A sine wave with a frequency of 10 Hz was applied as the fatigue load. The stress ratio of the cyclic load was constant at R = 0.1. In the stable crack growth stage, a constant amplitude load control experiment was conducted, and the maximum force F of the applied load was set. max = 1kN until the in-situ scanning electron microscope fatigue specimen breaks. During the fatigue test, the experiment and acoustic emission collection are suspended at regular intervals to record the fatigue crack SEM image, acoustic emission signal segment and current cyclic stress loading cycle N.

[0052] S2. Measure the maximum deflection angle of fatigue crack based on SEM image of fatigue crack propagation process. In specific application, the maximum deflection angle of fatigue crack is measured by image processing based on SEM image of fatigue crack propagation, specifically: measure the deflection angle of fatigue crack propagation trajectory, measure the angle between the extension line of the path before the crack deflects and the path after the crack deflects, which is the fatigue crack deflection angle, and take the maximum deflection angle of the propagated crack in the SEM image observed from the current fatigue cycle number to the last fatigue cycle number, which is the maximum deflection angle of fatigue crack.

[0053] In this embodiment, the maximum deflection angle of fatigue crack is measured according to the fatigue crack extension SEM image by image processing software, specifically: the deflection angle of fatigue crack extension trajectory is measured by using ImageJ software, and the two side trajectory segments closest to the inflection point (i.e., the point where the curvature changes significantly) on the crack path 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 is deflected; the subsequent crack path refers to the crack path after the crack is deflected; the maximum deflection angle of the fatigue crack is obtained by observing the extension crack in the SEM image from the current fatigue cycle number to the last 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, 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; wherein 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 extension SEM image by image processing software;

[0062] The original acoustic emission signal is subjected to Fourier transform to obtain the spectrum, the spectrum is bandpass filtered, the 100-700kHz 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, 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 by the N(X) curve, and the average of the number of peaks in the SPC curve is taken to evaluate nonlinearity, thereby producing 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, and the value of LL needs to be explored. In this embodiment, a number of 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, there is a decreasing relationship between the SPC-I characteristic parameters and the maximum deflection angle of the fatigue crack. UL is usually 100% or the highest peak value, 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.

[0072] Low carbon steel is mainly composed of ferrite and pearlite. There are many mechanisms that cause fatigue crack deflection. Different mechanisms will produce different acoustic emission signals. This embodiment only analyzes the deflection that occurs in 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+(dd 0 ) / k]

[0075] Among them, SPC-I(α) is the single characteristic parameter corresponding to the maximum deflection angle α of fatigue crack, α is the maximum deflection angle of fatigue crack, d is the grain size of ferrite in BMT specimen, d 0 is the grain size of ferrite in the WAAM specimen, 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+Ax 0 ]

[0078] x 0 =(dd 0 ) / k

[0079] α=[α 1 , α 2 , α 3 , α 4 , β 1 , β 2 , β 3 , β 4 , ] 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, x 0 is the angular translation related to the grain size of the specimen, d is the grain size of BMT, and d 0 is the grain size of WAAM, set the 0-1 matrix A = [1 1 1 1 0 0 0 0] T Convenient vector group calculation. Then, enter the initial parameter range and sample data of a, b, 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] Through the maximum likelihood estimation function and the built-in fminsearch function of MATLAB software, the SPC-I(α) estimate is continuously iterated until it converges to the SPC-I(α) sample value. Then, 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] Other specimens with the same microstructure characteristics were randomly selected, and only the maximum deflection angle of fatigue cracks in pure ferrite and the corresponding acoustic emission segment were selected for fatigue crack maximum deflection angle measurement and acoustic emission SPC-I parameter calculation. LL was 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, when LL=0.035, the trends of the relationship between SPC-I and the maximum deflection angle of fatigue crack are similar for different specimens, which proves the effectiveness of the method and parameters proposed in the present invention.

[0086] S5. During fatigue monitoring, obtain the 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 detected, and solve the maximum deflection angle of the fatigue crack based on the relationship model of step S4, and be able to obtain the ferrite grain boundary angle information in the low-carbon steel material 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 body, 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, so the atomic arrangement on the grain boundary is often more irregular than the atomic arrangement in the grain, and has higher energy. Grain boundaries are an important factor affecting fatigue crack propagation. The extension of fatigue crack propagation 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 in crystallography that describe the boundaries between different crystallographic orientations in polycrystalline materials. They are mainly distinguished based on the orientation difference between the grains on both sides of the grain boundary. High-Angle Grain Boundaries (HAGBs) usually refer to grain boundaries where the orientation difference between the grains on both sides of the grain boundary is greater than 10 to 15 degrees. The structure of this type of grain boundary is relatively complex, the energy is high, and it has a greater impact on the mechanical properties of the material. HAGBs can prevent the movement of dislocations, thereby enhancing the strength and hardness of the material. During the deformation of the material, HAGBs are conducive to the reorientation of grains, and are therefore closely related to the plastic deformation ability of the material. In contrast, low-angle grain boundaries (LAGBs) refer to grain boundaries where the orientation difference between the grains on both sides of the grain boundary is less than 10 to 15 degrees. This type of grain boundary structure is relatively simple and has low energy, which has a significant impact on the electronic structure and physical properties of the material, but has little impact on the mechanical properties. In the actual fatigue monitoring of arc additively manufactured low-carbon steel, the solved maximum deflection angle of fatigue crack can be used for auxiliary monitoring of crack growth in fatigue monitoring. By obtaining the information on the number of high grain boundary angles of ferrite in the low-carbon steel material, the strength and hardness of the low-carbon steel material can be quickly determined.

[0087] The present invention realizes real-time capture of the microscopic fatigue crack growth process by synchronously carrying out in-situ scanning electron microscope fatigue crack growth experiment and acoustic emission in-situ monitoring. This innovative method not only ensures the clarity and accuracy of the crack growth image, but also significantly improves the accuracy and real-time performance of the maximum deflection angle of fatigue crack characterization through the instant feedback of acoustic emission signals, providing more reliable data support for the evaluation of material fatigue performance. By performing noise reduction processing on the acoustic emission signal and introducing the characteristic parameter SPC-I, a quantitative relationship between the parameter and the maximum deflection angle of fatigue crack is established. The establishment of this relationship model provides a new perspective and tool for in-depth analysis of crack growth behavior. In the verification stage, the present invention uses multiple groups of test pieces to verify the effectiveness of the quantitative relationship between the acoustic emission SPC-I characteristic parameters and the maximum deflection angle of fatigue crack, ensuring the wide applicability and high repeatability of the experimental results. This feature makes the method of the present invention not only suitable for the characterization of the maximum deflection angle of fatigue crack under specific conditions, but also can be promoted and applied in a wider range of material fatigue research fields.

[0088] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design 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 all fall within the protection scope 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 in that: It includes the following steps: S1. Prepare low-carbon steel test blocks for arc additive manufacturing, cut BMT test pieces and WAAM test pieces at different parts of the test blocks and process initial cracks respectively, then conduct in-situ scanning electron microscope fatigue crack growth experiments and synchronously perform in-situ acoustic emission monitoring, collect SEM images during fatigue crack growth and corresponding acoustic emission signal segments; 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: 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; 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: SPC-I(α)=a+b[α+(d-d0) / k] Among them, 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; 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 detected 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 micro fatigue cracks based on acoustic emission signals according to claim 1 is characterized in that: 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.

3. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1 is characterized in that: The formula of SPC-I in step S32 can be expressed as: SPC-I=Average(SPC(T)), (LL≤T≤UL).

4. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 1 is characterized in that: Step S1 specifically includes the following sub-steps: S11, preparing a low carbon steel test block and machining a pre-crack on the test block as a test piece 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 is characterized in that: In step S11, a low-carbon steel test block is prepared by 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, and 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 is characterized in that: The preparation of BMT specimens and WAAM specimens in step S11 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 wires are 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, a BMT specimen is cut from the substrate part of the low-carbon steel test block, and a WAAM specimen is cut from the filling part of the low-carbon steel test block, and then initial cracks are processed respectively.

7. The method for predicting the maximum deflection angle of micro fatigue cracks based on acoustic emission signals according to claim 4 is 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 conducting a lead breaking experiment to verify the quality of acoustic emission signal acquisition, conducting an in-situ scanning electron microscope fatigue crack growth experiment on the specimen, and synchronously acquiring 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 100-700kHz 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 extension SEM image, specifically: the deflection angle of the fatigue crack extension 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. The maximum deflection angle of the extended 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 SPC characteristic parameters are calculated after the spectrum is obtained.

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