A macro-micro coupled electromagnetic ultrasonic simulation method for nondestructive characterization of metal polycrystal microstructure

Abstract

The present application belongs to the technical field of electromagnetic ultrasonic nondestructive testing, and particularly relates to a macro-micro coupled electromagnetic ultrasonic simulation method for nondestructively characterizing metal polycrystal microstructure, which comprises the following steps: preparing pure iron samples with different grain sizes and duplex stainless steel sample groups with different ferrite-austenite phase proportion ratios, and adjusting the grain size and ferrite phase content through a heat treatment process; linearly cutting the sample groups after heat treatment to obtain samples for metallographic observation and electromagnetic ultrasonic signal measurement; observing the microstructure of the samples through a metallographic microscope and adopting ImageJ software for metallographic observation, so as to establish a micro model with different grain sizes and phase proportions; measuring the ultrasonic signals of the cut sample groups by using an electromagnetic ultrasonic probe, processing the data and drawing a time-echo amplitude curve. The method improves the measurement efficiency, reduces the detection time and resource cost, and expands the application range.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic ultrasonic nondestructive testing technology, specifically relating to a nondestructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation. Background Technology

[0002] Steel materials are widely used in aerospace, high-speed rail, rail transportation, and major equipment. Their microstructure (grain size, phase composition, second-phase particles, etc.) directly determines their mechanical properties. Microstructure control in steel materials is crucial for improving product performance and quality. However, effective characterization is a prerequisite for this control. Only by accurately characterizing the morphology, distribution, and evolution of the microstructure can reliable data be provided for optimizing hot working processes and controlling the microstructure, thereby ensuring the strength, toughness, and long-term stability of key components under complex service environments. However, complex processing methods (such as high-temperature rolling, welding thermal cycling, or aging treatment) can easily cause changes in the material's microstructure, leading to performance variations. Therefore, establishing reliable non-destructive testing methods to accurately characterize changes in its microstructure has become a critical link in the quality control of the entire polycrystalline metal materials industry chain.

[0003] Traditional nondestructive testing techniques, such as conventional ultrasonic testing, require coupling agents and are ill-suited to harsh conditions such as high temperatures and rough surfaces, and their detection dimensions are relatively limited. Electromagnetic testing and eddy current testing can only detect the surface and near-surface areas, making it difficult to perform online and accurate characterization of the internal microstructure of materials. These methods are limited by their testing principles and lack sensitivity to differences in the physical properties of the internal microstructure of materials, failing to achieve accurate analysis of grain size and phase content. Destructive testing methods such as metallographic analysis rely on offline sampling, which cannot meet the needs of real-time monitoring and dynamic control in industrial production, leading to a break in the feedback chain between microstructure evolution and process parameters.

[0004] Electromagnetic ultrasound (EMAT), as a novel non-destructive testing method integrating ultrasound and electromagnetic technologies, boasts unique advantages due to its fusion of deep ultrasonic volume wave detection capabilities and non-contact electromagnetic excitation characteristics. It eliminates the need for coupling agents and contact probes found in conventional ultrasound, making it suitable for complex working conditions. Furthermore, it effectively penetrates the material thickness direction, overcoming the skin effect limitations of electromagnetic detection, and can acquire acoustic parameters such as internal grain scattering and sound velocity. Combined with electromagnetic properties, it enables effective characterization of the internal microstructure of materials, becoming a major research direction for high-precision, non-contact, online non-destructive testing of the microstructure of steel materials. However, current EMAT research focuses on detecting defects such as cracks and welds, or on simulating and optimizing the parameters of EMAT transducers to improve their transduction efficiency. At the microstructure level, it primarily focuses on grain number and size, without emphasizing the precise characterization and prediction of microscopic components such as phase composition. Therefore, qualitatively analyzing the influence of grain size and phase ratio on the properties of EMAT and clarifying their relationship with EMAT response is crucial for advancing the precision of this technology and is of great significance for the microstructure regulation and quality control of steel materials. Summary of the Invention

[0005] The purpose of this invention is to provide a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation. This method improves measurement efficiency, reduces testing time and resource costs, and expands the scope of application.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation, comprising the following steps: S1. Prepare industrial pure iron sample groups with different grain sizes and duplex stainless steel sample groups with ferrite phase ratios, and control the grain size and phase ratio through heat treatment process. S2. The heat-treated pure iron and duplex stainless steel samples were wire-cut to cut out samples for metallographic observation and electromagnetic ultrasonic signal measurement, respectively. S3. Observe the microstructure of the sample using a metallographic microscope and perform metallographic observation using ImageJ software; S4. Place the cut sample under the electromagnetic ultrasonic testing probe to measure the electromagnetic ultrasonic signal of different samples, process the data and plot the normalized time-echo amplitude curve, calculate the experimental ultrasonic attenuation coefficient and establish a macroscopic electromagnetic ultrasonic testing model for pure iron. S5. Based on the results of metallographic observation and statistics and the relevant model parameters obtained from the macroscopic model, establish microscopic ultrasonic models with different grain numbers and ferrite phase ratios, and calculate the simulated ultrasonic attenuation coefficient. S6. The acoustic attenuation coefficient obtained through experiments and simulations varies with different micro-components, clarifying the response characteristics between different micro-structural changes and electromagnetic ultrasonic signals.

[0007] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S2, wire cutting of the heat-treated sample specifically includes: The heat-treated sample was wire-cut to obtain a 10×10×5mm³ sample for metallographic observation and a 10×10×5mm³ sample for electromagnetic ultrasonic signal measurement.

[0008] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S3, observing the microstructure of the sample using a metallographic microscope and performing metallographic observation using ImageJ software specifically includes: The microstructure of the sample was observed using a metallographic microscope, and the diameters of ferrite and austenite grains were measured in the metallographic image using the scribing tool in ImageJ software, according to the scale. When measuring the proportion of the ferrite phase, the Threshold function in ImageJ was used to statistically analyze the area ratio of the ferrite phase.

[0009] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S4, the electromagnetic ultrasonic testing probe EMAT includes a permanent magnet, a coil, and a sample, and is a high-frequency signal exciter that integrates excitation and reception.

[0010] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S4, placing the cut sample below the electromagnetic ultrasonic testing probe to achieve electromagnetic ultrasonic signal measurement of different samples specifically includes: The samples, cut into 10×10×5mm³ sizes, were placed under the electromagnetic ultrasonic testing probe. The excitation signal, excitation frequency, and other parameters were set for measurement. The electromagnetic ultrasonic testing probe was connected to the interfaces of the high-frequency signal excitation instrument and the digital oscilloscope, and then connected to the computer, so that the electromagnetic ultrasonic signal was directly displayed on the computer.

[0011] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, step S4, processing data and plotting the normalized time-echo amplitude curve, specifically includes: The measured electromagnetic ultrasound signal data are sample point-amplitude curves, which are converted into time-echo amplitude curves by the sampling frequency and normalized to the [-1, 1] interval. During data processing, the sample points are transformed into time t (μs) using the following formula: T=1 / f; t = n × T; Where T is the time interval between two adjacent sampling points (μs), f is the excitation frequency of the high-frequency signal excitation instrument (MHz), and n is the number of sampling points; the vertical axis is the actual amplitude, and the result data is the amplitude normalized value; the average of the five measurements of each sample is imported into Origin software to plot the time-echo amplitude curve.

[0012] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasound simulation according to the present invention, preferably, in step S4, calculating the experimental ultrasound attenuation coefficient specifically includes: Based on the processed ultrasonic echo signal curve, the experimentally measured ultrasonic attenuation coefficient α is calculated using the following formula: ; The calculations show that A1 and A2 are the first and second bottom surface echoes of the ultrasonic wave inside the sample; d is the propagation distance of the ultrasonic wave inside the sample, which is the sample thickness.

[0013] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S4, establishing a macroscopic electromagnetic ultrasonic testing model for pure iron specifically includes: Due to the large computational load of electromagnetic ultrasonic simulation, all models adopt a two-dimensional modeling approach. First, the geometric modeling of the permanent magnet, coil, and sample is completed, followed by differentiated coarse and fine mesh generation. Then, based on the excitation signal, excitation frequency, and other parameters used in the electromagnetic ultrasonic testing system, the material properties, physical field parameters, and boundary conditions are set. Finally, for pure iron as a ferromagnetic material, magnetostrictive multiphysics is coupled to realize the energy conversion of EMAT.

[0014] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to the present invention, preferably, in step S5, establishing micro-ultrasonic models with different grain numbers and ferrite phase ratios specifically includes: While ensuring that the input frequency, excitation function, waveform, and other related settings are consistent with those in the experiment and the macroscopic model, ultrasonic waves are excited in the form of boundary loads, just like in the macroscopic model. For pure iron samples with different grain sizes, the Voronoi algorithm can be used to generate grain geometric models of different sizes, numbers, and distributions, followed by the generation of vector graphics, which are then imported into the simulation software. For 2205 samples with different ferrite-austenite phase ratios, with a fixed grain number model generated, a program can be written in the software to achieve random distribution of ferrite and austenite after inputting the phase ratio value. At the same time, since austenite has a stronger response to scattering than ferrite, a finer mesh needs to be used in the austenite region during mesh generation.

[0015] As a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasound simulation according to the present invention, preferably, in step S5, calculating the simulated ultrasound attenuation coefficient specifically includes: After the simulation is completed, along the direction of ultrasound propagation, avoiding the skin layer, and after normalizing the propagation distance from the wave source, eight points are selected at a depth of 20%-90% to obtain the ultrasound echo signal. The echo envelope is then obtained by applying a Hilbert transform to obtain the pulse peak value at each point. Substituting this into the formula: ; Where An is the pulse peak value at a depth n% from the wave source, α is the acoustic attenuation coefficient to be measured, d is the sample thickness, and coefficient 2 represents the round-trip propagation path of the sound wave. Taking the natural logarithm of both sides of the equation: ; Then, a univariate linear regression was performed with different depths from the wave source as independent variables. The absolute value of the slope of the fitted line is the simulated sound attenuation coefficient obtained.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention employs an innovative electromagnetic ultrasonic nondestructive testing method to characterize grain size and ferrite phase ratio in polycrystalline metallic materials. It is applicable to measurements of smaller samples, not just large steel plates. Compared to traditional methods, it improves measurement efficiency, reduces time and resource costs, and expands its applicability. Through electromagnetic ultrasonic characterization, accurate electromagnetic ultrasonic parameter data (such as sound attenuation coefficient and amplitude) are obtained, further clarifying the influence of grain size and phase ratio variations on electromagnetic ultrasonic signals within the multi-microscopic component structure of polycrystalline metallic materials. Therefore, this method provides a feasible approach for nondestructively characterizing important microscopic components and properties of steel using electromagnetic ultrasonic technology, which is of great significance for materials research, industrial applications, and academic research in related fields. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic flowchart of a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation, provided as an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of an electromagnetic ultrasonic testing system provided in an embodiment of this application.

[0019] Figure 3 This is a schematic diagram of the heat treatment curve of industrial pure iron provided in the embodiments of this application.

[0020] Figure 4 The following are metallographic diagrams of industrial pure iron with different grain sizes at 100x magnification provided in the embodiments of this application; wherein, (a) is the metallographic structure of untreated industrial pure iron, (b) is the metallographic structure after annealing at 930°C for 30 min, (c) is the metallographic structure after annealing at 930°C for 60 min, (d) is the metallographic structure after annealing at 930°C for 90 min, and (e) is the metallographic structure after annealing at 930°C for 120 min.

[0021] Figure 5 This is a schematic diagram of the normalized time-amplitude curves corresponding to pure iron microstructures of different grain sizes measured by the electromagnetic ultrasonic testing system provided in the embodiments of this application.

[0022] Figure 6 A schematic diagram of the aging temperature-grain size / acoustic attenuation coefficient variation curves of pure iron microstructure after different heat treatment regulation, obtained by calculating and processing the experimental data provided in the embodiments of this application.

[0023] Figure 7 The diagram shows the mesh division of the macroscopic electromagnetic ultrasonic model provided in the embodiments of this application; wherein, (a) is the overall mesh diagram of the macroscopic model, and (b) is the magnified area diagram of the coil part.

[0024] Figure 8 A schematic diagram of a microscopic ultrasonic model with different numbers of grains provided in the embodiments of this application.

[0025] Figure 9 This is a schematic diagram of the normalized time-amplitude curves at different depths when the number of grains is 50, obtained by calculation using a microscopic model according to an embodiment of this application.

[0026] Figure 10 This is a schematic diagram of the variation curves of different grain numbers and acoustic attenuation coefficients obtained after processing the simulation data provided in the embodiments of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figure 1-10 The present invention provides the following technical solutions: Example 1 like Figure 1 As shown, the present invention provides a non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation, comprising the following steps: S1. Prepare industrial pure iron sample groups with different grain sizes and duplex stainless steel sample groups with ferrite phase ratios, and control the grain size and phase ratio through heat treatment process. S2. The heat-treated pure iron and duplex stainless steel samples were wire-cut to cut out samples for metallographic observation and electromagnetic ultrasonic signal measurement, respectively. S3. Observe the microstructure of the sample using a metallographic microscope and perform metallographic observation using ImageJ software; S4. Place the cut sample under the electromagnetic ultrasonic testing probe to measure the electromagnetic ultrasonic signal of different samples, process the data and plot the normalized time-echo amplitude curve, calculate the experimental ultrasonic attenuation coefficient and establish a macroscopic electromagnetic ultrasonic testing model for pure iron. S5. Based on the results of metallographic observation and statistics and the relevant model parameters obtained from the macroscopic model, establish microscopic ultrasonic models with different grain numbers and ferrite phase ratios, and calculate the simulated ultrasonic attenuation coefficient. S6. The acoustic attenuation coefficient obtained through experiments and simulations varies with different micro-components, clarifying the response characteristics between different micro-structural changes and electromagnetic ultrasonic signals.

[0029] Furthermore, wire cutting of the heat-treated sample specifically includes: The heat-treated sample was wire-cut to obtain a 10×10×5mm³ sample for metallographic observation and a 10×10×5mm³ sample for electromagnetic ultrasonic signal measurement.

[0030] Furthermore, in S3, the microstructure of the sample is observed using a metallographic microscope, and metallographic observation is performed using ImageJ software, specifically including: The microstructure of the sample was observed using a metallographic microscope, and the diameters of ferrite and austenite grains were measured in the metallographic image using the scribing tool in ImageJ software, according to the scale. When measuring the proportion of the ferrite phase, the Threshold function in ImageJ was used to statistically analyze the area ratio of the ferrite phase.

[0031] Furthermore, in S4, the Electromagnetic Ultrasonic Testing Probe (EMAT) includes a permanent magnet, a coil, and a sample, serving as a high-frequency signal exciter that integrates excitation and reception. A permanent magnet with north at the top and south at the bottom is used to provide a magnetic field along the vertical direction, and a helical coil is employed to generate ultrasonic volume waves that overcome the skin effect and propagate within the material sample, thus enabling the detection of the material's internal microstructure.

[0032] Furthermore, in S4, the cut sample is placed below the electromagnetic ultrasonic testing probe to achieve electromagnetic ultrasonic signal measurement of different samples, specifically including: The samples, cut into 10×10×5mm³ sizes, were placed under the electromagnetic ultrasonic testing probe. The excitation signal, excitation frequency, and other parameters were set for measurement. The electromagnetic ultrasonic testing probe was connected to the interfaces of the high-frequency signal excitation instrument and the digital oscilloscope, and then connected to the computer, so that the electromagnetic ultrasonic signal was directly displayed on the computer.

[0033] Furthermore, in S4, processing the data and plotting the normalized time-echo amplitude curve specifically includes: The measured electromagnetic ultrasound signal data are sample point-amplitude curves, which are converted into time-echo amplitude curves by the sampling frequency and normalized to the [-1, 1] interval. During data processing, the sample points are transformed into time t (μs) using the following formula: T=1 / f; t = n × T; Where T is the time interval between two adjacent sampling points (μs), f is the excitation frequency of the high-frequency signal excitation instrument (MHz), and n is the number of sampling points; the vertical axis is the actual amplitude, and the result data is the amplitude normalized value; the average of the five measurements of each sample is imported into Origin software to plot the time-echo amplitude curve.

[0034] Furthermore, in S4, the calculation of the experimental ultrasonic attenuation coefficient specifically includes: Based on the processed ultrasonic echo signal curve, the experimentally measured ultrasonic attenuation coefficient α is calculated using the following formula: ; The calculations show that A1 and A2 are the first and second bottom surface echoes of the ultrasonic wave inside the sample; d is the propagation distance of the ultrasonic wave inside the sample, which is the sample thickness.

[0035] Furthermore, in S4, the establishment of a macroscopic electromagnetic ultrasonic testing model for pure iron specifically includes: Due to the large computational load of electromagnetic ultrasonic simulation, all models adopt a two-dimensional modeling approach. First, the geometric modeling of the permanent magnet, coil, and sample is completed, followed by differentiated coarse and fine mesh generation. Then, based on the excitation signal, excitation frequency, and other parameters used in the electromagnetic ultrasonic testing system, the material properties, physical field parameters, and boundary conditions are set. Finally, for pure iron as a ferromagnetic material, magnetostrictive multiphysics is coupled to realize the energy conversion of EMAT.

[0036] Furthermore, in S5, establishing microscopic ultrasonic models with different grain numbers and ferrite phase ratios specifically includes: While ensuring that the input frequency, excitation function, waveform, and other related settings are consistent with those in the experiment and the macroscopic model, ultrasonic waves are excited in the form of boundary loads, just like in the macroscopic model. For pure iron samples with different grain sizes, the Voronoi algorithm can be used to generate grain geometric models of different sizes, numbers, and distributions, followed by the generation of vector graphics, which are then imported into the simulation software. For 2205 samples with different ferrite-austenite phase ratios, with a fixed grain number model generated, a program can be written in the software to achieve random distribution of ferrite and austenite after inputting the phase ratio value. At the same time, since austenite has a stronger response to scattering than ferrite, a finer mesh needs to be used in the austenite region during mesh generation.

[0037] Furthermore, in S5, the calculation of the simulated ultrasonic attenuation coefficient specifically includes: After the simulation is completed, along the direction of ultrasound propagation, avoiding the skin layer, and after normalizing the propagation distance from the wave source, eight points are selected at a depth of 20%-90% to obtain the ultrasound echo signal. The echo envelope is then obtained by applying a Hilbert transform to obtain the pulse peak value at each point. Substituting this into the formula: ; Where An is the pulse peak value at a depth n% from the wave source, α is the acoustic attenuation coefficient to be measured, d is the sample thickness, and coefficient 2 represents the round-trip propagation path of the sound wave. Taking the natural logarithm of both sides of the equation: ; Then, a univariate linear regression was performed with different depths from the wave source as independent variables. The absolute value of the slope of the fitted line is the simulated sound attenuation coefficient obtained.

[0038] Specifically, the specific implementation steps of the method of the present invention are as follows: 1. Industrial pure iron and 2205 duplex stainless steel were heat-treated under different holding and cooling conditions. Pure iron samples with different grain sizes and 2205 duplex stainless steel samples with similar grain sizes but different ferrite phase ratios were obtained.

[0039] 2. The heat-treated steel sample was wire-cut to obtain a metallographic observation sample of size 10×10×5mm3 and an electromagnetic ultrasonic testing sample of size 10×10×5mm3.

[0040] 3. The microstructure of the samples was observed using a scanning electron microscope (SEM). Using the scribing tool in ImageJ software, the diameters of ferrite and austenite grains were measured in the metallographic images according to a 10 μm scale. When measuring the ferrite phase ratio, the Threshold function in ImageJ was used to statistically analyze the area percentage of the ferrite phase. Using area percentage to characterize the phase fraction directly reflects the distribution of the ferrite phase in a two-dimensional plane. For both measurement methods, 10 uniformly selected SEM images were taken from each observed sample. For the ferrite phase ratio measurement, the diameters of 10 grains were uniformly selected from each SEM image, and the average value was calculated after all measurements were completed.

[0041] 4. Cut the prepared test samples with different microstructures into 10×10×5mm³ specimens and place them below the electromagnetic ultrasonic probe. Connect the electromagnetic ultrasonic probe to the high-frequency signal excitation instrument, which is simultaneously connected to the computer. The connections are as follows: Figure 2 As shown. The excitation frequency was set to 7MHz during the measurement.

[0042] 5. The experimental data consisted of electromagnetic ultrasonic signal data, presented as sampling point-amplitude curves. During data processing, the sampling frequency was converted into a time-echo amplitude curve, and the data was normalized to the [-1,1] interval. The average of five measurements for each sample was imported into Origin, and a normalized time-amplitude curve was plotted. The acoustic attenuation coefficient was calculated using a formula, and the change curve of the acoustic attenuation coefficient was plotted.

[0043] 6. In finite element simulation software, establish a macroscopic electromagnetic ultrasonic model and microstructure models of different micro-components. Set the excitation signal, excitation frequency, and other parameters to be the same so that the model generates the same volume wave. Using the data obtained from the model calculations, time-amplitude curves at different depths along the ultrasonic propagation path can be obtained by setting domain point probes. The sound attenuation coefficient can then be calculated using formulas, and the change curve of the sound attenuation coefficient can be plotted.

[0044] Example 2 According to step 1 of the patent, pure iron and 2205 duplex stainless steel are first subjected to aging treatments for different durations, under the following conditions: Figure 3After processing, each sample was cut according to step 2 of the patent, and the microstructure of the sample was observed and the grain size and phase fraction were measured according to the method in step 3 of the patent. Partial metallographic photographs under various heat treatment conditions are shown below. Figure 4 As shown in (a)-(e), the results of pure iron grain size measurement under different aging time conditions are shown in Table 1. Combining the metallographic photographs and grain size data, it can be seen that the heat-treated samples are pure iron samples with different grain sizes.

[0045] Table 1

[0046] Subsequently, electromagnetic ultrasonic testing samples were prepared and an electromagnetic ultrasonic testing system was constructed according to the method in step 4 of the patent. Electromagnetic ultrasonic signals were detected on pure iron samples with different grain sizes, and the measurement data were processed to plot the corresponding normalized time-amplitude curves, such as... Figure 5 As shown. The acoustic attenuation coefficient is calculated according to the formula in step 4 of the patent, and the corresponding experimental aging temperature-grain size / acoustic attenuation coefficient variation curve is plotted, as shown. Figure 6 As shown, it can be observed that as the grain size increases, the corresponding acoustic attenuation coefficient also increases. Then, according to the method in step 4 of the patent, a macroscopic electromagnetic ultrasonic model is established in the finite element simulation software, such as... Figure 7 As shown, the geometric modeling of each part and the mesh division with different thicknesses can be seen.

[0047] Then, following the method in step 5 of the patent, microscopic ultrasonic models with different grain numbers are established, such as... Figure 8 As shown, the excitation frequency and other parameters are set in a manner consistent with the macroscopic model, and the excited component in the model is an ultrasonic volume wave. The normalized time-amplitude curves at different depths with the same grain number calculated by the model are imported into Origin for processing, such as... Figure 9 As shown. Then, the different grain number-acoustic attenuation variation curves obtained after processing according to the formula are as follows. Figure 10 As shown, it can be observed that the acoustic attenuation coefficient decreases with increasing grain number, i.e., decreasing grain size. Conclusions corresponding to the experiments were obtained, clarifying the response relationship between different microstructures and electromagnetic ultrasound, and proving the effectiveness of electromagnetic ultrasound detection.

[0048] This invention employs heat treatment to prepare pure iron samples with different grain sizes and duplex stainless steel samples with different ferrite phase ratios. Electromagnetic ultrasonic signals are measured on these samples using a constructed electromagnetic ultrasonic probe to clarify the correspondence between microstructure components and electromagnetic ultrasonic signals, revealing the corresponding response mechanism of electromagnetic ultrasonic detection, and characterizing the grain size in pure iron and the proportion of the ferrite phase in duplex stainless steel. This improved design can accurately characterize the differences in electromagnetic ultrasonic signals caused by variations in grain size and ferrite phase ratio, providing important experimental data and theoretical support for research and application in the electromagnetic ultrasonic characterization of steel microstructures.

[0049] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0050] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation, characterized in that, Includes the following steps: S1. Prepare industrial pure iron sample groups with different grain sizes and duplex stainless steel sample groups with ferrite phase ratios, and control the grain size and phase ratio through heat treatment process. S2. The heat-treated pure iron and duplex stainless steel samples were wire-cut to cut out samples for metallographic observation and electromagnetic ultrasonic signal measurement, respectively. S3. Observe the microstructure of the sample using a metallographic microscope and perform metallographic observation using ImageJ software; S4. Place the cut sample under the electromagnetic ultrasonic testing probe to measure the electromagnetic ultrasonic signal of different samples, process the data and plot the normalized time-echo amplitude curve, calculate the experimental ultrasonic attenuation coefficient and establish a macroscopic electromagnetic ultrasonic testing model for pure iron. S5. Based on the results of metallographic observation and statistics and the relevant model parameters obtained from the macroscopic model, establish microscopic ultrasonic models with different grain numbers and ferrite phase ratios, and calculate the simulated ultrasonic attenuation coefficient. S6. The acoustic attenuation coefficient obtained through experiments and simulations varies with different micro-components, clarifying the response characteristics between different micro-structural changes and electromagnetic ultrasonic signals.

2. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S2, wire cutting of the heat-treated sample specifically includes: The heat-treated sample was wire-cut to obtain a 10×10×5mm³ sample for metallographic observation and a 10×10×5mm³ sample for electromagnetic ultrasonic signal measurement.

3. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S3, observing the microstructure of the sample using a metallographic microscope and performing metallographic observation using ImageJ software specifically includes: The microstructure of the sample was observed using a metallographic microscope, and the diameters of ferrite and austenite grains were measured in the metallographic image using the scribing tool in ImageJ software, according to the scale. When measuring the proportion of the ferrite phase, the Threshold function in ImageJ was used to statistically analyze the area ratio of the ferrite phase.

4. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In S4, the electromagnetic ultrasonic testing probe EMAT includes a permanent magnet, a coil, and a sample, and is a high-frequency signal exciter that integrates excitation and reception.

5. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S4, placing the cut sample below the electromagnetic ultrasonic testing probe to perform electromagnetic ultrasonic signal measurement on different samples specifically includes: The samples, cut into 10×10×5mm³ sizes, were placed under the electromagnetic ultrasonic testing probe. The excitation signal, excitation frequency, and other parameters were set for measurement. The electromagnetic ultrasonic testing probe was connected to the interfaces of the high-frequency signal excitation instrument and the digital oscilloscope, and then connected to the computer, so that the electromagnetic ultrasonic signal was directly displayed on the computer.

6. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S4, processing the data and plotting the normalized time-echo amplitude curve specifically includes: The measured electromagnetic ultrasound signal data are sample point-amplitude curves, which are converted into time-echo amplitude curves by the sampling frequency and normalized to the [-1,1] interval. During data processing, the sample points are transformed into time t (μs) using the following formula: T=1 / f; t = n × T; Where T is the time interval between two adjacent sampling points (μs), f is the excitation frequency of the high-frequency signal excitation instrument (MHz), and n is the number of sampling points; the vertical axis is the actual amplitude, and the result data is the amplitude normalized value; the average of the five measurements of each sample is imported into Origin software to plot the time-echo amplitude curve.

7. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S4, calculating the experimental ultrasonic attenuation coefficient specifically includes: Based on the processed ultrasonic echo signal curve, the experimentally measured ultrasonic attenuation coefficient α is calculated using the following formula: ; The calculations show that A1 and A2 are the first and second bottom surface echoes of the ultrasonic wave inside the sample; d is the propagation distance of the ultrasonic wave inside the sample, which is the sample thickness.

8. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S4, establishing a macroscopic model for electromagnetic ultrasonic testing of pure iron specifically includes: Due to the large computational load of electromagnetic ultrasonic simulation, all models adopt a two-dimensional modeling approach. First, the geometric modeling of the permanent magnet, coil, and sample is completed, and then differentiated coarse and fine meshes are generated. Then, based on the excitation signal, excitation frequency, and other parameters used in the electromagnetic ultrasonic testing system, the material properties, physical field parameters, and boundary conditions are set. Finally, for pure iron as a ferromagnetic material, magnetostrictive multiphysics is coupled to realize the energy conversion of EMAT.

9. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S5, establishing microscopic ultrasonic models with different grain numbers and ferrite phase ratios specifically includes: While ensuring that the input frequency, excitation function, waveform, and other related settings are consistent with those in the experiment and the macroscopic model, ultrasonic waves are excited in the form of boundary loads, just like in the macroscopic model. For pure iron samples with different grain sizes, the Voronoi algorithm can be used to generate grain geometric models of different sizes, numbers, and distributions, followed by the generation of vector graphics, which are then imported into the simulation software. For 2205 samples with different ferrite-austenite phase ratios, with a fixed grain number model generated, a program can be written in the software to achieve random distribution of ferrite and austenite after inputting the phase ratio value. At the same time, since austenite has a stronger response to scattering than ferrite, a finer mesh needs to be used in the austenite region during mesh generation.

10. The non-destructive characterization method for the microstructure of polycrystalline metals using macro-micro coupled electromagnetic ultrasonic simulation according to claim 1, characterized in that: In step S5, calculating the simulated ultrasonic attenuation coefficient specifically includes: After the simulation is completed, along the direction of ultrasound propagation, avoiding the skin layer, and after normalizing the propagation distance from the wave source, eight points are selected at a depth of 20%-90% to obtain the ultrasound echo signal. The echo envelope is then obtained by applying a Hilbert transform to obtain the pulse peak value at each point. Substituting this into the formula: ; Where An is the pulse peak value at a depth n% from the wave source, α is the acoustic attenuation coefficient to be measured, d is the sample thickness, and coefficient 2 represents the round-trip propagation path of the sound wave; taking the natural logarithm of both sides of the equation: ; Then, a univariate linear regression was performed with different depths from the wave source as independent variables. The absolute value of the slope of the fitted line is the simulated sound attenuation coefficient obtained.

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