Ferromagnetic metal material recrystallization rate rapid characterization method based on magnetic domain motion characteristics

Abstract

The invention discloses a ferromagnetic metal material recrystallization rate rapid characterization method based on magnetic domain motion characteristics, which comprises the following steps: firstly, preparing a sample; secondly, the characteristic microstructure-recrystallization rate of the ferromagnetic metal material is obtained; then, performing electromagnetic nondestructive testing on the ferromagnetic metal material based on a multi-magnetic testing device to obtain electromagnetic signals representing reversible and irreversible motion of a magnetic domain, and defining and extracting corresponding electromagnetic characteristic parameters of the ferromagnetic metal material at the same time; and finally, by analyzing the relationship between the electromagnetic characteristic parameters and the characteristic microstructure, screening out the electromagnetic characteristic parameters as input, and establishing a deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of the magnetic domain reversible and irreversible motion. And parameters of the characteristic microstructure deep neural network representation model based on electromagnetic characteristic parameters of magnetic domain reversible and irreversible motion are adjusted. According to the invention, nondestructive testing of the recrystallization rate of the ferromagnetic metal material is realized.

Description

Technical Field

[0001] This invention relates to electromagnetic nondestructive testing technology for ferromagnetic materials, and more specifically, to a rapid characterization method for the recrystallization rate of ferromagnetic metallic materials based on the magnetic domain motion characteristics. Background Technology

[0002] Currently, steel mills both domestically and internationally primarily rely on destructive methods such as optical metallography and EBSD (Electron Back Scatter Diffraction) to obtain the microstructure of their products. These experimental methods cannot achieve online detection, have long testing cycles, lack intelligence, and result in significant resource waste. Guided by concepts such as carbon neutrality and environmental protection, rapid and non-destructive technologies for detecting microstructure are becoming increasingly important.

[0003] In this field, the motion characteristics of magnetic domains can be categorized into reversible and irreversible motions. Incremental permeability technology based on reversible domain motion and magnetic Barkhausen technology based on irreversible domain motion are feasible methods for detecting the microstructure of materials. Under the excitation of an external magnetic field, different microstructures have varying effects on the reversible and irreversible motions of magnetic domains, and also affect the structure of the domains. Therefore, extracting electromagnetic nondestructive testing signals that characterize the reversible and irreversible motions of magnetic domains, performing signal analysis, and extracting corresponding features can reflect the microstructure of the material under test.

[0004] Current technologies mostly focus on methods for testing the mechanical properties of ferromagnetic materials. As for methods for the microstructure of ferromagnetic materials, they are limited to destructive testing techniques. Therefore, no non-destructive testing techniques for the microstructure of ferromagnetic materials have been found that can be directly applied. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a rapid characterization method for the recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics, thereby achieving non-destructive testing of the recrystallization rate of ferromagnetic metallic materials.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics;

[0008] First, prepare the sample;

[0009] Secondly, the characteristic microstructure of ferromagnetic metallic materials—recrystallization rate—was obtained.

[0010] Then, electromagnetic non-destructive testing of ferromagnetic metal materials is carried out based on multi-magnetic detection equipment to obtain electromagnetic signals characterizing the reversible and irreversible motion of magnetic domains, and at the same time, the corresponding electromagnetic characteristic parameters of ferromagnetic metal materials are defined and extracted.

[0011] Finally, by analyzing the relationship between electromagnetic characteristic parameters and characteristic microstructure, electromagnetic characteristic parameters as inputs were selected, and a deep neural network representation model of characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion was established. The parameters of the deep neural network representation model of characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion were adjusted to obtain the optimal deep neural network representation model of characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion, thus realizing the non-destructive detection of characteristic microstructure and recrystallization rate of ferromagnetic metal materials.

[0012] Preferably, the rapid characterization method for recrystallization rate of the ferromagnetic metallic material specifically includes the following steps:

[0013] S1, Prepare samples for material characterization using the EBSD method and samples for detection using a multimagnetic detection device;

[0014] S2, The recrystallization rate of the microstructure of ferromagnetic metal materials was quantitatively characterized by the EBSD method.

[0015] S3. Repeat steps S1 and S2 to obtain the quantitative characterization results of different ferromagnetic metal materials.

[0016] S4. Based on step S1, use a multi-magnetic detection device to perform electromagnetic non-destructive testing on different ferromagnetic metal materials, and extract the electromagnetic characteristic parameters of the reversible and irreversible motion of the magnetic domains of the ferromagnetic metal materials.

[0017] S5. Based on the quantitative characterization results in step S3, the characteristic microstructure of the ferromagnetic material is obtained and a correlation is established with the electromagnetic characteristic parameters of the reversible and irreversible motion of the magnetic domains of the ferromagnetic metal material obtained in step S4.

[0018] S6. Establish a deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion.

[0019] S7 uses confidence level to evaluate the accuracy of the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion.

[0020] S8. Following the process of steps S6 and S7, the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion is repeatedly trained ten times, and the average of the ten training results is taken as the final detection value.

[0021] Preferably, the sample size used for material characterization by the EBSD method is 10mm*10mm;

[0022] The sample size used for testing by the multi-magnetic detection equipment is 150mm*600mm.

[0023] Preferably, step S4 specifically includes:

[0024] Twelve electromagnetic characteristic parameters, including MBN and MIP, were measured using a multi-magnetic detection device. Each sample was tested for 120 seconds, resulting in more than 100 sets of electromagnetic characteristic data. After outlier cleaning, 100 sets of data were retained for subsequent analysis.

[0025] Preferably, the 12 electromagnetic characteristic parameters are as follows:

[0026] MBN includes MMAX, MMEAN, HCM, DH25M, DH50M, and DH75M;

[0027] The MIP series includes UMAX, UMEAN, HCU, DH25U, DH50U, and DH75U.

[0028] Preferably, the multi-magnetic detection device applies different excitation magnetic fields using the magnetic Barkhausen noise method, incremental permeability method, tangential magnetic field harmonic analysis method, or multi-frequency eddy current detection method.

[0029] Preferably, in step S6, during the training process of the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion, the logsig, tansig, purelin activation functions and trainlm training function are used, the error threshold is set to 0.000001, the initial learning rate is set to 0.01, and the number of iterations is set to 1000.

[0030] Preferably, the confidence level in step S7 is defined as:

[0031]

[0032] In the formula, N 10 To verify the predicted value Y of the ensemble model ci Compared with the true value Y i The number of validation sets with an error ρ less than 10%, where N is the total number of validation sets, i.e.:

[0033]

[0034] The present invention provides a rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics, which has the following advantages:

[0035] (1) To achieve non-destructive testing of the characteristic microstructure and recrystallization rate of ferromagnetic steel materials, with a testing accuracy greater than 95%, an error less than 10%, and a confidence rate greater than 85%.

[0036] (2) It provides a digital and rapid evaluation method for characterizing the microstructure of ferromagnetic steel materials, thereby improving detection efficiency and reducing detection costs. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the rapid characterization method for recrystallization rate of ferromagnetic metallic materials according to the present invention.

[0038] Figure 2 This is a schematic diagram of the EBSD characterization results of the microstructure in step S2 of the rapid characterization method for recrystallization rate of ferromagnetic metal materials in this invention.

[0039] Figure 3 This is a schematic diagram showing the relationship between recrystallization rate and yield strength in step S3 of the rapid characterization method for recrystallization rate of ferromagnetic metal materials of the present invention.

[0040] Figure 4 This is a schematic diagram of the probe composition of the multi-magnetic detection device in step S4 of the rapid characterization method for recrystallization rate of ferromagnetic metallic materials of the present invention.

[0041] Figure 5 This is a schematic diagram of the MBN (MIP) butterfly curve in step S4 of the rapid characterization method for recrystallization rate of ferromagnetic metal materials in this invention.

[0042] Figure 6 This is a schematic diagram of the correlation between the electromagnetic characteristic parameters MBN and MIP in step S5 of the rapid characterization method for recrystallization rate of ferromagnetic metal materials in this invention. (a) represents MBN and (b) represents MIP.

[0043] Figure 7 This is a schematic diagram of the evaluation results of step S8 in the rapid characterization method for recrystallization rate of ferromagnetic metal materials of the present invention.

[0044] Figure 4 In the middle, 1-shell, 2-electronic board (preamplifier), 3-magnetic yoke, 4-electromagnetic coil, 5-connecting cable, 6-Hall sensor, 7-transmitter coil, 8-receiver coil, 9-electromagnetic non-destructive testing sample. Detailed Implementation

[0045] To better understand the above-mentioned technical solutions of the present invention, the technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0046] The present invention provides a rapid characterization method for recrystallization rate of ferromagnetic metal materials based on magnetic domain motion characteristics. Based on material characterization techniques such as EBSD, the microstructure of ferromagnetic metal materials is quantitatively characterized. At the same time, based on the analysis of the intrinsic physical relationship between microstructure and mechanical properties, the characteristic parameters of microstructure that have a great influence on the mechanical properties of ferromagnetic metal materials are determined. This process is called determining the characteristic microstructure of ferromagnetic metal materials.

[0047] Meanwhile, based on multi-magnetic detection equipment, electromagnetic non-destructive testing is performed on ferromagnetic materials to obtain electromagnetic parameters characterizing the magnetic properties of the ferromagnetic metal material.

[0048] Using electromagnetic parameters as input and characteristic microstructure as output, a deep neural network (DNN) characterization model of characteristic microstructure (recrystallization rate) based on electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion is established, thereby realizing non-destructive testing of characteristic microstructure-recrystallization rate of ferromagnetic materials.

[0049] Combination Figure 1 As shown, the rapid characterization method for recrystallization rate of ferromagnetic metallic materials of the present invention specifically includes the following steps:

[0050] S1 uses wire cutting technology to process ferromagnetic metal materials into small-sized samples of 10mm*10mm (for material characterization using EBSD technology) and large-sized samples of 150mm*600mm (for detection by multi-magnetic detection equipment).

[0051] S2, The recrystallization rate of the microstructure of ferromagnetic metallic materials was quantitatively characterized using the EBSD method. The quantitative characterization results are as follows: Figure 2 As shown;

[0052] S3. Repeat steps S1 and S2 to obtain the quantitative characterization results of different ferromagnetic metallic materials. Simultaneously, based on the correlation analysis between the microstructure and mechanical properties of different ferromagnetic metallic materials, it was found that as the recrystallization rate increases, the yield strength gradually decreases, and the grain size becomes more concentrated, leading to an increase in yield strength (e.g., ...). Figure 3 (As shown). Analysis of the overall trend reveals that the characteristic microstructure of ferromagnetic materials is the recrystallization rate;

[0053] S4, based on step S1, uses a multi-magnetic detection device to perform electromagnetic non-destructive testing on different ferromagnetic metal materials, and extracts the electromagnetic characteristics of reversible and irreversible magnetic domain motion in the ferromagnetic metal materials. This multi-magnetic detection device integrates multiple electromagnetic detection methods, applying different excitation magnetic fields to stimulate different electromagnetic principles, such as the magnetic Barkhausen noise method, incremental permeability method, tangential magnetic field harmonic analysis method, and multi-frequency eddy current detection method. During testing, different alternating magnetic field excitations are applied to the ferromagnetic material, from zero magnetic field excitation to positive saturation and back to zero magnetic field excitation, then increasing to negative saturation and back to zero magnetic field excitation. Throughout the entire repeated magnetization cycle, the magnetization process of different microstructures is different. The cumulative effect of magnetic domain motion leads to changes in the material's permeability, deepening the magnetization, and ultimately causing changes in the entire magnetization curve. The signals obtained in this process can reflect the magnetic properties and microstructural characteristics of different ferromagnetic materials.

[0054] like Figure 4 As shown, the probe of the multi-magnetic testing device inherits four micro-electromagnetic non-destructive testing techniques and selects 41 micro-electromagnetic properties to characterize the magnetic parameters of automotive sheet steel. The magnetic Barkhausen noise method (MBN) and incremental permeability method (MIP) were used in the experimental analysis, involving a total of 12 micro-electromagnetic properties.

[0055] In MBN technology, a high-amplitude, low-frequency sinusoidal current is fed into a yoke coil 4 wound around a U-shaped yoke 3. To ensure that the signal of irreversible magnetic domain motion is detected by the receiving coil 8, the applied magnetization amplitude is sufficient to excite the detected ferromagnetic material to saturation.

[0056] The detected MBN signal undergoes a combination of bandpass and low-pass / high-pass filters, followed by amplification, post-amplification, and signal smoothing rectification. The MBN butterfly curve is shown below. Figure 5 As shown, it uses the digitally transformed and smoothed MBN amplitude timing signal as the vertical axis and the excitation magnetic field strength corresponding to the MBN amplitude signal as the horizontal axis, resulting in a butterfly graph (the curve shape resembles a butterfly with outstretched wings) of the MBN signal. From this, the maximum amplitude (the maximum value of the MBN signal, MMAX) can be derived as a test statistic. Correspondingly, the magnetic field strength at MMAX is assigned to the test statistic (the horizontal axis value corresponding to the maximum value, HCM). The expansion of the profile curve is evaluated at 25%, 50%, and 75% of MMAX (referred to as the width, defined as "the distance (width) between the two intersections of the vertical axis value at the 25%, 50%, and 75% positions of the maximum value with the two points of the butterfly graph," DH25M, DH50M, and DH75M). An additional test statistic is MMEAN, which is the average value of the profile curve over a certain period (the average value of the MBN signal amplitude over one butterfly cycle).

[0057] During the reorganization of magnetic domains, displacement of the Bloch walls occurs, happening in a discrete, hopping manner. Bloch domain walls are influenced by different microstructures, thus exhibiting different motion characteristics. These microstructural variations can be reflected by the properties of the MBN. MMAX test statistics can be used to quantitatively obtain finishing conditions such as depth hardness and surface hardness. When grain boundaries represent the main barrier to Bloch wall displacement, HCM can be quantitatively correlated with grain size. Relationships between the expansion of profile curves (DH25M, DH50M, and DH75M) and internal stress or plastic deformation have been observed.

[0058] Unlike the MBN detection method, in the MIP technique, high- and low-frequency sinusoidal currents are necessary for obtaining reversible motion information. Similar to the MBN method, the high-amplitude, low-frequency (10-1000Hz) excitation of the yoke coil 4 surrounding the U-shaped yoke 3 generates hysteresis loops in the material. Simultaneously, a low-amplitude (milliampere level), high-frequency (10kHz-1MHz) sinusoidal current is required to be fed into the transmitter coil, similar to the MFEC method, to generate small asymmetric hysteresis loops that superimpose on the main hysteresis curve.

[0059] Similar to MBN, the maximum amplitude of the MIP (the maximum value of the signal, UMAX) is extracted as an important feature. The magnetic field strength at UMAX (the abscissa value at its maximum, HCU) is also derived as a statistical parameter. Furthermore, the curve extensions at 25%, 50%, and 75% (defined as above, DH25U, DH50U, and DH75U) and the average UMEAN over the time period are also used as MIP features. MIP can be used to characterize near-surface (surface hardened) material properties. Shell depth information is derived from the amplitude of the UMAX signal received from the core structure, and hardness information can be obtained from the associated forced field strength HCU. Stress state information is quantitatively described using curve extensions (DH25U, DH50U, and DH75U).

[0060] By using the aforementioned multimagnetic detection equipment to conduct electromagnetic nondestructive testing experiments on ferromagnetic material samples, a total of 12 electromagnetic characteristic parameters, including MBN and MIP, can be obtained. Each sample is tested for 120 seconds, resulting in more than 100 sets of electromagnetic characteristic data. After outlier cleaning, 100 sets of data are retained for subsequent analysis.

[0061] S5. Based on the microstructure of the ferromagnetic metal material obtained in step S3, establish a correlation with the MBN and MIP electromagnetic characteristics of the ferromagnetic metal material obtained in step S4. Plot the MBN electromagnetic characteristic parameters of the ferromagnetic metal material on the ordinate, and the MBN and MIP electromagnetic characteristic parameters on the ordinate, and the microstructure of the ferromagnetic metal material on the abscissa, as shown below. Figure 6As shown, the electromagnetic characteristics and microstructures characterizing the reversible and irreversible motion of magnetic domains exhibit a monotonic trend. Therefore, the electromagnetic characteristics characterizing both reversible and irreversible motion of magnetic domains can be used to characterize the microstructure of ferromagnetic metallic materials—recrystallization rate.

[0062] S6. A deep neural network representation model of the microstructure of ferromagnetic steel materials based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion was established. A 3-hidden-layer DNN model (with 9, 10, and 21 nodes per layer) was built, using the MBN electromagnetic characteristic parameters of ferromagnetic steel materials as input and the true value of the microstructure of ferromagnetic steel materials as output. During the training of the DNN model, the activation functions "logsig", "tansig", and "purelin" and the training function "trainlm" were used, with the error threshold set to 0.000001, the initial learning rate set to 0.01, and the number of iterations set to 1000.

[0063] S7 uses confidence scores to evaluate the accuracy of a deep neural network representation model of the characteristic microstructure based on the reversible and irreversible motion of magnetic domains. The confidence score is defined as:

[0064]

[0065] In the formula, N 10 To verify the predicted value Y of the ensemble model ci Compared with the true value Y i The number of validation sets with an error ρ less than 10%, where N is the total number of validation sets, i.e.:

[0066]

[0067] S8. Following steps S6 and S7, the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion is trained ten times, and the average of the ten training results is taken as the final detection value. The evaluation accuracy of recrystallization rate is: under the condition that the error ρ is less than 10%, the confidence rate is greater than 85% (e.g., ...). Figure 7 (As shown).

[0068] Example

[0069] This embodiment takes pickled steel from a cold-rolling production line in a steel plant as an example and proposes a rapid characterization method for the recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics. The specific flowchart is as follows. Figure 1 As shown. The quantitative characterization results of the microstructure are as follows. Figure 2 As shown, the relationship between microstructure and mechanical properties is as follows: Figure 3 As shown, the recrystallization rate of pickled steel can be determined based on its characteristic microstructure; multi-magnetic detection equipment such as... Figure 4As shown; the signals characterizing reversible and irreversible magnetic domain motion are as follows: Figure 5 As shown; the relationship between the reversible and irreversible electromagnetic eigenvalues ​​of magnetic domains and the characteristic microstructure of ferromagnetic metallic materials is as follows. Figure 6 As shown, it specifically includes:

[0070] A three-layer DNN model (with 9, 10, and 21 nodes per layer) was established, using the MBN and MIP electromagnetic feature parameters of pickled steel as input and the microstructure of pickled steel as output. The training set consisted of 145 data points, and the validation set consisted of 44 data points. During the training of the DNN model, the activation functions "logsig", "tansig", and "purelin" and the training function "trainlm" were used. The error threshold was set to 0.000001, the initial learning rate was set to 0.01, and the number of iterations was set to 1000. Following the above modeling and training process, the training was repeated ten times, and the average of the ten training results was taken as the final detection value. The accuracy of the recrystallization rate assessment was greater than 95%: the confidence rate was greater than 85% while meeting the condition that the error was less than 10% (e.g., ...). Figure 7 (As shown).

[0071] Those skilled in the art should recognize that the above embodiments are merely illustrative of the present invention and are not intended to limit the present invention. Any variations or modifications to the above embodiments that are within the spirit and essence of the present invention will fall within the scope of the claims of the present invention.

Claims

1. A rapid characterization method for the recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics, characterized in that: First, prepare the sample; Secondly, the characteristic microstructure of ferromagnetic metallic materials—recrystallization rate—was obtained. Then, electromagnetic non-destructive testing of ferromagnetic metal materials is carried out based on multi-magnetic detection equipment to obtain electromagnetic signals characterizing the reversible and irreversible motion of magnetic domains, and at the same time, the corresponding electromagnetic characteristic parameters of ferromagnetic metal materials are defined and extracted. Finally, by analyzing the relationship between electromagnetic characteristic parameters and characteristic microstructure, electromagnetic characteristic parameters as inputs were selected, and a deep neural network representation model of characteristic microstructure based on electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion was established. The parameters of the deep neural network representation model of characteristic microstructure based on electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion were adjusted to obtain the optimal deep neural network representation model of characteristic microstructure based on electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion, thus realizing the non-destructive detection of characteristic microstructure and recrystallization rate of ferromagnetic metal materials.

2. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 1, characterized in that, The rapid characterization method for recrystallization rate of ferromagnetic metallic materials specifically includes the following steps: S1, Prepare samples for material characterization using the EBSD method and samples for detection using a multimagnetic detection device; S2, The recrystallization rate of the microstructure of ferromagnetic metal materials was quantitatively characterized by the EBSD method. S3. Repeat steps S1 and S2 to obtain the quantitative characterization results of different ferromagnetic metal materials. S4. Based on step S1, use a multi-magnetic detection device to perform electromagnetic non-destructive testing on different ferromagnetic metal materials, and extract the electromagnetic characteristic parameters of the reversible and irreversible motion of the magnetic domains of the ferromagnetic metal materials. S5. Based on the quantitative characterization results in step S3, the characteristic microstructure of the ferromagnetic material is obtained and a correlation is established with the electromagnetic characteristic parameters of the reversible and irreversible motion of the magnetic domains of the ferromagnetic metal material obtained in step S4. S6. Establish a deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion. S7 uses confidence level to evaluate the accuracy of the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion. S8. Following the process of steps S6 and S7, the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion is repeatedly trained ten times, and the average of the ten training results is taken as the final detection value.

3. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 2, characterized in that: The sample size used for material characterization using the EBSD method is 10mm*10mm; The sample size used for testing by the multi-magnetic detection equipment is 150mm*600mm.

4. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 3, characterized in that, Step S4 specifically includes: Twelve electromagnetic characteristic parameters, including MBN and MIP, were measured using a multi-magnetic detection device. Each sample was tested for 120 seconds, resulting in more than 100 sets of electromagnetic characteristic data. After outlier cleaning, 100 sets of data were retained for subsequent analysis.

5. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 4, characterized in that, The 12 electromagnetic characteristic parameters are as follows: MBN includes MMAX, MMEAN, HCM, DH25M, DH50M, and DH75M; The MIP series includes UMAX, UMEAN, HCU, DH25U, DH50U, and DH75U.

6. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 2, characterized in that: The multi-magnetic detection device applies different excitation magnetic fields using the magnetic Barkhausen noise method, incremental permeability method, tangential magnetic field harmonic analysis method, or multi-frequency eddy current detection method.

7. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 2, characterized in that: In step S6, during the training process of the deep neural network representation model of the characteristic microstructure based on the electromagnetic characteristic parameters of reversible and irreversible magnetic domain motion, the logsig, tansig, purelin activation functions and trainlm training function are used, the error threshold is set to 0.000001, the initial learning rate is set to 0.01, and the number of iterations is set to 1000.

8. The rapid characterization method for recrystallization rate of ferromagnetic metallic materials based on magnetic domain motion characteristics according to claim 7, characterized in that, In step S7, the confidence level is defined as: In the formula, N 10 To verify the predicted value Y of the ensemble model ci Compared with the true value Y i The number of validation sets with an error ρ less than 10%, where N is the total number of validation sets, i.e.:

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