Medium manganese steel oxygen vacancy defect identification method and device

Abstract

The invention provides a medium manganese steel oxygen vacancy defect identification method and device. The method comprises the following steps: obtaining a first oxygen vacancy defect area based on a measurement result of a positron annihilation lifetime spectrum and a Doppler broadening spectrum on a target sample block; aiming at each ultra-thin sample, observing the ultra-thin sample through a high-resolution transmission electron microscope, and collecting a bright field image and a dark field image of the ultra-thin sample; obtaining a second oxygen vacancy defect area based on the bright field image, the dark field image and the first oxygen vacancy defect area; performing electron energy loss spectrum analysis on the second oxygen vacancy defect area, and determining a third oxygen vacancy defect area and corresponding oxygen vacancy defect concentration in the ultrathin sample; and determining each target oxygen vacancy defect region and oxygen vacancy defect concentration of the target medium manganese steel sample according to the third oxygen vacancy defect region and oxygen vacancy defect concentration in each ultrathin sample. According to the invention, information such as position, number, size and distribution pattern of oxygen vacancy defects can be accurately obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of microstructure analysis, and in particular to a method and device for identifying oxygen vacancy defects in medium manganese steel. Background Art

[0002] Medium-manganese steel, due to its excellent combination of strength and ductility, holds broad application prospects in the automotive, aerospace, and other fields. Specifically, oxygen vacancies in medium-manganese steel have lower formation energy than other steel grades, and their concentration is relatively high and controlled by the Al / Mn ratio. Oxygen vacancy defects within the crystal structure of medium-manganese steel have a significant impact on its mechanical and electrical properties. However, current research on oxygen vacancy defects in medium-manganese steel faces numerous challenges.

[0003] Traditional detection methods, such as X-ray diffraction (XRD) and scanning electron microscopy (SEM), can provide some crystal structure information, but they struggle to accurately identify and fully characterize oxygen vacancy defects, a microscopic and complex feature. The inability to accurately determine the location, quantity, size, and distribution of oxygen vacancy defects severely limits our understanding and optimization of medium-manganese steel performance. Summary of the Invention

[0004] The embodiments of the present invention provide a method and apparatus for identifying oxygen vacancy defects in medium manganese steel to solve the problem of being unable to accurately obtain information such as the position, quantity, size, and distribution morphology of oxygen vacancy defects.

[0005] In a first aspect, an embodiment of the present invention provides a method for identifying oxygen vacancy defects in medium manganese steel, comprising: Based on the results of measuring the target sample block by positron annihilation lifetime spectrum and Doppler broadening spectrum, the oxygen vacancy defect area of the target sample block was preliminarily obtained, which was recorded as the first oxygen vacancy defect area; the target sample block was obtained by cutting the target medium manganese steel sample.

[0006] For each ultra-thin sample, the ultra-thin sample is observed by a high-resolution transmission electron microscope, and bright field images and dark field images of the ultra-thin sample are collected; each ultra-thin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect area.

[0007] Based on the bright field image, the dark field image and the first oxygen vacancy defect region, the position of the oxygen vacancy defect of the ultra-thin sample is re-determined and recorded as the second oxygen vacancy defect region.

[0008] Electron energy loss spectrum analysis is performed on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0009] According to the third oxygen vacancy defect region and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region in each ultra-thin sample, the target oxygen vacancy defect regions of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region are determined.

[0010] In one possible implementation, based on the bright field image, the dark field image, and the first oxygen vacancy defect region, the position of the oxygen vacancy defect of the ultra-thin sample is re-determined and recorded as the second oxygen vacancy defect region, including: Acquire a high-angle annular dark-field image of the ultrathin sample.

[0011] Based on the bright field image, a dark spot area in the bright field image is determined and recorded as a first target area.

[0012] Based on the dark field image, a bright spot area in the dark field image is determined and recorded as a second target area.

[0013] Based on the high-angle annular dark field image, a dark spot area in the high-angle annular dark field image is determined and recorded as a third target area.

[0014] The region in the first oxygen vacancy defect region where the first target region, the second target region, and the third target region overlap is recorded as the second oxygen vacancy defect region.

[0015] In one possible implementation, based on the bright field image, determining a dark spot area in the bright field image, recorded as a first target area, includes: The grayscale value of the bright field image is obtained, and the area of the bright field image whose grayscale value is less than the first threshold is regarded as the dark spot area of the bright field image, which is recorded as the first target area.

[0016] Based on the dark field image, a bright spot area in the dark field image is determined and recorded as a second target area, including: The grayscale value of the dark field image is obtained, and the area of the dark field image where the grayscale value is greater than the second threshold is regarded as the bright spot area of the dark field image, which is recorded as the second target area.

[0017] Based on the high-angle annular dark field image, a dark spot area in the high-angle annular dark field image is determined and recorded as a third target area, including: The grayscale value of the high-angle annular dark field image is obtained, and the area of the high-angle annular dark field image whose grayscale value is less than the third threshold is taken as the dark spot area of the high-angle annular dark field image, recorded as the third target area.

[0018] In one possible implementation, performing electron energy loss spectroscopy analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region includes: The electron energy loss spectrum of the second oxygen vacancy defect region is analyzed to obtain the oxygen element characteristic energy loss peak and the oxygen element characteristic energy loss peak intensity of the second oxygen vacancy defect region.

[0019] Based on the characteristic energy loss peak of the oxygen element, the area where the characteristic energy loss peak of the oxygen element is less than the fourth threshold is eliminated from the second oxygen vacancy defect area to obtain the third oxygen vacancy defect area in the ultra-thin sample.

[0020] Based on the peak area of the characteristic energy loss peak of the oxygen element, and based on the difference between the intensity of the characteristic energy loss peak of the oxygen element and the intensity of the characteristic energy loss peak of the oxygen element in the non-oxygen vacancy defect area, the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect area in the ultra-thin sample is determined.

[0021] In one possible implementation, based on the results of measuring the positron annihilation lifetime spectrum and the Doppler broadening spectrum of the target sample block, an oxygen vacancy defect region of the target sample block is preliminarily obtained, which is recorded as the first oxygen vacancy defect region, including: The target sample block is measured based on the positron annihilation lifetime spectrum to obtain the positron lifetime of each area of the target sample block.

[0022] The target sample block is measured based on the Doppler broadening spectrum to obtain the Doppler broadening parameters.

[0023] Based on the positron lifetime and Doppler broadening parameters of each region of the target sample block, the first oxygen vacancy defect region is obtained.

[0024] In one possible implementation, obtaining the first oxygen vacancy defect region based on the positron lifetime and Doppler broadening parameter of each region of the target sample block includes: Based on the positron lifetimes of various regions of the target sample block, regions where the positron lifetimes are greater than a fifth threshold are recorded as first oxygen vacancy defect regions to be determined.

[0025] Based on the Doppler broadening parameter, the region where the Doppler broadening parameter meets the preset conditions is recorded as the second undetermined oxygen vacancy defect region.

[0026] The set of the first undetermined oxygen vacancy defect region and the second undetermined oxygen vacancy defect region is referred to as the first oxygen vacancy defect region.

[0027] In one possible implementation, the positron source for the positron annihilation lifetime spectrum is 22 Na; The target sample block is a cube with a side length of 1-2 cm, or a cylinder with a diameter of 1-2 cm and a thickness of 0.5-1 cm.

[0028] In one possible implementation, the method further includes: Obtain various target oxygen vacancy defect regions of a target medium manganese steel sample at multiple target angles; wherein the target angles are angles other than preset angles.

[0029] Construct a target medium manganese steel sample model.

[0030] Each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles is projected onto the target medium manganese steel sample model to obtain an oxygen vacancy defect display model.

[0031] In one possible implementation, the method further includes: The oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles is obtained.

[0032] According to the oxygen vacancy defect concentration corresponding to different target oxygen vacancy defect regions, a grayscale value or color is assigned to each target oxygen vacancy defect region in the oxygen vacancy defect display model.

[0033] In a second aspect, an embodiment of the present invention provides a device for identifying oxygen vacancy defects in medium manganese steel, comprising: The first processing module is used to measure the target sample block based on the results of the positron annihilation lifetime spectrum and the Doppler broadening spectrum, and preliminarily obtain the oxygen vacancy defect area of the target sample block, which is recorded as the first oxygen vacancy defect area; the target sample block is obtained by cutting the target medium manganese steel sample.

[0034] The second processing module is used to observe each ultra-thin sample through a high-resolution transmission electron microscope and collect bright field images and dark field images of the ultra-thin sample; each ultra-thin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect area.

[0035] The third processing module is used to re-determine the position of the oxygen vacancy defect of the ultra-thin sample based on the bright field image, the dark field image and the first oxygen vacancy defect region, and record it as the second oxygen vacancy defect region.

[0036] The fourth processing module is used to perform electron energy loss spectrum analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0037] The fifth processing module is used to determine the target oxygen vacancy defect regions of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region based on the third oxygen vacancy defect region in each ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0038] In an embodiment of the present invention, the first oxygen vacancy defect region at a macroscopic perspective is obtained by positron annihilation lifetime spectroscopy and Doppler broadening spectroscopy technology, and the third oxygen vacancy defect region in the first oxygen vacancy defect region at a microscopic perspective is obtained by high-resolution transmission electron microscopy observation and electron energy loss spectroscopy, so that the macro data and the micro data are accurately correlated, and the macro information and the micro information can be integrated to accurately obtain information such as the position, quantity, size and distribution morphology of the oxygen vacancy defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 1 is a flow chart of a method for identifying oxygen vacancy defects in medium manganese steel provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the medium manganese steel oxygen vacancy defect identification device provided by the embodiment of the present invention. Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , which shows a flow chart of the implementation of the method for identifying oxygen vacancy defects in medium manganese steel provided by an embodiment of the present invention, and is described in detail as follows: Step 101: Based on the results of measuring the positron annihilation lifetime spectrum and the Doppler broadening spectrum of the target sample block, a preliminary oxygen vacancy defect region of the target sample block is obtained, which is recorded as the first oxygen vacancy defect region. The target sample block is obtained by cutting the target medium manganese steel sample.

[0042] Exemplarily, the target medium manganese steel sample is cut into target sample blocks using a wire cutting device. Exemplarily, the target sample blocks are cubes with a side length of 1-2 cm, or cylinders with a diameter of 1-2 cm and a thickness of 0.5-1 cm.

[0043] In some specific embodiments, step 101 may include: The target sample block is measured based on the positron annihilation lifetime spectrum to obtain the positron lifetime of each area of the target sample block.

[0044] The target sample block is measured based on the Doppler broadening spectrum to obtain the Doppler broadening parameters.

[0045] Based on the positron lifetime and Doppler broadening parameters of each region of the target sample block, the first oxygen vacancy defect region is obtained.

[0046] In some specific embodiments, obtaining the first oxygen vacancy defect region based on the positron lifetime and Doppler broadening parameter of each region of the target sample block includes: Based on the positron lifetimes of various regions of the target sample block, regions where the positron lifetimes are greater than a fifth threshold are recorded as first oxygen vacancy defect regions to be determined.

[0047] Based on the Doppler broadening parameter, the region where the Doppler broadening parameter meets the preset conditions is recorded as the second undetermined oxygen vacancy defect region.

[0048] The set of the first undetermined oxygen vacancy defect region and the second undetermined oxygen vacancy defect region is referred to as the first oxygen vacancy defect region.

[0049] For example, positron annihilation lifetime spectroscopy (PALS) can be used to determine the different lifetime components of positrons in a material, corresponding to vacancy defects of different types and sizes, and to preliminarily confirm the presence of oxygen vacancy defects. Doppler broadening spectroscopy (DBS) can provide information on the electron density distribution around the vacancy, further assisting in determining the characteristics of the oxygen vacancy. The sample region subjected to positron annihilation testing is then marked to obtain a marked first oxygen vacancy defect region.

[0050] For example, the positron source for the positron annihilation lifetime spectrum is 22 Na (sodium-22). Different types of vacancies have different abilities to capture positrons at different energies. Accelerator-based positron sources allow for precise adjustment of the positron beam energy. Through multiple experiments, an energy range of 5-10 keV (preferably 8 keV) has been identified where positrons are more likely to be captured by oxygen vacancies, while the probability of capture by other vacancies is relatively low. During positron annihilation experiments, the positron energy is set within this range to enhance the oxygen vacancy signal and suppress interference from other vacancies. The annihilation lifetime of oxygen vacancies in medium-manganese steel is typically 180-220 ps. The annihilation lifetime of precipitates or grain boundaries is even longer (>300 ps). A PALS lifetime of approximately 200 ps supports oxygen vacancy dominance; the presence of a component >300 ps indicates interference from precipitates or grain boundaries.

[0051] For example, the region where the Doppler broadening parameter meets the preset conditions refers to the region where the absolute value of the S parameter is in the range of 0.52–0.55 and the absolute value of the W parameter is in the range of 0.40–0.45. In addition, if there is a standard sample without oxygen vacancy defects, the relative values of the S parameter and the W parameter can also be calculated. Then, the region where the Doppler broadening parameter meets the preset conditions refers to the region where the relative value of the S parameter is in the range of 1.02–1.08 and the relative value of the W parameter is in the range of 0.85–0.95.

[0052] Step 102: Observe each ultrathin sample using a high-resolution transmission electron microscope and collect a bright field image and a dark field image of the ultrathin sample. Each ultrathin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect region.

[0053] For example, focused ion beam (FIB) technology was used to prepare ultrathin samples from the marked region (the region with the marked first oxygen vacancy defect) for high-resolution transmission electron microscopy (HRTEM) and electron energy loss spectroscopy (EELS) analysis. A 0.5 mm thick slice was cut from the marked region and mounted on the FIB sample stage. The slice was then milled layer by layer using the FIB's ion beam. During the milling process, the ion beam energy and beam current were precisely controlled to ensure uniform thickness and no significant damage to the resulting ultrathin sample. Ultimately, an ultrathin sample with a thickness of 50-100 nm was produced for subsequent analysis. This method uses EELS to measure the characteristic energy loss peak of oxygen to determine the presence and concentration of oxygen vacancies, but the information obtained deeper in the sample is limited. Positron annihilation technology can detect vacancies deeper within the material. Combining the two, positron annihilation is used to determine the approximate distribution of oxygen vacancies at different depths within the sample. Then, during HRTEM-EELS analysis, the sample block is sliced into ultra-thin nanometer-thin samples, targeting key areas at different depths indicated by positron annihilation, for targeted electron energy loss spectroscopy acquisition. This synergistic depth-directed analysis provides a more comprehensive understanding of the three-dimensional distribution characteristics of oxygen vacancy defects within the medium-manganese steel crystal. The macroscopic vacancy information obtained by positron annihilation can be precisely correlated with the microstructural and oxygen elemental information obtained by HRTEM and EELS, enabling a comprehensive study of oxygen vacancy defects from the macro to the micro level.

[0054] Step 103 : Based on the bright field image, the dark field image, and the first oxygen vacancy defect region, the position of the oxygen vacancy defect of the ultra-thin sample is re-determined and recorded as the second oxygen vacancy defect region.

[0055] In some specific embodiments, step 103 may include: Acquire a high-angle annular dark-field image of the ultrathin sample.

[0056] Based on the bright field image, a dark spot area in the bright field image is determined and recorded as a first target area.

[0057] Based on the dark field image, a bright spot area in the dark field image is determined and recorded as a second target area.

[0058] Based on the high-angle annular dark field image, a dark spot area in the high-angle annular dark field image is determined and recorded as a third target area.

[0059] The region in the first oxygen vacancy defect region where the first target region, the second target region, and the third target region overlap is recorded as the second oxygen vacancy defect region.

[0060] In one possible implementation, based on the bright field image, determining a dark spot area in the bright field image, recorded as a first target area, includes: The grayscale value of the bright field image is obtained, and the area of the bright field image whose grayscale value is less than the first threshold is regarded as the dark spot area of the bright field image, which is recorded as the first target area.

[0061] In one possible implementation, based on the dark field image, determining a bright spot area in the dark field image, recorded as a second target area, includes: The grayscale value of the dark field image is obtained, and the area of the dark field image where the grayscale value is greater than the second threshold is regarded as the bright spot area of the dark field image, which is recorded as the second target area.

[0062] In one possible implementation, based on the high-angle annular dark field image, determining a dark spot area in the high-angle annular dark field image, recorded as a third target area, includes: The grayscale value of the high-angle annular dark field image is obtained, and the area of the high-angle annular dark field image whose grayscale value is less than the third threshold is taken as the dark spot area of the high-angle annular dark field image, recorded as the third target area.

[0063] For example, bright field images (BF-TEM) were taken under dual beam conditions (g = {200}, s ≈ 0.1 nm). -1 ) to observe the first target area (e.g., the dark spot area). Using a dark-field image (DF-TEM), locate the image and switch to a g = {110} dark-field image to confirm the second target area (e.g., the bright spot area). Using a high-angle annular dark-field image (HAADF-STEM), confirm the third target area (e.g., the dark spot area). The overall idea is that regardless of the image type, oxygen vacancy defect areas can be identified based on different characteristics. Therefore, the intersection of the first, second, and third target areas must be an area with oxygen vacancy defects.

[0064] Example, bright field image (BF-TEM): under dual beam conditions (e.g. g = {200}, s ≈ 0.1 nm -1 ), local dark spots will appear in the bright field image.

[0065] Dark field image (DF-TEM): In the case of a specific diffraction vector (such as g={110}), bright spots (oxygen vacancy defect areas) will appear in the dark field image.

[0066] High-angle annular dark-field (HAADF-STEM) imaging: At a specific diffraction vector (such as g = {110}) (this must be the same as the specific diffraction vector in the dark-field image, meaning that the environmental conditions for both images must be the same), dark spots (corresponding to oxygen vacancy defects) appear in the HADF image. However, these dark spots in HADF images may be caused by other interference and are not always due to oxygen vacancy defects. To ensure the accuracy of the results, the "oxygen K-edge intensity" and "atomic proton number" obtained through EELS analysis can be used to jointly determine the third target region, making the obtained third target region more accurate. Specifically, EELS can directly determine the atomic proton number in the defect region. In this case, if the proton number of oxygen atoms is present, it can be determined that an oxygen vacancy defect exists in this region. The oxygen K-edge intensity reflects the number of oxygen atoms and the electron state density. If the oxygen K-edge intensity is low (the peak value is relatively low) compared to the K-edge intensity of an oxygen-free region, it can be considered that an oxygen vacancy defect exists in this region.

[0067] For example, bright and dark spots are determined by grayscale values. In BF-TEM, the grayscale value of the matrix is about 150, and the grayscale value of the oxygen vacancy defect area is about 135 (dark spot), which is about 10% lower than the grayscale value of the matrix. In DF-TEM, the grayscale value of the matrix is about 50, and the grayscale value of the oxygen vacancy defect area is about 60 (bright spot), which is about 20% higher than the grayscale value of the matrix. In HAADF-STEM, the grayscale value of the matrix is about 200, and the grayscale value of the oxygen vacancy defect area is about 170 (dark spot), which is about 15% lower than the grayscale value of the matrix.

[0068] Step 104 : performing electron energy loss spectroscopy analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0069] In some specific embodiments, step 104 may include: The electron energy loss spectrum of the second oxygen vacancy defect region is analyzed to obtain the oxygen element characteristic energy loss peak and the oxygen element characteristic energy loss peak intensity of the second oxygen vacancy defect region.

[0070] Based on the characteristic energy loss peak of the oxygen element, the area where the characteristic energy loss peak of the oxygen element is less than the fourth threshold is eliminated from the second oxygen vacancy defect area to obtain the third oxygen vacancy defect area in the ultra-thin sample.

[0071] Based on the peak area of the characteristic energy loss peak of the oxygen element, and based on the difference between the intensity of the characteristic energy loss peak of the oxygen element and the intensity of the characteristic energy loss peak of the oxygen element in the non-oxygen vacancy defect area, the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect area in the ultra-thin sample is determined.

[0072] For example, when acquiring electron energy loss spectroscopy (EELS), appropriate acquisition parameters, such as energy resolution and acquisition time, are set to ensure accurate measurement of the characteristic energy loss peak of oxygen. By comparing the intensity of the characteristic energy loss peak of oxygen in normal and defective regions, the presence of oxygen vacancies can be confirmed and their concentration estimated.

[0073] Step 105: Determine each target oxygen vacancy defect region of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region based on the third oxygen vacancy defect region in each ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0074] In some specific embodiments, the method further comprises: Multiple ultra-thin samples were obtained by cutting at different angles on the first oxygen vacancy defect region.

[0075] Obtain various target oxygen vacancy defect areas of the target medium manganese steel sample at multiple angles.

[0076] Construct a target medium manganese steel sample model.

[0077] The target oxygen vacancy defect areas of the target medium manganese steel sample at multiple angles are projected onto the target medium manganese steel sample model to obtain an oxygen vacancy defect display model.

[0078] For example, positron annihilation, Doppler broadening spectrum, high-resolution transmission electron microscopy observation and electron energy loss spectrum analysis are performed on medium manganese steel samples from multiple different angles to obtain the target oxygen vacancy defect areas of the target medium manganese steel samples at multiple angles. It is advisable to collect a set of data at intervals of 5°-10° to ensure that sufficient multi-view information is obtained for three-dimensional model construction. These images and data are imported into a specially developed three-dimensional reconstruction algorithm program. In the program, the image is first preprocessed, including image denoising, contrast enhancement and other operations to improve image quality. The Gaussian filtering algorithm is used to remove noise interference in the image, and the histogram equalization method is used to enhance the image contrast, making the oxygen vacancy defects more clearly discernible in the image.

[0079] Using a 3D reconstruction algorithm, a 3D model of oxygen vacancy defects is constructed through iterative calculations based on the position of oxygen vacancy defects in images from different angles and the defect concentration information provided by the electron energy loss spectrum. During this iterative calculation, the coordinates of the voxels (three-dimensional pixels) in 3D space are determined based on the projected positions of the oxygen vacancy defects in the images from different angles using the principle of triangulation.

[0080] In some specific embodiments, the method further comprises: The oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region of the target medium manganese steel sample at multiple angles is obtained.

[0081] According to the oxygen vacancy defect concentration corresponding to different target oxygen vacancy defect regions, a grayscale value or color is assigned to each target oxygen vacancy defect region in the oxygen vacancy defect display model.

[0082] For example, based on the defect concentration information obtained from the electron energy loss spectrum, each voxel is assigned a corresponding attribute value, such as color or grayscale, to intuitively reflect the difference in oxygen vacancy concentration. This process is repeated continuously, gradually filling the voxels in the three-dimensional space and constructing a complete three-dimensional model of oxygen vacancy defects.

[0083] The above-mentioned method for identifying oxygen vacancy defects in medium manganese steel obtains the first oxygen vacancy defect region from a macroscopic perspective through positron annihilation lifetime spectroscopy and Doppler broadening spectrum technology, and obtains the third oxygen vacancy defect region in the first oxygen vacancy defect region from a microscopic perspective through high-resolution transmission electron microscopy observation and electron energy loss spectroscopy, so that the macro data and micro data are accurately correlated, and macro information and micro information can be integrated to accurately obtain information such as the position, quantity, size and distribution morphology of oxygen vacancy defects.

[0084] One embodiment of the present application also provides a medium manganese steel oxygen vacancy defect recognition model, which is trained using a large amount of data in the joint database and can automatically identify and analyze the characteristics of oxygen vacancy defects to obtain the oxygen vacancy defect area and oxygen vacancy defect concentration.

[0085] For example, by learning from multi-dimensional data such as lifetime and Doppler broadening parameters from positron annihilation data, defect morphology characteristics from HRTEM images, and characteristic peaks of oxygen elements from EELS spectra, the team achieved automatic identification and classification of oxygen vacancy defects (e.g., distinguishing oxygen vacancies of different sizes and concentrations), determining the oxygen vacancy defect region and concentration for each ultra-thin sample. Furthermore, utilizing a machine learning feedback mechanism, the team continuously optimized detection parameters and processes based on the analysis results, further improving the accuracy and efficiency of oxygen vacancy defect research in ultra-thin samples. Finally, a complete three-dimensional model of oxygen vacancy defects was constructed by analyzing the target oxygen vacancy defect regions of the target medium-manganese steel sample from multiple angles.

[0086] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0088] Figure 2 The following is a schematic diagram showing the structure of a device for identifying oxygen vacancy defects in medium manganese steel according to an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are described in detail as follows: like Figure 2 As shown, the medium manganese steel oxygen vacancy defect identification device includes: The first processing module 201 is used to measure the target sample block based on the results of the positron annihilation lifetime spectrum and the Doppler broadening spectrum, and preliminarily obtain the oxygen vacancy defect region of the target sample block, which is recorded as the first oxygen vacancy defect region; the target sample block is obtained by cutting the target medium manganese steel sample.

[0089] The second processing module 202 is used to observe each ultra-thin sample through a high-resolution transmission electron microscope and collect bright field images and dark field images of the ultra-thin sample; each ultra-thin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect area.

[0090] The third processing module 203 is used to re-determine the position of the oxygen vacancy defect of the ultra-thin sample based on the bright field image, the dark field image and the first oxygen vacancy defect region, and record it as the second oxygen vacancy defect region.

[0091] The fourth processing module 204 is used to perform electron energy loss spectrum analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0092] The fifth processing module 205 is used to determine the target oxygen vacancy defect regions of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region based on the third oxygen vacancy defect region in each ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

[0093] In a possible implementation, the third processing module 203 is further configured to: Acquire a high-angle annular dark-field image of the ultrathin sample.

[0094] Based on the bright field image, a dark spot area in the bright field image is determined and recorded as a first target area.

[0095] Based on the dark field image, a bright spot area in the dark field image is determined and recorded as a second target area.

[0096] Based on the high-angle annular dark field image, a dark spot area in the high-angle annular dark field image is determined and recorded as a third target area.

[0097] The region in the first oxygen vacancy defect region where the first target region, the second target region, and the third target region overlap is recorded as the second oxygen vacancy defect region.

[0098] In a possible implementation, the third processing module 203 is further configured to: The grayscale value of the bright field image is obtained, and the area of the bright field image whose grayscale value is less than the first threshold is regarded as the dark spot area of the bright field image, which is recorded as the first target area.

[0099] In a possible implementation, the third processing module 203 is further configured to: The grayscale value of the dark field image is obtained, and the area of the dark field image where the grayscale value is greater than the second threshold is regarded as the bright spot area of the dark field image, which is recorded as the second target area.

[0100] In a possible implementation, the third processing module 203 is further configured to: The grayscale value of the high-angle annular dark field image is obtained, and the area of the high-angle annular dark field image whose grayscale value is less than the third threshold is taken as the dark spot area of the high-angle annular dark field image, recorded as the third target area.

[0101] In a possible implementation, the fourth processing module 204 is further configured to: The electron energy loss spectrum of the second oxygen vacancy defect region is analyzed to obtain the oxygen element characteristic energy loss peak and the oxygen element characteristic energy loss peak intensity of the second oxygen vacancy defect region.

[0102] Based on the characteristic energy loss peak of the oxygen element, the area where the characteristic energy loss peak of the oxygen element is less than the fourth threshold is eliminated from the second oxygen vacancy defect area to obtain the third oxygen vacancy defect area in the ultra-thin sample.

[0103] Based on the peak area of the characteristic energy loss peak of the oxygen element, and based on the difference between the intensity of the characteristic energy loss peak of the oxygen element and the intensity of the characteristic energy loss peak of the oxygen element in the non-oxygen vacancy defect area, the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect area in the ultra-thin sample is determined.

[0104] In a possible implementation, the first processing module 201 is further configured to: The target sample block is measured based on the positron annihilation lifetime spectrum to obtain the positron lifetime of each area of the target sample block.

[0105] The target sample block is measured based on the Doppler broadening spectrum to obtain the Doppler broadening parameters.

[0106] Based on the positron lifetime and Doppler broadening parameters of each region of the target sample block, the first oxygen vacancy defect region is obtained.

[0107] In a possible implementation, the first processing module 201 is further configured to: Based on the positron lifetimes of various regions of the target sample block, regions where the positron lifetimes are greater than a fifth threshold are recorded as first oxygen vacancy defect regions to be determined.

[0108] Based on the Doppler broadening parameter, the region where the Doppler broadening parameter meets the preset conditions is recorded as the second undetermined oxygen vacancy defect region.

[0109] The set of the first undetermined oxygen vacancy defect region and the second undetermined oxygen vacancy defect region is referred to as the first oxygen vacancy defect region.

[0110] In one possible implementation, the positron source for the positron annihilation lifetime spectrum is 22 Na; The target sample block is a cube with a side length of 1-2 cm, or a cylinder with a diameter of 1-2 cm and a thickness of 0.5-1 cm.

[0111] In a possible implementation, the fifth processing module 205 is further configured to: Obtain various target oxygen vacancy defect regions of a target medium manganese steel sample at multiple target angles; wherein the target angles are angles other than preset angles.

[0112] Construct a target medium manganese steel sample model.

[0113] Each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles is projected onto the target medium manganese steel sample model to obtain an oxygen vacancy defect display model.

[0114] In a possible implementation, the fifth processing module 205 is further configured to: The oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles is obtained.

[0115] According to the oxygen vacancy defect concentration corresponding to different target oxygen vacancy defect regions, a grayscale value or color is assigned to each target oxygen vacancy defect region in the oxygen vacancy defect display model.

[0116] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, the electronic device 5 of this embodiment includes: a processor 50 and a memory 51. The memory 51 stores a computer program 52. When the processor 50 executes the computer program 52, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 50 executes the computer program 52, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0117] For example, the computer program 52 may be divided into one or more modules / units, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the electronic device 5.

[0118] The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 5 may also include input and output devices, network access devices, buses, etc.

[0119] The processor 50 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0120] The memory 51 can be an internal storage unit of the electronic device 5, such as the hard drive or memory of the electronic device 5. The memory 51 can also be an external storage device of the electronic device 5, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 5. Furthermore, the memory 51 can include both the internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store the computer program 52 and other programs and data required by the electronic device 5. The memory 51 can also be used to temporarily store data that has been output or is about to be output.

[0121] For the sake of convenience and brevity, the division of the above functional modules / units is only used as an example. In actual applications, the above functions can be assigned to different functional modules / units as needed. The above modules / units can be implemented in the form of hardware, software, or a combination of hardware and software.

[0122] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in the above-mentioned method embodiments.

[0123] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the methods in the above-mentioned method embodiments.

[0124] The term "computer program" includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. Computer-readable media may include any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.

[0125] In the above embodiments, the descriptions of each embodiment have their own focus. For parts not described or recorded in detail in one embodiment, please refer to the relevant descriptions of other embodiments. Unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features of different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0126] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for identifying oxygen vacancy defects in medium manganese steel, characterized in that: include: Based on the results of measuring the target sample block by positron annihilation lifetime spectrum and Doppler broadening spectrum, the oxygen vacancy defect region of the target sample block is preliminarily obtained, which is recorded as the first oxygen vacancy defect region; The target sample block is obtained by cutting the target medium manganese steel sample; For each ultra-thin sample, observe the ultra-thin sample through a high-resolution transmission electron microscope, and collect a bright field image and a dark field image of the ultra-thin sample; each ultra-thin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect region; Re-determining the position of the oxygen vacancy defect of the ultra-thin sample based on the bright field image, the dark field image, and the first oxygen vacancy defect region, and recording it as a second oxygen vacancy defect region; Performing electron energy loss spectroscopy analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region; According to the third oxygen vacancy defect region in each of the ultra-thin samples and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region, the target oxygen vacancy defect regions of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region are determined.

2. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 1, wherein: The step of re-determining the position of the oxygen vacancy defect of the ultra-thin sample based on the bright field image, the dark field image, and the first oxygen vacancy defect region, and recording the position as a second oxygen vacancy defect region, includes: Acquire a high-angle annular dark-field image of the ultrathin sample; Based on the bright field image, determining a dark spot area in the bright field image as a first target area; Based on the dark field image, determining a bright spot area in the dark field image, and recording it as a second target area; Based on the high-angle annular dark field image, determining a dark spot area in the high-angle annular dark field image, and recording it as a third target area; The region in the first oxygen vacancy defect region where the first target region, the second target region, and the third target region overlap is recorded as the second oxygen vacancy defect region.

3. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 2, wherein: The step of determining a dark spot area in the bright field image based on the bright field image, recording the dark spot area as a first target area, includes: Acquiring a grayscale value of the bright field image, and taking an area of the bright field image whose grayscale value is less than a first threshold as a dark spot area of the bright field image, recorded as the first target area; The step of determining a bright spot area in the dark field image based on the dark field image, recording the bright spot area as a second target area, includes: Acquiring a grayscale value of the dark field image, and taking an area of the dark field image where the grayscale value is greater than a second threshold as a bright spot area of the dark field image, recorded as the second target area; The step of determining a dark spot area in the high-angle annular dark field image based on the high-angle annular dark field image, which is recorded as a third target area, includes: The grayscale value of the high-angle annular dark field image is obtained, and the area of the high-angle annular dark field image whose grayscale value is less than a third threshold is taken as the dark spot area of the high-angle annular dark field image, recorded as the third target area.

4. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 1, wherein: The performing electron energy loss spectroscopy analysis on the second oxygen vacancy defect region to determine the third oxygen vacancy defect region in the ultra-thin sample and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region includes: Performing electron energy loss spectrum analysis on the second oxygen vacancy defect region to obtain an oxygen element characteristic energy loss peak and an oxygen element characteristic energy loss peak intensity in the second oxygen vacancy defect region; Based on the characteristic energy loss peak of the oxygen element, removing the region where the characteristic energy loss peak of the oxygen element is less than a fourth threshold from the second oxygen vacancy defect region to obtain a third oxygen vacancy defect region in the ultra-thin sample; Based on the peak area of the characteristic energy loss peak of the oxygen element, and based on the difference between the intensity of the characteristic energy loss peak of the oxygen element and the intensity of the characteristic energy loss peak of the oxygen element in the non-oxygen vacancy defect area, the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect area in the ultra-thin sample is determined.

5. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 1, wherein: The oxygen vacancy defect region of the target sample block is preliminarily obtained based on the results of measuring the positron annihilation lifetime spectrum and the Doppler broadening spectrum, and is recorded as the first oxygen vacancy defect region, including: Measuring the target sample block based on the positron annihilation lifetime spectrum to obtain the positron lifetime of each region of the target sample block; measuring the target sample block based on the Doppler broadening spectrum to obtain Doppler broadening parameters; A first oxygen vacancy defect region is obtained based on the positron lifetime of each region of the target sample block and the Doppler broadening parameter.

6. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 5, characterized in that: The step of obtaining a first oxygen vacancy defect region based on the positron lifetime of each region of the target sample block and the Doppler broadening parameter comprises: Based on the positron lifetimes of various regions of the target sample block, recording the region where the positron lifetime is greater than a fifth threshold as a first undetermined oxygen vacancy defect region; Based on the Doppler broadening parameter, recording the region where the Doppler broadening parameter meets a preset condition as a second undetermined oxygen vacancy defect region; The set of the first undetermined oxygen vacancy defect region and the second undetermined oxygen vacancy defect region is used as the first oxygen vacancy defect region.

7. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 1, wherein: The positron source of the positron annihilation lifetime spectrum is 22 Na; the target sample block is a cube with a side length of 1-2 cm, or a cylinder with a diameter of 1-2 cm and a thickness of 0.5-1 cm.

8. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 1, wherein: The method further comprises: Acquire target oxygen vacancy defect regions of a target medium manganese steel sample at multiple target angles; wherein the target angles are angles other than preset angles; Construct a target medium manganese steel sample model; Each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles is projected onto the target medium manganese steel sample model to obtain an oxygen vacancy defect display model.

9. The method for identifying oxygen vacancy defects in medium manganese steel according to claim 8, wherein: The method further comprises: Obtaining the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region of the target medium manganese steel sample at multiple target angles; According to the oxygen vacancy defect concentrations corresponding to different target oxygen vacancy defect regions, a grayscale value or color is assigned to each target oxygen vacancy defect region in the oxygen vacancy defect display model.

10. A medium manganese steel oxygen vacancy defect identification device, characterized in that: include: A first processing module is configured to measure the target sample block based on the results of the positron annihilation lifetime spectrum and the Doppler broadening spectrum, and preliminarily obtain the oxygen vacancy defect region of the target sample block, which is recorded as the first oxygen vacancy defect region; The target sample block is obtained by cutting the target medium manganese steel sample; a second processing module, configured to observe each ultrathin sample through a high-resolution transmission electron microscope and collect a bright-field image and a dark-field image of the ultrathin sample; wherein each ultrathin sample is obtained by cutting at a preset angle on the first oxygen vacancy defect region; a third processing module, configured to re-determine a position of the oxygen vacancy defect of the ultra-thin sample based on the bright field image, the dark field image, and the first oxygen vacancy defect region, and record the position as a second oxygen vacancy defect region; a fourth processing module, configured to perform electron energy loss spectroscopy analysis on the second oxygen vacancy defect region to determine a third oxygen vacancy defect region in the ultra-thin sample and an oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region; The fifth processing module is used to determine the target oxygen vacancy defect regions of the target medium manganese steel sample and the oxygen vacancy defect concentration corresponding to each target oxygen vacancy defect region based on the third oxygen vacancy defect region in each of the ultra-thin samples and the oxygen vacancy defect concentration corresponding to the third oxygen vacancy defect region.

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