Energy dispersive x-ray fluorescence based energy spectrum analysis method and system

Abstract

The application provides a kind of energy dispersive X-ray fluorescence based energy spectrum analysis method and system. Wherein, the application first obtains the energy spectrum data of the energy dispersive X-ray fluorescence of sample surface;Second, according to the layered structure characteristics of multilayer composite metal material, the energy spectrum data of multiple measurement positions is corrected;Then the corrected energy spectrum data is superimposed according to the layered structure of material;Then based on the concentration gradient characteristics of each layer element, the interface diffusion effect compensation is carried out to the superimposed energy spectrum data;Finally, the element distribution information is parsed from the energy spectrum data compensated by interface diffusion effect;The technical scheme provided by the application not only realizes the high-precision analysis of the element distribution form of the interface region of multilayer composite metal material, but also improves the accuracy and reliability of interface element detection.

Description

Technical Field

[0001] This application relates to the field of materials analysis technology, and in particular to an energy dispersive X-ray fluorescence-based energy spectroscopy analysis method and system. Background Technology

[0002] In the electronics industry, the precise evaluation of interfacial element diffusion behavior is required for multilayer metal composite materials prepared by precision rolling. Traditional methods are difficult to achieve accurate analysis of element distribution in the interfacial region.

[0003] The existing technical solution adopts the energy dispersive X-ray fluorescence spectrometry analysis method based on standard matrix correction. By establishing a matrix correction model of the standard sample, the energy spectrum data of the test sample is calibrated and analyzed as a whole to obtain the elemental composition information of the material.

[0004] However, this scheme has obvious flaws. Its overall correction mode cannot effectively distinguish the difference between the interface diffusion signal and the matrix signal, which makes it difficult to accurately identify the element diffusion boundary and concentration gradient distribution characteristics when analyzing the interlayer interface region. In particular, it has insufficient detection sensitivity for thin layer interfaces and trace diffusion elements. Summary of the Invention

[0005] This application provides an energy dispersive X-ray fluorescence-based energy spectrum analysis method and system to solve the problem that the overall correction mode in the prior art cannot effectively distinguish the difference between the interface diffusion signal and the matrix signal, which makes it difficult to accurately identify the element diffusion boundary and concentration gradient distribution characteristics when analyzing the interlayer interface region, especially the problem of insufficient detection sensitivity for thin layer interfaces and trace diffused elements.

[0006] In a first aspect, this application provides an energy-dispersive X-ray fluorescence-based energy spectroscopy analysis method, including:

[0007] Acquire energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample under test;

[0008] Based on the layered structure characteristics of multilayer composite metal materials, angle-dependent corrections are performed on the energy spectrum data at multiple measurement locations to obtain corrected energy spectrum data.

[0009] The corrected energy spectrum data are superimposed according to the layered structure of the material to obtain the superimposed energy spectrum data.

[0010] Based on the concentration gradient characteristics of elements in each layer of multilayer composite metal materials, interfacial diffusion effect compensation is performed on the superimposed energy spectrum data.

[0011] The elemental distribution information of the interlayer interface region was extracted from the energy spectrum data compensated for by the interface diffusion effect.

[0012] Optionally, energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample to be tested are acquired, including:

[0013] Micro-area X-ray beams are obtained by beam shaping operations using the raw X-ray beam emitted from an X-ray source.

[0014] Based on the layered structure characteristics of the multilayer composite metal material, a measurement path perpendicular to the layered structure interface is defined on the surface of the sample to be tested.

[0015] The micro-area X-ray beam is used to perform a step-by-step scan along the measurement path, and X-ray photon energy distribution data is collected at each step point of the measurement path;

[0016] Record the spatial location information of each step point and the corresponding X-ray photon energy distribution data to form an energy spectrum data sequence with spatial location correlation.

[0017] Optionally, based on the layered structural characteristics of the multilayer composite metal material, angle-dependent correction is performed on the energy spectrum data at multiple measurement locations to obtain corrected energy spectrum data, including:

[0018] Determine the angle between the incident direction of the X-ray beam and the normal of the characteristic interface of the layered structure at each measurement location;

[0019] Based on the anisotropic properties of each layer in the multilayer composite metal material, a correspondence between the included angle information and the intensity of the X-ray fluorescence signal is established.

[0020] Based on the aforementioned correspondence, the signal intensity of the X-ray photon energy distribution data collected at each measurement location is adjusted to obtain X-ray photon energy distribution data at multiple measurement locations after signal intensity adjustment.

[0021] X-ray photon energy distribution data from multiple measurement locations, after signal intensity adjustment, were used as calibration energy spectrum data.

[0022] Optionally, the corrected energy spectrum data are superimposed according to the layered structure of the material to obtain superimposed energy spectrum data, including:

[0023] Based on the layered structure characteristics of the multilayer composite metal material, the distribution position of each material layer on the measurement path is determined;

[0024] The corrected energy spectrum data are classified according to the corresponding material layers;

[0025] Based on the differences in physical properties of each material layer, the weighting coefficients of the corrected energy spectrum data of different material layers in the stacking process are determined;

[0026] According to the weighting coefficients, the corrected energy spectrum data belonging to the same material layer are weighted and combined to obtain the weighted energy spectrum data of each material layer.

[0027] The weighted energy spectrum data of each material layer are integrated and superimposed according to the layer sequence to form the superimposed energy spectrum data representing the entire layered structure.

[0028] Optionally, according to the weighting coefficients, the corrected energy spectrum data belonging to the same material layer are weighted and combined to obtain the weighted energy spectrum data of each material layer, including:

[0029] Obtain calibrated energy spectrum data for all measurement locations belonging to the same material layer;

[0030] Determine the relative importance of the corrected energy spectrum data at each measurement location in the corresponding material layer;

[0031] Based on the weighting coefficients and the relative importance, the importance of the calibrated energy spectrum data at each measurement location is adjusted.

[0032] All corrected energy spectrum data of the same material layer, after importance adjustment, are synthesized to obtain weighted energy spectrum data of each material layer.

[0033] Optionally, based on the concentration gradient characteristics of elements in each layer of the multilayer composite metal material, interfacial diffusion effect compensation is performed on the superimposed energy spectrum data, including:

[0034] Based on the concentration gradient characteristics, the theoretical distribution pattern of each element in the interface region under the influence of diffusion is determined.

[0035] The measured signal distribution of each element in the superimposed energy spectrum data is compared point by point with the theoretical distribution of the corresponding element to obtain the signal difference data of each measurement point.

[0036] Based on the interlayer interface characteristics of the material, a morphological difference mapping relationship between the measured signal distribution and the theoretical distribution is established based on the signal difference data.

[0037] Based on the aforementioned morphological difference mapping relationship, the measured signal distribution of each element in the superimposed energy spectrum data is compensated and corrected to obtain the energy spectrum data after interface diffusion compensation.

[0038] Optionally, the elemental distribution information of the interlayer interface region is extracted from the energy spectrum data after compensation for interfacial diffusion effects, including:

[0039] Identify the characteristic energy signals of each element in the energy spectrum data after the interface diffusion effect compensation;

[0040] Based on the layered structure characteristics of the multilayer composite metal material, the spatial range of the interlayer interface region is determined;

[0041] The characteristic energy signals of each element are correlated with the spatial range of the interlayer interface region to generate spatial signal correlation data.

[0042] Based on the intensity changes of the characteristic energy signals of each element in the spatial signal correlation data, a distribution relationship between signal intensity and spatial location is established.

[0043] Based on the distribution relationship between signal strength and spatial location, the distribution pattern of each element in the interface region is determined;

[0044] The distribution pattern information of each element is used as the element distribution information of the interlayer interface area.

[0045] Secondly, this application provides an energy dispersive X-ray fluorescence-based energy spectral analysis system, comprising:

[0046] The acquisition module is used to acquire energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample under test;

[0047] The calibration module is used to perform angle-dependent calibration on the energy spectrum data at multiple measurement locations based on the layered structure characteristics of the multilayer composite metal material, so as to obtain calibrated energy spectrum data.

[0048] The superposition module is used to superimpose the corrected energy spectrum data according to the layered structure of the material to obtain the superimposed energy spectrum data.

[0049] The compensation module is used to compensate for the interface diffusion effect of the superimposed energy spectrum data based on the concentration gradient characteristics of elements in each layer of the multilayer composite metal material.

[0050] The analysis module is used to extract the elemental distribution information of the interlayer interface region from the energy spectrum data compensated for by the interface diffusion effect.

[0051] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement an energy dispersive X-ray fluorescence-based energy spectral analysis method as described in the first aspect above.

[0052] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an energy dispersive X-ray fluorescence-based energy spectral analysis method as described in the first aspect.

[0053] The energy dispersive spectroscopy (EDS) method provided in this application is specifically designed for the structural characteristics of multilayer composite metal materials, effectively improving the accuracy of interface element analysis. This method first eliminates the influence of measurement geometry on signal intensity through multi-location EDS acquisition and angle-dependent correction. Then, based on the layered characteristics of the material, data superposition and interface diffusion compensation are performed, ultimately achieving precise analysis of the elemental distribution morphology in the interlayer interface region, significantly improving the characterization ability of the interface diffusion behavior of composite metal materials.

[0054] Furthermore, by establishing spatial signal correlation data and distribution relationship models, the accurate reconstruction of the element distribution morphology in the interface region was achieved. This scheme systematically correlates energy spectrum data with spatial location information. By analyzing the variation of signal intensity with spatial location, the distribution characteristics of each element in the interface region can be clearly presented, providing reliable data support for the interface quality assessment of multilayer composite metal materials.

[0055] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A flowchart of an energy dispersive X-ray fluorescence-based energy spectroscopy analysis method provided in this application is shown;

[0058] Figure 2 A schematic diagram of the structure of an energy dispersive X-ray fluorescence-based energy spectroscopy analysis system provided in this application is shown;

[0059] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0061] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0062] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] Figure 1 This application provides a flowchart of an energy dispersive X-ray fluorescence-based energy spectroscopy analysis method, such as... Figure 1 As shown, the method includes:

[0064] Step 101: Obtain energy dispersive X-ray fluorescence energy spectrum data at multiple measurement locations on the surface of the sample to be tested.

[0065] Optionally, step 101 may specifically include the following steps:

[0066] Step 1011: Using the raw X-ray beam emitted by the X-ray source, a micro-area X-ray beam is obtained through beam shaping operation;

[0067] Step 1012: Based on the layered structure characteristics of the multilayer composite metal material, define a measurement path perpendicular to the layered structure interface on the surface of the sample to be tested.

[0068] Step 1013: Use the micro-area X-ray beam to perform a step-by-step scan along the measurement path, and collect X-ray photon energy distribution data at each step point of the measurement path;

[0069] Step 1014: Record the spatial location information of each step point and the corresponding X-ray photon energy distribution data to form an energy spectrum data sequence with spatial location correlation.

[0070] In the above scheme, the sample surface to be tested refers to the external region of the material to be analyzed; multiple measurement positions refer to a series of detection points selected on the surface; energy dispersive X-ray fluorescence refers to the characteristic fluorescence signal generated after the sample is excited by X-rays; energy spectrum data records the distribution information of fluorescence signal intensity at different energies; X-ray source is the device that generates the raw X-ray beam; the raw X-ray beam is the unprocessed X-ray emitted directly from the X-ray source; micro-area X-ray beam is a small X-ray beam formed by focusing through an optical system; multilayer composite metal material is a composite material formed by combining different metal layers through processes such as rolling; layered structure characteristics refer to the arrangement and interface properties of different metal layers in the material; perpendicular to the layered structure interface refers to the orientation perpendicular to the layering direction of the material; measurement path is a scanning trajectory pre-set on the sample surface; each step point of the measurement path refers to the detection position set at fixed intervals on the path; X-ray photon energy distribution data records the number of photons of different energies collected at each detection point; spatial position information records the coordinate data of each detection point on the sample surface.

[0071] In this scheme, firstly, step 1011 generates a raw X-ray beam using an X-ray source and uses an optical focusing system to beam-shape the raw beam, focusing it into a micro-region X-ray beam with a smaller diameter. Secondly, step 1012, based on the layered structure characteristics of the multilayer composite metal material, determines a measurement path perpendicular to the layered interface on the sample surface. Then, step 1013 uses the micro-region X-ray beam to perform a step-by-step scanning along this measurement path, stopping at each step point to excite the sample and simultaneously acquiring the X-ray photon energy distribution data generated at that point. Finally, step 1014 records the coordinate position of each step point and its corresponding X-ray photon energy distribution data, integrating this information into a spatially correlated energy spectrum data sequence.

[0072] For example, taking a multi-layer composite metal sheet produced by Factory A as an example, this sheet is made of copper and steel layers through a rolling composite process. During testing, the X-ray source is first turned on to generate a raw X-ray beam, which is then focused into a micro-area X-ray beam with a diameter of 50 micrometers using a focusing lens group. Based on the layered structure of the sheet, a measurement path perpendicular to the rolling direction is drawn on the sample surface. A precision moving platform is used to move the sample step by step along this path, collecting X-ray photon energy distribution data every 20 micrometers. The coordinate position and X-ray photon energy distribution data of each point are recorded, ultimately obtaining an energy spectrum data sequence containing 200 measurement points.

[0073] This method obtains spatially resolved energy spectrum data through a precisely defined measurement path and micro-area scanning method, providing accurate basic data for subsequent analysis and ensuring that the detection results can truly reflect the elemental distribution of the material at different locations.

[0074] Step 102: Based on the layered structure characteristics of the multilayer composite metal material, the energy spectrum data at multiple measurement locations are corrected for angle dependence to obtain corrected energy spectrum data.

[0075] Optionally, step 102 may specifically include the following steps:

[0076] Step 1021: Determine the angle information between the incident direction of the X-ray beam and the normal of the layered structure feature interface at each measurement location;

[0077] Step 1022: Based on the anisotropic properties of each layer of the multilayer composite metal material, establish the correspondence between the included angle information and the intensity of the X-ray fluorescence signal;

[0078] Step 1023: According to the correspondence, the signal intensity of the X-ray photon energy distribution data collected at each measurement position is adjusted to obtain X-ray photon energy distribution data of multiple measurement positions after signal intensity adjustment.

[0079] Step 1024: Use the X-ray photon energy distribution data at multiple measurement locations after signal intensity adjustment as the calibration energy spectrum data.

[0080] In the above scheme, the energy spectrum data at multiple measurement locations refers to the set of records of X-ray fluorescence signal intensity as a function of energy, collected from different locations on the sample surface; the calibrated energy spectrum data refers to the energy spectrum data after correction for angle effects; the X-ray beam incident direction refers to the direction in which X-rays irradiate the sample surface; the interface normal of the layered structure refers to the direction perpendicular to the interface of the material's layered structure; the included angle information refers to the angle value between the X-ray incident direction and the interface normal; the anisotropy characteristic refers to the property of the material exhibiting different physical properties in different directions; the correspondence of X-ray fluorescence signal intensity refers to the law of signal intensity change under different incident angles; signal intensity adjustment refers to the process of correcting the measured value according to the angle dependence; the X-ray photon energy distribution data at multiple measurement locations after signal intensity adjustment refers to the energy spectrum data of each measurement point after correction.

[0081] In this scheme, firstly, step 1021 determines the angle between the incident X-ray beam direction and the normal of the layered structure interface at each measurement location through geometric measurements. Secondly, step 1022 establishes a correspondence model between the angle information and the X-ray fluorescence signal intensity based on the anisotropic properties of each layer in the multilayer composite metal material. Then, step 1023 adjusts the signal intensity of the X-ray photon energy distribution data collected at each measurement location according to the established correspondence model. Finally, step 1024 integrates the X-ray photon energy distribution data from multiple measurement locations after signal intensity adjustment into calibrated energy spectrum data.

[0082] Following the specific implementation of the previous scheme, and specifically the testing case of the copper-steel composite plate produced by Factory A in step 101, based on the obtained energy dispersive spectral data sequence containing 200 measurement points, and considering the anisotropic characteristics of this copper-steel composite plate, the angle between the X-ray incident direction and the normal to the rolling interface at each measurement point was first calculated, revealing an angle distribution ranging from 8 to 22 degrees. Based on the anisotropic parameters of the copper and steel layers, a correction model for the relationship between angle and signal intensity was established. This model was then used to perform point-by-point intensity correction on the energy dispersive spectral data of the 200 measurement points. After angle-dependent correction, the signal deviation caused by differences in measurement angles was eliminated, resulting in corrected energy dispersive spectral data that accurately reflects the elemental distribution.

[0083] This approach effectively eliminates signal intensity deviations caused by measurement geometry by establishing an angle-dependent model and correcting the measurement data, thereby improving the accuracy and reliability of the energy spectrum data and providing a more precise data foundation for subsequent analysis.

[0084] Step 103: The corrected energy spectrum data is superimposed according to the layered structure of the material to obtain the superimposed energy spectrum data.

[0085] Optionally, step 103 may specifically include the following steps:

[0086] Step 1031: Determine the distribution position of each material layer on the measurement path based on the layered structure characteristics of the multilayer composite metal material;

[0087] Step 1032: Classify the corrected energy spectrum data according to the corresponding material layers;

[0088] Step 1033: Based on the differences in physical properties of each material layer, determine the weighting coefficients of the corrected energy spectrum data of different material layers in the stacking process;

[0089] Step 1034: According to the weighting coefficients, the corrected energy spectrum data belonging to the same material layer are weighted and combined to obtain the weighted energy spectrum data of each material layer;

[0090] Step 1034 may specifically include the following steps:

[0091] Obtain the corrected energy spectrum data of all measurement locations belonging to the same material layer; determine the relative importance of the corrected energy spectrum data of each measurement location in the corresponding material layer; adjust the importance of the corrected energy spectrum data of each measurement location based on the weighting coefficient and the relative importance; synthesize all the corrected energy spectrum data of the same material layer after the importance adjustment to obtain the weighted energy spectrum data of each material layer.

[0092] Step 1035: The weighted energy spectrum data of each material layer are integrated and superimposed according to the layer sequence to form superimposed energy spectrum data representing the entire layered structure.

[0093] In the above scheme, the superimposed energy spectrum data refers to the energy spectrum data representing the characteristics of the entire layered structure obtained through superposition processing; the distribution position of each material layer on the measurement path refers to the start and end position information of each material layer on the measurement path; the classification of corresponding material layers refers to grouping the corrected energy spectrum data according to the material layer to which it belongs; the difference in physical properties refers to the difference in physical properties of different material layers, such as density, atomic number, etc.; the weighting coefficient in the superposition process refers to the weight value of the data of different material layers in the superposition process; the weighted energy spectrum data of each material layer refers to the energy spectrum data after weighted combination of each material layer; the relative importance refers to the importance of the corrected energy spectrum data at each measurement position in the corresponding material layer; the importance adjustment refers to the process of adjusting the data according to the relative importance.

[0094] In this scheme, firstly, step 1031 determines the distribution position of each material layer on the measurement path based on the layered structure characteristics of the multilayer composite metal material, i.e., identifying the boundary and range of each material layer. Secondly, step 1032 classifies the calibration energy spectrum data according to the corresponding material layer, i.e., grouping the data according to the material layer to which the measurement position belongs. Then, step 1033 determines the weighting coefficients of the calibration energy spectrum data of different material layers in the superposition process based on the differences in the physical properties of each material layer, i.e., assigning weight values ​​according to material properties. Next, step 1034 performs weighted combination of calibration energy spectrum data belonging to the same material layer according to the weighting coefficients: obtaining calibration energy spectrum data of all measurement positions belonging to the same material layer; determining the relative importance of the calibration energy spectrum data of each measurement position in the corresponding material layer; adjusting the importance of the calibration energy spectrum data of each measurement position based on the weighting coefficients and relative importance; and synthesizing all calibration energy spectrum data of the same material layer after importance adjustment to obtain the weighted energy spectrum data of each material layer. The final step 1035 integrates and superimposes the weighted energy spectrum data of each material layer according to the layer order, that is, the weighted energy spectrum data is merged according to the order of the material layers to form the superimposed energy spectrum data representing the entire layered structure.

[0095] Following on from the previous specific implementation, and taking the calibration energy spectrum data of the copper-steel composite plate produced by Factory A in step 102, this plate consists of copper and steel layers. First, based on the layered structure of the plate, the distribution positions of the copper and steel layers along the measurement path are determined, with the copper layer covering the first 100 points and the steel layer covering the last 100 points. Second, the calibration energy spectrum data from the 200 measurement points are categorized by copper and steel layers. Then, based on the differences in physical properties between copper and steel (e.g., copper has lower density and atomic number), the weighting coefficient for the copper layer data is determined to be 0.6, and for the steel layer, 0.4. Next, for the 100 measurement points of the copper layer: all point data are acquired; the relative importance of each point is determined (e.g., edge points have lower importance); based on the weights and importance, each point data is adjusted (e.g., multiplied by weight and importance factors); the adjusted data are synthesized to obtain the weighted energy spectrum data of the copper layer. The steel layer data is processed in the same way. Finally, the weighted energy spectrum data of the copper and steel layers are superimposed in sequence to form the superimposed energy spectrum data representing the entire plate.

[0096] This scheme effectively integrates the energy spectrum data of each layer by classifying and weighting them according to material layers, highlighting the overall characteristics of the layered structure, eliminating the interference caused by interlayer differences, and providing a clearer and more accurate data foundation for subsequent interface analysis.

[0097] Step 104: Based on the concentration gradient characteristics of elements in each layer of the multilayer composite metal material, perform interface diffusion effect compensation on the superimposed energy spectrum data.

[0098] Optionally, step 104 may specifically include the following steps:

[0099] Step 1041: Based on the concentration gradient characteristics, determine the theoretical distribution pattern of each element in the interface region under the influence of diffusion;

[0100] Step 1042: The measured signal distribution of each element in the superimposed energy spectrum data is compared point by point with the theoretical distribution of the corresponding element to obtain the signal difference data of each measurement point.

[0101] Step 1043: Based on the interlayer interface characteristics of the material, establish a morphological difference mapping relationship between the measured signal distribution and the theoretical distribution based on the signal difference data;

[0102] Step 1044: Based on the morphological difference mapping relationship, the measured signal distribution of each element in the superimposed energy spectrum data is compensated and corrected to obtain the energy spectrum data after interface diffusion compensation.

[0103] In the above scheme, the concentration gradient characteristics of each element in the layer refer to the characteristics of the gradual change in concentration of each element in the material near the interlayer interface; the interface diffusion effect compensation refers to the process of correcting the signal deviation caused by element diffusion; the theoretical distribution morphology refers to the ideal distribution shape of the element in the interface region predicted based on the material properties; the measured signal distribution refers to the distribution of the element signal intensity obtained by actual measurement as a function of position; the theoretical distribution morphology of the corresponding element refers to the ideal distribution shape predicted for a specific element; the signal difference data of each measurement point refers to the difference data between the measured signal and the theoretical signal at each measurement point; the interlayer interface characteristics of the material refer to the physical and chemical properties of the interface region between layers; the morphological difference mapping relationship refers to the quantitative relationship between the measured distribution and the theoretical distribution; and the energy spectrum data after interface diffusion compensation refers to the final energy spectrum data after correcting the diffusion effect.

[0104] In this scheme, firstly, step 1041 determines the theoretical distribution morphology of each element in the interface region under the influence of diffusion based on the concentration gradient characteristics of each element in the multilayer composite metal material. Secondly, step 1042 compares the measured signal distribution of each element in the superimposed energy dispersive spectroscopy data with the theoretical distribution morphology of the corresponding elements point by point to obtain the signal difference data of each measurement point. Then, step 1043 establishes a morphological difference mapping relationship between the measured signal distribution and the theoretical distribution morphology based on the obtained signal difference data, according to the interlayer interface characteristics of the material. Finally, step 1044 compensates and corrects the measured signal distribution of each element in the superimposed energy dispersive spectroscopy data based on the established morphological difference mapping relationship to obtain the energy dispersive spectroscopy data after interface diffusion compensation.

[0105] Following the specific implementation of the previous scheme, and taking the superimposed energy dispersive spectroscopy (EDS) data of the copper-steel composite plate produced by Factory A in step 103, the following steps are taken: First, based on the concentration gradient characteristics of copper and steel elements in the plate, it is determined that the theoretical distribution of copper in the interface region should exhibit a smooth transition. Then, the measured copper signal distribution is compared point by point with the theoretical smooth distribution, revealing a signal intensity deviation in the interface region. Based on the diffusion characteristics of the copper-steel interface, a mapping relationship between the measured and theoretical distributions is established. Finally, based on this mapping relationship, the copper signal distribution is compensated and corrected to make the signal distribution in the interface region closer to the theoretical prediction, ultimately obtaining compensated EDS data that accurately reflects the true elemental distribution.

[0106] This scheme effectively corrects signal deviations caused by element diffusion by establishing a theoretical distribution model and compensating for diffusion effects, thereby improving the detection accuracy of element distribution in the interface region and providing a more reliable data foundation for the final analysis of element distribution information.

[0107] Step 105: Extract the elemental distribution information of the interlayer interface region from the energy spectrum data compensated for by the interface diffusion effect.

[0108] Optionally, step 105 may specifically include the following steps:

[0109] Step 1051: Identify the characteristic energy signals of each element in the energy spectrum data after the interface diffusion effect compensation;

[0110] Step 1052: Determine the spatial range of the interlayer interface region based on the layered structural characteristics of the multilayer composite metal material;

[0111] Step 1053: Correlate the characteristic energy signals of each element with the spatial range of the interlayer interface region to generate spatial signal correlation data;

[0112] Step 1054: Based on the intensity changes of the characteristic energy signals of each element in the spatial signal association data, establish the distribution relationship between signal intensity and spatial location;

[0113] Step 1055: Determine the distribution pattern of each element in the interface region based on the distribution relationship between signal strength and spatial location;

[0114] Step 1056: Use the distribution pattern information of each element as the element distribution information of the interlayer interface region.

[0115] In the above scheme, elemental distribution information refers to detailed information about the distribution of each element in the interface region; characteristic energy signal refers to the unique X-ray fluorescence energy characteristic signal of each element; spatial range of the interlayer interface region refers to the specific location range of the interface in the material; spatial signal correlation data refers to the data set that correlates elemental signals with spatial locations; intensity variation of characteristic energy signals of each element refers to the variation of elemental signal intensity at different locations; distribution relationship between signal intensity and spatial location refers to the regularity of signal intensity variation with location; and distribution morphology of each element in the interface region refers to the specific distribution shape characteristics of the elements in the interface region.

[0116] In this scheme, firstly, step 1051 identifies the characteristic energy signals of each element in the energy spectrum data after interfacial diffusion effect compensation through energy spectrum analysis, i.e., identifies the characteristic energy peaks corresponding to different elements. Secondly, step 1052 determines the specific spatial range of the interlayer interface region based on the layered structure characteristics of the multilayer composite metal material, i.e., defines the start and end positions of the interface region. Then, step 1053 correlates the characteristic energy signals of each element with the spatial range of the interlayer interface region to generate correlation data containing spatial location and signal intensity. Next, step 1054 establishes a quantitative distribution relationship between signal intensity and spatial location based on the intensity changes of the characteristic energy signals of each element in the spatial signal correlation data. Then, step 1055 determines the specific distribution morphology characteristics of each element in the interface region based on the distribution relationship between signal intensity and spatial location. Finally, step 1056 integrates the distribution morphology information of each element as the final element distribution information of the interlayer interface region.

[0117] Following the specific implementation of the previous scheme, and taking the energy dispersive spectral data of the copper-steel composite plate after interface diffusion effect compensation in step 104, the characteristic energy signals of copper and iron are first identified. Based on the layered structure of the plate, the copper-steel interface region is located between points 90 and 110 of the measurement path. The characteristic signals of copper and iron are correlated with this interface region to generate spatial signal correlation data containing location and signal intensity. Analysis reveals that the copper signal intensity gradually weakens while the iron signal intensity gradually strengthens within the interface region. Based on this variation, the distribution relationship between signal intensity and location is established, determining that copper and iron elements exhibit a gradient diffusion distribution pattern in the interface region. Finally, the elemental distribution information of element diffusion in the interface region is obtained.

[0118] This scheme accurately resolves the elemental distribution morphology in the interface region by systematically analyzing the compensated energy spectrum data, providing detailed elemental diffusion information, which provides an important basis for evaluating the interface quality and process performance of materials, and realizes a complete characterization of the interface properties of multilayer composite metal materials.

[0119] Figure 2 This application provides a schematic diagram of the structure of an energy dispersive X-ray fluorescence-based energy spectroscopy analysis system, as shown below. Figure 2 As shown, the system includes:

[0120] The acquisition module 21 is used to acquire the energy dispersive X-ray fluorescence energy spectrum data at multiple measurement locations on the surface of the sample to be tested;

[0121] The correction module 22 is used to perform angle-dependent correction on the energy spectrum data at multiple measurement locations based on the layered structure characteristics of the multilayer composite metal material, so as to obtain the corrected energy spectrum data.

[0122] The superposition module 23 is used to superimpose the corrected energy spectrum data according to the layered structure of the material to obtain the superimposed energy spectrum data.

[0123] Compensation module 24 is used to compensate for interface diffusion effects on the superimposed energy spectrum data based on the concentration gradient characteristics of elements in each layer of the multilayer composite metal material.

[0124] The analysis module 25 is used to analyze the element distribution information of the interlayer interface region from the energy spectrum data compensated for by the interface diffusion effect.

[0125] Figure 2 The energy dispersive X-ray fluorescence-based energy spectroscopy analysis system described above can perform... Figure 1 The implementation principle and technical effects of the energy-dispersive X-ray fluorescence-based energy spectroscopy analysis method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the energy-dispersive X-ray fluorescence-based energy spectroscopy analysis system described in the above embodiments have been detailed in the embodiments related to this method, and will not be elaborated upon here.

[0126] In one possible design, Figure 2 The energy dispersive X-ray fluorescence-based energy spectroscopy analysis system of the illustrated embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0127] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0128] The processing component 32 is used for the above Figure 1 The embodiment describes an energy dispersive X-ray fluorescence-based energy spectroscopy analysis method.

[0129] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0130] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0131] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0132] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0133] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0134] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0135] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment presents an energy dispersive X-ray fluorescence-based energy spectroscopy analysis method.

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An energy-dispersive X-ray fluorescence (EDXRF) spectral analysis method, characterized in that, include: Acquire energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample under test; Based on the layered structure characteristics of multilayer composite metal materials, angle-dependent corrections are performed on the energy spectrum data at multiple measurement locations to obtain corrected energy spectrum data. The corrected energy spectrum data is superimposed according to the layered structure of the material to obtain superimposed energy spectrum data. This process includes: determining the distribution position of each material layer along the measurement path based on the layered structural characteristics of the multilayer composite metal material; classifying the corrected energy spectrum data according to the corresponding material layers; determining the weighting coefficients of the corrected energy spectrum data of different material layers during the superposition process based on the differences in the physical properties of each material layer; weighting the corrected energy spectrum data belonging to the same material layer according to the weighting coefficients to obtain weighted energy spectrum data for each material layer; and integrating and superimposing the weighted energy spectrum data of each material layer according to the layer sequence to form superimposed energy spectrum data representing the entire layered structure. Based on the concentration gradient characteristics of elements in each layer of a multilayer composite metal material, interface diffusion effect compensation is performed on the superimposed energy dispersive spectroscopy (EDS) data. This includes: determining the theoretical distribution morphology of each element in the interface region under diffusion influence based on the concentration gradient characteristics; comparing the measured signal distribution of each element in the superimposed EDS data with the theoretical distribution morphology point by point to obtain signal difference data at each measurement point; establishing a morphological difference mapping relationship between the measured signal distribution and the theoretical distribution morphology based on the signal difference data according to the interlayer interface characteristics of the material; and compensating and correcting the measured signal distribution of each element in the superimposed EDS data based on the morphological difference mapping relationship to obtain EDS data after interface diffusion compensation. Extracting elemental distribution information from interlayer interface regions from energy spectrum data compensated for interface diffusion effects includes: identifying the characteristic energy signals of each element in the energy spectrum data after interface diffusion effect compensation; determining the spatial range of the interlayer interface region based on the layered structure characteristics of the multilayer composite metal material; correlating the characteristic energy signals of each element with the spatial range of the interlayer interface region to generate spatial signal correlation data; establishing a distribution relationship between signal intensity and spatial location based on the intensity changes of the characteristic energy signals of each element in the spatial signal correlation data; determining the distribution morphology of each element in the interface region based on the distribution relationship between signal intensity and spatial location; and using the distribution morphology information of each element as the elemental distribution information of the interlayer interface region.

2. The method according to claim 1, characterized in that, Acquire energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample under test, including: Micro-area X-ray beams are obtained by beam shaping operations using the raw X-ray beam emitted from an X-ray source. Based on the layered structure characteristics of the multilayer composite metal material, a measurement path perpendicular to the layered structure interface is defined on the surface of the sample to be tested. The micro-area X-ray beam is used to perform a step-by-step scan along the measurement path, and X-ray photon energy distribution data is collected at each step point of the measurement path; Record the spatial location information of each step point and the corresponding X-ray photon energy distribution data to form an energy spectrum data sequence with spatial location correlation.

3. The method according to claim 1, characterized in that, Based on the layered structure characteristics of multilayer composite metal materials, angle-dependent corrections are performed on energy spectrum data from multiple measurement locations to obtain corrected energy spectrum data, including: Determine the angle between the incident direction of the X-ray beam and the normal of the characteristic interface of the layered structure at each measurement location; Based on the anisotropic properties of each layer in the multilayer composite metal material, a correspondence between the included angle information and the intensity of the X-ray fluorescence signal is established. Based on the aforementioned correspondence, the signal intensity of the X-ray photon energy distribution data collected at each measurement location is adjusted to obtain X-ray photon energy distribution data at multiple measurement locations after signal intensity adjustment. X-ray photon energy distribution data from multiple measurement locations, after signal intensity adjustment, were used as calibration energy spectrum data.

4. The method according to claim 1, characterized in that, According to the aforementioned weighting coefficients, the corrected energy spectrum data belonging to the same material layer are weighted and combined to obtain the weighted energy spectrum data of each material layer, including: Obtain calibrated energy spectrum data for all measurement locations belonging to the same material layer; Determine the relative importance of the corrected energy spectrum data at each measurement location in the corresponding material layer; Based on the weighting coefficients and the relative importance, the importance of the calibrated energy spectrum data at each measurement location is adjusted. All corrected energy spectrum data of the same material layer, after importance adjustment, are synthesized to obtain weighted energy spectrum data of each material layer.

5. An energy dispersive X-ray fluorescence (EDXRF)-based energy spectroscopy system, applied to the energy dispersive X-ray fluorescence-based energy spectroscopy method according to any one of claims 1-4, characterized in that, include: The acquisition module is used to acquire energy dispersive X-ray fluorescence spectral data at multiple measurement locations on the surface of the sample under test; The calibration module is used to perform angle-dependent calibration on the energy spectrum data at multiple measurement locations based on the layered structure characteristics of the multilayer composite metal material, so as to obtain calibrated energy spectrum data. The superposition module is used to superimpose the corrected energy spectrum data according to the layered structure of the material to obtain the superimposed energy spectrum data. The compensation module is used to compensate for the interface diffusion effect of the superimposed energy spectrum data based on the concentration gradient characteristics of elements in each layer of the multilayer composite metal material. The analysis module is used to extract the elemental distribution information of the interlayer interface region from the energy spectrum data compensated for by the interface diffusion effect.

6. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the energy dispersive X-ray fluorescence-based energy spectroscopy analysis method as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an energy-dispersive X-ray fluorescence-based spectral analysis method as described in any one of claims 1 to 4.

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