Microstructure quantitative characterization and feature dimension reduction method for titanium alloy welding seam

Abstract

The invention discloses a microstructure quantitative characterization and feature dimension reduction method for a titanium alloy welding seam. The method comprises the following steps that S1, a welding seam section sample is obtained; s2, collecting the surface of the weld joint section sample to obtain a weld joint full-view image; s3, carrying out observation area division on a base metal area, a heat affected area and a fusion area in the full view image of the welding seam, and selecting a to-be-represented observation area; s4, carrying out an EBSD experiment on the weld joint section sample, and carrying out data acquisition on the to-be-represented observation area to obtain a micro-area structure and a structural feature image; s5, performing quantitative analysis on the tissue and structural feature images, and extracting feature information as feature parameters; and S6, performing data dimension reduction on the feature parameters by adopting a feature extraction dimension reduction technology. According to the method, the EBSD technology and image processing software are combined, quantitative characterization is carried out on titanium alloy welding seam structures obtained under different process parameters, characteristic parameters related to the process and performance are extracted, dimension reduction processing is further adopted, and welding seam microstructure information is simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of titanium alloy structure characterization, and in particular to a method for quantitative characterization and feature dimension reduction of the microstructure of a titanium alloy weld. Background Art

[0002] Titanium alloys are valued for their high specific strength, high temperature resistance, corrosion resistance and other excellent properties, and are widely used in high-end fields such as aerospace, marine engineering, chemical industry and nuclear power. However, high-end equipment has increasingly higher quality requirements for titanium alloy welded components, and welding quality is directly related to product reliability and safety. Especially in the titanium alloy welding process, the microstructure of the weld and heat affected zone (HAZ) will change due to thermal cycles, resulting in inhomogeneity of microstructural characteristics such as grain size, phase change and texture. The evolution of microstructure will directly affect the mechanical properties of the welded joint, such as strength, hardness and fatigue life. Traditional welding quality assessment methods mainly rely on experience and qualitative analysis, lack of quantitative analysis of microstructure characteristics, and are not conducive to accurately studying the relationship between "process-structure-performance". Therefore, the quantitative characterization of the microstructure of titanium alloy welds is crucial for the establishment of a microstructure-mechanical property relationship model, and is of great significance for optimizing welding processes, predicting and improving the performance of welded joints.

[0003] Although traditional characterization methods such as metallography and scanning electron microscopy (SEM) can provide intuitive organizational information, it is difficult to accurately capture subtle changes in non-uniform organizations and achieve a more comprehensive quantitative analysis. Electron backscatter diffraction analysis technology (EBSD) technology can realize microtexture analysis and grain orientation distribution measurement, and can quantitatively characterize the correspondence between crystal structure and orientation information and microstructural morphology, thereby more accurately analyzing the relationship between material microstructural characteristics and performance from multiple dimensions. However, facing the research needs of building quantitative relationships, there are many microstructural characteristic parameters that affect the mechanical properties of titanium alloys, and the data processing is complex and inefficient.

[0004] In view of this, this application is hereby filed. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for quantitative characterization and feature dimensionality reduction of the microstructure of titanium alloy welds. By screening and multi-scale quantitative characterization of the weld tissue characteristics obtained under different process parameters, characteristic parameters related to the process and performance are extracted, and further dimensionality reduction processing is performed to retain the comprehensiveness of the information while extracting key feature information, thereby simplifying the weld microstructure information and laying the foundation for establishing a quantitative relationship between the microstructure and mechanical properties of titanium alloy welds.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention provides a method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld, comprising the following steps:

[0008] The steps include:

[0009] S1, weld cross-section samples were obtained from weld joints prepared under different process parameters;

[0010] S2, collecting the surface of the weld cross-section sample to obtain the overall image of the weld;

[0011] S3, dividing the base metal area (BM), heat affected zone (HAZ), and fusion zone (FZ) in the overall weld image into observation areas, and selecting the observation area to be characterized;

[0012] S4, performing an EBSD experiment on the weld cross-section sample, collecting data on the observation area to be characterized, and obtaining a microstructure and structural feature image;

[0013] S5, quantitatively analyzing the tissue and structure feature images, and extracting feature information as feature parameters;

[0014] S6, using feature extraction and dimensionality reduction technology to reduce the extracted multi-dimensional feature parameters.

[0015] In a specific embodiment, the specific method of step S1 is: according to the national standard GB / 3298-91 for metal microstructure inspection methods, representative samples are cut from titanium alloy welded joints prepared under different process parameters, and the weld cross section is appropriately surface ground and polished to ensure that the surface to be inspected is scratch-free and achieves a mirror effect; then the weld sample is immersed in a chemical etching solution for 10 to 15 seconds, washed with alcohol and then blown dry; wherein the chemical etching solution is a mixed solution of V(HF):V(HNO3):V(H2O)=1:4:15.

[0016] In a specific implementation, the specific method of step S2 is: using an optical microscope to observe the weld cross-section sample at 100 times, collecting images and splicing them into a full-view photo of the weld using Photoshop software.

[0017] In a specific embodiment, in step S3, the divided observation area subdivides the areas that may affect the target performance according to the differences in composition and organizational morphology, specifically including: BM1, BM2, HAZ1, HAZ2, the fusion zone close to the HAZ1 side, the fusion zone close to the HAZ2 side, the area near the fusion line between the weld metal and the base material 1, the area near the fusion line between the weld metal and the base material 2, the bottom of the weld area, the middle of the weld area, and the top of the weld area.

[0018] In a specific embodiment, in step S4, first, EBSD sample preparation is performed: the weld cross-section sample of S1 is electropolished to relieve stress using an electrolyte composition of 6% perchloric acid, 34% n-butanol, and 60% methanol to ensure that the sample resolution meets the observation requirements. During the electropolishing process, the electrolyte temperature is maintained at about -25°C and the current is set to 0.4A.

[0019] The microstructure and structural feature images include: grain orientation imaging, grain boundary map, phase distribution map, large and small angle grain boundary distribution map, IPF map, PF map, and KAM map.

[0020] In a specific embodiment, in step S5, the characteristic information includes the total α phase and total β phase volume fraction, the volume fraction of α phase in different forms, the average aspect ratio of the needle phase, the α cluster size, the α phase grain size, the thickness of the lamellar α phase, the proportion of large and small angle grain boundaries, the average KAM value, and the proportion of texture types.

[0021] In a specific embodiment, the total α phase and the total β phase volume fractions are expressed as area ratios using an area measurement method;

[0022] Different forms of α phases are distinguished by the aspect ratio of the fitted ellipse: the aspect ratio of the equiaxed and nearly equiaxed α phases ranges from 1 to 5.5, the aspect ratio of the lamellar α phase ranges from 5.5 to 8.5, and the aspect ratio of the needle-like phase is greater than 8.5.

[0023] In a specific embodiment, the α cluster size is represented by the average intercept length obtained by the average intercept method;

[0024] The α-phase grain size is represented by an average equivalent circle diameter;

[0025] The thickness of the α-phase layer is the length of the minor axis of the fitted ellipse;

[0026] The large and small angle grain boundaries are: high angle grain boundaries (HAGBs)>10°, and low angle grain boundaries (LAGBs) are 2°-10°;

[0027] The texture type accounts for <0001> / / The proportion of RD, where the deviation angle range is 0°~45°.

[0028] In a specific embodiment, in step S5, AZtecCrystal software or Image-Pro plus software is used to process the EBSD data and images for quantitative analysis.

[0029] In a specific implementation, in step S6, the feature extraction and dimensionality reduction technology uses principal component analysis.

[0030] Specifically, the original characteristic parameter data matrix is ​​standardized, the covariance matrix is ​​calculated, the eigenvalues ​​and eigenvectors are calculated, and the interpretation degree VE and the cumulative interpretation degree AVE of each order eigenvalue are calculated; the first two order principal components with AVE values ​​of 90% are selected to form a new two-dimensional feature space as the quantitative characterization parameter of the microstructure of the weld area;

[0031] In a specific embodiment, for titanium alloy welds with different compositions, processes, and structures, the previous steps can be repeated, and the high-resolution characterization of the microstructure can be guided by the macroscopic metallographic selection to ensure the correspondence between multi-scale characterization data, form a microstructure map and a quantitative characterization data package, which are used to support the subsequent process-structure-performance quantitative relationship model construction.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] 1. A method for quantitative characterization and feature dimensionality reduction of the microstructure of a titanium alloy weld provided in an embodiment of the present invention screens and quantitatively extracts some main organizational features by the significant degree of influence of different organizational feature parameters on the performance of titanium alloy, thereby preliminarily reducing the feature parameters. At the same time, based on the existing feature parameters, a dimensionality reduction technique is further adopted to extract key feature information while retaining the comprehensiveness of the information, thereby further simplifying the data.

[0034] 2. A method for quantitative characterization and feature dimensionality reduction of the microstructure of a titanium alloy weld provided in an embodiment of the present invention combines EBSD technology and image processing software to quantitatively characterize the titanium alloy weld structure obtained under different process parameters, extract characteristic parameters associated with the process and performance, and further adopt dimensionality reduction processing to simplify the weld microstructure information;

[0035] 3. A method for quantitative characterization and feature dimension reduction of the microstructure of a titanium alloy weld provided in an embodiment of the present invention can be applied to titanium alloy welds of different compositions, processes, and structures. It can quickly and reasonably realize the systematic characterization of the microstructure of the titanium alloy weld and the quantitative extraction of characteristic parameters. The obtained characteristic parameters have real physical meanings, which help to build a systematic understanding of the organizational structure of the titanium alloy weld, and on this basis can further reduce the dimension of the characteristic parameters, laying a data foundation for efficiently establishing a process-organization-performance quantitative relationship model for titanium alloy welds. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for quantitative characterization and feature dimensionality reduction of microstructures provided in an embodiment of the present invention;

[0038] Figure 2 A metallographic observation area division diagram of a titanium alloy weld provided in an embodiment of the present invention;

[0039] Figure 3 The phase distribution diagram of the micro-region organization and structural characteristic image provided by the embodiment of the present invention is used to obtain the total α phase volume fraction;

[0040] Figure 4 The grain orientation imaging diagram of the micro-region organization and structural characteristic image provided by the embodiment of the present invention is used to obtain the average grain size of the α phase;

[0041] Figure 5 The diffraction imaging diagram of the micro-region organization and structural characteristic image provided by the embodiment of the present invention is used to obtain the volume fraction of α phases of different forms;

[0042] Figure 6 The grain orientation imaging diagram of the micro-region organization and structural characteristic image provided by the embodiment of the present invention is used to obtain the average aspect ratio of the α phase;

[0043] Figure 7 The large and small angle grain boundary distribution map of the micro-region organization and structural feature image provided in the embodiment of the present invention is used to obtain the proportion of large and small angle grain boundaries;

[0044] Figure 8 The KAM map of the micro-region organization and structural feature image provided by the embodiment of the present invention is used to obtain the average KAM value;

[0045] Fig. 9 The PF map of the micro-region organization and structural feature image provided by the embodiment of the present invention is used to obtain the proportion of texture types;

[0046] Fig.10 A flow chart of a feature dimensionality reduction algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0048] In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is apparent to one of ordinary skill in the art that these specific details need not be employed to practice the present invention. In other embodiments, in order to avoid confusing the present invention, well-known materials or methods are not specifically described.

[0049] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment," "an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0050] The "range" disclosed in the present application is defined in the form of a lower limit and an upper limit, and a given range is defined by selecting a lower limit and an upper limit, and the selected lower limit and upper limit define the boundaries of the particular range. The range defined in this way can be inclusive or exclusive of the end values, and can be arbitrarily combined, that is, any lower limit can be combined with any upper limit to form a range. For example, if a range of 60 to 120 and 80 to 110 is listed for a particular parameter, it is understood that a range of 60 to 110 and 80 to 120 is also expected. In addition, if the minimum range values ​​1 and 2 are listed, and if the maximum range values ​​3, 4 and 5 are listed, the following ranges can all be expected: 1 to 3, 1 to 4, 1 to 5, 2 to 3, 2 to 4 and 2 to 5. In the present application, unless otherwise specified, the numerical range "a to b" represents an abbreviation of any real number combination between a and b, where a and b are both real numbers. For example, the numerical range "0-5" means that all real numbers between "0-5" are listed in this document, and "0-5" is just an abbreviation of these numerical combinations. In addition, when a parameter is expressed as an integer ≥ 2, it is equivalent to disclosing that the parameter is, for example, an integer of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, etc.

[0051] If there is no special explanation, all steps of the present application can be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), which means that the method may include steps (a) and (b) performed sequentially, or may include steps (b) and (a) performed sequentially. For example, the method may further include step (c), which means that step (c) may be added to the method in any order. For example, the method may include steps (a), (b) and (c), or may include steps (a), (c) and (b), or may include steps (c), (a) and (b), etc.

[0052] Although traditional characterization methods such as metallography and scanning electron microscopy (SEM) can provide intuitive organizational information, it is difficult to accurately capture subtle changes in non-uniform organizations and achieve a more comprehensive quantitative analysis. Electron backscatter diffraction analysis technology (EBSD) technology can realize microtexture analysis and grain orientation distribution measurement, and can quantitatively characterize the correspondence between crystal structure and orientation information and microstructural morphology, thereby more accurately analyzing the relationship between material microstructural characteristics and performance from multiple dimensions. However, facing the research needs of building quantitative relationships, there are many microstructural characteristic parameters that affect the mechanical properties of titanium alloys, and the data processing is complex and inefficient.

[0053] In order to solve the above technical problems, the present invention provides a method for quantitative characterization and feature dimension reduction of the microstructure of a titanium alloy weld, comprising the following steps:

[0054] The steps include:

[0055] S1, weld cross-section samples were obtained from weld joints prepared under different process parameters;

[0056] S2, collecting the surface of the weld cross-section sample to obtain the overall image of the weld;

[0057] S3, dividing the base metal area (BM), heat affected zone (HAZ), and fusion zone (FZ) in the overall weld image into observation areas, and selecting the observation area to be characterized;

[0058] S4, performing an EBSD experiment on the weld cross-section sample, collecting data on the observation area to be characterized, and obtaining a microstructure and structural feature image;

[0059] S5, quantitatively analyzing the tissue and structure feature images, and extracting feature information as feature parameters;

[0060] S6, using feature extraction and dimensionality reduction technology to reduce the extracted multi-dimensional feature parameters.

[0061] In a specific embodiment, the specific method of step S1 is: according to the national standard GB / 3298-91 for metal microstructure inspection methods, representative samples are cut from titanium alloy welded joints prepared under different process parameters, and the weld cross section is appropriately surface ground and polished to ensure that the surface to be inspected is scratch-free and achieves a mirror effect; then the weld sample is immersed in a chemical etching solution for 10 to 15 seconds, washed with alcohol and then blown dry; wherein the chemical etching solution is a mixed solution of V(HF):V(HNO3):V(H2O)=1:4:15.

[0062] In a specific implementation, the specific method of step S2 is: using an optical microscope to observe the weld cross-section sample at 100 times, collecting images and splicing them into a full-view photo of the weld using Photoshop software.

[0063] In a specific embodiment, in step S3, the divided observation area subdivides the areas that may affect the target performance according to the differences in composition and organizational morphology, specifically including: BM1, BM2, HAZ1, HAZ2, the fusion zone close to the HAZ1 side, the fusion zone close to the HAZ2 side, the area near the fusion line between the weld metal and the base material 1, the area near the fusion line between the weld metal and the base material 2, the bottom of the weld area, the middle of the weld area, and the top of the weld area.

[0064] In a specific embodiment, in step S4, first, EBSD sample preparation is performed: the weld cross-section sample of S1 is electropolished to relieve stress using an electrolyte composition of 6% perchloric acid, 34% n-butanol, and 60% methanol to ensure that the sample resolution meets the observation requirements. During the electropolishing process, the electrolyte temperature is maintained at about -25°C and the current is set to 0.4A.

[0065] The microstructure and structural feature images include: grain orientation imaging, grain boundary map, phase distribution map, large and small angle grain boundary distribution map, IPF map, PF map, and KAM map.

[0066] In a specific embodiment, in step S5, the characteristic information includes the total α phase and total β phase volume fraction, the volume fraction of α phase in different forms, the average aspect ratio of the needle phase, the α cluster size, the α phase grain size, the thickness of the lamellar α phase, the proportion of large and small angle grain boundaries, the average KAM value, and the proportion of texture types.

[0067] In a specific embodiment, the total α phase and the total β phase volume fractions are expressed as area ratios using an area measurement method;

[0068] Different forms of α phases are distinguished by the aspect ratio of the fitted ellipse: the aspect ratio of the equiaxed and nearly equiaxed α phases ranges from 1 to 5.5, the aspect ratio of the lamellar α phase ranges from 5.5 to 8.5, and the aspect ratio of the needle-like phase is greater than 8.5.

[0069] In a specific embodiment, the α cluster size is represented by the average intercept length obtained by the average intercept method;

[0070] The α-phase grain size is represented by an average equivalent circle diameter;

[0071] The thickness of the α-phase layer is the length of the minor axis of the fitted ellipse;

[0072] The large and small angle grain boundaries are: high angle grain boundaries (HAGBs)>10°, and low angle grain boundaries (LAGBs) are 2°-10°;

[0073] The texture type accounts for <0001> / / The proportion of RD, where the deviation angle range is 0°~45°.

[0074] In a specific embodiment, in step S5, AZtechCrystal software or Image-Pro plus software is used to process the EBSD data and images for quantitative analysis.

[0075] In a specific implementation, in step S6, the feature extraction and dimensionality reduction technology uses principal component analysis.

[0076] Specifically, the original characteristic parameter data matrix is ​​standardized, the covariance matrix is ​​calculated, the eigenvalues ​​and eigenvectors are calculated, and the interpretation degree VE and the cumulative interpretation degree AVE of each order eigenvalue are calculated; the first two order principal components with AVE values ​​of 90% are selected to form a new two-dimensional feature space as the quantitative characterization parameter of the microstructure of the weld area;

[0077] In a specific embodiment, for titanium alloy welds with different compositions, processes, and structures, the previous steps can be repeated, and the high-resolution characterization of the microstructure can be guided by the macroscopic metallographic selection to ensure the correspondence between multi-scale characterization data, form a microstructure map and a quantitative characterization data package, which are used to support the subsequent process-structure-performance quantitative relationship model construction.

[0078] The present invention screens and quantitatively extracts some main organizational features through the significant degree of influence of different organizational characteristic parameters on the performance of titanium alloy, and preliminarily reduces the characteristic parameters. At the same time, based on the existing characteristic parameters, the dimensionality reduction technology is further adopted to extract key characteristic information while retaining the comprehensiveness of the information, so as to further simplify the data.

[0079] The present invention combines EBSD technology and image processing software to quantitatively characterize the titanium alloy weld microstructure obtained under different process parameters, extract characteristic parameters associated with the process and performance, and further adopt dimensionality reduction processing to simplify the weld microstructure information;

[0080] The present invention can be applied to titanium alloy welds of different compositions, processes and structures. It can quickly and reasonably realize the systematic characterization of the microstructure of titanium alloy welds and the quantitative extraction of characteristic parameters. The obtained characteristic parameters have real physical meanings, which are helpful to build a systematic understanding of the organizational structure of titanium alloy welds. On this basis, the characteristic parameter dimensions can be further reduced, laying a data foundation for efficiently establishing a process-organization-performance quantitative relationship model for titanium alloy welds.

[0081] Example 1

[0082] like Figure 1 As shown, an embodiment of the present invention provides a method for quantitative characterization and feature dimension reduction of the microstructure of a titanium alloy weld, taking a titanium alloy pipe butt joint as an example, comprising the following steps:

[0083] The weld base material uses two near-α titanium alloys with different compositions, namely BM1 and BM2, and the filler wire metal is close to the composition of the BM1 base material; the weld preparation in this embodiment adopts pulse TIG welding technology, which is divided into two welding processes: base and filler wire. In order to ensure the uniformity of the weld width, each process is divided into 7 process intervals with different peak currents. According to the different angles from the arc starting point, the 7 process intervals are pre-melting zone (0°~30°), zone I (31°~60°), zone II (61°~120°), zone III (121°~180°), zone IV (181°~240°), zone V (241°~300°), and zone VI (301°~370°); based on the existing process, by overall expanding or reducing the peak current level during welding, n groups of welding joints under different process parameters are prepared in parallel while ensuring a good weld forming surface;

[0084] (1) Preparation of weld samples: For the obtained n groups of weld joints, representative samples were cut from the titanium alloy weld joints prepared under different process parameters according to the national standard GB / 3298-91 for metal microstructure inspection methods, and the weld cross section was properly surface ground and polished to ensure that the surface to be inspected was free of scratches and achieved a mirror effect. The selection of representative samples in this embodiment was based on existing temperature field research, and the cross section of zone V was used as the observation surface.

[0085] (2) Metallographic sample etching: Immerse the weld sample in a chemical etching solution for 10 to 15 seconds until a clear molten pool morphology appears on the sample surface, wash it with alcohol and then blow it dry; the chemical etching solution is a mixed solution of V(HF):V(HNO3):V(H2O)=1:4:15.

[0086] (3) Metallographic observation and image acquisition: The metallographic samples of the welds were observed at 100 times using an optical microscope, and images were collected and stitched into a full-view photo of the weld using Photoshop software.

[0087] (4) Selection of microstructure observation area: The base metal area (BM), heat affected zone (FAZ), and fusion zone (FZ) in the overall weld photo can be divided. The areas that may affect the performance can be further subdivided according to the differences in composition and microstructure morphology. The observation area is subdivided into 11 areas, such as Figure 2 As shown, it specifically includes: BM1, HAZ1, fusion zone near the side of HAZ1, the area near the fusion line where the weld metal combines with the base material 1, the bottom of the weld zone, the middle of the weld zone, the top of the weld zone, the area near the fusion line where the weld metal combines with the base material 2, the fusion zone near the side of HAZ2, HAZ2, BM2, and is distinguished by numbering. According to the existing qualitative research on process-structure-fatigue performance, combined with the fatigue performance test requirements of this embodiment, the middle of the weld zone (observation zone No. 6) is selected from the 11 observation areas as the observation area to be further characterized, and a 350-fold local photo is taken to supplement the high-magnification organizational morphology information in the microstructure atlas.

[0088] (5) EBSD sample preparation: The surface of the metallographic sample in step (3) is subjected to stress relief treatment by electrolytic polishing or mechanical polishing to ensure that the sample resolution meets the observation requirements. The electrolytic polishing is performed using an electrolyte composition of 6% perchloric acid, 34% n-butanol, and 60% methanol; the electrolyte temperature is maintained at about -25°C, and the current is set to 0.4A.

[0089] (6) EBSD experiment and image acquisition: The prepared samples were subjected to EBSD experiment, and data was collected from the middle of the weld area (observation area No. 6) selected in step (4). The data was then denoised to obtain images of the relevant organizational and structural features in the sample micro-area.

[0090] Among them, Figure 3 to Figure 9 As shown, the images of the organizational and structural features include: grain orientation imaging, grain boundary map, phase distribution map, large and small angle grain boundary distribution map, IPF map, PF map, and KAM map.

[0091] (7) Quantitative extraction and analysis: AZtecCrystal software and Image-Pro plus software were used to process the EBSD data and images for quantitative analysis. Based on the principle of stereology, characteristic information that affects the fatigue performance of the weld in this embodiment was extracted as characteristic parameters from the perspectives of morphology, content, size, and distribution.

[0092] Based on the organizational characteristics of near-α titanium alloys, this embodiment selects the following characteristic information as characteristic parameters of the organization of this region, including: volume fraction of total α phase and total β phase, volume fraction of α phase in different forms, average aspect ratio of needle phase, α cluster size, α phase grain size, thickness of lamellar α phase, proportion of large and small angle grain boundaries, average KAM value, and proportion of texture types.

[0093] Among them, the volume fractions of total α phase and total β phase are expressed as area ratio based on the planimetric method;

[0094] The α phase morphology is distinguished by the aspect ratio of the fitted ellipse, the ellipse aspect ratio of the equiaxed and nearly equiaxed α phases ranges from 1 to 5.5, the fitting ellipse aspect ratio of the lamellar α phase ranges from 5.5 to 8.5, and the ellipse aspect ratio of the needle phase is greater than 8.5; wherein, the grain orientation imaging image is subjected to binary image preprocessing, and the adhered α phase is manually separated, and then the average aspect ratio of the needle phase is extracted using Image-Pro plus software.

[0095] Among them, the α cluster size is expressed by the average intercept length obtained by the average intercept method;

[0096] Among them, the α-phase grain size is expressed by the average equivalent circle diameter;

[0097] Among them, the thickness of the α-phase layer is the length of the minor axis of the fitted ellipse;

[0098] Among them, large and small angle grain boundaries are defined as: high-angle grain boundaries (HAGBs)>10°, low-angle grain boundaries (LAGBs) are 2°-10°;

[0099] Among them, the proportion of texture types is <0001> / / The proportion of RD, where the deviation angle range is 0°~45°.

[0100] (8) Dimensionality reduction: The p-dimensional feature parameters extracted in step (7) are reduced in dimension using feature extraction and dimensionality reduction technology. The m-dimensional feature parameters obtained after dimensionality reduction need to retain most of the original features. The feature extraction and dimensionality reduction technology is principal component analysis (PCA). Fig.10As shown, specifically: standardize the original characteristic parameter data matrix, calculate the covariance matrix, calculate the eigenvalues ​​and eigenvectors, and calculate the interpretation degree VE and cumulative interpretation degree AVE of each order eigenvalue; select the first two principal components with AVE value of 90% to form a new two-dimensional feature space as the quantitative characterization parameter of the microstructure of the weld area.

[0101] (9) Summarize steps 1)-8). For titanium alloy welds with different compositions, processes, and structures, repeat the above steps (1)-(8). The high-resolution characterization of the microstructure is guided by the macroscopic metallographic selection to ensure the correspondence between multi-scale characterization data. A microstructure map and a quantitative characterization data package can be formed to support the subsequent process-structure-performance quantitative relationship model building.

[0102] The present invention screens and quantitatively extracts some main organizational features through the significant degree of influence of different organizational characteristic parameters on the performance of titanium alloy, and preliminarily reduces the characteristic parameters. At the same time, based on the existing characteristic parameters, the dimensionality reduction technology is further adopted to extract key characteristic information while retaining the comprehensiveness of the information, so as to further simplify the data.

[0103] The present invention combines EBSD technology and image processing software to quantitatively characterize the titanium alloy weld microstructure obtained under different process parameters, extract characteristic parameters associated with the process and performance, and further adopt dimensionality reduction processing to simplify the weld microstructure information;

[0104] The present invention can be applied to titanium alloy welds of different compositions, processes and structures. It can quickly and reasonably realize the systematic characterization of the microstructure of titanium alloy welds and the quantitative extraction of characteristic parameters. The obtained characteristic parameters have real physical meanings, which are helpful to build a systematic understanding of the organizational structure of titanium alloy welds. On this basis, the characteristic parameter dimensions can be further reduced, laying a data foundation for efficiently establishing a process-organization-performance quantitative relationship model for titanium alloy welds.

[0105] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for quantitative characterization and feature dimension reduction of microstructure of titanium alloy welds, characterized in that: The steps include: S1, weld cross-section samples were obtained from weld joints prepared under different process parameters; S2, collecting the surface of the weld cross-section sample to obtain the overall image of the weld; S3, dividing the base metal area, heat affected zone, and fusion zone in the overall weld image into observation areas, and selecting the observation area to be characterized; S4, performing an EBSD experiment on the weld cross-section sample, collecting data on the observation area to be characterized, and obtaining a microstructure and structural feature image; S5, quantitatively analyzing the tissue and structure feature images, and extracting feature information as feature parameters; S6, using feature extraction and dimensionality reduction technology to reduce the extracted multi-dimensional feature parameters.

2. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: The specific method of step S1 is: cutting a weld cross section from a weld joint prepared under different process parameters, grinding and polishing the weld cross section, and then immersing it in a chemical etching solution for treatment.

3. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: The specific method of step S2 is: using an optical microscope to observe the weld cross-section sample, and collecting images to obtain a full-view photo of the weld.

4. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: In step S3, the divided observation areas include: base material zone 1, base material zone 2, heat affected zone 1, heat affected zone 2, fusion zone close to the heat affected side 1, fusion zone close to the heat affected side 2, area near the fusion line where the weld metal and base material 1 are combined, area near the fusion line where the weld metal and base material 2 are combined, bottom of the weld zone, middle of the weld zone, and top of the weld zone.

5. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: In step S4, the micro-region organization and structural characteristic images include: grain orientation imaging map, grain boundary map, phase distribution map, large and small angle grain boundary distribution map, IPF map, PF map, and KAM map.

6. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: In step S5, the characteristic information includes the total α phase and total β phase volume fraction, the volume fraction of α phase in different forms, the average aspect ratio of the needle phase, the α cluster size, the α phase grain size, the thickness of the lamellar α phase, the proportion of large and small angle grain boundaries, the average KAM value, and the proportion of texture types.

7. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 6, characterized in that: The total α phase and total β phase volume fractions are expressed as area ratios using the area measurement method; Different forms of α phases are distinguished by the aspect ratio of the fitted ellipse: the aspect ratio of the ellipse of the equiaxed and nearly equiaxed α phases ranges from 1 to 5.5, the aspect ratio of the fitted ellipse of the lamellar α phase ranges from 5.5 to 8.5, and the aspect ratio of the ellipse of the needle-shaped phase is greater than 8.

5.

8. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 6, characterized in that: The α cluster size is represented by the average intercept length obtained by the average intercept method; The α-phase grain size is represented by an average equivalent circle diameter; The thickness of the α-phase layer is the length of the minor axis of the fitted ellipse; The large and small angle grain boundaries are: high angle grain boundaries (HAGBs)>10°, and low angle grain boundaries (LAGBs) are 2°-10°; The texture type accounts for <0001> / / The proportion of RD, where the deviation angle range is 0°~45°.

9. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: In step S5, the quantitative analysis is performed using AZtecCrystal software or Image-Pro plus software.

10. The method for quantitative characterization and feature dimension reduction of microstructure of a titanium alloy weld according to claim 1, characterized in that: In step S6, the feature extraction and dimensionality reduction technology adopts principal component analysis.

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