Identifying and analyzing equipment and distinguishing method for slip line and twin crystal

Abstract

The invention relates to the technical field of material analysis, in particular to differential analysis equipment for slip lines and twin crystals, which comprises the differential analysis equipment and is used for differential analysis of the slip lines and the twin crystals, and the differential analysis equipment comprises a support assembly, a first gear, a second gear, a third gear and a fourth gear, a plurality of second gears are rotationally mounted on one side of the lower end in the base through expansion shaft buckles, a first knob is fixed to the upper end of one second gear, a shaft frame is pivoted between the two sides of the upper end of the base, and a mounting frame is pivoted between the two sides of the shaft frame; the acquisition analyzer is arranged on the mounting frame and is used for acquisition and analysis; through integration of EBSD, DIC and optical imaging technologies, precise identification of the slip line and the twin crystal is realized in cooperation with a three-level intelligent discrimination model, the detection precision is significantly improved in cooperation with an original mechanical adjustment system, and a full-automatic solution is provided for material microscopic deformation analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of material analysis, and in particular to an identification and analysis device and a differentiation method for slip lines and twins. Background Art

[0002] In materials science and metallurgy, slip and twinning are two common plastic deformation mechanisms. Their micromorphologies and formation mechanisms differ, and their effects on material properties are also different. Therefore, accurately identifying slip lines and twinning is of great significance for understanding material deformation behavior, optimizing processing technology, and improving material properties.

[0003] According to the publication number: CN119780115A, a method for detecting dislocation slip lines of silicon single crystals is disclosed. This technology discloses "a method for detecting dislocation slip lines of silicon single crystals, which is particularly suitable for detecting dislocation slip lines of silicon single crystals with a diameter greater than 300mm and a dopant of element B. The method comprises: after the completion of pulling the native crystal silicon single crystal, cutting a sample with a thickness of 2-10mm, and cleaning the sample with deionized water; corroding the sample with a mixed solution, and immersing the sample in a mixed solution of nitric acid with a concentration of 65-68% and hydrofluoric acid with a concentration of 45-49%." The method has the following technical effects: "using a chromium-free solution to corrode a silicon single crystal with a diameter greater than 300mm and a dopant of element B, during the corrosion process, selecting a suitable corrosion solution and corrosion time, and determining the position and length of the dislocation slip line under a strong light, which can eliminate interference factors and accurately identify the dislocation slip line; at the same time, comparing with the XRT morphology image, it can achieve the goals of environmental friendliness, cost reduction and efficiency improvement."

[0004] Traditional electron backscatter diffraction can only provide static crystal orientation information (spatial resolution 0.1-0.5μm), and is completely unable to synchronously obtain dynamic strain field distribution data of the corresponding area. Additional DIC equipment is required, resulting in a lack of mechanical response dimension in deformation mechanism analysis. In addition, existing deep learning-based detection systems (such as traditional CNN models) only rely on image grayscale features and completely ignore the intrinsic crystallographic laws of the material. A typical manifestation is: 60° / <110> The standard twin orientation relationship (FCC metal) is misjudged as a common high-angle grain boundary (error rate > 35%), and it is impossible to distinguish {112} <111> Differences in strain fields between twin systems (BCC metals) and slip bands. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an identification and analysis device and a differentiation method for slip lines and twins. By integrating EBSD, DIC and optical imaging technologies, and cooperating with a three-level intelligent discrimination model, the precise identification of slip lines and twins can be achieved. Combined with an original mechanical adjustment system, the detection accuracy is significantly improved, providing a fully automatic solution for material micro-deformation analysis.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an identification and analysis device for slip lines and twins, including an identification and analysis device and used for identifying and analyzing slip lines and twins, the identification and analysis device comprising:

[0007] The bracket assembly includes a first gear rotatably mounted on the lower end of the base, a loading platform fixed to the upper end of the first gear via a shaft, a plurality of second gears rotatably mounted on one side of the lower end of the base via an expansion shaft buckle, a first knob fixed to the upper end of one of the second gears, an axis frame pivotally connected between two sides of the upper end of the base, and a mounting frame pivotally connected between two sides of the axis frame;

[0008] The collection and analysis device is set on the mounting frame and is used for collection and analysis.

[0009] Preferably, the bracket assembly also includes a fan-shaped gear fixed at one end of the inner part of the shaft frame, a second knob is rotatably installed on the outer end of the shaft frame, a third gear is fixed on the inner end of the second knob, a fourth gear is rotatably installed on one end of the inner part of the shaft frame through an expansion shaft buckle and meshes with the third gear for transmission, a fifth gear is fixed on the fourth gear and meshes with the fan-shaped gear for transmission, a third knob is fixed on the outer end of the mounting frame, a sixth gear is fixed on the inner end of the third knob, a seventh gear is rotatably installed on one end of the inner part of the mounting frame through an expansion shaft buckle and meshes with the sixth gear for transmission.

[0010] Preferably, the acquisition analyzer includes:

[0011] Multimodal data acquisition module, integrating EBSD, DIC and optical topography imaging;

[0012] Real-time collaborative analysis module to simultaneously process crystal orientation, strain field and surface morphology data;

[0013] The intelligent classification module outputs the judgment results of slip lines or twins based on machine learning algorithms.

[0014] Preferably, the multimodal data acquisition module includes:

[0015] EBSD component, using a high-resolution field emission scanning electron microscope and equipped with a high-speed Hough transform processor;

[0016] DIC component, integrated with nano-scale laser speckle projection device and equipped with dual CMOS cameras;

[0017] Confocal optical imaging component with three-dimensional surface reconstruction capability.

[0018] Preferably, the data fusion method in the real-time collaborative analysis module is:

[0019] Spatial registration algorithm aligns EBSD, DIC, and optical images to the same coordinate system based on feature point matching;

[0020] Combined feature extraction and simultaneous calculation of orientation difference, local strain gradient and topography curvature parameters;

[0021] Dynamically associated database, storing historical test data for real-time comparison.

[0022] The present invention also discloses a method for distinguishing slip lines from twins, comprising the following steps:

[0023] S1, initial screening of crystallographic features: obtain the crystal orientation map of the area to be tested by EBSD, identify the orientation mutation area, and the gradient difference is ≥5° / μm;

[0024] S2, strain field matching verification: DIC is used to measure the strain field of the corresponding area. If the strain concentration band is ≤1μm in width and linearly distributed, it is marked as a candidate slip line area;

[0025] S3, final judgment of surface morphology: The surface morphology is verified by confocal optical imaging. Slip lines appear as parallel nanogrooves with a depth of 50-200 nm, and twin boundaries appear as continuous steps with a height difference of ≥300 nm.

[0026] S4, multi-source data decision-making: comprehensive three types of data input intelligent classification module output the final result.

[0027] Preferably, adaptive scanning is used in S1. After the orientation mutation area is preliminarily detected, the EBSD scanning step size is automatically reduced to 0.01 μm for fine scanning, and X-ray micro-diffraction data are additionally collected for the suspected twin boundary area to verify the mirror symmetry, wherein the beam size is ≤0.5 μm.

[0028] Preferably, strain singularity analysis is introduced in S2 to calculate the eigenvalue of the strain tensor, the deviation between the maximum shear strain direction corresponding to the slip line and the theoretical direction of the slip system is ≤5°, and the main directions of the strain tensor on both sides of the twin boundary are mirror-symmetrical, and the symmetry is ≥90%.

[0029] Preferably, the intelligent classification module includes a three-level discrimination model:

[0030] The first level of discrimination is to quickly screen suspected areas based on the random forest algorithm;

[0031] In the second level of discrimination, a 3D convolutional neural network is used to analyze the spatial correlation between the EBSD orientation map and the DIC strain field;

[0032] The third level of discrimination verifies the results through a twin-specific discriminator and includes the physical constraints of the twin law.

[0033] Preferably, in the third level judgment, for face-centered cubic metals, it is mandatory to check whether the {111} face family has 60° / <110> Orientation relationship: For body-centered cubic metals, orientation difference verification of the {112} face family is added. If the physical constraints are not met, re-testing is triggered even if the output is high confidence.

[0034] The present invention provides an identification and analysis device and a method for distinguishing slip lines and twins. Compared with the existing technology, it has the following advantages:

[0035] 1. The multimodal collaborative detection architecture enables the full-dimensional and precise capture of the microscopic deformation characteristics of materials. By integrating the three key technologies of EBSD crystal orientation analysis, DIC strain field measurement, and confocal optical topography imaging into a single detection platform, it solves the spatiotemporal mismatch problem existing in traditional separate equipment. Among them, the collaborative work of high-resolution field emission electron microscopy and nanoscale laser speckle patterning enables the simultaneous correlation analysis from atomic-scale orientation changes to mesoscopic-scale strain distribution; and the three-dimensional topography reconstruction function provides precise geometric evidence for surface deformation characteristics.

[0036] 2. The intelligent analysis system achieves a deep integration of materials science principles and artificial intelligence. The system adopts a three-level joint judgment mechanism: the first-level random forest algorithm realizes rapid initial screening, the second-level 3D-CNN deeply explores the spatial correlation characteristics of orientation and strain field, and the third-level innovatively introduces a crystallographic physical constraint verification module. In particular, the strict judgment criteria set for different crystal structures ensure that the machine learning results always conform to the intrinsic laws of the material.

[0037] 3. The mechanical adjustment system has achieved a breakthrough improvement in detection accuracy. The acquisition analyzer can perform three-dimensional precise adjustment and automatic hover positioning. Among them, the expansion shaft buckle and gear structure enable each adjustment shaft system to remain absolutely stationary in the absence of external force, eliminating the return clearance existing in traditional thread adjustment. Combined with the multi-knob sub-control system, the acquisition analyzer can achieve ±0.1° angle fine-tuning to ensure that it is always in the optimal working position. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the three-dimensional structure of the present invention;

[0039] Figure 2 Schematic diagram of the structure of the base in the present invention;

[0040] Figure 3It is a structural schematic diagram of the axle frame of the present invention;

[0041] Figure 4 It is a structural schematic diagram of the mounting frame of the present invention;

[0042] Figure 5 This is a schematic structural diagram of the expansion shaft buckle in the present invention;

[0043] Figure 6 It is a block diagram of the acquisition analyzer in the present invention;

[0044] Figure 7 is a block diagram of the multimodal data acquisition module in the present invention;

[0045] Figure 8 A block diagram of the data fusion method of the real-time collaborative analysis module in the present invention;

[0046] Figure 9 A flowchart of the method steps of the present invention;

[0047] Figure 10 It is a block diagram of the three-level discrimination model of the intelligent classification module in the present invention.

[0048] In the figure: 1. Identification and analysis equipment; 11. Bracket assembly; 111. Base; 112. First gear; 113. Stage; 114. Expansion shaft buckle; 115. Second gear; 116. First knob; 117. Axle frame; 118. Fan gear; 119. Second knob; 1110. Third gear; 1111. Fourth gear; 1112. Fifth gear; 1113. Mounting frame; 1114. Third knob; 1115. Sixth gear; 1116. Seventh gear; 12. Acquisition analyzer; 121. Multimodal data acquisition module; 122. Real-time collaborative analysis module; 123. Intelligent classification module. DETAILED DESCRIPTION

[0049] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] See also Figure 1 - Figure 10 The present invention provides a technical solution: an identification and analysis device for slip lines and twins, including an identification and analysis device 1 for identifying and analyzing slip lines and twins, the identification and analysis device 1 comprising:

[0051] The bracket assembly 11 includes a first gear 112 rotatably mounted on the lower end of a base 111. A loading platform 113 is fixed to the upper end of the first gear 112 via a shaft. A plurality of second gears 115 are rotatably mounted on one side of the lower end of the base 111 via an expansion shaft buckle 114. A first knob 116 is fixed to the upper end of one of the second gears 115. A shaft frame 117 is pivotally connected between the two sides of the upper end of the base 111. A mounting frame 1113 is pivotally connected between the two sides of the shaft frame 117.

[0052] The collection and analysis device 12 is mounted on the mounting frame 1113 and is used for collection and analysis.

[0053] In this embodiment, the second gear 115 is driven to rotate by rotating the first knob 116, and the second gear 115 drives the stage 113 to rotate through the first gear 112, thereby adjusting the angle of the object to be identified placed on the stage 113; since the expansion shaft buckle 114 rebounds and tightens the fixed object, and the expansion shaft buckle 114 is fixed on the gear, the gear installed by the expansion shaft buckle 114 will not rotate independently, and will only rotate when driven by other gears to which external force is applied.

[0054] Specifically, the bracket assembly 11 also includes a fan-shaped gear 118 fixed to one end of the inner part of the shaft frame 117, a second knob 119 is rotatably installed on the outer end of the shaft frame 117, a third gear 1110 is fixed on the inner end of the second knob 119, a fourth gear 1111 is rotatably installed on one end of the inner part of the shaft frame 117 through an expansion shaft buckle 114 and meshes with the third gear 1110 for transmission, a fifth gear 1112 is fixed on the fourth gear 1111 and meshes with the fan-shaped gear 118 for transmission, a third knob 1114 is fixed on the outer end of the mounting frame 1113, a sixth gear 1115 is fixed on the inner end of the third knob 1114, a seventh gear 1116 is rotatably installed on one end of the inner part of the mounting frame 1113 through the expansion shaft buckle 114 and meshes with the sixth gear 1115 for transmission.

[0055] In this embodiment, the fourth gear 1111 is driven to rotate by rotating the second knob 119 in cooperation with the third gear 1110. The fourth gear 1111 drives the shaft frame 117 to rotate through the fifth gear 1112 and the sector gear 118. The shaft frame 117 drives the adjustment of the front and rear tilt angle of the collection and analyzer 12 on the mounting frame 1113. In addition, since the fourth gear 1111 is rotatably mounted with the shaft frame 117 through the expansion shaft buckle 114, the fourth gear 1111 can only rotate when a certain external force is applied, thereby allowing the collection and analyzer 12 to have a hovering effect after the front and rear tilt angle is adjusted.

[0056] By rotating the third knob 1114, the sixth gear 1115 is driven to rotate, and the sixth gear 1115 drives the adjustment of the left and right tilt angle of the acquisition analyzer 12 through the mounting bracket 1113. Moreover, when the sixth gear 1115 rotates, it also drives the seventh gear 1116 to rotate. Since the seventh gear 1116 is rotatably mounted with the mounting bracket 1113 through the expansion shaft buckle 114, the seventh gear 1116 can only rotate when a certain external force is applied, so that the acquisition analyzer 12 has a hovering effect after the left and right tilt angle is adjusted.

[0057] Specifically, the acquisition analyzer 12 includes:

[0058] Multimodal data acquisition module 121, integrating EBSD, DIC and optical topography imaging;

[0059] A real-time collaborative analysis module 122 that simultaneously processes crystal orientation, strain field, and surface topography data;

[0060] The intelligent classification module 123 outputs the determination result of slip line or twin based on the machine learning algorithm.

[0061] In this embodiment, efficient and accurate identification of slip lines and twins is achieved through an innovative three-module collaborative architecture: the multimodal data acquisition module integrates EBSD, DIC and optical morphology imaging technologies into a single platform for the first time, solving the problem of data fragmentation in traditional detection; the real-time collaborative analysis module uses advanced data fusion algorithms to achieve simultaneous analysis of crystal orientation, strain field and surface morphology characteristics; the intelligent classification module creatively combines machine learning with the physical laws of crystallography, significantly improving the reliability of identifying fine deformation features.

[0062] Specifically, the multimodal data acquisition module 121 includes:

[0063] EBSD component, using a high-resolution field emission scanning electron microscope and equipped with a high-speed Hough transform processor;

[0064] DIC component, integrated with nano-scale laser speckle projection device and equipped with dual CMOS cameras;

[0065] Confocal optical imaging component with three-dimensional surface reconstruction capability.

[0066] In this embodiment, the multimodal acquisition module constructs a full-scale synchronous acquisition system for microscopic crystal orientation-mesoscopic strain field-macroscopic surface morphology by integrating the high-precision EBSD analysis of a field emission electron microscope, the DIC dynamic measurement enhanced by nanoscale laser speckle, and the confocal three-dimensional morphology reconstruction function. Its modular design supports the in-situ fusion of cross-modal data, significantly improving the spatiotemporal resolution and dimensional integrity of the characterization of the material's service behavior, and providing an accurate experimental data foundation for establishing the multi-scale constitutive relationship of the material.

[0067] Specifically, the data fusion method in the real-time collaborative analysis module 122 is:

[0068] Spatial registration algorithm aligns EBSD, DIC, and optical images to the same coordinate system based on feature point matching;

[0069] Combined feature extraction and simultaneous calculation of orientation difference, local strain gradient and topography curvature parameters;

[0070] Dynamically associated database, storing historical test data for real-time comparison.

[0071] In this embodiment, a four-dimensional material evolution map is constructed through real-time fusion of multimodal data to achieve dynamic correlation analysis of microstructure, mechanical response and surface morphology. The innovatively established material gene digital twin can support in-situ service behavior prediction. At the same time, its automated feature correlation mechanism significantly compresses the multi-scale characterization cycle, providing a new cross-scale and cross-modal collaborative design paradigm for advanced material research and development.

[0072] The present invention also discloses a method for distinguishing slip lines from twins, comprising the following steps:

[0073] S1, initial screening of crystallographic features: obtain the crystal orientation map of the area to be tested by EBSD, identify the orientation mutation area, and the gradient difference is ≥5° / μm;

[0074] S2, strain field matching verification: DIC is used to measure the strain field of the corresponding area. If the strain concentration band is ≤1μm in width and linearly distributed, it is marked as a candidate slip line area;

[0075] S3, final judgment of surface morphology: The surface morphology is verified by confocal optical imaging. Slip lines appear as parallel nanogrooves with a depth of 50-200 nm, and twin boundaries appear as continuous steps with a height difference of ≥300 nm.

[0076] S4, multi-source data decision-making: comprehensive three types of data input intelligent classification module output the final result.

[0077] In this embodiment, accurate identification of slip lines and twins is achieved through collaborative analysis of multimodal data: first, potential deformation areas are identified through EBSD crystallographic screening (gradient difference ≥ 5° / μm), then slip line candidate areas are screened in combination with DIC strain field analysis (bandwidth ≤ 1μm), and finally, confocal optical imaging is used to verify nanogrooves (50-200nm) and step features (≥300nm). This three-level crystallographic-mechanical-morphological joint judgment mechanism improves detection accuracy and efficiency compared to single-modal methods, and is particularly suitable for micro-defect analysis under complex deformation conditions, providing a reliable technical means for material performance evaluation.

[0078] Specifically, adaptive scanning is used in S1. After the orientation mutation area is preliminarily detected, the EBSD scanning step size is automatically reduced to 0.01μm for fine scanning, and additional X-ray micro-diffraction data are collected for the suspected twin boundary area to verify the mirror symmetry, where the beam size is ≤0.5μm.

[0079] In this embodiment, after EBSD preliminarily detects the orientation mutation area, the system automatically reduces the scanning step to 0.01μm to perform fine scanning, and at the same time intelligently triggers μ-XRD micro-area analysis (beam size ≤0.5μm) for the suspected twin boundary area to verify the mirror symmetry.

[0080] Specifically, strain singularity analysis is introduced in S2 to calculate the eigenvalues ​​of the strain tensor. The deviation between the maximum shear strain direction corresponding to the slip line and the theoretical direction of the slip system is ≤5°, and the main directions of the strain tensor on both sides of the twin boundary are mirror-symmetrical, with a symmetry of ≥90%.

[0081] In this embodiment, by introducing strain singularity analysis technology, an accurate discrimination standard based on the eigenvalue of the strain tensor is established: for slip lines, the deviation between the maximum shear strain direction and the theoretical slip system direction is required to be strictly controlled within 5° to ensure the crystallographic consistency of the slip deformation; for twin boundaries, the mirror symmetry of the main direction of the strain tensor is quantified (symmetry ≥ 90%) to effectively distinguish twin deformation from ordinary slip bands.

[0082] Specifically, the intelligent classification module 123 includes a three-level discrimination model:

[0083] The first level of discrimination is to quickly screen suspected areas based on the random forest algorithm;

[0084] In the second level of discrimination, a 3D convolutional neural network is used to analyze the spatial correlation between the EBSD orientation map and the DIC strain field;

[0085] The third level of discrimination verifies the results through a twin-specific discriminator and includes the physical constraints of the twin law.

[0086] In this embodiment, accurate identification of slip lines and twins is achieved through a three-level cascade discrimination model: the first level uses a random forest algorithm to achieve rapid initial screening (processing speed of 1500 points / second), the second level uses 3D-CNN to deeply mine the three-dimensional spatial correlation characteristics of EBSD orientation and DIC strain field, and the third level innovatively introduces a crystallographic physical constraint verification module (such as FCC metal 60° / <110> Twin law), building a dual protection mechanism of machine identification + physical verification.

[0087] Specifically, in the third level of discrimination, for face-centered cubic metals, it is mandatory to check whether the {111} face family has a 60° / <110> Orientation relationship: For body-centered cubic metals, orientation difference verification of the {112} face family is added. If the physical constraints are not met, re-testing is triggered even if the output is high confidence.

[0088] In this embodiment, by forcibly embedding crystallographic physical constraints in the third level judgment (FCC metals must meet 60° / <110> Twin orientation relationship, BCC metals need to verify the characteristic orientation difference of the {112} plane family), achieving deep coupling of machine learning results and the intrinsic laws of the material. Even if the algorithm outputs a high confidence level (≥95%), it will still trigger re-testing due to physical condition discrepancies. This improves the accuracy of twin identification and reduces the misjudgment rate of slip lines. It also forms a closed-loop detection system of intelligent identification-physical verification-dynamic correction, significantly improving the reliability of deformation mechanism analysis. It is especially suitable for advanced materials such as TWIP steel and titanium alloys where slip and twinning are easily confused.

[0089] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0090] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Equipment for the identification and analysis of slip lines and twins, characterized by: The invention comprises an identification and analysis device (1) and is used for identifying and analyzing slip lines and twins. The identification and analysis device (1) comprises: The bracket assembly (11) includes a first gear (112) rotatably mounted on the lower end of a base (111), a loading platform (113) being fixed to the upper end of the first gear (112) via a shaft, a plurality of second gears (115) being rotatably mounted on one side of the lower end of the base (111) via an expansion shaft buckle (114), a first knob (116) being fixed to the upper end of one of the second gears (115), a shaft frame (117) being pivotally connected between two sides of the upper end of the base (111), and a mounting frame (1113) being pivotally connected between two sides of the shaft frame (117); The collection and analysis device (12) is arranged on the mounting frame (1113) and is used for collection and analysis.

2. The device for identifying and analyzing slip lines and twins according to claim 1, characterized in that: The bracket assembly (11) further comprises a sector gear (118) fixed to one end of the interior of the shaft frame (117); a second knob (119) is rotatably mounted on the outer end of the shaft frame (117); a third gear (1110) is fixed to the inner end of the second knob (119); a fourth gear (1111) is rotatably mounted on one end of the interior of the shaft frame (117) via an expansion shaft buckle (114) and meshes with the third gear (1110) for transmission; a fifth gear (1112) is fixed on the fourth gear (1111) and meshes with the sector gear (118) for transmission; a third knob (1114) is fixed to the outer end of the mounting frame (1113); a sixth gear (1115) is fixed to the inner end of the third knob (1114); a seventh gear (1116) is rotatably mounted on one end of the interior of the mounting frame (1113) via an expansion shaft buckle (114) and meshes with the sixth gear (1115) for transmission.

3. The device for identifying and analyzing slip lines and twins according to claim 1, characterized in that: The acquisition analyzer (12) comprises: A multimodal data acquisition module (121) integrating EBSD, DIC and optical topography imaging; A real-time collaborative analysis module (122) synchronously processes crystal orientation, strain field and surface morphology data; and an intelligent classification module (123) outputs a determination result of slip lines or twins based on a machine learning algorithm.

4. The device for identifying and analyzing slip lines and twins according to claim 3, characterized in that: The multimodal data acquisition module (121) comprises: EBSD component, using a high-resolution field emission scanning electron microscope and equipped with a high-speed Hough transform processor; DIC component, integrated with nano-scale laser speckle projection device and equipped with dual CMOS cameras; Confocal optical imaging component with three-dimensional surface reconstruction capability.

5. The slip line and twin identification and analysis device according to claim 3, characterized in that: The data fusion method in the real-time collaborative analysis module (122) is: Spatial registration algorithm aligns EBSD, DIC, and optical images to the same coordinate system based on feature point matching; Combined feature extraction and simultaneous calculation of orientation difference, local strain gradient and topography curvature parameters; Dynamically associated database, storing historical test data for real-time comparison.

6. The method for distinguishing slip lines from twins according to any one of claims 1 to 5, characterized in that: The following steps are involved: S1, initial screening of crystallographic features: obtain the crystal orientation map of the area to be tested by EBSD, identify the orientation mutation area, and the gradient difference must be ≥5° / μm; S2, strain field matching verification: DIC is used to measure the strain field of the corresponding area. If the strain concentration band is ≤1μm in width and linearly distributed, it is marked as a candidate slip line area; S3, final judgment of surface morphology: The surface morphology is verified by confocal optical imaging. Slip lines appear as parallel nanogrooves with a depth of 50-200 nm, and twin boundaries appear as continuous steps with a height difference of ≥300 nm. S4, multi-source data decision-making: comprehensive three types of data input intelligent classification module output the final result.

7. The method for distinguishing slip lines from twins according to claim 6, wherein: Adaptive scanning is used in the S1. After the orientation mutation area is preliminarily detected, the EBSD scanning step size is automatically reduced to 0.01 μm for fine scanning. X-ray micro-diffraction data are additionally collected for the suspected twin boundary area to verify the mirror symmetry, where the beam size is ≤0.5 μm.

8. The method for distinguishing slip lines from twins according to claim 6, wherein: Strain singularity analysis is introduced in S2 to calculate the eigenvalue of the strain tensor. The deviation between the maximum shear strain direction corresponding to the slip line and the theoretical direction of the slip system is ≤5°. The main directions of the strain tensor on both sides of the twin boundary are mirror-symmetrical, and the symmetry is ≥90%.

9. The method for distinguishing slip lines from twins according to claim 3, wherein: The intelligent classification module (123) includes a three-level discrimination model: The first level of discrimination is to quickly screen suspected areas based on the random forest algorithm; In the second level of discrimination, a 3D convolutional neural network is used to analyze the spatial correlation between the EBSD orientation map and the DIC strain field; The third level of discrimination verifies the results through a twin-specific discriminator and includes the physical constraints of the twin law.

10. The method for distinguishing slip lines from twins according to claim 9, wherein: In the third level judgment, for face-centered cubic metals, it is mandatory to check whether the {111} face family has 60° / <110> Orientation relationship: For body-centered cubic metals, orientation difference verification of the {112} face family is added. If the physical constraints are not met, re-testing is triggered even if the output is high confidence.

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