A computer addressing technology-based failure analysis starting cracking area determination method

Abstract

A method for determining the initial crack zone in failure analysis based on computer addressing technology, using scanning electron microscopy to analyze the crack zone, includes the following steps: Step S1, system calibration and coordinate mapping acquisition; Step S2, preliminary determination of the range of the failure area to be tested and construction of a corresponding mapping table; Step S3, image capture using the scanning electron microscope according to the corresponding mapping table; Step S4, performing a series of image processing and feature extraction on the acquired images, the features including: average crack width w, average curvature k, gray-level variance Var, and texture entropy E; Step S5, performing multi-feature fusion scoring and analysis based on the features, and generating a standardized failure analysis report. This invention has the advantage of improving the efficiency and accuracy of failure analysis.

Description

Technical Field

[0001] This invention relates to the field of material failure analysis technology, and in particular to a method for determining the initial crack zone in failure analysis based on computer addressing technology. Background Technology

[0002] Fracture failures frequently occur in critical components used in fields such as mechanical equipment, aerospace, and electronic packaging. The initial crack zone, as the core area for crack initiation, is a crucial basis for determining failure modes, analyzing fracture mechanisms, and tracing the root causes of failure. Scanning electron microscopy (SEM), with its high resolution and large depth of field, has become the core equipment for observing microscopic morphology and locating the initial crack zone in fracture failure analysis.

[0003] Traditional methods for determining the initiation zone in failure analysis primarily rely on technicians manually operating a scanning electron microscope to scan field by field, subjectively judging the crack origin location based on their personal morphological analysis experience. This method has several drawbacks: First, it has extremely low detection efficiency; for large failure areas, continuous observation for hours or even days is often required. Second, it suffers from significant human error; differences in experience and observation angles among technicians easily lead to discrepancies in judgments, lacking objectivity and standardization. Third, it easily misses micro-crack initiation sites; for narrow micro-crack initiation zones, manual visual observation is insufficient for effective identification. Fourth, its positioning accuracy is inadequate; the large positioning error from manually operating the stage makes it impossible to accurately reproduce and quantitatively analyze the initiation zone.

[0004] While some automatic crack identification technologies have emerged in the existing technology, most of them rely solely on single image features for crack identification. They lack a unified mapping mechanism between the physical coordinates of the scanning electron microscope stage, the image pixel coordinates, and the electron beam scanning field coordinates. This makes it impossible to achieve full-area coverage addressing scanning of the failure area and to accurately reproduce and locate the initial cracking zone. Furthermore, the accuracy of single feature identification is low, failing to effectively distinguish between the crack initiation zone, propagation zone, and instantaneous fracture zone, thus failing to meet the high-precision and standardized requirements of failure analysis in the engineering field.

[0005] Therefore, developing a method for determining the initial crack zone that can achieve full-domain addressing, automatic imaging, objective judgment, and accurate repositioning, and addressing the shortcomings of traditional methods and existing technologies, is of great engineering significance for improving the automation and standardization of failure analysis. Summary of the Invention

[0006] The purpose of this invention is to provide a method for determining the initial crack zone in failure analysis based on computer addressing technology, which has the advantages of improving the efficiency and accuracy of failure analysis.

[0007] To achieve the above objectives, this invention provides a method for determining the initial crack zone in failure analysis based on computer addressing technology. The method involves analyzing the crack zone using a scanning electron microscope. The method includes: Step S1, system calibration and coordinate mapping acquisition; Step S2, preliminary determination of the range of the failure area to be tested and construction of a corresponding mapping table; Step S3, image capture using the scanning electron microscope according to the corresponding mapping table; Step S4, performing a series of image processing and feature extractions on the acquired images, the features including: average crack width w, average curvature k, grayscale variance Var, and texture entropy E; Step S5, performing multi-feature fusion scoring and analysis based on the features, and generating a standardized failure analysis report.

[0008] Preferably, step S1 includes: Step S11, establishing a three-dimensional rigid affine transformation model of physical coordinates, image pixel coordinates, and electron beam scanning field coordinates, with the affine transformation formulas being X=a×u+b×v+c, Y=d×u+e×v+f, where (X, Y) are physical coordinates, (u, v) are pixel coordinates; a, b, c, d, e, and f are all affine transformation coefficients; a is the scaling + principal spin shear coefficient in the u direction; b is the shear coupling coefficient in the v direction about the X axis; c is the overall translation in the X direction; d is the shear coupling coefficient in the u direction about the Y axis; e is the scaling + secondary spin shear coefficient in the v direction; and f is the overall translation in the Y direction; Step S12, selecting at least three calibration points with known physical coordinates, collecting their pixel coordinates, and using the least squares method to solve for the affine transformation coefficients a, b, c, d, e, and f, to achieve a bidirectional accurate mapping between physical coordinates and pixel coordinates.

[0009] Preferably, step S2 includes: step S21, recording the physical origin coordinates (X0, Y0) of the upper left corner of the area, measuring the physical height H and physical width W of the area; calculating the number of grid rows and columns M=ceil(H / Step) and N=ceil(W / Step) according to the preset grid step size Step, completing the division of an M×N regular grid; step S22, assigning a unique two-dimensional address (i, j) to each grid, where i is the row number and j is the column number; step S23, calculating the physical center coordinates (X0, Y0) of each grid. c Y c Xc=X0+(j-0.5)・Step, Yc=Y0+(i-0.5)・Step, establish a mapping table between the address and the physical center coordinates.

[0010] Preferably, step S3 includes: Step S31, the scanning electron microscope automatically generates an optimal serpentine scanning path based on the address-coordinate mapping table. After scanning one row from left to right along the row direction, the stage moves one grid step along the column direction and then scans the next row from right to left, and so on; Step S32, the computer calls the scanning electron microscope control system through the communication interface, and drives the stage to move to the physical center coordinates of each grid according to the address sequence of the serpentine scanning path. After the automatic focusing function is activated and precise focusing is completed, a high-resolution image is acquired; each acquired image is associated with and stored with the corresponding address and physical coordinates to ensure a one-to-one correspondence between the image and the spatial location information.

[0011] Preferably, step S4 includes: step S41, converting the color image to a grayscale image to reduce the amount of data; then performing Gaussian filtering using a 3×3 or 5×5 Gaussian kernel σ=1.2 to eliminate image noise; step S42, enhancing image contrast to highlight crack features; then using dual thresholds, a high threshold of 80 and a low threshold of 30, edge detection is performed to extract crack edges, and morphological closing operations of 3×3 rectangular structural elements are used to repair minor fractures at crack edges and eliminate noise points; step S43, performing contour extraction and noise filtering to remove non-crack contours and retain real crack contours; step S44, based on the processed crack contours, quantizing and extracting four core features: average width feature value w, average curvature k, grayscale variance Var, and texture entropy E.

[0012] Preferably, step S5 includes: step S51, performing minimum-maximum normalization on the four extracted crack features; performing reverse processing on the normalization results of average curvature and average width; analyzing each crack segment using a weighted fusion model to obtain the source seed segment; step S52, starting from the source seed segment, combining the spatial coordinate information of each crack segment, stitching together all crack contours to construct a crack topology map of the entire failure area to be tested, reflecting the connectivity, path, and branching of the cracks; determining the actual expansion direction of the crack based on the crack topology map, and performing reverse tracing along the opposite direction of expansion, following the rule of decreasing crack width and increasing crack curvature; step S53, extracting the physical center coordinates of the initial cracking zone from the crack topology map, and transmitting these coordinates back to the scanning electron microscope control system via a communication interface; after receiving the coordinates, the control system drives the stage to move precisely along the X and Y axes to the target position, realizing automatic reproduction and positioning of the initial cracking zone with a positioning repeatability accuracy of <1μm; simultaneously, the computer automatically integrates the information of the component under test and SEM data. The system generates a standardized failure analysis report by analyzing scanning parameters, mesh addressing information, crack characteristic data, coordinates of the initial cracking zone, and crack propagation path.

[0013] Preferably, the initial probability score S for each crack segment is calculated; S = 0.3 ・ (1 − C norm )+0.3・(1−w norm )+0.2・E norm +0.2・Var norm Among them, C norm The normalized mean curvature; w norm E represents the normalized average width eigenvalue. norm The normalized texture entropy; Var norm The normalized gray variance is used to sort all crack segments from high to low score, and the top 25% of high-scoring segments are selected as source seed segments.

[0014] In summary, compared with the prior art, the failure analysis method for determining the initial crack zone based on computer addressing technology provided by the present invention has the following beneficial effects:

[0015] First, the present invention proposes a failure analysis method for determining the initial crack zone based on computer addressing technology. It establishes a unified three-dimensional rigid affine transformation model of scanning electron microscope physical coordinates, pixel coordinates and electron beam scanning field coordinates, realizing bidirectional accurate coordinate mapping. This lays the core foundation for full-domain addressing scanning and accurate positioning reproduction, and solves the problem of no unified coordinate mapping in the existing technology.

[0016] Secondly, the present invention proposes a failure analysis method for determining the initial crack zone based on computer addressing technology. It adopts a global gridded addressing partitioning and a serpentine optimal scanning path to achieve automatic sequential scanning of the failure area to be tested without blind spots, avoiding blind spots and missed detection problems of manual scanning. At the same time, the serpentine path minimizes the movement distance of the workbench and improves scanning efficiency.

[0017] Third, the present invention proposes a failure analysis method for determining the initial crack zone based on computer addressing technology. It extracts four features of crack: average width, average curvature, gray-level variance, and texture entropy. Combined with a multi-feature fusion scoring model, it realizes a quantitative evaluation of the probability of crack initiation, replacing subjective human judgment, eliminating human error, and improving the objectivity and standardization of the judgment results.

[0018] Fourth, this invention proposes a failure analysis method for determining the initial crack zone based on computer addressing technology. Based on a crack topology map, a reverse tracing algorithm is designed to track the crack propagation evolution. The initial crack zone is determined through convergence under explicit quantitative conditions, achieving high-precision determination with a location repeatability accuracy of <1μm, far exceeding the accuracy of manual location. This method automates the entire process from system calibration, full-domain scanning, feature extraction, source determination to automatic reproduction and location, and report output, eliminating the need for manual intervention and significantly improving failure analysis efficiency. Furthermore, this invention is compatible with various commercial scanning electron microscopes, requiring no major equipment modifications. It boasts strong compatibility and practicality, applicable to various fracture failure analyses of metals, ceramics, composite materials, and electronic packaging structures, and has broad engineering application prospects. Attached Figure Description

[0019] Figure 1 This is a flowchart of a failure analysis method for determining the initial crack zone based on computer addressing technology proposed in this invention. Detailed Implementation

[0020] The following will be combined with the appendix in the embodiments of the present invention. Figure 1 The technical solutions, structural features, objectives and effects achieved in the embodiments of the present invention will be described in detail.

[0021] It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions. They are only used to facilitate and clarify the purpose of illustrating the embodiments of the present invention, and are not intended to limit the implementation conditions of the present invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationship, or adjustments to the size should still fall within the scope of the technical content disclosed in the present invention, provided that they do not affect the effects and objectives that the present invention can produce.

[0022] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only the expressly listed elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0023] like Figure 1 As shown, this invention proposes a failure analysis method for determining the initial crack zone based on computer addressing technology. The crack zone is analyzed using a scanning electron microscope. The method includes:

[0024] Step S1: System calibration and coordinate mapping acquisition;

[0025] Step S2: Initially determine the range of the failure area to be tested and construct the corresponding mapping table;

[0026] Step S3: The scanning electron microscope captures images according to the corresponding mapping table;

[0027] Step S4: Perform a series of image processing and feature extraction on the acquired image. The features include: average crack width w, average curvature k, gray-level variance Var, and texture entropy E.

[0028] Step S5: Perform multi-feature fusion scoring and analysis based on the features, and generate a standardized failure analysis report.

[0029] Specifically, step S1 includes:

[0030] Step S11: Establish a three-dimensional rigid affine transformation model of physical coordinates, image pixel coordinates, and electron beam scanning field coordinates. The affine transformation formulas are X = a × u + b × v + c, Y = d × u + e × v + f, where (X, Y) are physical coordinates, (u, v) are pixel coordinates; a, b, c, d, e, and f are affine transformation coefficients; a is the scaling + principal spin shear coefficient in the u direction; b is the shear coupling coefficient in the v direction about the X-axis; c is the overall translation in the X direction; d is the shear coupling coefficient in the u direction about the Y-axis; e is the scaling + secondary spin shear coefficient in the v direction; and f is the overall translation in the Y direction.

[0031] Step S12: Select at least three calibration points with known physical coordinates, collect their pixel coordinates, and use the least squares method to solve for the affine transformation coefficients a, b, c, d, e, and f, so as to achieve a bidirectional and accurate mapping between physical coordinates and pixel coordinates.

[0032] Specifically, step S2 includes:

[0033] Step S21: Record the physical origin coordinates (X0, Y0) of the upper left corner of the area, and measure the physical height H and physical width W of the area; according to the preset grid step size Step, calculate the number of grid rows and columns M=ceil (H / Step) and N=ceil (W / Step) to complete the division of the M×N regular grid;

[0034] In specific embodiments, the step size is set according to the SEM imaging accuracy, the size of the area to be measured, and the calibration error requirements. The smaller the step size, the denser the grid, the higher the mapping accuracy, but the computation time increases accordingly. It can be preset according to the device resolution to match the physical resolution of the SEM image pixels; 1 pixel corresponds to the actual size, which is the basic step size.

[0035] Step S22: Assign a unique two-dimensional address (i, j) to each grid, where i is the row number and j is the column number;

[0036] Step S23, calculate the physical center coordinates (X) of each grid. c Y c Xc = X0 + (j - 0.5) • Step, Yc = Y0 + (i - 0.5) • Step, establish a mapping table between the address and the physical center coordinates;

[0037] This step enables full-domain address management of the failed area under test.

[0038] Specifically, step S3 includes:

[0039] Step S31: The scanning electron microscope automatically generates the optimal serpentine scanning path based on the address-coordinate mapping table. After scanning a row from left to right along the row direction, the stage moves one grid step along the column direction and then scans the next row from right to left along the row direction, and so on.

[0040] In step S32, the computer calls the scanning electron microscope control system through the communication interface, and drives the stage to move to the physical center coordinates of each grid in sequence according to the address order of the serpentine scanning path. After the automatic focusing function is activated to complete the precise focusing, high-resolution images are acquired. Each acquired image is associated with and stored with the corresponding address and physical coordinates to ensure a one-to-one correspondence between the image and the spatial location information.

[0041] Specifically, step S4 includes:

[0042] Step S41: Convert the color image to a grayscale image to reduce the amount of data; then use a 3×3 or 5×5 Gaussian kernel (σ=1.2) to perform Gaussian filtering to eliminate image noise;

[0043] Step S42: Enhance image contrast to highlight crack features; then use dual threshold (high threshold 80, low threshold 30) edge detection to extract crack edges, and use morphological closing operation of 3×3 rectangular structuring elements to repair minor fractures and eliminate noise points at crack edges.

[0044] Step S43: Perform contour extraction and noise filtering, remove non-crack contours, and retain real crack contours.

[0045] Step S44: Based on the processed crack profile, extract four core features: average width feature value w, average curvature k, grayscale variance Var, and texture entropy E.

[0046] Among them, the average width feature value is designed for the width feature of fracture morphology evolution. The width of microcracks in the initial cracking zone is usually <0.5μm, and the gray difference with the matrix is ​​very small. The crack width is accurately quantified by the equidistant sampling method. Combined with the calibration coefficient, the pixel size is converted into the physical size in engineering, making the width feature the core quantitative basis for determining the initial cracking zone.

[0047] The average curvature is designed for the bending characteristics of cracks in the initial cracking zone. Cracks in the initial cracking zone usually exhibit a high curvature bending shape because they originate at stress concentration points or material defects. In contrast, cracks in the propagation zone have a significantly reduced curvature because they propagate along the path of least resistance. By fitting parametric equations, the crack curvature is accurately quantified, and the curvature characteristics are transformed into numerical indicators, which can effectively distinguish the crack morphology in the initial and propagation zones.

[0048] Gray-scale variance is designed for the material defect characteristics of the initial cracking zone. The initial cracking zone usually has material defects such as micro-inclusions and micropores, which leads to a large dispersion of gray-scale values ​​(high gray-scale variance) in the fracture SEM image of this area. In contrast, the fracture morphology of the extended zone is more uniform and the gray-scale variance is low. By calculating the gray-scale variance of the crack area, the visual characteristics of material defects are transformed into quantitative indicators, providing a texture gray-scale basis for the determination of the initial cracking zone.

[0049] Texture entropy is designed for the texture evolution characteristics of fracture morphology. The crack initiation in the initial crack zone is random, resulting in a highly complex texture (high texture entropy) in this area. In contrast, the cracks in the propagation zone exhibit regular morphological features (such as fatigue bands and cleavage surface river patterns), with low texture complexity (low texture entropy). By calculating texture entropy, the complexity of the fracture texture is transformed into a numerical index, effectively distinguishing the texture features of the initial and propagation zones.

[0050] Specifically, step S5 includes:

[0051] Step S51: Perform min-max normalization on the four extracted crack features; reverse the normalization results of average curvature and average width; use a weighted fusion model to analyze each crack segment and obtain the source seed segment.

[0052] Among them, the seed segment selection for tracing the source is designed to address the complexity of cracks across the entire fracture surface. Fracture surface SEM images contain various crack segments such as primary cracks, secondary cracks, and microcracks. Directly tracing the source of all crack segments in reverse would result in low efficiency and large deviations. By merging scores to select high-scoring segments as seed segments for tracing the source, the tracing focus is placed on the crack segments that best match the characteristics of the initial cracking zone, which greatly improves the efficiency and reliability of subsequent reverse tracing.

[0053] In a specific embodiment, the initiation probability score S of each crack segment is calculated;

[0054] S = 0.3 ・ (1 − C norm )+0.3・(1−w norm )+0.2・E norm +0.2・Var norm ;

[0055] Among them, C norm The normalized mean curvature; w norm E represents the normalized average width eigenvalue. norm The normalized texture entropy; Var norm The normalized gray variance;

[0056] All crack segments are sorted from highest to lowest score, and the top 25% of high-scoring segments are selected as source segments.

[0057] Step S52: Starting from the source seed segment, combine the spatial coordinate information of each crack segment to stitch together all crack outlines and construct a crack topology map of the entire failure area to be tested, reflecting the connectivity, path and branching of the cracks; determine the actual propagation direction of the crack based on the crack topology map, and perform reverse tracing along the opposite direction of propagation. The tracing process follows the rule of decreasing crack width and increasing crack curvature.

[0058] Step S53: Extract the physical center coordinates of the initial cracking zone from the crack topology map and transmit these coordinates back to the scanning electron microscope control system via the communication interface. After receiving the coordinates, the control system drives the stage to move precisely along the X and Y axes to the target position, realizing the automatic reproduction and positioning of the initial cracking zone with a positioning repeatability of <1μm. At the same time, the computer automatically integrates the information of the component under test, SEM scanning parameters, mesh addressing information, crack feature data, coordinates of the initial cracking zone, crack propagation path analysis, etc., to generate a standardized failure analysis report.

[0059] The following are specific examples.

[0060] This embodiment employs a field emission scanning electron microscope (FEM), equipped with an automated control system and a high-resolution imaging system. The computer control system utilizes Python and the OpenCV digital image processing library to achieve communication and automated control with the scanning electron microscope. The object of analysis is the fatigue fracture surface of an alloy component of an engine, requiring determination of the location of the initial cracking zone and analysis of the fatigue fracture mechanism.

[0061] The method for determining the initial crack zone in failure analysis based on computer addressing technology in this embodiment includes the following steps:

[0062] The system calibration and coordinate mapping involved adjusting the scanning electron microscope magnification to 500x, acquiring a clear image of the built-in standard scale, determining the corresponding pixel length of the scale to be 500 pixels, and the actual scale length to be 100μm. The pixel-physical size calibration coefficient was calculated as 100μm / 500 pixels = 0.2μm / pixel. Three calibration points with known physical coordinates were selected, and their pixel coordinates were acquired. The affine transformation coefficients were solved using the least squares method, yielding the affine transformation formulas X = 0.2u + 0.01v + 50 and Y = 0.01u + 0.2v + 50. This achieved a precise bidirectional mapping between physical coordinates and pixel coordinates with a mapping accuracy of <0.5μm.

[0063] The global mesh addressing was performed by manually scanning with a scanning electron microscope to determine the extent of the fatigue fracture test failure area. The coordinates of the physical origin at the top left corner of the area were recorded (X0=100μm, Y0=100μm). The physical height of the area was measured to be H=3000μm and the physical width to be W=2000μm. The mesh step size was set to Step=50μm, and the number of mesh rows and columns was calculated as M=ceil(3000 / 50)=60 and N=ceil(2000 / 50)=40, completing the 60×40 regular mesh generation. An addressing address (i, j) was assigned to each mesh (i=1-60, j=1-40), and the physical center coordinates of each mesh were calculated. For example, the center coordinates of the mesh with address (1,1) were X0, J0, and J1, J2, J3, J4, J4, J5, J6, J7, J8, J9, J1 ... c =100 + (1-0.5)×50=125μm, Y c =100+(1-0.5)×50=125μm; Establish a mapping table between the address and the physical center coordinates, and store it in the computer control system.

[0064] The SEM automated sequential scanning imaging generates an optimal serpentine scanning path based on an address-coordinate mapping table. The computer establishes communication with the scanning electron microscope control system via a network port, and drives the stage to move to the center of each grid sequentially according to the path. After the autofocus function is activated to complete precise focusing, high-resolution SEM images are acquired. A total of 1200 valid images were acquired in this experiment. All images are named with "address (i, j) - physical coordinates (Xc, Yc)" to achieve the association storage of image, address, and coordinates. The entire scanning process took 10 minutes.

[0065] Crack feature extraction and quantization: 1200 SEM images were sequentially subjected to grayscale conversion, 5×5 Gaussian filtering (σ=1.2), CLAHE enhancement, Canny edge detection (threshold 80 / 30), morphological closing operation of 3×3 rectangular structuring elements, contour extraction, and noise filtering. Based on the processed crack contours, four features of each crack segment were quantized and extracted: average width w, average curvature K, grayscale variance Var, and texture entropy E, to obtain the feature quantization data of each crack segment.

[0066] The multi-feature fusion scoring method performs min-max normalization on each of the four features, and reverses the normalization results for the average curvature and average width. A weighted fusion model is used to calculate the initiation probability score for each crack segment, with the scoring formula being S = 0.3・(1−C). norm )+0.3・(1−w norm )+0.2・E norm +0.2・Var norm All crack segments are sorted from high to low score, and the top 25% of high-scoring segments are selected as source seed segments. In this embodiment, a total of 30 source seed segments are selected.

[0067] Topology expansion and reverse tracing begin with 30 tracing seed segments. Combining the physical coordinates of each crack segment, all crack profiles are pieced together to construct a crack topology map of the fatigue fracture surface. The crack propagation direction is determined based on the topology map, and reverse tracing is performed along the opposite direction, following a rule of decreasing width and increasing curvature. Ultimately, all tracing paths converge to the same crack segment, which has an average width of 0.3μm-0.5μm and an average curvature of 0.15μm. -1 >0.1μm -1 Since there are no branches, this location is determined to be the initial cracking zone.

[0068] The coordinates of the physical center of the initial crack zone are extracted (Xs=1525μm, Ys=1275μm) and transmitted back to the scanning electron microscope control system. This drives the stage to move automatically to the target position, achieving automatic reproduction and positioning with a positioning repeatability of 0.8μm-1μm. At the same time, the computer automatically integrates alloy component information, SEM scanning parameters (magnification 500x, mesh step size 50μm), mesh addressing information, crack feature data, coordinates and morphology images of the initial crack zone, crack propagation path analysis, etc., to generate a standardized failure analysis report.

[0069] The method of this invention automates the entire process of determining the initial crack zone in failure analysis, effectively improving analysis efficiency and judgment accuracy, eliminating human error, and is applicable to various fracture failure analyses of metals, ceramics, composite materials, and electronic packaging structures, providing a standardized and intelligent technical solution for failure analysis in the engineering field.

[0070] The present invention has been described in detail through the above preferred embodiments, but it should be understood that the above description should not be considered as a limitation of the present invention. After reading the above content, various modifications and substitutions of the present invention will be obvious to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for determining the initial crack zone in failure analysis based on computer addressing technology, characterized in that, The method involves analyzing the cracked area using a scanning electron microscope, and includes: Step S1: System calibration and coordinate mapping acquisition; Step S2: Initially determine the range of the failure area to be tested and construct the corresponding mapping table; Step S3: The scanning electron microscope captures images according to the corresponding mapping table; Step S4: Perform a series of image processing and feature extraction on the acquired image. The features include: average crack width w, average curvature k, gray-level variance Var, and texture entropy E. Step S5: Perform multi-feature fusion scoring and analysis based on the features, and generate a standardized failure analysis report.

2. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 1, characterized in that, Step S1 includes: Step S11: Establish a three-dimensional rigid affine transformation model of physical coordinates, image pixel coordinates, and electron beam scanning field coordinates. The affine transformation formulas are X = a × u + b × v + c, Y = d × u + e × v + f, where (X, Y) are physical coordinates, (u, v) are pixel coordinates; a, b, c, d, e, and f are affine transformation coefficients; a is the scaling + principal spin shear coefficient in the u direction; b is the shear coupling coefficient in the v direction about the X-axis; c is the overall translation in the X direction; d is the shear coupling coefficient in the u direction about the Y-axis; e is the scaling + secondary spin shear coefficient in the v direction; and f is the overall translation in the Y direction. Step S12: Select at least three calibration points with known physical coordinates, collect their pixel coordinates, and use the least squares method to solve for the affine transformation coefficients a, b, c, d, e, and f, so as to achieve a bidirectional and accurate mapping between physical coordinates and pixel coordinates.

3. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 2, characterized in that, Step S2 includes: Step S21: Record the physical origin coordinates (X0, Y0) of the upper left corner of the area, and measure the physical height H and physical width W of the area; according to the preset grid step size Step, calculate the number of grid rows and columns M=ceil (H / Step) and N=ceil (W / Step) to complete the division of the M×N regular grid; Step S22: Assign a unique two-dimensional address (i, j) to each grid, where i is the row number and j is the column number; Step S23, calculate the physical center coordinates (X c , Y c ) of each grid, Xc=X0+(j-0.5)・Step, Yc=Y0+(i-0.5)・Step, and establish a corresponding mapping table of the addressing address and the physical center coordinates.

4. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 3, characterized in that, Step S3 includes: Step S31: The scanning electron microscope automatically generates the optimal serpentine scanning path based on the address-coordinate mapping table. After scanning a row from left to right along the row direction, the stage moves one grid step along the column direction and then scans the next row from right to left along the row direction, and so on. In step S32, the computer calls the scanning electron microscope control system through the communication interface, and drives the stage to move to the physical center coordinates of each grid in sequence according to the address order of the serpentine scanning path. After the automatic focusing function is activated to complete the precise focusing, high-resolution images are acquired. Each acquired image is associated with and stored with the corresponding address and physical coordinates to ensure a one-to-one correspondence between the image and the spatial location information.

5. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 4, characterized in that, Step S4 includes: Step S41: Convert the color image to a grayscale image to reduce the amount of data; then use a 3×3 or 5×5 Gaussian kernel σ=1.2 to perform Gaussian filtering to eliminate image noise; Step S42: Enhance image contrast to highlight crack features; then use dual thresholds, high threshold 80 and low threshold 30, to extract crack edges through edge detection, and repair minor fractures and eliminate noise points at crack edges through morphological closing operations of 3×3 rectangular structuring elements. Step S43: Perform contour extraction and noise filtering, remove non-crack contours, and retain real crack contours. Step S44: Based on the processed crack profile, extract four core features: average width feature value w, average curvature k, grayscale variance Var, and texture entropy E.

6. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 5, characterized in that, Step S5 includes: Step S51: Perform min-max normalization on the four extracted crack features; reverse the normalization results of average curvature and average width; use a weighted fusion model to analyze each crack segment and obtain the source seed segment. Step S52: Starting from the source seed segment, combine the spatial coordinate information of each crack segment to stitch together all crack outlines and construct a crack topology map of the entire failure area to be tested, reflecting the connectivity, path and branching of the cracks; determine the actual propagation direction of the crack based on the crack topology map, and perform reverse tracing along the opposite direction of propagation. The tracing process follows the rule of decreasing crack width and increasing crack curvature. Step S53: Extract the physical center coordinates of the initial cracking zone from the crack topology map and transmit these coordinates back to the scanning electron microscope control system via the communication interface. After receiving the coordinates, the control system drives the stage to move precisely along the X and Y axes to the target position, realizing the automatic reproduction and positioning of the initial cracking zone with a positioning repeatability of <1μm. At the same time, the computer automatically integrates the information of the component under test, SEM scanning parameters, mesh addressing information, crack feature data, coordinates of the initial cracking zone, crack propagation path analysis, etc., to generate a standardized failure analysis report.

7. The method for determining the initial crack zone in failure analysis based on computer addressing technology according to claim 6, characterized in that, Step S51 includes calculating the initial probability score S for each crack segment; S = 0.3 • (1 - C norm ) + 0.3 • (1 - w norm ) + 0.2 • E norm + 0.2 • Var norm ; wherein C norm is the normalized average curvature; w norm is the normalized average width eigenvalue; E norm is the normalized texture entropy; Var norm is the normalized gray level variance; and all crack segments are sorted by score from high to low, and the top 25% high-score segments are selected as the trace source seed segments.

Images