A method and system for quantifying the amount of pore wall blockage and defects of a directional porous structure

Abstract

The present application belongs to the field of porous structure detection, and particularly relates to a method and system for quantitatively characterizing the blockage and defect of the pore wall of a directional porous structure. The method comprises obtaining a cross-sectional image of the structure to be detected, and establishing an actual image pixel matrix containing scanning path identification bits and region attribute bits through binarization; a theoretical pore array template pixel matrix is generated in combination with the image resolution, scanning interval and spot size, and spatial registration and radial single-pore, axial single-wall and axial flow channel region division are performed. Through attribute difference between the actual matrix and the theoretical template pixel, blocked deviation pixels and wall defect deviation pixels are identified, and pore shape distortion rate, pore shape defect rate, pore wall distortion rate and pore wall discontinuity rate are calculated; according to the single-pore effective flow state and the flow channel axial communication state and bypass communication state, pore shape failure rate and flow channel failure rate are determined, and the parameter average value and distribution result of single cross-section or multiple cross-sections are output, so as to realize the classified quantitative characterization of the morphology deviation and flow failure.

Description

Technical Field

[0001] This invention belongs to the field of porous structure detection, specifically relating to a method and system for quantitative characterization of pore wall blockage and defects in directional porous structures. Background Technology

[0002] Laser additive manufacturing technology can prepare directional porous structures with complex internal channels by melting metal powder layer by layer. It is particularly suitable for the integral forming of micro-channel structures such as regularly arrayed through holes and stepped holes. These directional porous structures can be applied to scenarios such as sweating cooling, filtration, heat exchange, and fluid transport. The pore size, pore wall integrity, and channel connectivity directly affect the structure's permeability, flow distribution, and heat transfer performance. However, during laser additive manufacturing, factors such as laser heat input, molten pool fluctuations, powder adhesion, and incomplete fusion can easily lead to pore blockage caused by semi-molten powder or molten droplets intruding into the pores, as well as pore wall defects caused by incomplete fusion, fracture, or partial loss of the pore walls. In severe cases, this can even cause a single pore or the entire flow channel to completely lose its flow capacity. Figure 1 As shown.

[0003] In existing technologies, methods for detecting and characterizing additively manufactured porous structures mainly include overall porosity measurement and cross-sectional porosity measurement. Overall porosity measurement typically employs the mass-volume method, calculating the overall porosity of the structure based on the sample mass, external volume, and theoretical material density. Cross-sectional porosity measurement usually utilizes metallographic microscopy or micro-CT reconstruction of cross-sectional images, distinguishing between pore regions and solid regions through image binarization, and obtaining the pore porosity based on the ratio of the pixel area of ​​the pore region to the total cross-sectional area. Furthermore, some methods utilize micro-CT scanning and 3D reconstruction to obtain the 3D morphology of the pores or the overall volumetric porosity, allowing for observation and analysis of the channel morphology within the porous structure.

[0004] However, existing detection methods use overall porosity or cross-sectional porosity as the main evaluation index, which can only reflect the overall change in the proportion of pores. It is difficult to distinguish between pore shrinkage caused by powder or droplet intrusion and pore enlargement caused by the lack of fusion of pore walls. Therefore, it is impossible to quantify the degree of pore wall blockage and pore wall defects separately. For regular array oriented porous structures, existing methods usually do not compare the actual pore profile with the theoretically designed pore array pore by pore, making it difficult to obtain the deviation distribution at the level of a single pore, a single wall, and a single flow channel. At the same time, it is difficult to determine whether the pores are completely blocked based on the average porosity alone, and it is also difficult to determine whether there is a continuous flow path in the axial flow channel or whether a bypass flow is formed through adjacent flow channels. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a quantitative characterization method and system for pore wall blockage and defects in directional porous structures. This method and system can identify completely blocked pores and failed flow channels that have neither a direct axial flow path nor the ability to form a bypass flow through adjacent channels. Thus, the characterization results can reflect not only geometric shape deviations but also the actual flow function failure of the porous structure.

[0006] A method for quantitatively characterizing pore wall blockage and defects in directional porous structures, comprising: Obtain cross-sectional images of the directional porous structure to be measured and the corresponding forming process parameters. The cross-sectional images include radial cross-sectional images perpendicular to the channel extension direction and axial cross-sectional images along the channel extension direction. The cross-sectional image is binarized to distinguish between pore regions and solid wall regions, resulting in a binary image. An actual image pixel matrix is ​​then established based on the binary image. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used for hole wall forming in the cross-sectional image. The actual attribute bit is used to characterize whether the corresponding pixel belongs to a pore region or a solid wall region. Based on the resolution of the actual image pixel matrix, the laser scanning path when printing the directional porous structure to be measured, and the forming process parameters, a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix is ​​generated. The theoretical hole array template pixel matrix includes a theoretical pore region and a theoretical solid wall region. Spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix, and divide the radial section calculation region and axial section calculation region in the actual image pixel matrix according to the periodic array boundary determined by the laser scanning path. According to the index range of each calculation region in the actual image pixel matrix, extract the corresponding theoretical region with the same index range from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and the corresponding theoretical region. The attribute bits of the pixels at the same position in the corresponding theoretical region are differentially divided between each radial single hole calculation region and each axial single wall calculation region. Based on the differential results, the blocking deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area are identified. The hole shape distortion rate is determined by the ratio of the number of pixels with blockage deviation in each radial single-hole calculation area to the number of pixels in the corresponding theoretical pore area; the hole shape defect rate is determined by the ratio of the number of pixels with wall defect deviation in each radial single-hole calculation area to the number of pixels in the corresponding theoretical solid wall area; the hole wall distortion rate is determined by the ratio of the number of pixels with blockage deviation in each axial single-wall calculation area to the number of pixels in the corresponding theoretical pore area; and the hole wall discontinuity rate is determined by the ratio of the number of pixels with wall defect deviation in each axial single-wall calculation area to the number of pixels in the corresponding theoretical solid wall area. Failed pores are determined based on the effective flow state of each radial single-pore calculation region, and the pore shape failure rate is determined based on the ratio of the number of failed pores to the total number of pores in the radial section. Failed flow channels are determined based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and the flow channel failure rate is determined based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section. The characterization parameters are statistically analyzed, including the pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate. The average value and distribution of the characterization parameters of the cross-sectional image of the porous structure to be measured are output as the characterization result.

[0007] The effective flow state refers to the existence of an effective flow area within the pore; the failed pore is a pore that is completely blocked or whose effective flow area is less than a certain set threshold; the total number of pores in the radial section is the total number of pores in that radial section; the axial connectivity state is the continuous flow state of the channel along the extension direction of the pore; the bypass connectivity state is the flow state formed by a channel with an adjacent channel through a wall defect. The failed channel is a channel that is completely blocked along the extension direction of the pore, has no axial flow capacity, and does not form a bypass flow with an adjacent channel through a wall defect.

[0008] Optionally, acquiring a cross-sectional image of the porous structure to be measured includes: The porous structure to be measured is subjected to computed tomography, and the radial cross-sectional image and the axial cross-sectional image are obtained according to a preset truncation step size. Alternatively, a metallographic sample can be prepared for the porous structure to be measured, and the radial cross-sectional image and the axial cross-sectional image can be acquired.

[0009] Optionally, the actual image pixel matrix is ​​denoted as [ ], where each pixel unit is represented as , These are the identifier bits for the actual image pixel matrix. This represents the actual attribute bit; when the corresponding pixel is located on the laser scanning path... When the corresponding pixel is not located on the laser scanning path, When the corresponding pixel belongs to the pore region, When the corresponding pixel belongs to the solid wall area .

[0010] Optionally, the theoretical aperture array template pixel matrix is ​​denoted as [ ], where each pixel unit is represented as ,in, These are the identifier bits for the pixel matrix of the theoretical aperture array template. For theoretical attribute bits; When the corresponding pixel is located on the theoretical laser scanning path When the corresponding pixel is not located on the theoretical laser scanning path, When the corresponding pixel belongs to the theoretical pore region, When the corresponding pixel belongs to the theoretical solid wall region, ; Generating the theoretical aperture array template pixel matrix includes: Each pixel unit in the theoretical aperture array template pixel matrix is ​​initialized to (0, -1), and the pixel unit corresponding to the laser scanning path is assigned the value (1, 1). Select parallel lines that cross each other between their centerlines Two laser scanning paths with a laser scanning spacing are used to determine the pixel distance between the center lines of the two laser scanning paths along a direction perpendicular to the laser scanning path. and in accordance with Determine the physical size of a single pixel ,in, The laser scanning spacing The number of laser scanning intervals between the two laser scanning paths; according to Determine the number of wall extension pixels corresponding to one side of the laser scanning path. ,in, The laser spot size; Extending outwards from each of the laser scanning paths as the center, perpendicular to both sides of the laser scanning paths. Each pixel is assigned an attribute bit of 1 within the extended range and an attribute bit of -1 outside the extended range, so as to form the theoretical solid wall region and the theoretical pore region respectively.

[0011] Optionally, the radial section calculation region and the axial section calculation region are divided separately. Based on the index range of each calculation region in the actual image pixel matrix, corresponding theoretical regions with the same index range are extracted from the theoretical hole array template pixel matrix, including: In the radial section calculation region, the area enclosed by the periodic array boundary determined by the laser scanning path is taken as the radial single hole calculation region; In the axial section calculation region, the centerline of a theoretical solid wall is used as a reference, and the midline between the centerline and the centerlines of the adjacent theoretical solid walls on both sides is used as the region boundary to form the axial single wall calculation region. The axial single wall calculation region includes the theoretical solid wall region and the theoretical pore regions located on both sides of the theoretical solid wall region. The theoretical pore channel formed between two adjacent theoretical solid wall regions along the channel extension direction is used as the calculation region of the axial flow channel. The minimum bounding rectangles of each radial single-hole calculation region, each axial single-wall calculation region, and each axial flow channel calculation region are determined respectively, and the row index interval and column index interval corresponding to the minimum bounding rectangle are obtained. According to the same row index interval and column index interval, the corresponding theoretical region is extracted from the corresponding theoretical hole array template pixel matrix.

[0012] Optionally, the attribute bits of pixels at the same position in the corresponding theoretical region are differentiated between each of the radial single-hole calculation regions and each of the axial single-wall calculation regions, including: Subtract the actual image pixel submatrix corresponding to the radial single-hole calculation region or the axial single-wall calculation region from the theoretical template pixel submatrix corresponding to the corresponding theoretical region to obtain the difference pixel matrix; When the attribute bit in the differential pixel matrix is ​​-2, the corresponding pixel is the wall defect deviation pixel; when the attribute bit is 0, the corresponding pixel is the pore pixel whose actual state is consistent with the theoretical state; when the attribute bit is 1, the corresponding pixel is the solid wall pixel whose actual state is consistent with the theoretical state; when the attribute bit is 3, the corresponding pixel is the blockage deviation pixel.

[0013] Optionally, the step of spatially registering the actual image pixel matrix with the theoretical aperture array template pixel matrix includes: Using the pixel in the actual image pixel matrix that represents the pixel located at the laser scanning path projection position as the registration reference, the theoretical aperture array template pixel matrix is ​​subjected to a spatial transformation including at least translation and rotation. The registration target is to maximize the number of overlapping laser scanning path pixels in the theoretical aperture array template pixel matrix and laser scanning path pixels in the actual image pixel matrix, so that the corresponding periodic array boundaries in the two are at the same pixel index position, thereby achieving spatial registration.

[0014] A quantitative characterization system for pore wall blockage and defects in oriented porous structures, comprising: An image acquisition module is used to acquire cross-sectional images of the porous structure to be measured, the cross-sectional images including a radial cross-sectional image perpendicular to the extension direction of the pores and an axial cross-sectional image along the extension direction of the pores; The image preprocessing module is used to binarize the cross-sectional image to distinguish between pore regions and solid wall regions, obtain a binary image, and establish an actual image pixel matrix based on the binary image. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used for hole wall forming in the cross-sectional image, and the actual attribute bit is used to characterize whether the corresponding pixel belongs to a pore region or a solid wall region. The template generation module is used to generate a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix, based on the resolution of the actual image pixel matrix, the laser scanning path when printing the directional porous structure to be measured, and the forming process parameters. The theoretical hole array template pixel matrix includes a theoretical pore region and a theoretical solid wall region. The region registration and division module is used to spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix, and divide the radial section calculation region and axial section calculation region in the actual image pixel matrix according to the periodic array boundary determined by the laser scanning path. According to the index range of each calculation region in the actual image pixel matrix, the corresponding theoretical region with the same index range is extracted from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and the corresponding theoretical region. The first calculation module is used to perform a difference between the attribute bits of the radial single hole calculation area and the axial single wall calculation area and the pixels at the same position in the corresponding theoretical area, and to identify the blockage deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area based on the difference results. The second calculation module is used to determine the hole shape distortion rate based on the ratio of the number of blocking deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical pore area; to determine the hole shape defect rate based on the ratio of the number of wall defect deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical solid wall area; to determine the hole wall distortion rate based on the ratio of the number of blocking deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical pore area; and to determine the hole wall discontinuity rate based on the ratio of the number of wall defect deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical solid wall area. The distortion and defect parameter calculation module is used to determine the failed pores based on the effective flow state of each radial single-hole calculation region, and to determine the pore shape failure rate based on the ratio of the number of failed pores to the total number of pores in the radial section; to determine the failed flow channels based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and to determine the flow channel failure rate based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section; and to statistically analyze the characterization parameters, including the pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate, and output the average value and distribution results of the characterization parameters for a single section and / or multiple sections as the characterization results.

[0015] A terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs a quantitative characterization method for pore wall blockage and defects in a directional porous structure.

[0016] A computer-readable storage medium storing a computer program, wherein when the computer program is loaded and executed by a processor, a method for quantitative characterization of pore wall blockage and defects in a directional porous structure is employed.

[0017] The beneficial effects of this invention are: 1. Construct an actual image pixel matrix that simultaneously includes laser scanning path identifiers and material property bits, and generate a theoretical pore array template pixel matrix corresponding to the actual image; through spatial registration and pixel-by-pixel difference at the same index position, it is possible to directly distinguish between the blockage deviation of the actual formed entity in the theoretical pore area and the defect deviation of the actual formed pore in the theoretical entity wall area, thereby overcoming the limitation that traditional porosity can only reflect the overall proportion of pores and cannot identify the causes of different morphological deviations.

[0018] 2. The boundary of the periodic array is determined by laser scanning path, and the radial single-hole calculation region, axial single-wall calculation region, and axial flow channel calculation region are established respectively, so that the actual pores, pore walls and flow channels are in one-to-one correspondence with the theoretical design region. Then, the pore shape distortion rate, pore shape defect rate, pore wall distortion rate and pore wall discontinuity rate are obtained respectively, realizing the layered quantitative characterization from single pore, single wall to the whole cross section, and improving the precision and comparability of the evaluation of the morphological deviation of the regular array directional porous structure.

[0019] 3. Based on the quantification of local morphological deviations, the effective flow state of radial single holes and the axial connectivity and bypass connectivity of axial flow channels are further combined to determine the hole shape failure rate and flow channel failure rate. This can identify completely blocked holes and failed flow channels that have neither a direct axial flow path nor the ability to form a bypass flow through adjacent flow channels. Thus, the characterization results not only reflect geometric morphological deviations but also reflect the actual flow function failure of porous structures. Attached Figure Description

[0020] Figure 1 This is a schematic diagram showing the morphological deviations in the radial and axial sections of a directional porous structure. Figure 1 (a) represents the radial cross-sectional morphology deviation. Figure 1 (b) represents the axial section morphology deviation; Figure 2 This is a schematic diagram of an overall process of the method of the present invention; Figure 3 This is a schematic diagram of the metallographic and binarized radial cross-sectional images of a directional porous structure. Figure 3 (a) is a metallographic radial section image. Figure 3 (b) is a schematic diagram of the binary radial section; Figure 4 This is a schematic diagram of the actual image pixel matrix; Figure 5 Schematic diagram for initializing the pixel matrix of the theoretical aperture array template; Figure 6 A schematic diagram of the generated pixel matrix of the theoretical aperture array template; Figure 7 A schematic diagram illustrating spatial registration and computational region division between the actual image and the theoretical aperture array template; Figure 8 This is a schematic diagram showing the difference between the actual attribute bits and the theoretical attribute bits. Detailed Implementation

[0021] A quantitative characterization method for pore wall blockage and defects in porous structures, such as Figure 2 As shown, it includes: S1. Obtain cross-sectional images of the porous structure to be measured. The cross-sectional images include radial cross-sectional images perpendicular to the direction of pore extension and axial cross-sectional images along the direction of pore extension.

[0022] Specifically, a porous structure is a structure containing multiple pores or channels, with the pore walls formed by a solid material. A directional porous structure is a porous structure where the channels have a common or predetermined extension direction, and the pores or pore walls are arranged in a regular array or predictable periodicity. The channel extension direction is the main direction in which the fluid extends from the inlet to the outlet along a single channel under design conditions; it is also called the axial direction. The radial section is a section whose normal is parallel to the channel extension direction, i.e., the cross-sectional plane is perpendicular to the channel extension direction; this section mainly reflects the cross-sectional profile of a single pore.

[0023] Obtain cross-sectional images of the directional porous structure to be measured, including: Computed tomography was performed on the porous structure to be measured, and radial and axial cross-sectional images were obtained according to the preset truncation step size. Alternatively, prepare metallographic specimens of the porous structure to be measured and acquire radial and axial cross-sectional images.

[0024] Computed tomography includes micro-CT scans and 3D reconstruction.

[0025] S2. Perform binarization on the cross-sectional image to distinguish between the pore region and the solid wall region, and obtain a binary image. Based on the binary image, establish an actual image pixel matrix. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used to form the pore wall in the cross-sectional image. The actual attribute bit is used to characterize whether the corresponding pixel belongs to the pore region or the solid wall region. Specifically, the laser scanning path is the path along which the center of the laser beam moves according to a predetermined trajectory during the manufacturing process; in this application, it mainly refers to the theoretical center line used to form the contour of the hole wall or the wall surface.

[0026] The projection position of the laser scanning path is the pixel position mapped by the laser scanning path to the current radial or axial cross-sectional coordinate system, which usually corresponds to the intersection of the theoretical hole wall centerline or the center plane within the cross-section.

[0027] The laser scanning path is the route the laser takes, representing the theoretical centerline of the wall. However, the actual wall is affected by various factors, such as power and scanning speed. In some areas, due to lower energy density, a wall may not form, even though the laser has still scanned those areas. The marker bits are primarily used to align the actual pixel matrix with the theoretical pixel matrix. Without marker bits, it would be difficult to determine the theoretical centerline of the actual wall and the theoretical template centerline. However, with marker bits... = = 1. Spatial registration is possible.

[0028] Specifically, radial cross-sectional images such as Figure 3 As shown in (a), the binarized radial cross-section image is as follows: Figure 3 As shown in (b).

[0029] Specifically, the original image can be processed sequentially by non-uniform illumination correction, grayscale normalization, denoising, and edge preservation. Denoising can be achieved using median filtering, Gaussian filtering, nonlocal means, or other methods that do not significantly alter the position of the hole wall edges.

[0030] Binarization can be achieved using methods such as global thresholding and maximum inter-class variance thresholding.

[0031] like Figure 4 As shown, the actual image pixel matrix is ​​denoted as [ ], where each pixel unit is represented as , These are the identifier bits for the actual image pixel matrix. This represents the actual attribute bit; when the corresponding pixel is located on the laser scanning path... When the corresponding pixel is not located on the laser scanning path, When the corresponding pixel belongs to the aperture region, When the corresponding pixel belongs to the solid wall region .

[0032] The theoretical aperture array template pixel matrix is ​​denoted as [ ], where each pixel unit is represented as ,in, These are the identifier bits for the pixel matrix of the theoretical aperture array template. For theoretical attribute bits; When the corresponding pixel is located on the theoretical laser scanning path When the corresponding pixel is not located on the theoretical laser scanning path, When the corresponding pixel belongs to the theoretical pore region, When the corresponding pixel belongs to the theoretical solid wall region, ; Generate the theoretical aperture array template pixel matrix, such as Figure 5 As shown, it includes: Each pixel unit in the theoretical aperture array template pixel matrix is ​​initialized to (0, -1), and the pixel unit corresponding to the laser scanning path is assigned the value (1, 1). Select parallel lines that cross each other between their centerlines For each laser scanning path with a given laser scanning spacing, determine the pixel distance between the center lines of the two laser scanning paths along the direction perpendicular to the laser scanning path. and in accordance with Determine the physical size of a single pixel ,in, Laser scanning spacing This refers to the number of laser scanning intervals between the two laser scanning paths. according to Determine the number of wall extension pixels corresponding to one side of the laser scanning path. ,in, This refers to the size of the laser spot. like Figure 6 As shown, the laser scanning paths extend outwards to both sides perpendicular to the laser scanning paths, with each path as the center. For each pixel, the attribute bits of the pixel units within the extended range are assigned a value of 1, while the attribute bits of the pixel units outside the extended range are kept at -1, so as to form the theoretical solid wall region and the theoretical pore region respectively.

[0033] S3. Based on the resolution of the binary image, the laser scanning path identified from the binary image, and the forming process parameters including at least the laser scanning spacing and the laser spot size, generate a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix. The theoretical hole array template pixel matrix includes a theoretical hole region and a theoretical solid wall region. Specifically, the projected position of the laser scanning path can be inferred from the periodic aperture wall in the binary image. This projected position is used to represent the theoretical aperture wall centerline and does not require direct observation of the actual laser beam trajectory in the microscopic image.

[0034] Centerline extraction: The skeleton of the solid wall region is extracted, and long straight or nearly straight skeleton branches are extracted; straight line fitting and robust regression are used to remove outliers caused by local blockages, gaps and noise to obtain the centerline of the hole wall.

[0035] When a hole cannot be directly identified due to severe damage or blockage, extrapolation is performed based on the position of adjacent complete paths and the known period to complete the missing theoretical scanning path.

[0036] The identified scan path is represented as a set of pixels or a parameterized line. The center line, which is one pixel wide and covered by the path, is assigned a flag of 1, while the flags of the remaining pixels are assigned a flag of 0. If the center line is a sub-pixel position, the pixel closest to the center line can be assigned a value of 1.

[0037] The laser scanning path distribution is determined by the scanning spacing; the scanning spacing determines the distance between adjacent laser scanning paths. All process parameters are known and confirmed during the prototype molding process.

[0038] The identification mentioned here refers to the initial position identification. This is because the distribution of the laser scanning path, i.e., the theoretical centerline distribution of the wall, can only be determined through process parameters. However, the exact position is uncertain; it might be offset by tens of micrometers in all directions. The specific offset needs to be identified using a binary image. Process parameters determine the distribution, while a binary image determines the position.

[0039] Directional porous structures are manufactured by controlling process parameters, which result in different micron-sized arrays of holes. These parameters include laser power, scanning speed, scanning spacing, powder layer thickness, and laser spot size. Different parameters lead to varying degrees of defects; therefore, these defects need to be quantified to determine the relationship between process parameters and defects.

[0040] S4. Spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix. Based on the periodic array boundary determined by the laser scanning path, divide the actual image pixel matrix into radial and axial section calculation regions. According to the index range of each calculation region in the actual image pixel matrix, extract the corresponding theoretical regions with the same index range from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and its corresponding theoretical region. Figure 7 As shown; The radial section calculation region and the axial section calculation region are divided separately. Based on the index range of each calculation region in the actual image pixel matrix, corresponding theoretical regions with the same index range are extracted from the theoretical hole array template pixel matrix, including: In the radial cross-sectional image, the region enclosed by the periodic array boundary determined by the laser scanning path is taken as the radial single-hole calculation region; Specifically, the periodic array boundary is a repeating cell boundary determined by adjacent scan paths, hole wall centerlines, or their bisecting centerlines, used to divide the entire image into single-hole, single-wall, or single-channel computational regions.

[0041] The actual image pixel matrix is ​​a two-dimensional data array obtained by converting the actual binary image. Each element records the scan path identifier and the actual structural attributes, i.e., gaps or walls.

[0042] The theoretical aperture array template pixel matrix is ​​a two-dimensional theoretical array generated based on the scanning path position, scanning spacing, spot size and image resolution. Each element records the theoretical scanning path identifier and theoretical material properties.

[0043] In the axial section image, the centerline of a theoretical solid wall is used as a reference, and the midline between this centerline and the centerlines of the adjacent theoretical solid walls on both sides is used as the region boundary to form the axial single wall calculation region. The axial single wall calculation region includes the theoretical solid wall region and the theoretical pore regions located on both sides of the theoretical solid wall region. The theoretical pore channel formed along the extension direction of the pore between two adjacent theoretical solid wall regions is used as the calculation region of the axial flow channel. The minimum bounding rectangles of each radial single-hole calculation region, each axial single-wall calculation region, and each axial flow channel calculation region are determined respectively. The row index interval and column index interval corresponding to the minimum bounding rectangle are obtained. The corresponding theoretical regions are extracted from the corresponding theoretical hole array template pixel matrix according to the same row index interval and column index interval.

[0044] The radial single-hole calculation region is the area within the radial section enclosed by a periodic array of boundaries, used to evaluate the cross-sectional morphology of a hole.

[0045] The axial single-wall calculation region is the area within the axial section that is divided around a theoretical hole wall centerline. It includes the theoretical solid wall surface and part of the theoretical pores on both sides of it, and is used to simultaneously evaluate wall protrusions and wall discontinuities.

[0046] The axial flow channel calculation region is the pore channel region formed between adjacent theoretical solid walls within the axial section along the channel extension direction, used to evaluate the axial and bypass connectivity.

[0047] Spatial registration is performed between the actual image pixel matrix and the theoretical aperture array template pixel matrix, including: Specifically, spatial registration is the process of locating the corresponding scan paths and periodic boundaries in the actual matrix and the theoretical template at the same pixel index position through translation, rotation, and, if necessary, micro-scale correction.

[0048] Using the pixel in the actual image pixel matrix that represents the corresponding pixel located at the laser scanning path projection position as the registration reference, the theoretical aperture array template pixel matrix is ​​subjected to spatial transformation including at least translation and rotation. The registration target is to maximize the number of overlapping laser scanning path pixels in the theoretical aperture array template pixel matrix and laser scanning path pixels in the actual image pixel matrix, so that the corresponding periodic array boundaries in the two are at the same pixel index position, thus achieving spatial registration.

[0049] Specifically, after the template is generated, the scan path identifier is used as the main registration feature. Registration can include coarse registration and fine registration. Coarse registration makes the two matrices roughly coincide based on the first complete path, the array center or corner points; fine registration performs translation and rotation search on the theoretical template to maximize the number of overlapping pixels on the scan path.

[0050] To calculate the number of overlaps, the scan path identifiers of the actual matrix and the theoretical template can be converted into binary labeled images for processing.

[0051] Specifically, in the radial section, the periodic array boundary is formed by the centerlines of adjacent hole walls, the scan path, or the centerline between adjacent centerlines. The region enclosed by each set of closed boundaries is considered as a radial single-hole calculation region. Holes located at the image edge and with incomplete boundaries can be marked as edge holes and excluded from the statistics, or included only when a complete theoretical region can be determined, such as... Figure 8 As shown.

[0052] In the axial section, the centerline of a theoretical solid wall is used as a reference. The centerline between this centerline and the centerlines of the adjacent theoretical solid walls on both sides are used as the left and right boundaries. The inlet and outlet of the section detection range are used as the axial boundaries to form an axial single-wall calculation region. This region includes the central theoretical solid wall and the theoretical pore portions on both sides. Therefore, it can count both solid protrusions appearing in the theoretical pores and pore gaps appearing in the theoretical wall.

[0053] For the outermost hole wall that has only one adjacent wall, the boundary on the other side can be calculated outward based on the theoretical period; if the calculated boundary exceeds the valid image, the single-wall region can be excluded.

[0054] The theoretical pore channel formed between two adjacent theoretical entity wall regions along the channel extension direction is considered as an axial flow channel computational region. The inlet and outlet boundaries of the flow channel are the two end boundaries of this region along the channel extension direction, respectively. If the image orientation is reversed, the names of the inlet and outlet can be interchanged, and the connectivity determination remains unaffected.

[0055] For each computational region, determine the minimum bounding rectangle and record the minimum row index. Maximum row index Minimum column index and the largest column index Both the actual submatrix and the theoretical submatrix use the same index range i∈[ , ]、j∈[ , Extraction. If the computation region itself is not rectangular, a region mask can be created. : Within the area =1, outside the region =0, only for statistics =1 pixel count.

[0056] S5. Differentiate the attribute bits of the pixels at the same position in the corresponding theoretical region between each radial single hole calculation region and each axial single wall calculation region. Based on the difference results, identify the blocking deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area. The attribute bits of pixels at the same position in the corresponding theoretical region are subtracted from each radial single-hole calculation region and each axial single-wall calculation region, including: Subtract the actual image pixel submatrix corresponding to the radial single-hole calculation region or the axial single-wall calculation region from the theoretical template pixel submatrix corresponding to the theoretical region to obtain the difference pixel matrix; When the attribute bit in the differential pixel matrix is ​​-2, the corresponding pixel is a wall defect deviation pixel; when the attribute bit is 0, the corresponding pixel is a pore pixel whose actual state is consistent with the theoretical state; when the attribute bit is 1, the corresponding pixel is a solid wall pixel whose actual state is consistent with the theoretical state; when the attribute bit is 3, the corresponding pixel is a blockage deviation pixel.

[0057] Specifically, the blockage deviation pixel is a pixel whose theoretical attribute is a pore, but whose actual attribute is a solid. The wall defect deviation pixel is a pixel whose theoretical attribute is a solid, but whose actual attribute is a pore.

[0058] Specifically, after spatial registration and corresponding region extraction are completed, the actual attribute bits and theoretical attribute bits at the same index location are subtracted. The identifier bits are used for registration and boundary positioning and do not participate in material property differencing. The attribute difference is defined as follows:

[0059] in, The difference between attributes. For actual attribute bits, This refers to the theoretical attribute bit. and These represent the row index and column index in the current submatrix, respectively.

[0060] Based on the encoding of the actual attribute bits and the theoretical attribute bits, the correspondence between the difference and the pixel category is shown in Table 1: Table 1

[0061] Therefore, statistics A pixel value of 3 can be used to obtain the blockage area of ​​the theoretical pore occupied by the actual entity; statistics = A pixel of 2 can be used to obtain the defect area of ​​the theoretical wall that is occupied by the actual pores. =0 and =1 represents the normal pore and normal solid wall surfaces, respectively.

[0062] S6. Determine the hole shape distortion rate based on the ratio of the number of blocking deviation pixels in each radial single-hole calculation area to the number of pixels in the corresponding theoretical pore area; determine the hole shape defect rate based on the ratio of the number of wall defect deviation pixels in each radial single-hole calculation area to the number of pixels in the corresponding theoretical solid wall area; determine the hole wall distortion rate based on the ratio of the number of blocking deviation pixels in each axial single-wall calculation area to the number of pixels in the corresponding theoretical pore area; determine the hole wall discontinuity rate based on the ratio of the number of wall defect deviation pixels in each axial single-wall calculation area to the number of pixels in the corresponding theoretical solid wall area. Specifically, to avoid confusion between the theoretical pixel counts of the radial and axial regions, the following quantities are defined respectively. For a radial single-hole calculation region, This indicates the number of pixels with blocking deviation. This indicates the number of pixels indicating wall defect deviation. This indicates the number of theoretical aperture pixels within the corresponding theoretical region. This represents the number of pixels on the theoretical entity wall within the corresponding theoretical region, with the superscript 'r' indicating radial direction. For a single-walled computational region along an axial direction, This indicates the number of pixels with blocking deviation. This indicates the number of pixels indicating wall defect deviation. This indicates the number of theoretical aperture pixels within the corresponding theoretical region. This indicates the number of pixels on the theoretical entity wall within the corresponding theoretical region, with the superscript 'a' indicating the axial direction.

[0063] Aperture distortion rate The degree to which the actual solid encroaches on the theoretical pore within a radial single hole is characterized by the following formula:

[0064] in, This represents the aperture distortion rate, which is a dimensionless number. Indicates the radial single-hole calculation area =3, the number of pixels with blocking deviation; This indicates the total number of theoretical aperture pixels within the theoretical region corresponding to the radial single aperture.

[0065] Hole-shaped defect rate The degree of damage to the theoretical solid wall surrounding a radial single hole is characterized by the following formula: = /

[0066] in, This represents the porosity defect rate, which is a dimensionless number. Indicates the radial single-hole calculation area = 2. Number of pixels with wall defect deviation; This indicates the total number of theoretical solid wall pixels within the theoretical region corresponding to the radial single hole.

[0067] Hole wall distortion rate The degree to which the orifice wall protrusion encroaches on the adjacent theoretical flow channel within the axial single-wall region is characterized by the following formula:

[0068] in, This represents the hole wall distortion rate, which is a dimensionless number. Indicates the axial single-wall calculation area =3, the number of pixels with blocking deviation; This represents the total number of theoretical pore pixels within the theoretical region corresponding to the single wall along this axis.

[0069] Hole wall discontinuity The degree to which the theoretical wall surface is cut off or missing by pores within an axial single-wall region is characterized by the following formula: = /

[0070] in, This represents the discontinuity rate of the hole wall, and is a dimensionless number. Indicates the axial single-wall calculation area = 2. Number of pixels with wall defect deviation; This represents the total number of theoretical entity wall pixels within the theoretical region corresponding to this axial single wall.

[0071] The theoretical range for the above four ratios is 0 to 1. A parameter of 0 indicates that no such deviation was detected under the corresponding statistical caliber; a larger parameter indicates a higher proportion of the deviation pixel in the theoretical reference area. To ensure that the denominator is non-zero, each corresponding theoretical region should contain at least one theoretical pixel of the corresponding type during template generation and calculation region division.

[0072] Since the pixel area is e², the ratio of the number of pixels is equivalent to the ratio of the area. If the physical dimensions of the image pixels in two directions are respectively... and Then the area of ​​each pixel is If pixels within the same image have the same area, the ratio of the number of pixels is still the same as the ratio of their areas.

[0073] S7. Determine the failed pores based on the effective flow state of each radial single-hole calculation region, and determine the pore shape failure rate based on the ratio of the number of failed pores to the total number of pores in the radial section; determine the failed flow channels based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and determine the flow channel failure rate based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section; statistically analyze the characterization parameters, including pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate, and output the average value and distribution results of the characterization parameters for single section and / or multiple sections.

[0074] Specifically, according to the defined individual pore calculation regions, the six morphological characterization parameters of each pore are calculated one by one, and two types of statistical results are finally output. One is to take the arithmetic mean of the same parameters of all pores in the cross section to obtain the overall average characterization value of the cross section. The other is to statistically analyze the parameter distribution of each interval according to the preset parameter interval (such as 10%).

[0075] Determine whether all cross sections have been processed. If not all cross sections have been processed, return to step S2. If all cross sections have been processed, proceed to step S7.

[0076] The statistical parameters on a single cross section of S7 are statistically analyzed, and two types of statistical results are finally output. One is to take the arithmetic mean of the same parameters of all pores in the whole domain to obtain the overall average characterization value of the whole domain. The other is to statistically analyze the parameter distribution of each interval according to the preset parameter interval (such as 10%).

[0077] Total number of pores in radial section: Identifier based on theoretical template matrix For laser path pixels with a value of 1, the total number of closed unit cells formed by their enclosing arrangement is the total number of pores within that radial cross-section. If there are transverse... m If there are n laser paths running vertically, then it can be assumed that there should be n laser paths within this interface. A complete radial cross-section of internal pores, the number of which is the total number of internal pores in the radial cross-section.

[0078] Number of failed pores: Calculate the unit cell for each pore, counting only the identifier bits. The internal calculation region is equal to 0, and the difference within this region is... If the percentage of 3 is greater than a preset threshold, the hole is determined to be a failed pore. In this embodiment, the preset threshold is set to 90%, and this value can be modified according to actual product requirements.

[0079] Total number of flow channels in axial section: Identifier based on theoretical template matrix For an axial laser path pixel with a value of 1, the strip region formed between two adjacent parallel axial paths is an independent flow channel. The total number of strip regions in this axial section is the total number of flow channels in the axial section. If there are n axial laser paths in the axial section, the total number of flow channels in the axial section is n-1.

[0080] Effective flow state: If, along the entire axial length of the channel, there exists at least one connected path entirely composed of effective flow pixels, connecting both ends of the channel, then the channel is considered axially connected. If the channel has localized pore blockages or wall defects, but still retains at least one continuous effective flow path, it is still considered axially connected. The difference matrix obtained by subtracting the actual attribute bits and theoretical attribute bits at the same index position is defined as follows: Let the index row For the theoretical solid wall pixels along the flow channel axis, The theoretical solid wall pixel perpendicular to the flow channel axis defines the neighborhood in four directions: up, down, left, and right. And all pixel indices satisfy the matrix boundary constraints: For a path defined as a pixel sequence A sequence is called a connected path if it satisfies the following conditions: ① all pixels belong to a certain attribute set, and ② adjacent pixels in the sequence are neighbors in four directions. The effective flow-through pixel set actually consists of pixels that exhibit porosity characteristics and possess flow-through capability, corresponding to two types of values ​​in the difference matrix: That is, the normal porosity region. This refers to the area with wall defects. The effective set of circulating pixels is then... .remember Let S be the set of all connected components of set S, that is, the set of all pixels with the same attribute that are connected to pixels in set S. Normal porosity This is a wall defect.

[0081] Axial flow interruption: Along the entire axial length of the flow channel, there is no effective flow path connecting the two ends of the channel. The flow channel is completely blocked by solid material, and there is no axial flow capacity; this is determined as axial flow interruption. All effective flow pixels within the first row (a=1) of the axial flow channel are taken as the inlet set: Take all valid flow pixels within the last row (a=A) of the flow channel axis as the outlet set: If the connected components of the entry set and the connected components of the exit set have no intersection, that is: If the flow path is interrupted axially, it is determined that the flow channel does not have axial flow capacity.

[0082] No bypass connectivity: Within the theoretical wall region between two adjacent channels, if a pixel with a missing wall simultaneously forms a connection path with the effective flow regions of both channels, then the two channels are considered to have bypass connectivity through the wall defect; otherwise, they are considered to have no bypass connectivity. Let adjacent channels... The column range is b∈[ , ], flow channel The column range is b∈[ , The effective flow areas of the two channels are respectively and .

[0083] If the judgment formula is satisfied: Then it is determined that the two flow channels form a bypass flow through the wall defect; if the right side of the equation is an empty set. If so, it is determined that there is no bypass connection between the two channels.

[0084] Number of failed flow channels: A flow channel is considered a failed flow channel if it simultaneously meets both of the following conditions: ① Self-axial flow interruption: The flow channel itself has no effective flow path along the axial direction and does not have axial flow capacity; ② No bypass connection: This flow channel cannot form a bypass with adjacent flow channels through wall defects, and cannot achieve flow through adjacent flow channels. The judgment formula is:

[0085]

[0086] in, This is a set of pixels representing wall defects.

[0087] The number of flow channels that meet the above conditions within a specified axial section is the number of failed flow channels.

[0088] Hole shape distortion rate, hole shape defect rate, hole wall distortion rate, hole wall discontinuity rate, hole shape failure rate, and flow channel failure rate can be used as quantitative input indicators for optimizing the process of directional porous structures in laser additive manufacturing. They can be used to establish the correspondence between process parameters and morphological defects, support the reasonable matching and selection of process parameters and defect control targets, and provide a unified quantitative evaluation benchmark for the process control of directional porous structures.

[0089] A quantitative characterization system for pore wall blockage and defects in oriented porous structures, comprising: An image acquisition module is used to acquire cross-sectional images of the porous structure to be measured, the cross-sectional images including a radial cross-sectional image perpendicular to the extension direction of the pores and an axial cross-sectional image along the extension direction of the pores; The image preprocessing module is used to binarize the cross-sectional image to distinguish between pore regions and solid wall regions, obtain a binary image, and establish an actual image pixel matrix based on the binary image. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used for hole wall forming in the cross-sectional image, and the actual attribute bit is used to characterize whether the corresponding pixel belongs to a pore region or a solid wall region. The template generation module is used to generate a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix, based on the resolution of the actual image pixel matrix, the laser scanning path when printing the directional porous structure to be measured, and the forming process parameters. The theoretical hole array template pixel matrix includes a theoretical pore region and a theoretical solid wall region. The region registration and division module is used to spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix, and divide the radial section calculation region and axial section calculation region in the actual image pixel matrix according to the periodic array boundary determined by the laser scanning path. According to the index range of each calculation region in the actual image pixel matrix, the corresponding theoretical region with the same index range is extracted from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and the corresponding theoretical region. The first calculation module is used to perform a difference between the attribute bits of the radial single hole calculation area and the axial single wall calculation area and the pixels at the same position in the corresponding theoretical area, and to identify the blockage deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area based on the difference results. The second calculation module is used to determine the hole shape distortion rate based on the ratio of the number of blocking deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical pore area; to determine the hole shape defect rate based on the ratio of the number of wall defect deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical solid wall area; to determine the hole wall distortion rate based on the ratio of the number of blocking deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical pore area; and to determine the hole wall discontinuity rate based on the ratio of the number of wall defect deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical solid wall area. The distortion and defect parameter calculation module is used to determine the failed pores based on the effective flow state of each radial single-hole calculation region, and to determine the pore shape failure rate based on the ratio of the number of failed pores to the total number of pores in the radial section; to determine the failed flow channels based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and to determine the flow channel failure rate based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section; and to statistically analyze the characterization parameters, including the pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate, and output the average value and distribution results of the characterization parameters for a single section and / or multiple sections as the characterization results.

[0090] This application also discloses a terminal device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, a quantitative characterization method for pore wall blockage and defects of a directional porous structure is used.

[0091] The terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server. The terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0092] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0093] The memory can be an internal storage unit of the terminal device, such as a hard disk or RAM of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the terminal device. Furthermore, the memory can be a combination of internal storage units and external storage devices of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0094] In this terminal device, the quantification method for pore wall blockage and defects of a directional porous structure in the above embodiment is stored in the memory of the terminal device and loaded and executed on the processor of the terminal device for convenient use.

[0095] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it employs a quantitative characterization method for pore wall blockage and defects of a directional porous structure as described in the above embodiments.

[0096] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0097] The above-described method for quantitative characterization of pore wall blockage and defects in a directional porous structure is stored in the computer-readable storage medium and loaded and executed on a processor to facilitate the storage and application of the method.

[0098] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0099] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method for quantitatively characterizing pore wall blockage and defects in directional porous structures, characterized in that, include: Obtain cross-sectional images of the directional porous structure to be measured and the corresponding forming process parameters. The cross-sectional images include radial cross-sectional images perpendicular to the channel extension direction and axial cross-sectional images along the channel extension direction. The cross-sectional image is binarized to distinguish between pore regions and solid wall regions, resulting in a binary image. An actual image pixel matrix is ​​then established based on the binary image. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used for hole wall forming in the cross-sectional image. The actual attribute bit is used to characterize whether the corresponding pixel belongs to a pore region or a solid wall region. Based on the resolution of the actual image pixel matrix, the laser scanning path when printing the directional porous structure to be measured, and the forming process parameters, a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix is ​​generated. The theoretical hole array template pixel matrix includes a theoretical pore region and a theoretical solid wall region. Spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix, and divide the radial section calculation region and axial section calculation region in the actual image pixel matrix according to the periodic array boundary determined by the laser scanning path. According to the index range of each calculation region in the actual image pixel matrix, extract the corresponding theoretical region with the same index range from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and the corresponding theoretical region. The attribute bits of the pixels at the same position in the corresponding theoretical region are differentially divided between each radial single hole calculation region and each axial single wall calculation region. Based on the differential results, the blocking deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area are identified. The hole shape distortion rate is determined by the ratio of the number of pixels with blockage deviation in each radial single-hole calculation area to the number of pixels in the corresponding theoretical pore area; the hole shape defect rate is determined by the ratio of the number of pixels with wall defect deviation in each radial single-hole calculation area to the number of pixels in the corresponding theoretical solid wall area; the hole wall distortion rate is determined by the ratio of the number of pixels with blockage deviation in each axial single-wall calculation area to the number of pixels in the corresponding theoretical pore area; and the hole wall discontinuity rate is determined by the ratio of the number of pixels with wall defect deviation in each axial single-wall calculation area to the number of pixels in the corresponding theoretical solid wall area. Failed pores are determined based on the effective flow state of each radial single-pore calculation region, and the pore shape failure rate is determined based on the ratio of the number of failed pores to the total number of pores in the radial section. Failed flow channels are determined based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and the flow channel failure rate is determined based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section. The characterization parameters are statistically analyzed, including the pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate. The average value and distribution results of the characterization parameters of the cross-sectional image of the porous structure to be measured are output as the characterization results.

2. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 1, characterized in that, The process of acquiring a cross-sectional image of the oriented porous structure to be measured includes: The porous structure to be measured is subjected to computed tomography, and the radial cross-sectional image and the axial cross-sectional image are obtained according to a preset truncation step size. Alternatively, a metallographic sample can be prepared for the porous structure to be measured, and the radial cross-sectional image and the axial cross-sectional image can be acquired.

3. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 1, characterized in that it includes: The actual image pixel matrix is ​​denoted as [ ], where each pixel unit is represented as , These are the identifier bits for the actual image pixel matrix. This represents the actual attribute bit; when the corresponding pixel is located on the laser scanning path... When the corresponding pixel is not located on the laser scanning path, When the corresponding pixel belongs to the pore region, When the corresponding pixel belongs to the solid wall area .

4. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 3, characterized in that, include: The theoretical aperture array template pixel matrix is ​​denoted as […]. ], where each pixel unit is represented as ,in, These are the identifier bits for the pixel matrix of the theoretical aperture array template. For theoretical attribute bits; When the corresponding pixel is located on the theoretical laser scanning path When the corresponding pixel is not located on the theoretical laser scanning path, When the corresponding pixel belongs to the theoretical pore region, When the corresponding pixel belongs to the theoretical solid wall region, ; Generating the theoretical aperture array template pixel matrix includes: Each pixel unit in the theoretical aperture array template pixel matrix is ​​initialized to (0, -1), and the pixel unit corresponding to the laser scanning path is assigned the value (1, 1). Select parallel lines that cross each other between their centerlines Two laser scanning paths with a laser scanning interval are used to determine the pixel distance between the center lines of the two laser scanning paths along a direction perpendicular to the laser scanning path. and in accordance with Determine the physical size of a single pixel ,in, The laser scanning spacing The number of laser scanning intervals between the two laser scanning paths; according to Determine the number of wall extension pixels corresponding to one side of the laser scanning path. ,in, The laser spot size; Centered on each of the laser scanning paths, extend to both sides perpendicular to the laser scanning paths. Each pixel is assigned an attribute bit of 1 within the extended range and an attribute bit of -1 outside the extended range, so as to form the theoretical solid wall region and the theoretical pore region respectively.

5. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 1, characterized in that, The radial section calculation region and the axial section calculation region are divided separately. Based on the index range of each calculation region in the actual image pixel matrix, corresponding theoretical regions with the same index range are extracted from the theoretical hole array template pixel matrix, including: In the radial section calculation region, the area enclosed by the periodic array boundary determined by the laser scanning path is taken as the radial single hole calculation region; In the axial section calculation region, the centerline of a theoretical solid wall is used as a reference, and the midline between the centerline and the centerlines of the adjacent theoretical solid walls on both sides is used as the region boundary to form the axial single wall calculation region. The axial single wall calculation region includes the theoretical solid wall region and the theoretical pore regions located on both sides of the theoretical solid wall region. The theoretical pore channel formed between two adjacent theoretical solid wall regions along the channel extension direction is used as the calculation region of the axial flow channel. The minimum bounding rectangles of each radial single-hole calculation region, each axial single-wall calculation region, and each axial flow channel calculation region are determined respectively, and the row index interval and column index interval corresponding to the minimum bounding rectangle are obtained. According to the same row index interval and column index interval, the corresponding theoretical region is extracted from the corresponding theoretical hole array template pixel matrix.

6. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 4, characterized in that, The attribute bits of pixels at the same position in the corresponding theoretical region are differiated between each radial single-hole calculation region and each axial single-wall calculation region, including: Subtract the actual image pixel submatrix corresponding to the radial single-hole calculation region or the axial single-wall calculation region from the theoretical template pixel submatrix corresponding to the corresponding theoretical region to obtain the difference pixel matrix; When the attribute bit in the differential pixel matrix is ​​-2, the corresponding pixel is the wall defect deviation pixel; when the attribute bit is 0, the corresponding pixel is the pore pixel whose actual state is consistent with the theoretical state; when the attribute bit is 1, the corresponding pixel is the solid wall pixel whose actual state is consistent with the theoretical state; when the attribute bit is 3, the corresponding pixel is the blockage deviation pixel.

7. The method for quantitative characterization of pore wall blockage and defects in porous structures according to claim 1, characterized in that, The step of spatially registering the actual image pixel matrix with the theoretical aperture array template pixel matrix includes: Using the pixel in the actual image pixel matrix that represents the pixel located at the laser scanning path projection position as the registration reference, the theoretical aperture array template pixel matrix is ​​subjected to a spatial transformation including at least translation and rotation. The registration target is to maximize the number of overlapping laser scanning path pixels in the theoretical aperture array template pixel matrix and laser scanning path pixels in the actual image pixel matrix, so that the corresponding periodic array boundaries in the two are at the same pixel index position, thereby achieving spatial registration.

8. A quantitative characterization system for pore wall blockage and defects in directional porous structures, characterized in that, include: An image acquisition module is used to acquire cross-sectional images of the porous structure to be measured, the cross-sectional images including a radial cross-sectional image perpendicular to the extension direction of the pores and an axial cross-sectional image along the extension direction of the pores; The image preprocessing module is used to binarize the cross-sectional image to distinguish between pore regions and solid wall regions, obtain a binary image, and establish an actual image pixel matrix based on the binary image. Each pixel unit in the actual image pixel matrix includes an identifier bit and an actual attribute bit. The identifier bit is used to characterize whether the corresponding pixel is located at the projection position of the laser scanning path used for hole wall forming in the cross-sectional image, and the actual attribute bit is used to characterize whether the corresponding pixel belongs to a pore region or a solid wall region. The template generation module is used to generate a theoretical hole array template pixel matrix with the same resolution as the actual image pixel matrix, based on the resolution of the actual image pixel matrix, the laser scanning path when printing the directional porous structure to be measured, and the forming process parameters. The theoretical hole array template pixel matrix includes a theoretical pore region and a theoretical solid wall region. The region registration and division module is used to spatially register the actual image pixel matrix with the theoretical aperture array template pixel matrix, and divide the radial section calculation region and axial section calculation region in the actual image pixel matrix according to the periodic array boundary determined by the laser scanning path. According to the index range of each calculation region in the actual image pixel matrix, the corresponding theoretical region with the same index range is extracted from the theoretical aperture array template pixel matrix to establish a one-to-one correspondence between each calculation region and the corresponding theoretical region. The first calculation module is used to perform a difference between the attribute bits of the radial single hole calculation area and the axial single wall calculation area and the pixels at the same position in the corresponding theoretical area, and to identify the blockage deviation pixels that are actually solid walls in the theoretical pore area and the wall defect deviation pixels that are actually pores in the theoretical solid wall area based on the difference results. The second calculation module is used to determine the hole shape distortion rate based on the ratio of the number of blocking deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical pore area; to determine the hole shape defect rate based on the ratio of the number of wall defect deviation pixels in each radial single hole calculation area to the number of pixels in the corresponding theoretical solid wall area; to determine the hole wall distortion rate based on the ratio of the number of blocking deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical pore area; and to determine the hole wall discontinuity rate based on the ratio of the number of wall defect deviation pixels in each axial single wall calculation area to the number of pixels in the corresponding theoretical solid wall area. The distortion and defect parameter calculation module is used to determine the failed pores based on the effective flow state of each radial single-hole calculation region, and to determine the pore shape failure rate based on the ratio of the number of failed pores to the total number of pores in the radial section; to determine the failed flow channels based on the axial connectivity and bypass connectivity of each axial flow channel calculation region, and to determine the flow channel failure rate based on the ratio of the number of failed flow channels to the total number of flow channels in the axial section; and to statistically analyze the characterization parameters, including the pore shape distortion rate, pore shape defect rate, pore wall distortion rate, pore wall discontinuity rate, pore shape failure rate, and flow channel failure rate, and output the average value and distribution results of the characterization parameters for a single section and / or multiple sections as the characterization results.

9. A terminal device, comprising a memory and a processor, characterized in that, The memory stores a computer program that can run on a processor, and when the processor loads and executes the computer program, it employs the method described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs the method described in any one of claims 1 to 7.

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