3D printing grid structure multi-domain feature defect detection method based on CT image

Abstract

The invention provides a 3D printing grid structure multi-domain feature defect detection method based on a CT image, and the method comprises the steps: converting a 3D printing grid model into RAW data through computed tomography, extracting a 3D printing grid structure, and extracting the geometric features of the 3D printing grid structure in a spatial domain through spatial domain feature analysis; determining the distribution of the repetitive structure through frequency domain analysis, drawing a change curve of a characteristic frequency domain, determining a repetitive unit by combining the information of the spatial domain and the change curve of the characteristic frequency domain, extracting the characteristics of the repetitive unit based on the characteristics of a fast point characteristic histogram, and carrying out registration, by traversing and comparing the features of the repetitive units in the registered point clouds and obtaining the result and displaying the result in the form of the heat map, a user can visually and comprehensively observe the difference of the model through visualization, potential printing defects can be quickly positioned and analyzed, and the beneficial effects of high detection precision and high efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing, and in particular to a method for detecting multi-domain characteristic defects of a 3D printing grid structure based on CT images. Background Art

[0002] 3D printing is a technology that generates three-dimensional entities based on three-dimensional CAD model data by continuously stacking physical layers and adding materials layer by layer. Simply put, it uses a computer to cut the 3D model of the formed part into a series of "slices" of a certain thickness. The 3D printing equipment manufactures each layer of "slices" from bottom to top, and finally stacks them to form a three-dimensional solid part.

[0003] In the current manufacturing industry, metal 3D printing technology has become an innovative manufacturing method. It can shorten the processing cycle, reduce production costs, and the forming process is not limited by the complexity. However, the current 3D printed grid model is composed of periodically arranged repetitive structures, which are complex inside and difficult to accurately locate and identify surface defects. Printing defects and dimensional errors often occur during the 3D printing process, which seriously reduces the structural performance and even causes collapse. Therefore, identifying these defects is an important part of structural quality inspection. However, existing inspection methods mostly rely on two-dimensional image analysis, which can only identify the surface part and is prone to misjudgment, and requires a large number of images to be taken from different angles, resulting in high costs and low efficiency. Summary of the invention

[0004] The present invention provides a 3D printed grid structure multi-domain feature defect detection method based on CT images, which is used to solve the defects of the prior art that it relies on two-dimensional image analysis, can only identify the surface part and is prone to misjudgment, and requires a large number of images to be taken from different angles, resulting in high cost and low efficiency.

[0005] The present invention provides a method for detecting multi-domain characteristic defects of a 3D printed grid structure based on CT images, comprising: Converting the 3D printed mesh model into data in a raw data format by computer tomography, and extracting the 3D printed mesh structure by using a voxel histogram; Extracting geometric features of the 3D printed grid structure in the spatial domain by spatial domain feature analysis, wherein the spatial domain feature analysis includes obtaining a repetitive structure by counting the standard deviation of the dimension of the three-dimensional point cloud model of the 3D printed grid structure; Determine the distribution of the repetitive structure by frequency domain analysis and draw a change curve of the characteristic frequency domain, wherein the frequency domain analysis includes converting the information of the space domain into the information of the frequency domain by fast Fourier transform; Determine the repeating unit by combining the information in the spatial domain and the change curve in the characteristic frequency domain, and identify and automatically extract the repeating unit at the end of a cycle, where the end of the cycle is a wave peak and a wave trough; Based on the fast point feature histogram feature, the feature of the repeating unit is extracted by a feature matching algorithm and the corresponding feature point pairs in the point cloud are found to obtain an initial transformation matrix, the initial transformation matrix is ​​used for coarse registration, a second transformation matrix is ​​obtained by the result after the coarse registration, the second transformation matrix is ​​used as the input of the ICP algorithm and the ICP iteration is performed to register the point cloud containing the repeating unit; By traversing the features of the repeating unit in the point cloud after comparison and registration and obtaining a measurement result; The defects are analyzed based on the measurement results and the analysis results are displayed in the form of a heat map.

[0006] According to a method for detecting multi-domain characteristic defects of a 3D printed mesh structure based on CT images provided by the present invention, the method of extracting the 3D printed mesh structure using a voxel histogram includes: segmenting the data of the 3D printed mesh model by selecting a voxel threshold in the voxel histogram and screening out voxel points representing the 3D printed mesh structure to obtain the 3D printed mesh structure.

[0007] According to a method for detecting multi-domain feature defects of a 3D printed mesh structure based on CT images provided by the present invention, the repetitive structure is a concentrated or regularly distributed data point, and the standard deviation is used to identify the periodicity of the planar features of the 3D printed mesh structure, wherein the statistical standard deviation of the dimension of the three-dimensional point cloud model of the 3D printed mesh structure specifically includes: Get three-dimensional point cloud data including x-coordinate, y-coordinate and z-coordinate; The point cloud data is grouped according to the x-coordinate value, and the standard deviation of the y- and z-coordinate values ​​corresponding to each x-coordinate value is calculated to obtain a standard deviation variation curve of the y-plane and the z-plane along the x-axis direction; By taking the geometric mean of the y-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the x-coordinate value; By taking the geometric mean of the x-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the y-coordinate value; By taking the geometric mean of the x-plane standard deviation and the y-plane standard deviation and plotting it against the z-coordinate value.

[0008] According to a multi-domain characteristic defect detection method for 3D printed grid structures based on CT images provided by the present invention, the distribution of the repeated structure is determined by frequency domain analysis and a change curve of the characteristic frequency domain is plotted. The frequency domain analysis includes converting the information in the spatial domain into information in the frequency domain by fast Fourier transform, specifically including: The x-coordinate value is fixed, and the y-coordinate value and the z-coordinate value corresponding to the x-coordinate value are processed in the characteristic frequency domain by the fast Fourier transform; The characteristic frequency domains of the y plane and the z plane are respectively plotted into characteristic frequency domain curves based on the x coordinate value; Performing geometric averaging on the characteristic frequency domain curve of the y plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the x coordinate value changes; Performing geometric averaging on the characteristic frequency domain curve of the x plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the y coordinate value changes; The characteristic frequency domain curve of the x-plane and the characteristic frequency domain curve of the y-plane are geometrically averaged and a characteristic frequency domain variation curve graph varying with the z-coordinate value is plotted.

[0009] According to a method for detecting multi-domain characteristic defects of a 3D printed mesh structure based on CT images provided by the present invention, the fixed x coordinate value, and the characteristic frequency domain processing of the y coordinate value and the z coordinate value corresponding to the x coordinate value by the fast Fourier transform, include: The data of the 3D printed grid structure in the spatial domain are grouped according to the x-axis value. For a fixed x-coordinate value, the corresponding y and z-coordinate values ​​are extracted to form a two-dimensional data group. A complex array is obtained after transformation by the fft function in the Python numpy library. Each element in the complex array corresponds to a frequency component. The position number of the element in the complex array represents the frequency index. The relationship between the frequency index and the corresponding actual frequency value is: , where f is the actual frequency, k is the frequency index, N is the data length, and fs is the sampling frequency.

[0010] According to a 3D printing grid structure multi-domain feature defect detection method based on CT images provided by the present invention, the peak and the trough are determined by setting a discrete data sequence, the peak being the local maximum value in the discrete data sequence, and the and Determine the local maximum, where i is a data point in the discrete data sequence, is the local maximum value, the peak is obtained by filtering out the noise points through the median filtering method, and the trough is the local minimum value in the discrete data sequence. and Determine the local minimum, where m is a data point in the discrete data sequence, is the local minimum value, and the trough is obtained by filtering out noise points using the median filtering method.

[0011] According to a 3D printing grid structure multi-domain feature defect detection method based on CT images provided by the present invention, constructing the fast point feature histogram includes: weighting the simple point feature histogram of the neighborhood point relative to the selected point, and then combining the simple point feature histogram of the selected point with the weighted simple point feature histogram of the neighborhood point, specifically using the formula:

[0012] Where: is the selected point, is the fast point feature histogram of the selected point, is the simple point feature histogram of the selected point, is the simple point feature histogram of the neighborhood points, For the selected point and neighboring points The weight between them, k represents the number of neighborhood points relative to the selected point.

[0013] According to a 3D printing grid structure multi-domain feature defect detection method based on CT images provided by the present invention, the features of the repeating units in the point cloud after traversal, comparison and registration are obtained and the measurement results are obtained, including: The points of the repeating unit are traversed and compared with the points of the same position of the adjacent repeating unit to obtain the Euclidean distance and the average value, and the average value is used as the difference value of the points of the repeating unit. The points of the repeating unit are replaced and the operation is repeated.

[0014] According to a method for detecting multi-domain characteristic defects of a 3D printed grid structure based on CT images provided by the present invention, the specific method of obtaining the average value is:

[0015] Wherein, x is the point of the repeating unit, is the point cloud of the repeating unit numbered i, q is , N is the total number of repeating units.

[0016] According to a method for detecting multi-domain characteristic defects of a 3D printed mesh structure based on CT images provided by the present invention, the defect is analyzed according to the measurement result and the analysis result is displayed in the form of a heat map, including: Mapping the indicator value of the defect to the color range of the heat map; Annotating axes, scales and color legends on the heat map; Add interactive functions, including displaying the defect information by hovering the mouse, and being able to zoom in or out the heat map.

[0017] The present invention provides a 3D printed grid structure multi-domain feature defect detection method based on CT images, which converts the 3D printed grid model into data in raw data format through computer tomography, extracts the 3D printed grid structure using voxel histogram, and extracts the geometric features of the 3D printed grid structure in the spatial domain through spatial domain feature analysis, wherein the spatial domain feature analysis includes obtaining the repetitive structure by counting the standard deviation of the dimension of the three-dimensional point cloud model, and furthermore, determines the distribution of the repetitive structure through frequency domain analysis and draws a characteristic frequency domain change curve, wherein the frequency domain analysis includes converting the spatial domain information into frequency domain information through fast Fourier transform, determines the repetitive unit by combining the spatial domain information and the characteristic frequency domain change curve, and identifies and automatically extracts the repetitive unit at the end of the cycle. , the end of the cycle is the peak and the trough, based on the fast point feature histogram feature, the feature matching algorithm is used to extract the features of the repeating unit and find the corresponding feature point pairs in the point cloud to obtain an initial transformation matrix, the initial transformation matrix is ​​used for coarse registration, and the second transformation matrix is ​​obtained through the result after the coarse registration, the second transformation matrix is ​​used as the input of the ICP algorithm and the ICP iteration is performed to align the point cloud containing the repeating unit, finally, the features of the repeating unit in the point cloud after traversal and comparison registration are obtained and the measurement results are obtained, the defects are analyzed according to the measurement results and the analysis results are displayed in the form of a heat map, the visualization of the defect results allows the user to intuitively observe the differences in the model, which is helpful to quickly locate and analyze potential printing defects, and has the beneficial effects of high detection accuracy and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a method for detecting multi-domain characteristic defects of a 3D printed mesh structure based on CT images provided by an embodiment of the present invention; Figure 2 It is a flow chart of the statistical standard deviation of the multi-domain feature defect detection method of 3D printing grid structure based on CT images provided by an embodiment of the present invention; Figure 3 It is a flow chart of frequency domain analysis of a method for detecting multi-domain characteristic defects of a 3D printed mesh structure based on CT images provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The present invention provides a method for detecting multi-domain feature defects of a 3D printed grid structure based on CT images, comprising: converting a 3D printed grid model into data in an original data format by computer tomography, and extracting the 3D printed grid structure by using a voxel histogram; extracting the geometric features of the 3D printed grid structure in the spatial domain by spatial domain feature analysis, wherein the spatial domain feature analysis comprises obtaining a repetitive structure by counting the standard deviation of the dimension of a three-dimensional point cloud model, wherein the repetitive structure is a data point that is concentrated or regularly distributed, and wherein the standard deviation is used to identify the periodicity of a plane feature; determining the distribution of the repetitive structure by frequency domain analysis and drawing a change curve of a feature frequency domain, wherein the frequency domain analysis comprises converting the information of the spatial domain into information of the frequency domain by fast Fourier transform ; Determine the repeating unit by combining the information in the spatial domain and the change curve in the characteristic frequency domain, identify and automatically extract the repeating unit at the end of the period, and the end of the period is the peak and the trough; Based on the fast point feature histogram feature, extract the features of the repeating unit through the feature matching algorithm and find the corresponding feature point pairs in the point cloud to obtain the initial transformation matrix, the initial transformation matrix is ​​used for coarse alignment, and the second transformation matrix is ​​obtained through the result after the coarse alignment, and the second transformation matrix is ​​used as the input of the ICP algorithm and ICP iteration is performed to align the point cloud containing the repeating unit; traverse and compare the features of the repeating unit in the point cloud after alignment and obtain the measurement results; Analyze the defects according to the measurement results and display the analysis results in the form of a heat map.

[0022] Among them, the full name of CT is "Computed Tomography", which means computer tomography, and CT image is a computer tomography image. In addition, the extracting of the 3D printing grid structure by using a voxel histogram includes: performing data segmentation of the 3D printing grid model by selecting a voxel threshold in the voxel histogram to screen out voxel points representing the 3D printing grid structure, so as to obtain the 3D printing grid structure.

[0023] The statistical standard deviation of the dimension of the three-dimensional point cloud model of the 3D printed grid structure specifically includes: Get three-dimensional point cloud data including x-coordinate, y-coordinate and z-coordinate; The point cloud data is grouped according to the x-coordinate value, and the standard deviation of the y- and z-coordinate values ​​corresponding to each x-coordinate value is calculated to obtain a standard deviation variation curve of the y-plane and the z-plane along the x-axis direction; By taking the geometric mean of the y-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the x-coordinate value; By taking the geometric mean of the x-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the y-coordinate value; By taking the geometric mean of the x-plane standard deviation and the y-plane standard deviation and plotting it against the z-coordinate value.

[0024] The distribution of the repetitive structure is determined by frequency domain analysis and a characteristic frequency domain change curve is plotted. The frequency domain analysis includes converting the spatial domain information into frequency domain information by fast Fourier transform, specifically including: The x-coordinate value is fixed, and the y-coordinate value and the z-coordinate value corresponding to the x-coordinate value are processed in the characteristic frequency domain by the fast Fourier transform; The characteristic frequency domains of the y plane and the z plane are respectively plotted into characteristic frequency domain curves based on the x coordinate value; Performing geometric averaging on the characteristic frequency domain curve of the y plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the x coordinate value changes; Performing geometric averaging on the characteristic frequency domain curve of the x plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the y coordinate value changes; The characteristic frequency domain curve of the x-plane and the characteristic frequency domain curve of the y-plane are geometrically averaged and a characteristic frequency domain variation curve graph varying with the z-coordinate value is plotted.

[0025] The fixed x-coordinate value performs characteristic frequency domain processing on the y-coordinate value and the z-coordinate value corresponding to the x-coordinate value through the fast Fourier transform, including: The data of the 3D printed grid structure in the spatial domain are grouped according to the x-axis value. For a fixed x-coordinate value, the corresponding y and z-coordinate values ​​are extracted to form a two-dimensional data group. A complex array is obtained after transformation by the fft function in the Python numpy library. Each element in the complex array corresponds to a frequency component. The position number of the element in the complex array represents the frequency index. The relationship between the frequency index and the corresponding actual frequency value is: , where f is the actual frequency, k is the frequency index, N is the data length, and fs is the sampling frequency.

[0026] The peak and the trough are found by setting a discrete data sequence, wherein the peak is the local maximum value in the discrete data sequence, and the peak is the local maximum value in the discrete data sequence. and Determine the local maximum, where i is a data point in the discrete data sequence, is the local maximum value, and the peak is obtained by filtering out the noise points through the median filtering method. The trough is opposite to the peak, and the trough is the local minimum value in the discrete data sequence. and Determine the local minimum, where m is a data point in the discrete data sequence, is the local minimum value, and the trough is obtained by filtering out noise points through the median filtering method.

[0027] Wherein, constructing the fast point feature histogram includes: weighting the simple point feature histogram of the neighborhood points relative to the selected point, and then combining the simple point feature histogram of the selected point with the weighted simple point feature histogram of the neighborhood points, using the formula:

[0028] Where: is the selected point, is the fast point feature histogram of the selected point, is the simple point feature histogram of the selected point, is the simple point feature histogram of the neighborhood points, For the selected point and neighboring points The weight between them, k represents the number of neighborhood points relative to the selected point.

[0029] Among them, the features of the repeating unit in the point cloud after traversal comparison and registration are obtained and the measurement results are obtained, including: traversing and comparing the points of the repeating unit with the points at the same position of the adjacent repeating unit to obtain the Euclidean distance and obtain the average value, the average value is used as the difference value of the points of the repeating unit, and the points of the repeating unit are replaced to repeat the operation.

[0030] The specific method of obtaining the average value is as follows:

[0031] Wherein, x is the point of the repeating unit, is the point cloud of the repeating unit numbered i, and q is points in the point cloud, and N is the total number of the repeating units.

[0032] Among them, analyzing the defects according to the measurement results and displaying the analysis results in the form of a heat map includes: mapping the indicator value of the defect to the color range of the heat map; marking the coordinate axis, scale and color legend on the heat map; adding interactive functions, and the interactive functions include displaying the information of the defect when the mouse is hovered, and being able to zoom in or out the heat map.

[0033] In this embodiment, please refer to Figure 1 , Figure 2 and Figure 3 , which are respectively a flow chart of a 3D printing mesh structure multi-domain feature defect detection method based on CT images provided in an embodiment of the present invention, a flow chart of a statistical standard deviation of a 3D printing mesh structure multi-domain feature defect detection method based on CT images provided in an embodiment of the present invention, and a flow chart of a frequency domain analysis of a 3D printing mesh structure multi-domain feature defect detection method based on CT images provided in an embodiment of the present invention.

[0034] Specifically, in this embodiment, Figure 1 As shown, the method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images provided in an embodiment of the present invention includes: S1: converting the 3D printed mesh model into data in a raw data format by computer tomography, and extracting the 3D printed mesh structure by using a voxel histogram; S2: extracting geometric features of the 3D printed grid structure in the spatial domain through spatial domain feature analysis; S3: determining the distribution of the repetitive structure by frequency domain analysis and drawing a change curve of the characteristic frequency domain, wherein the frequency domain analysis includes converting the information in the space domain into the information in the frequency domain by fast Fourier transform; S4: Determine a repeating unit by combining the information in the spatial domain and the change curve in the characteristic frequency domain, and identify and automatically extract the repeating unit at the end of the cycle; S5: Based on the fast point feature histogram feature, extract the features of the repeating unit through a feature matching algorithm and find the corresponding feature point pairs in the point cloud to obtain an initial transformation matrix, and register the point cloud containing the repeating unit through an ICP algorithm; S6: Obtaining measurement results by traversing and comparing the features of the repeating units in the point cloud after registration; S7: Analyze the defects according to the measurement results and display the analysis results in the form of a heat map.

[0035] In this embodiment, the above steps are described in detail, wherein S1: converting the 3D printed mesh model into data in raw data format through computer tomography, and extracting the 3D printed mesh structure using a voxel histogram. In this embodiment, extracting the 3D printed mesh structure using a voxel histogram includes: performing data segmentation of the 3D printed mesh model by selecting a voxel threshold in the voxel histogram to screen out voxel points representing the 3D printed mesh structure, thereby obtaining the 3D printed mesh structure.

[0036] In this embodiment, specifically, S2: extracting the geometric features of the 3D printed grid structure in the spatial domain by spatial domain feature analysis, wherein the spatial domain feature analysis includes obtaining a repetitive structure by counting the standard deviation of the three-dimensional point cloud model dimension, the repetitive structure is a concentrated or regularly distributed data point, and the standard deviation is used to identify the periodicity of the plane feature. In this embodiment, the statistical standard deviation of the three-dimensional point cloud model dimension is as follows: Figure 2 As shown, specifically including: S10: Acquire three-dimensional point cloud data including x-coordinates, y-coordinates and z-coordinates; S20: grouping the point cloud data according to the x-coordinate value, and calculating the standard deviation of the y- and z-coordinate values ​​corresponding to each x-coordinate value to obtain a standard deviation variation curve of the y-plane and the z-plane along the x-axis direction; S30: Taking the geometric mean of the y-plane standard deviation and the z-plane standard deviation and drawing a curve graph showing the change with the x-coordinate value.

[0037] In addition, the curve graph that follows the change of the y-coordinate value and the curve graph that follows the change of the z-coordinate value are similar to the above-mentioned S10~S30, specifically, the point cloud data are grouped according to the y-coordinate value, and the standard deviation of the x and z-coordinate values ​​corresponding to each y-coordinate value is calculated to obtain the standard deviation change curve of the x plane and the z plane along the y-axis direction, and the curve graph that follows the change of the y-coordinate value is drawn by taking the geometric average of the x-plane standard deviation and the z-plane standard deviation; the point cloud data are grouped according to the z-coordinate value, and the standard deviation of the x and y-coordinate values ​​corresponding to each z-coordinate value is calculated to obtain the standard deviation change curve of the x plane and the y plane along the z-axis direction, and the curve graph that follows the change of the z-coordinate value is drawn by taking the geometric average of the x-plane standard deviation and the y-plane standard deviation.

[0038] In this embodiment, specifically, S3: determine the distribution of the repetitive structure by frequency domain analysis and draw a characteristic frequency domain change curve, the frequency domain analysis includes converting the spatial domain information into frequency domain information by fast Fourier transform, such as Figure 3 As shown, specifically including: S100: fixing the x coordinate value, and performing characteristic frequency domain processing on the y coordinate value and the z coordinate value corresponding to the x coordinate value through the fast Fourier transform; S200: drawing the characteristic frequency domains of the y plane and the z plane into characteristic frequency domain curves based on the x coordinate value respectively; S300: geometrically average the characteristic frequency domain curve of the y plane and the characteristic frequency domain curve of the z plane and draw a characteristic frequency domain variation curve graph as the x coordinate value changes.

[0039] In addition, the characteristic frequency domain change curve graph that follows the change of the y-coordinate value and the characteristic frequency domain change curve graph that changes with the z-coordinate value are similar to the above-mentioned S100~S300, specifically, the y-coordinate value is fixed, the x-coordinate and the z-coordinate corresponding to the y-coordinate value are subjected to characteristic frequency domain processing through the fast Fourier transform, the characteristic frequency domains of the x-plane and the z-plane are respectively plotted into characteristic frequency domain curves based on the y-coordinate value, the characteristic frequency domain curve of the x-plane and the characteristic frequency domain curve of the z-plane are geometrically averaged, and a characteristic frequency domain change curve graph that changes with the y-coordinate value is plotted; the z-coordinate value is fixed, the x-coordinate and the y-coordinate corresponding to the z-coordinate value are subjected to characteristic frequency domain processing through the fast Fourier transform, the characteristic frequency domains of the x-plane and the y-plane are respectively plotted into characteristic frequency domain curves based on the z-coordinate value, the characteristic frequency domain curve of the x-plane and the characteristic frequency domain curve of the y-plane are geometrically averaged, and a characteristic frequency domain change curve graph that changes with the z-coordinate value is plotted.

[0040] In this embodiment, the fixed x coordinate value performs characteristic frequency domain processing on the y coordinate value and the z coordinate value corresponding to the x coordinate value through the fast Fourier transform, including: The data of the 3D printed grid structure in the spatial domain are grouped according to the x-axis value. For a fixed x-coordinate value, the corresponding y and z-coordinate values ​​are extracted to form a two-dimensional data group. A complex array is obtained after transformation by the fft function in the Python numpy library. Each element in the complex array corresponds to a frequency component. The position number of the element in the complex array represents the frequency index. The relationship between the frequency index and the corresponding actual frequency value is: , where f is the actual frequency, k is the frequency index, N is the data length, and fs is the sampling frequency. Similarly, the y coordinate value is fixed, and the x coordinate and z coordinate corresponding to the y coordinate value are processed in the characteristic frequency domain through the fast Fourier transform, and the z coordinate value is fixed, and the x coordinate and y coordinate corresponding to the z coordinate value are processed in the characteristic frequency domain through the fast Fourier transform. Their processing methods are similar to the above-mentioned method of fixing the x coordinate value and processing the y coordinate value and z coordinate value corresponding to the x coordinate value through the fast Fourier transform, and will not be repeated.

[0041] In this embodiment, S4: combining the information of the spatial domain and the change curve of the characteristic frequency domain to determine the repeating unit, identifying and automatically extracting the repeating unit at the end of the cycle, wherein the end of the cycle is the peak and the trough, specifically, by setting a discrete data sequence to find the peak and the trough, the peak is the local maximum value in the discrete data sequence, and by and Determine the local maximum, where i is a data point in the discrete data sequence, is the local maximum value, and the peak is obtained by filtering out the noise points through the median filtering method. The trough is opposite to the peak, and the trough is the local minimum value in the discrete data sequence. and Determine the local minimum, where m is a data point in the discrete data sequence, is the local minimum value, and the trough is obtained by filtering out noise points through the median filtering method.

[0042] In this embodiment, S5: based on the fast point feature histogram feature, the feature matching algorithm is used to extract the features of the repeated unit and find the corresponding feature point pairs in the point cloud to obtain an initial transformation matrix, and the point cloud containing the repeated unit is aligned by the ICP algorithm. Specifically, the initial transformation matrix is ​​used for coarse alignment, and the second transformation matrix is ​​calculated according to the result after the coarse alignment. The second transformation matrix is ​​used as the input of the ICP algorithm and the ICP iteration is performed to align the point cloud containing the repeated unit. In this embodiment, the Chinese name of the ICP algorithm is the iterative closest point algorithm. Constructing the fast point feature histogram includes: weighting the simple point feature histogram of the neighborhood points of the selected point, and then combining the simple point feature histogram of the selected point with the weighted simple point feature histogram, using the formula:

[0043] Where: is the selected point, is the fast point feature histogram of the selected point, is the simple point feature histogram of the selected point, For the selected point and neighboring points The weight between them, k represents the number of neighborhood points of the selected point.

[0044] In this embodiment, specifically, S6: obtaining the measurement results by traversing and comparing the features of the repeating unit in the point cloud after the registration, including: traversing and comparing the points of the repeating unit with the points at the same position of the adjacent repeating unit to obtain the Euclidean distance and obtain the average value, the average value is used as the difference value of the points of the repeating unit, and the points of the repeating unit are replaced to repeat the operation.

[0045] The specific method of obtaining the average value is as follows:

[0046] Wherein, x is the point of the repeating unit, is the point cloud of the repeating unit numbered i, and q is points in the point cloud, and N is the total number of the repeating units.

[0047] In this embodiment, specifically, S7: analyzing the defect according to the measurement result and displaying the analysis result in the form of a heat map, including: mapping the indicator value of the defect to the color range of the heat map; marking the coordinate axis, scale and color legend on the heat map; adding interactive functions, the interactive functions including displaying the information of the defect when the mouse is hovered, and being able to zoom in or out the heat map.

[0048] Specifically, in this embodiment, the best processing implementation method is described in detail. The 3D printed mesh model is converted into RAW format data through CT scanning, and the 3D printed mesh structure part is accurately extracted using the voxel histogram. The voxel histogram of the 3D printed mesh model data can better understand the structure of the 3D printed mesh model by counting the number of voxels and the attribute distribution. Different voxel value distributions show different structures. For example, a part of the structure in the 3D printed mesh structure is stronger than other parts, which is reflected in the histogram as being greater than a certain voxel threshold. By selecting different voxel thresholds for three-dimensional model segmentation, voxel points representing the 3D printed mesh structure are screened out, and finally the incomplete structure around the 3D printed mesh model is removed to obtain the best 3D printed mesh model.

[0049] The 3D printing grid model is composed of a number of repetitive structures arranged periodically. This embodiment reveals its repetitive structure by statistically analyzing the standard deviation of each dimension of the three-dimensional point cloud model. The standard deviation describes the degree to which the data points deviate from the mean. The larger the value, the more dispersed the data. After fixing the coordinate axis values, the point cloud distribution is analyzed by calculating the standard deviation on each plane, such as the yz, xz, and xy planes. The repetitive structure is manifested as the concentration or regular distribution of data points in the region. The standard deviation calculation can accurately identify the periodicity of plane features.

[0050] In this embodiment, the spatial domain feature analysis includes the following steps: first, load the three-dimensional point cloud data containing x, y, and z coordinates, then group the three-dimensional point cloud data by x value, calculate the standard deviation of the y and z coordinates corresponding to each x value, and obtain the standard deviation change curve of each plane along the x-axis direction, and finally, calculate the geometric mean of the y and z standard deviations to comprehensively measure the variability of each plane (y and z dimensions), and draw a curve chart of its change with the x value. Similarly, the feature curves of the planes corresponding to the other two dimensions (y and z) can be obtained.

[0051] Frequency domain analysis is introduced to assist in determining the distribution of repetitive structures and accurately extract them. Fast Fourier transform (FFT) is an efficient algorithm for calculating discrete Fourier transform (DFT). The amplitude of the Fourier transform represents the intensity of each frequency present in the function. The frequency of a periodic function is recorded as a local maximum in the amplitude of its Fourier transform. In this embodiment, each x-axis value is first fixed, and the y and z coordinates are subjected to fast Fourier transform (FFT) analysis. After ignoring the zero-frequency component, the main frequency amplitude of each plane is plotted as a curve based on the x value to analyze the characteristic changes. Then, the frequency domain amplitude curves (y and z value curves) of the plane are geometrically averaged and visualized for analysis. Similarly, the characteristic frequency domain change curves of each dimension of the three-dimensional space are obtained for the y and z axes.

[0052] In this embodiment, because the 3D printed mesh material is composed of multiple repeating small structures and has obvious spatial and dimensional periodicity, the characteristic curve can be divided block by block. By combining the spatial domain and frequency domain characteristic curves, the minimum repeating unit can be determined and identified and extracted at the end of the period, usually at the valley peaks and valley points.

[0053] For finding peaks and troughs, it is assumed that there is a function or a discrete data sequence. The peak condition can be understood as a local maximum. In this scenario, the data is a number of discrete points, and the peak is the local maximum in the discrete data sequence. and Determine the local maximum, where i is a data point in the discrete data sequence, is the local maximum value, and the peak is obtained by filtering out the noise points through the median filtering method. The trough is opposite to the peak, and the trough is the local minimum value in the discrete data sequence. and Determine the local minimum, where m is a data point in the discrete data sequence, is the local minimum value, and the trough is obtained by filtering out noise points through the median filtering method.

[0054] The periodic curve part may not be completely consistent due to inconsistent point distribution and the influence of noise points, but the basic trend remains unchanged. The segmentation points appear in a periodic manner. Automatic extraction can be completed by setting a certain valley peak and valley point interval. The same method is used for the y-axis and z-axis dimensions to completely extract the repeating units.

[0055] In this embodiment, an improved ICP registration method based on fast point feature histogram (FPFH) is adopted. FPFH is a simplified improvement of point feature histogram (PFH). For each selected point, FPFH only calculates the relationship between itself and adjacent points, which are called simplified point feature histogram (SPFH). At the same time, the neighborhood points of each point are re-determined, and the final histogram (FPFH) is weighted using the adjacent SPFH. The calculation formula is as follows:

[0056] Where: is the selected point, is the simple point feature histogram of the selected point, For the selected point and neighboring points The weight between them, k represents the number of neighborhood points of the selected point. Based on the FPFH feature descriptor, the corresponding feature point pairs in the two point clouds are found by using the hybrid KD tree search parameters, and these points are used to generate the initial transformation matrix to complete the rough registration. Then, the second transformation matrix is ​​calculated according to the result after the rough registration, and the second transformation matrix is ​​used as the input of the ICP algorithm. The ICP iteration process is performed until the termination condition is met, thereby completing the high-precision registration.

[0057] In this embodiment, in order to reduce the influence of small structures with large errors, a method is adopted in which the average distance is calculated by sequentially calculating the difference between the currently measured repeating unit and other repeating units in the 3D printed mesh model.

[0058] Traverse and compare a point of a repeating unit with the points of the same position of other repeating units, calculate the Euclidean distance and take the average value as the difference value of the point. Repeat this operation to obtain the difference value of each position point of the repeating unit. For example, taking the calculation process of the repeating unit numbered 0 as an example, the final average distance calculation formula is: , where x is the point of the repeating unit, is the point cloud of the repeating unit numbered i, and q is Points in the point cloud, N is the total number of the repeating units, where each point belongs to the repeating unit point cloud numbered 0, is the point cloud of the repeating unit numbered i, and N is the total number of repeating structures in the 3D printed mesh model. Repeating this operation can complete the traversal calculation of the entire 3D printed mesh model. This traversal comparison method can quickly and accurately measure each repeating unit to provide a basis for subsequent difference visualization and defect analysis.

[0059] In this embodiment, point cloud processing technology is used to describe different features based on color information in the 3D printed mesh three-dimensional model to assist in the detection of defect differences in the 3D printed mesh three-dimensional model. The entire 3D printed mesh three-dimensional model is visualized in the form of a heat map, and point cloud segmentation is performed based on the color intensity threshold. As the threshold increases, the number of point clouds identified and retained gradually decreases. The higher the threshold, the farther the point distance is, the greater the difference is, and the shape of the defect will also be intuitively visualized.

[0060] The preprocessing method based on CT scanning combined with voxel histogram and threshold segmentation in the present invention can efficiently extract the three-dimensional geometric structure of the model. This method can quickly and automatically generate accurate three-dimensional point cloud data, greatly improving the efficiency of data processing. In addition, the multi-domain feature analysis method combines the dual features of the spatial domain and the frequency domain, especially characterizing the local changes in the spatial domain through the standard deviation, which can more sensitively capture tiny geometric changes. The frequency domain analysis of Fourier transform increases the recognition ability of periodic and repetitive structures, making the system more suitable for complex grid structures and can automatically extract and analyze the basic units of the model.

[0061] In addition, the present invention can automatically detect structural differences and defects by combining multi-domain features with registration algorithms. The improved application of the registration algorithm greatly improves the accuracy of difference detection, the traversal calculation improves the detection efficiency, and the visualization of the difference detection results allows users to intuitively observe the differences in the models, which helps to quickly locate and analyze potential printing defects.

[0062] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0063] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images, characterized in that: include: Converting the 3D printed mesh model into data in a raw data format by computer tomography, and extracting the 3D printed mesh structure by using a voxel histogram; Extracting geometric features of the 3D printed grid structure in the spatial domain by spatial domain feature analysis, wherein the spatial domain feature analysis includes obtaining a repetitive structure by counting the standard deviation of the dimension of the three-dimensional point cloud model of the 3D printed grid structure; Determine the distribution of the repetitive structure by frequency domain analysis and draw a change curve of the characteristic frequency domain, wherein the frequency domain analysis includes converting the information of the space domain into the information of the frequency domain by fast Fourier transform; Determine the repeating unit by combining the information in the spatial domain and the change curve in the characteristic frequency domain, and identify and automatically extract the repeating unit at the end of a cycle, where the end of the cycle is a wave peak and a wave trough; Based on the fast point feature histogram feature, the feature of the repeating unit is extracted by a feature matching algorithm and the corresponding feature point pairs in the point cloud are found to obtain an initial transformation matrix, the initial transformation matrix is ​​used for coarse registration, a second transformation matrix is ​​obtained by the result after the coarse registration, the second transformation matrix is ​​used as the input of the ICP algorithm and the ICP iteration is performed to register the point cloud containing the repeating unit; By traversing the features of the repeating unit in the point cloud after comparison and registration and obtaining a measurement result; The defects are analyzed based on the measurement results and the analysis results are displayed in the form of a heat map.

2. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: The extracting of the 3D printing grid structure by using a voxel histogram includes: segmenting the data of the 3D printing grid model by selecting a voxel threshold in the voxel histogram and screening out voxel points representing the 3D printing grid structure to obtain the 3D printing grid structure.

3. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: The repetitive structure is a concentrated or regularly distributed data point, and the standard deviation is used to identify the periodicity of the planar features of the 3D printed grid structure, wherein the statistical standard deviation of the dimension of the three-dimensional point cloud model of the 3D printed grid structure specifically includes: Get three-dimensional point cloud data including x-coordinate, y-coordinate and z-coordinate; The point cloud data is grouped according to the x-coordinate value, and the standard deviation of the y- and z-coordinate values ​​corresponding to each x-coordinate value is calculated to obtain a standard deviation variation curve of the y-plane and the z-plane along the x-axis direction; By taking the geometric mean of the y-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the x-coordinate value; By taking the geometric mean of the x-plane standard deviation and the z-plane standard deviation and plotting a curve graph that changes with the y-coordinate value; By taking the geometric mean of the x-plane standard deviation and the y-plane standard deviation and plotting it against the z-coordinate value.

4. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: The distribution of the repetitive structure is determined by frequency domain analysis and a characteristic frequency domain change curve is plotted. The frequency domain analysis includes converting the spatial domain information into frequency domain information by fast Fourier transform, specifically including: The x-coordinate value is fixed, and the y-coordinate value and the z-coordinate value corresponding to the x-coordinate value are processed in the characteristic frequency domain by the fast Fourier transform; The characteristic frequency domains of the y plane and the z plane are respectively plotted into characteristic frequency domain curves based on the x coordinate value; Performing geometric averaging on the characteristic frequency domain curve of the y plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the x coordinate value changes; Performing geometric averaging on the characteristic frequency domain curve of the x plane and the characteristic frequency domain curve of the z plane and drawing a characteristic frequency domain variation curve graph as the y coordinate value changes; The characteristic frequency domain curve of the x-plane and the characteristic frequency domain curve of the y-plane are geometrically averaged and a characteristic frequency domain variation curve graph varying with the z-coordinate value is plotted.

5. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 4, characterized in that: The fixed x-coordinate value, and performing characteristic frequency domain processing on the y-coordinate value and the z-coordinate value corresponding to the x-coordinate value through the fast Fourier transform, include: The data of the 3D printed grid structure in the spatial domain are grouped according to the x-axis value. For a fixed x-coordinate value, the corresponding y and z-coordinate values ​​are extracted to form a two-dimensional data group. A complex array is obtained after transformation by the fft function in the Python numpy library. Each element in the complex array corresponds to a frequency component. The position number of the element in the complex array represents the frequency index. The relationship between the frequency index and the corresponding actual frequency value is: , where f is the actual frequency, k is the frequency index, N is the data length, and fs is the sampling frequency.

6. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: The peak and the trough are determined by setting a discrete data sequence, wherein the peak is a local maximum value in the discrete data sequence. and Determine the local maximum, where i is a data point in the discrete data sequence, is the local maximum value, the peak is obtained by filtering out the noise points through the median filtering method, and the trough is the local minimum value in the discrete data sequence. and Determine the local minimum, where m is a data point in the discrete data sequence, is the local minimum value, and the trough is obtained by filtering out noise points using the median filtering method.

7. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: Constructing the fast point feature histogram includes: weighting the simple point feature histogram of the neighborhood points relative to the selected point, and then combining the simple point feature histogram of the selected point with the weighted simple point feature histogram of the neighborhood points, specifically using the formula: Where: is the selected point, is the fast point feature histogram of the selected point, is the simple point feature histogram of the selected point, is the simple point feature histogram of the neighborhood points, For the selected point and neighboring points The weight between them, k represents the number of neighborhood points relative to the selected point.

8. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: By traversing the features of the repeating unit in the point cloud after comparison and registration and obtaining the measurement result, it includes: The points of the repeating unit are traversed and compared with the points of the same position of the adjacent repeating unit to obtain the Euclidean distance and the average value, and the average value is used as the difference value of the points of the repeating unit. The points of the repeating unit are replaced and the operation is repeated.

9. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 8, characterized in that: The specific method of obtaining the average value is: Wherein, x is the point of the repeating unit, is the point cloud of the repeating unit numbered i, q is , N is the total number of repeating units.

10. The method for detecting multi-domain characteristic defects of 3D printed mesh structures based on CT images according to claim 1, characterized in that: Analyzing defects according to the measurement results and displaying the analysis results in the form of a heat map includes: Mapping the indicator value of the defect to the color range of the heat map; Annotating axes, scales and color legends on the heat map; Add interactive functions, including displaying the defect information by hovering the mouse, and being able to zoom in or out the heat map.

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