Method and system for detecting three-dimensional roughness of surface of fiber reinforced ceramic matrix composite material

Abstract

The application is a kind of fiber reinforced ceramic matrix composite surface three-dimensional roughness detection method and system, belongs to the technical field of material processing surface quality detection;Method is, first scanning fiber reinforced ceramic matrix composite processing surface to get the original processing surface three-dimensional model, and export point cloud data;Secondly, the coordinate transformation processing is carried out to the original three-dimensional model point cloud data, and the model is put right, the height difference of adjacent point cloud data is taken as the feature, the abnormal data points are screened and removed, the internal hole exposed on the processing surface is removed, the processing surface is reconstructed and the three-dimensional roughness S a Is calculated;Then determine the measurement area;Finally, according to the determined measurement area, the above scanning, hole removal, model reconstruction, S a The calculation work, namely the three-dimensional roughness of the processing surface is determined. The three-dimensional surface roughness value calculated by the application is no longer affected by the hole data, which ensures the effectiveness of the data.

Description

Technical Field

[0001] This invention belongs to the field of surface quality testing technology for material processing, specifically relating to a method and system for detecting the three-dimensional roughness of processed surfaces of fiber-reinforced ceramic matrix composites. Background Technology

[0002] Fiber-reinforced ceramic matrix composites are ceramic matrix composites composed of high-strength fibers and a ceramic matrix. They possess excellent properties such as high specific modulus, high specific strength, low coefficient of thermal expansion, high temperature resistance, corrosion resistance, and wear resistance, and have broad application prospects in aerospace and military fields. Many researchers have studied the preparation and processing of fiber-reinforced ceramic matrix composites. However, due to limitations in the preparation process, it is difficult to achieve high density in fiber-reinforced ceramic matrix composites. They typically contain 10% to 15% porosity, which becomes visible after secondary processing, affecting the measurement and evaluation of the processed surface quality.

[0003] Traditional two-dimensional roughness evaluation methods are no longer applicable to fiber-reinforced ceramic matrix composites. Researchers generally use three-dimensional roughness characteristic parameters to evaluate the surface quality of processed surfaces. Huang Qinglin et al. published a paper titled "SiC" in *Aerospace Materials and Processes*, 2022, 52(01):89-93. f The surface roughness (S) of the milled SiC composite material was analyzed using a KEYENCE VHX-7000 ultra-depth-of-field microscope under various milling parameters. a The values ​​were measured, and the influence of milling parameters on milling force and surface roughness was analyzed. However, the measurement was conducted in areas where holes were exposed on the machined surface, avoiding these areas. Measurements were only performed along the two fiber directions and away from the holes. The measurement area was 0.8 mm × 0.8 mm, with a side length smaller than that of the SiC used in the experiment. f / SiC composite material single fiber bundle width. Liu Cong et al. published "SiC f The experimental study on the evaluation method of milled surface quality of SiC composite materials compared and investigated the two-dimensional roughness R of milled surfaces. a and three-dimensional roughness S a The stability of the measurement data was assessed, and the S... a The measurement area size has an impact on the measurement results, but it includes the exposed pore area on the surface within the measurement range of three-dimensional surface roughness. This may result in the measured value reflecting the impact of the exposed pores on the surface quality to a greater extent, which is not applicable to fiber-reinforced composite materials with high porosity.

[0004] Existing studies measuring the three-dimensional roughness of processed surfaces of fiber-reinforced ceramic matrix composites (FRC) have not addressed the exposed macroscopic porosity of the material itself. The width of a single fiber bundle in FRC is approximately 1 mm, and the number, orientation, and matrix material of these fiber bundles all affect the measurement results of the three-dimensional surface roughness characteristics. To avoid the influence of material porosity on the measurement results, researchers often avoid pores and select smaller areas for measurement; some studies have even chosen measurement areas smaller than 1 mm × 1 mm. Such small measurement areas cannot characterize the overall quality level of the processed surface. Furthermore, as the measurement area increases, exposed pore areas are included within the measurement range, further impacting the results. Therefore, addressing the exposed macroscopic porosity of the processed surface is an urgent need and a prerequisite for the validity and accuracy of the three-dimensional roughness measurement data and evaluation results of FRC surface processed FRC. Summary of the Invention

[0005] The technical problem to be solved:

[0006] To avoid the influence of the inherent porosity of fiber-reinforced ceramic matrix composites on the measurement of three-dimensional surface roughness, and to accurately and effectively characterize and analyze the processed surface of fiber-reinforced ceramic matrix composites, this invention provides a method and system for detecting the three-dimensional roughness of processed surfaces of fiber-reinforced ceramic matrix composites. By determining a suitable measurement area, based on the height difference characteristics of the original processed surface topography point cloud data, abnormal data points are identified and removed, exposed material pore areas on the processed surface are removed, and the three-dimensional surface roughness S is calculated based on the reconstructed surface. a This invention solves the problem of the influence of macroscopic pores exposed on the surface of the processed surface on the measurement results of three-dimensional roughness of fiber-reinforced ceramic matrix composites.

[0007] The technical solution of this invention is: a method for detecting the three-dimensional roughness of the surface of fiber-reinforced ceramic matrix composite materials, the specific steps of which are as follows:

[0008] Step 1: Select the processing parameters for fiber-reinforced ceramic matrix composites and conduct processing tests;

[0009] Step 2: Obtain the original processed surface data, obtain the original 3D model of the processed surface, and export the point cloud data of the original 3D model;

[0010] Step 3: Based on the point cloud data from Step 2, restore and align the original 3D model of the processed surface;

[0011] Step 4: Remove the holes from the 3D model of the machined surface and reconstruct the model after removing the holes;

[0012] Step 5: Calculate the 3D surface roughness S of the model reconstructed in Step 4.a ;

[0013] Step 6: Determine the measurement area;

[0014] Step 7: Calculate and determine the three-dimensional roughness of the machined surface based on the measurement area determined in Step 6.

[0015] A further technical solution of the present invention is: in step 1, ultrasonic parameters and process parameters are selected as processing parameters, and ultrasonic vibration-assisted milling test is performed on fiber-reinforced ceramic matrix composite material.

[0016] A further technical solution of the present invention is as follows: In step 2, a scanning instrument is selected based on the surface characteristics of the fiber-reinforced ceramic matrix composite material to scan the processed surface, obtain the original three-dimensional model of the processed surface, and export the corresponding point cloud data; the scanning instrument is used to measure the three-dimensional roughness of the original processed surface and the three-dimensional surface roughness of the processed surface material itself, which is manually avoided from the pore area.

[0017] A further technical solution of the present invention is as follows: In step 3, the point cloud data is read, and the three-dimensional coordinates of all points are exported sequentially and stored in the coordinate matrix M. The storage order of the coordinate points is sorted in ascending order according to the Y-axis and X-axis coordinate values. The original three-dimensional model of the fiber-reinforced ceramic matrix composite material is restored by drawing a scatter plot. The coordinate matrix M of the restored three-dimensional model of the processed surface is rotated using the Rodriguez rotation formula to make the processed surface perpendicular to the z-axis, that is, the model is aligned, and the coordinate matrix N of the three-dimensional model of the processed surface after the rotational coordinate transformation is obtained.

[0018] A further technical solution of the present invention is as follows: In step 4, after the three-dimensional model of the fiber-reinforced ceramic matrix composite material processing surface is aligned, the z-coordinate of the point cloud data in the normal area without holes on the surface fluctuates up and down along the X and Y axes within a very small range, and the absolute value of the height difference between adjacent point clouds can also be constrained within a very small range [0, m]. In the area where there are holes on the surface, the z-coordinate of the point cloud data will fluctuate violently along the X and Y axes at the edge of the hole, and at this time the absolute value of the height difference between adjacent point clouds will exceed the constraint range. That is, with m as the threshold, the point cloud data at the edge of the hole is removed by the absolute value of the height difference between the point clouds. After the outer edge of the hole is removed, the point cloud data at the edge of the hole is removed by the absolute value of the height difference of the z coordinate of the adjacent point clouds, still with m as the threshold. After repeated calculation and removal of point cloud data, the hole removal of the three-dimensional model of the processing surface is completed, and the coordinate matrix C of the reconstructed three-dimensional model of the processing surface after hole removal is obtained. Using the coordinate matrix C as data, the three-dimensional model of the fiber-reinforced ceramic matrix composite material processing surface is reconstructed by drawing a scatter plot.

[0019] A further technical solution of the present invention is: the method for removing holes in the three-dimensional model of the processed surface is as follows:

[0020] Subtracting the previous row from all rows of matrix N yields a new matrix N'. The absolute value of the third column of N' is the absolute value of the height difference of the point cloud data along the Y-axis. At this point, using m as the threshold, the hole contours of the point cloud data along the Y-axis are removed, and a new 3D model coordinate matrix N1 of the processed surface is formed. Subtracting the previous row from all rows of matrix N1 yields a new matrix N1', and using m as the threshold, the second layer contour of the holes is removed. Through repeated calculations, the holes along the Y-axis are removed, and a new 3D model coordinate matrix B of the processed surface is obtained.

[0021] The B matrix is ​​rearranged into a V matrix, where the coordinate points are stored in ascending order of X-axis and Y-axis coordinate values. Subtracting the previous row from each row of the V matrix yields a new matrix V'. The absolute value of the third column of V' represents the absolute value of the height difference of the point cloud data along the X-axis. Using m as a threshold, the hole contours along the X-axis of the point cloud data are removed, simultaneously forming a new 3D model coordinate matrix V1 for the processed surface. Subtracting the previous row from each row of the V1 matrix yields a new matrix V1', which uses m as a threshold to remove the second layer of hole contours. This process is repeated to remove holes along the X-axis, resulting in the reconstructed 3D model coordinate matrix C for the processed surface.

[0022] A further technical solution of the present invention is: in step 5, according to the three-dimensional surface roughness calculation formula provided by the international standard, the coordinate matrix C of the reconstructed three-dimensional model of the machined surface after removing the holes is substituted to calculate the three-dimensional surface roughness S. a .

[0023] A further technical solution of the present invention is: in step 6, based on steps 3, 4, and 5, measurement areas of different sizes are selected, and the three-dimensional surface roughness S of the reconstructed processing surface in each area is measured. a Taking into account both measurement efficiency and data stability, a suitable S is selected. a Size of the measurement area.

[0024] A further technical solution of the present invention is: in step 7, the three-dimensional surface roughness S determined in step 6 is... a The measurement area is scanned using a 3D model of multiple machined surfaces of the same size on the sample to be measured. Steps 3, 4, and 5 are repeated to calculate S. a The average value was used as the three-dimensional roughness value of the processed surface of the fiber-reinforced ceramic matrix composite.

[0025] A three-dimensional roughness detection system for the processed surface of fiber-reinforced ceramic matrix composites includes surface processing equipment, scanning instruments, and a control center;

[0026] Processing tests were conducted on fiber-reinforced ceramic matrix composites using the aforementioned surface processing equipment;

[0027] The processing surface is scanned using the scanning instrument to obtain a three-dimensional model of the original processing surface;

[0028] The control center includes a processor, a memory, and an application program. The application program is stored in the memory and configured to be executed by the processor. The application program is configured to perform the three-dimensional roughness detection method for the processed surface of the fiber-reinforced ceramic matrix composite material.

[0029] Beneficial effects

[0030] The beneficial effects of this invention are as follows:

[0031] 1. This invention identifies and removes abnormal data points based on the height difference features of the original surface topography point cloud data. It can effectively identify and remove exposed hole areas on the surface without affecting the machined surface area, solving the problem of limited measurement area caused by the presence of surface holes. As a result, the calculated three-dimensional surface roughness value is no longer affected by hole data and is a true reflection of the influence of experimental parameters on the surface topography, ensuring the validity of the data and providing better guidance for processing work.

[0032] 2. This invention explores the effect of measurement area size on the reconstructed three-dimensional roughness S of the machined surface. a The influence pattern, select and determine S a The measurement area can obtain stable, reliable and unique measurement results while maintaining measurement efficiency, thus enabling accurate evaluation of the three-dimensional roughness of the processed surface of fiber-reinforced ceramic matrix composites. Attached Figure Description

[0033] Figure 1 It is a comparison chart of the original three-dimensional roughness of the machined surface measured by a scanning instrument and the three-dimensional surface roughness of the machined surface material itself, avoiding the pore areas of the machined surface material itself.

[0034] Figure 2 It is SiC f Comparison of 3D models of the original and reconstructed surfaces of SiC ceramic matrix composite materials;

[0035] Figure 3 This is a comparison chart of the three-dimensional surface roughness measured by a scanning instrument, which manually avoids the pore areas of the material itself on the machined surface, and the reconstructed three-dimensional surface roughness calculated by MATLAB R2019a.

[0036] Figure 4 Different measurement area sizes affect SiC fThe influence of SiC ceramic matrix composites on the three-dimensional roughness of reconstructed surfaces. Detailed Implementation

[0037] The following is in conjunction with the appendix Figures 1-4 The specific implementation methods described in the examples will be used to illustrate the content of the present invention in detail. However, this should not be construed as limiting the scope of the present invention to the following examples. Various substitutions or modifications made based on common knowledge and practices in the art without departing from the technical concept of the present invention should be included within the scope of the present invention.

[0038] The method for detecting and evaluating the three-dimensional roughness of machined surfaces proposed in this embodiment is applicable to fiber-reinforced ceramic matrix composites. In this embodiment, only SiC is used. f The process for processing SiC ceramic matrix composites involves several steps. First, an optical scanning instrument is used to scan the surface of the fiber-reinforced ceramic matrix composite to obtain a 3D model of the original surface, and the point cloud data of the original 3D model is exported. Second, coordinate transformation is performed on the point cloud data of the original 3D model to align the model. Using the height difference between adjacent point cloud data as a feature, outlier data points are filtered and removed. Exposed internal holes on the surface are removed, the surface is reconstructed, and the 3D roughness S is calculated. a Then, the effect of different measurement area sizes on the reconstructed three-dimensional roughness S of the machined surface was studied. a The influence of the data is analyzed to determine the appropriate measurement area size; finally, the scanning, hole removal, model reconstruction, and S-curve reconstruction are repeated based on the appropriate measurement area. a The calculation process can determine the three-dimensional roughness of the machined surface of fiber-reinforced ceramic matrix composites. The three-dimensional roughness detection and evaluation method provided by this invention can effectively identify and remove exposed hole areas on the surface without affecting the machined surface area. Therefore, the calculated three-dimensional surface roughness value is no longer affected by the hole data, ensuring the validity of the data.

[0039] Method Implementation Examples:

[0040] A method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites, comprising the following steps:

[0041] Step 1: Machining Parameter Selection and Testing: Four sets of machining tests were conducted. The selected machining and ultrasonic parameters are shown in Table 1. A BMC-630V five-axis CNC milling machine and an HSK63A-20-9210014 ultrasonic tool holder provided by Xi'an Chaoken Ultrasonic Technology Research Institute Co., Ltd. were used for machining SiC. f Ultrasonic vibration-assisted milling test was conducted on SiC ceramic matrix composite material;

[0042] Table 1 Processing test parameters

[0043]

[0044] Step 2: Acquisition of raw machined surface data: SiC f The surface of SiC ceramic matrix composites contains pores of varying sizes, making contact scanning instruments unsuitable. Since optical instruments do not directly contact the material surface, the Infinite Focus G4 Alicona optical 3D scanner was used to scan the surface, obtaining a 3D model of the original surface and exporting the corresponding point cloud data. The scanner was then used to measure the 3D roughness of the original surface and the 3D surface roughness after manually avoiding the porous areas of the material itself. Figure 1 As shown, it can be seen that the inherent pores exposed on the material's processed surface will affect S. a The measured values ​​have a significant impact. The measurement results of the original machined surface largely reflect the influence of exposed macroscopic pores on the machined surface. The influence of exposed pores must be removed to ensure the accuracy of the measured S... a Only when the value is reasonable and meaningful;

[0045] Step 3: Restoration and Alignment of the Original Machining Surface 3D Model: Using MATLAB R2019a, the point cloud data is read, and the 3D coordinates of all points are exported sequentially and stored in the coordinate matrix M. The coordinate points are stored in ascending order of Y-axis and X-axis coordinate values. The SiC model is then restored by plotting a scatter plot. f A three-dimensional model of the original processed surface of the SiC ceramic matrix composite material. This model was created using the Rodrigues rotation formula.

[0046] A rotational coordinate transformation is performed on the coordinate matrix M of the 3D model of the machined surface so that the machined surface is perpendicular to the z-axis. At this time, the point cloud data is displayed in different colors according to the size of the z-coordinate, which can clearly distinguish the holes on the machined surface. At the same time, the coordinate matrix N of the 3D model of the machined surface after the rotational coordinate transformation is obtained.

[0047] Step 4: Hole Removal and Model Reconstruction: SiC fAfter alignment, the z-coordinate of the point cloud data in the normal area (without holes) of the SiC ceramic matrix composite material processing surface fluctuates within a small range along the X and Y axes, and the absolute value of the height difference between adjacent point clouds is also constrained within a small range [0, 50]. However, when holes exist on the surface, the z-coordinate of the point cloud data fluctuates drastically along the X and Y axes at the edge of the hole. At this time, the absolute value of the height difference between adjacent point clouds exceeds the constraint range. That is, using an m value of 50 as a threshold, the point cloud data at the edge of the hole can be removed by using the absolute value of the height difference between the point clouds. After the outer edge of the hole is removed, the point cloud data at the second outermost edge of the hole is removed by using an m value of 50 as a threshold and the absolute value of the height difference of the z-coordinate of adjacent point clouds. By repeating this calculation and removal of point cloud data, the hole removal of the processing surface 3D model can be achieved. At the same time, the coordinate matrix C of the reconstructed processing surface 3D model after hole removal is obtained. Using the coordinate matrix C as data, the SiC is reconstructed by drawing a scatter plot. f Three-dimensional model of the processed surface of SiC ceramic matrix composite material;

[0048] The specific calculation process is as follows: Subtract the previous row from all rows of matrix N to obtain a new matrix N'. The absolute value of the third column of N' is the absolute value of the height difference of the point cloud data along the Y-axis. At this time, with m as the threshold, the hole contour of the point cloud data along the Y-axis can be removed, and a new three-dimensional model coordinate matrix N1 of the processed surface is formed. Subtract the previous row from all rows of matrix N1 to obtain a new matrix N1'. With m as the threshold, the second layer contour of the hole is removed. ... By repeating the calculation, the hole is removed along the Y-axis, and a new three-dimensional model coordinate matrix B of the processed surface is obtained.

[0049] The B matrix is ​​rearranged into a V matrix, where the coordinate points are stored in ascending order of X-axis and Y-axis coordinate values. Subtracting the previous row from each row of the V matrix yields a new matrix V'. The absolute value of the third column of V' represents the absolute value of the height difference of the point cloud data along the X-axis. Using a threshold of m=50, the hole contours along the X-axis of the point cloud data can be removed, simultaneously forming a new 3D model coordinate matrix V1 for the processed surface. Subtracting the previous row from each row of the V1 matrix yields a new matrix V1'. Using a threshold of m=50, the second layer of hole contours is removed… This process is repeated to remove holes along the X-axis, resulting in the reconstructed 3D model coordinate matrix C for the processed surface.

[0050] Reference Figure 2 As shown, it is SiC f A comparison of the three-dimensional models of the original and reconstructed surfaces of the SiC ceramic matrix composite material clearly shows that the macroscopic pores on the surface of the test piece were effectively identified and removed, and the morphology of the reconstructed machined surface did not affect the pore-free areas.

[0051] Step 5: Calculate the three-dimensional surface roughness S a According to the three-dimensional surface roughness calculation formula provided by the international standard ISO 25178. Input the coordinate matrix C of the reconstructed 3D model of the machined surface after removing the holes, and calculate the 3D surface roughness S. a ;

[0052] Reference Figure 3 The image shows a comparison between the 3D surface roughness measured by a scanning instrument (manually avoiding the porous areas of the material itself) and the reconstructed 3D surface roughness calculated by MATLAB R2019a. The S values ​​under the two measurement methods are shown in the image. a The maximum relative deviation of the numerical value was only 2.7343%, which proves the accuracy of the proposed three-dimensional surface roughness measurement method based on reconstructed surface morphology.

[0053] Step 6: Determine the measurement area: Select 10 measurement areas within the range of 0.5mm×0.5mm to 17mm×17mm, and scan three corresponding SiC areas on the machined surface from top to bottom. f The surface model of the SiC ceramic matrix composite material (due to the displacement limitation of the Alicona optical 3D scanner stage, the 0.5mm×0.5mm measurement area uses a 50x microscope scanning model, the 1mm×1mm measurement area uses a 20x microscope scanning model, and the remaining measurement areas use a 5x microscope scanning model) is calculated through steps 3, 4, and 5, and the average of the three calculation results is taken as the 3D surface roughness S of the measurement area. a Draw a line chart. For example... Figure 4 As shown, the effects of different measurement area sizes on the three-dimensional surface roughness S are compared and analyzed. a The influence of the measurement area was investigated, and it was found that the three-dimensional surface roughness S increases as the measurement area gradually increases. a The overall trend also shows an increasing trend. When the measurement area is greater than or equal to 11mm × 11mm, the three-dimensional surface roughness value is relatively stable. Furthermore, as the measurement area increases, the scanning model takes longer (as shown in Table 2). Considering both the scanning time and the stability of the calculated data, 11mm × 11mm was chosen as the measurement area for SiC. f / Three-dimensional roughness S of SiC ceramic matrix composite surface a The measurement area.

[0054] Table 2 SiC f / Scanning time of different measurement areas of SiC

[0055]

[0056]

[0057] Step 7: Determine the three-dimensional roughness of the machined surface: Since the three-dimensional roughness value of the material in the 11mm×11mm area has already been calculated in Step 6, it does not need to be recalculated. The average of the three calculation results is directly used as the SiC value. f The three-dimensional roughness value of the processed surface of the SiC ceramic matrix composite material was measured. When subsequently measuring the three-dimensional roughness value of other processed surfaces of the same material, it is only necessary to scan multiple three-dimensional models of the processed surfaces of the same size on the sample under test, based on the determined measurement area of ​​11mm × 11mm, and repeat steps 3, 4, and 5 to calculate S. a The average value can be used as the SiC to be tested. f Three-dimensional roughness values ​​of the processed surface of / SiC ceramic matrix composite material.

[0058] In summary, the three-dimensional roughness detection and evaluation method provided by this invention can effectively identify and remove exposed hole areas on the surface without affecting the machined surface area. Therefore, the calculated three-dimensional surface roughness value is no longer affected by the hole data, ensuring the validity of the data. Furthermore, the study investigates the effect of different measurement area sizes on the three-dimensional surface roughness S. a The influence of the scanning time and the stability of the calculated data were considered when selecting SiC. f / Three-dimensional roughness S of SiC ceramic matrix composite surface a The measurement area was 11mm × 11mm. This determined the SiC... f The method for detecting and evaluating the three-dimensional surface roughness of SiC ceramic matrix composites is as follows: Within a measurement area of ​​11mm × 11mm, based on the height difference characteristics of the original surface topography point cloud data, abnormal data points are identified and removed, exposed material pore areas on the processed surface are removed, and the three-dimensional surface roughness S is calculated based on the reconstructed surface. a Therefore, the three-dimensional roughness of the four machined surfaces are 7.0435μm, 4.3847μm, 2.9248μm, and 2.5927μm, respectively.

[0059] System Implementation Example:

[0060] A three-dimensional roughness detection system for the processed surface of fiber-reinforced ceramic matrix composites includes surface processing equipment, scanning instruments, and a control center;

[0061] The surface processing equipment was used to perform processing tests on fiber-reinforced ceramic matrix composites. The equipment used was a BMC-630V five-axis CNC milling machine and an HSK63A-20-9210014 ultrasonic tool holder provided by Xi'an Chaoken Ultrasonic Technology Research Institute Co., Ltd., for processing SiC... f Ultrasonic vibration-assisted milling test was conducted on SiC ceramic matrix composite material;

[0062] The processing surface is scanned by the scanning instrument to obtain the original three-dimensional model of the processing surface; the scanning instrument is the Infinite Focus G4Alicona optical three-dimensional scanner to scan the processing surface.

[0063] The control center includes a processor, memory, and an application program. The application program is stored in the memory and configured to be executed by the processor. The application program is configured to execute the three-dimensional roughness detection method for the processed surface of the fiber-reinforced ceramic matrix composite material. Specifically, it uses MATLAB R2019a software to read point cloud data, restore and align the original three-dimensional model of the processed surface, remove holes and reconstruct the model, and calculate the three-dimensional surface roughness S. a .

[0064] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites, characterized in that... The specific steps are as follows: Step 1: Select the processing parameters for fiber-reinforced ceramic matrix composites and conduct processing tests; Step 2: Obtain the original processed surface data, obtain the original 3D model of the processed surface, and export the point cloud data of the original 3D model; Step 3: Based on the point cloud data from Step 2, restore and align the original 3D model of the processed surface; Step 4: Remove the holes from the 3D model of the machined surface and reconstruct the model after removing the holes; After the 3D model of the fiber-reinforced ceramic matrix composite surface is aligned, the z-coordinate of the point cloud data in the normal area without holes fluctuates within a very small range along the X and Y axes, and the absolute value of the height difference between adjacent point clouds can also be constrained within a very small range [0, m]. In the area with holes, the z-coordinate of the point cloud data fluctuates drastically along the X and Y axes at the edge of the hole, and the absolute value of the height difference between adjacent point clouds will exceed the constraint range. That is, with m as the threshold, the point cloud data at the edge of the hole is removed by the absolute value of the height difference between the point clouds. After the outer edge of the hole is removed, the point cloud data at the second outermost edge of the hole is removed by the absolute value of the height difference of the z-coordinate of adjacent point clouds, still with m as the threshold. After repeated calculations and point cloud data removal, the hole removal of the 3D model of the machined surface is completed, and the coordinate matrix of the reconstructed 3D model of the machined surface after hole removal is obtained. C , in coordinate matrix C Using the data, a three-dimensional model of the processed surface of fiber-reinforced ceramic matrix composites was reconstructed by plotting scatter plots; Step 5: Calculate the 3D surface roughness of the model reconstructed in Step 4. S a ; Step 6: Determine the measurement area; Step 7: Calculate and determine the three-dimensional roughness of the machined surface based on the measurement area determined in Step 6.

2. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 1, characterized in that: In step 1, ultrasonic parameters and process parameters are selected as processing parameters to conduct ultrasonic vibration-assisted milling tests on fiber-reinforced ceramic matrix composites.

3. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 1, characterized in that: In step 2, a scanning instrument is selected based on the surface characteristics of the fiber-reinforced ceramic matrix composite material to scan the processed surface, obtain the original three-dimensional model of the processed surface, and export the corresponding point cloud data; the scanning instrument is used to measure the three-dimensional roughness of the original processed surface and the three-dimensional surface roughness of the processed surface material itself, which is manually avoided in the pore area.

4. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 3, characterized in that: In step 3, the point cloud data is read, the three-dimensional coordinates of all points are exported sequentially, and stored in a coordinate matrix. M In the diagram, the coordinate points are stored in ascending order of Y-axis and X-axis coordinate values. A scatter plot is used to reconstruct the original 3D model of the fiber-reinforced ceramic matrix composite material's surface. The coordinate matrix of the reconstructed 3D model is then calculated using the Rodriguez rotation formula. M Perform a rotational coordinate transformation to make the machined surface perpendicular to the z-axis, i.e., align the model, and obtain the 3D model coordinate matrix of the machined surface after the rotational coordinate transformation. N .

5. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 1, characterized in that: The method for removing holes from the three-dimensional model of the machined surface is as follows: Will N Subtracting the previous row from each row of a matrix results in a new matrix. N’ , N’ The absolute value in the third column represents the absolute value of the height difference of the point cloud data along the Y-axis. Using m as the threshold, the hole contours in the point cloud data along the Y-axis are removed, simultaneously forming a new coordinate matrix for the 3D model of the processed surface. N 1; will N Subtracting the previous row from each row of a matrix results in a new matrix. N 1 ’ The second layer contour of the hole is removed using m as a threshold. Through repeated calculations, the holes along the Y-axis were removed, and the coordinate matrix of the three-dimensional model of the newly machined surface was obtained. B ; Will B The matrix is ​​rearranged so that the coordinate points are stored in ascending order according to their X-axis and Y-axis coordinate values. V Matrix, will V Subtracting the previous row from each row of a matrix results in a new matrix. V’ , V’ The absolute value in the third column represents the absolute value of the height difference of the point cloud data along the X-axis. Using m as the threshold, the hole contours in the point cloud data along the X-axis are removed, simultaneously forming a new coordinate matrix for the 3D model of the processed surface. V 1; will V Subtracting the previous row from each row of a matrix results in a new matrix. V 1 ’ The second layer contour of the hole is removed using m as a threshold. Through repeated calculations, the holes along the X-axis were removed, and the coordinate matrix of the reconstructed 3D model of the machined surface was obtained. C .

6. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 1, characterized in that: In step 5, the coordinate matrix of the reconstructed three-dimensional model of the machined surface after removing holes is substituted into the three-dimensional surface roughness calculation formula provided by the international standard. C Calculate three-dimensional surface roughness S a .

7. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 6, characterized in that: In step 6, based on steps 3, 4, and 5, measurement areas of different sizes are selected, and the three-dimensional surface roughness of the reconstructed processing surface in each area is measured. S a Taking into account both measurement efficiency and data stability, a suitable method is selected. S a Size of the measurement area.

8. The method for detecting the three-dimensional surface roughness of fiber-reinforced ceramic matrix composites according to claim 7, characterized in that: In step 7, the three-dimensional surface roughness is determined according to step 6. S a The measurement area is scanned using a 3D model of multiple machined surfaces of the same size on the sample to be measured. Steps 3, 4, and 5 are repeated to obtain the calculation results. S a The average value was used as the three-dimensional roughness value of the processed surface of the fiber-reinforced ceramic matrix composite.

9. A three-dimensional roughness detection system for the processed surface of fiber-reinforced ceramic matrix composites, characterized in that: This includes surface processing equipment, scanning instruments, and a control center; Processing tests were conducted on fiber-reinforced ceramic matrix composites using the aforementioned surface processing equipment; The processing surface is scanned using the scanning instrument to obtain a three-dimensional model of the original processing surface; The control center includes a processor, a memory, and an application program. The application program is stored in the memory and configured to be executed by the processor. The application program is configured to perform the three-dimensional roughness detection method for the processed surface of the fiber-reinforced ceramic matrix composite material according to any one of claims 1-8.

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