Ceramic matrix composite machining surface defect identification and process parameter optimization method

Abstract

This invention discloses a method for identifying surface defects and optimizing process parameters in ceramic matrix composites, comprising: scanning a ceramic matrix composite sample to obtain the three-dimensional morphological features of the initial surface and initial pores; performing ultrasonic-assisted grinding; scanning the processed ceramic matrix composite block to export STL sheet data and height contour maps of the processed surface; differentiating, removing, and patching the initial pores; calculating the true surface roughness; identifying and establishing a mathematical correlation model between the true surface roughness and processing damage characteristics; and optimizing the process parameters. This application establishes a mathematical model between the true surface roughness and various processing damage characteristics, obtains the correlation between various processing damages and true roughness, and proposes a new method for optimizing processing parameters that simultaneously considers roughness and processing damage, thereby improving the accuracy of surface defect identification and the scientific rigor of process parameter optimization in ceramic matrix composites.

Description

Technical Field

[0001] This application relates to the field of composite material processing technology, and in particular to a method for identifying surface defects and optimizing process parameters in ceramic matrix composite material processing. Background Technology

[0002] Ceramic matrix composites (CMCs), due to their high specific strength, high specific modulus, excellent high-temperature resistance, and corrosion resistance, have become key core materials in high-end equipment fields such as aerospace and defense, and are widely used in engine hot-end components and spacecraft structural parts. Three-dimensional needle-punched preforms, as the basic blanks for CMC preparation, directly determine the densification effect of subsequent chemical vapor deposition (CVI) and precursor impregnation pyrolysis (PIP) processes based on their surface roughness and porosity, ultimately affecting the mechanical properties, sealing performance, and service life of CMC components. Therefore, accurate measurement of the surface roughness of CMCs is an important prerequisite for ensuring the quality of composite component preparation and achieving controllable performance of high-end equipment, and is of great significance for promoting the engineering application of CMCs.

[0003] In the prior art, Chinese patent CN119670469A discloses a method for analyzing the drilling quality and material removal mechanism of micro-holes in ceramic matrix composites, including: using drilling simulation and experiments, for micro-holes in ceramic matrix composites with diameters of Φ0.5~2mm, determining the damage model and performance parameters of carbon fiber, the damage model and performance parameters of SiC matrix, and the damage model and material properties of the interface layer; Chinese patent CN116772754A discloses a method and system for detecting the three-dimensional roughness of fiber-reinforced ceramic matrix composite surfaces, including: using an optical scanning instrument to measure the three-dimensional surface roughness, using the height difference of adjacent point cloud data as a feature to screen and remove abnormal data points, removing the internal holes exposed on the processed surface, reconstructing the processed surface and calculating the three-dimensional roughness Sa.

[0004] The pores on the surface of CMCs are characterized by varying sizes, random distribution, and irregular shapes. Automatic point cloud filtering methods can only remove relatively obvious noise points and cannot effectively distinguish between pores and the real surface. This can easily lead to the loss of real surface contour information or the retention of pore areas, or even the mistaken removal of surface damage information as pores, greatly reducing the reliability of processing quality evaluation. However, the aforementioned existing technologies have not studied or solved these problems, and the accuracy of surface defect identification and the scientific nature of process parameter optimization for ceramic matrix composites need to be improved. Summary of the Invention

[0005] This application provides a method for identifying surface defects and optimizing process parameters in the processing of ceramic matrix composites, in order to address the problem that the accuracy of surface defect identification and the scientific nature of process parameter optimization in the prior art need to be improved.

[0006] On the one hand, this application provides a method for identifying surface defects and optimizing process parameters in ceramic matrix composites, including the following steps:

[0007] Step 1: Use a laser confocal microscope to scan the ceramic matrix composite sample to obtain the three-dimensional morphological features of the initial surface and initial pores.

[0008] Step 2: Perform uncooled ultrasonic-assisted grinding on the ceramic matrix composite sample block to obtain a ceramic matrix composite processed block.

[0009] Step 3: Use a laser confocal microscope to scan the ceramic matrix composite material processing block and export the STL sheet data and height contour map of the processing surface.

[0010] Step 4: Based on the height cloud map and the initial pore 3D morphology features, the initial pores of the processed surface STL sheet data are distinguished, eliminated, and patched to make the processed surface STL sheet data converge and be exported as calculable .xyz 3D point cloud data.

[0011] Step 5: Use a three-dimensional roughness measurement system to calculate the true roughness of the machined surface based on the .xyz three-dimensional point cloud data.

[0012] Step 6: After reducing the scanning range using a laser confocal microscope, observe the ceramic matrix composite material processing block, identify processing damage characteristics, and establish a mathematical correlation model between the actual surface roughness of the processed surface and the processing damage characteristics.

[0013] Step 7: Based on the mathematical correlation model, optimize the process parameters of ultrasonic-assisted grinding with the goal of minimizing the actual surface roughness and the processing damage characteristics.

[0014] In one possible implementation, in step two, the ultrasonic-assisted grinding process uses an electroplated diamond grinding tool and employs a face grinding process.

[0015] The carbon fiber winding direction is consistent on the processed surface of the ceramic matrix composite sample.

[0016] In one possible implementation, in step two, the process parameters of the ultrasonic-assisted grinding process include: grinding speed, grinding feed rate, grinding depth, ultrasonic amplitude, and ultrasonic frequency.

[0017] In one possible implementation, in step three, the height cloud map distinguishes surface features through height abrupt change boundaries, identifies the size and location of initial pores, and provides data support for the identification, removal, and repair of initial pores in step four.

[0018] In one possible implementation, step four, the mending includes:

[0019] Use a point at zero height from the reference plane to stitch up the missing part at the location where the initial pores were removed.

[0020] In one possible implementation, in step six, the processing damage features include: processing holes, fiber pull-out, and fiber debonding.

[0021] In one possible implementation, step six proposes quantitative standards for the initial pore area ratio, processing damage area, and depth to quantify the characteristics of initial pores and processing damage.

[0022] The quantification standard for the initial pore area ratio includes: quantifying the initial pores by the ratio of the number of pixels projected onto the processing surface normal to the total number of scanned pixels.

[0023] The quantification criteria for the area and depth of processing damage include: quantifying processing damage characteristics by the percentage of the damage distribution area affecting height characteristics to the total area.

[0024] In one possible implementation, in step seven, the optimization process of the process parameters adopts the controlled variable method to obtain the optimal process parameters for each process parameter of ultrasonic assisted grinding.

[0025] The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials presented in this application has the following advantages:

[0026] By establishing a mathematical model of the true surface roughness and various processing damage characteristics, the correlation between various processing damages and true surface roughness is obtained. A new method for optimizing processing parameters that simultaneously considers roughness and processing damage is proposed, improving the accuracy of defect identification and the scientific rigor of process parameter optimization for ceramic matrix composite materials. Specifically, when calculating the true surface roughness, the method avoids significantly reducing the measurement area to avoid pore interference, thus maximizing the accuracy of the measurement results and reflecting the actual quality of the processed surface.

[0027] By differentiating, removing, and patching the initial pores in the STL sheet data of the processed surface based on height cloud maps and initial pore 3D morphology features, a judgment comparison between the initial surface and the processed surface was established, ensuring the effectiveness and accuracy of pore removal and avoiding excessive removal of surface processing damage features.

[0028] By using a laser confocal microscope, compared to probe-based roughness measurement, the visualization is higher and the scanning area is larger. At the same time, it avoids the surface scratch problems that may be caused by probe-based roughness measurement, and is suitable for undensified ceramic matrix composites.

[0029] By proposing quantitative standards for the proportion of initial pore area, processing damage area and depth, the characteristics of initial pores and processing damage are quantified, providing reliable support for establishing a mathematical correlation model between the true roughness of the processed surface and the characteristics of processing damage, thereby improving the reliability of process parameter optimization. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to an embodiment of this application.

[0032] Figure 2 A schematic diagram of the three-dimensional morphological features of the initial surface and initial pores provided in the embodiments of this application;

[0033] Figure 3 A schematic diagram of the STL sheet data and height contour map of the processed surface provided in the embodiments of this application;

[0034] Figure 4 A schematic diagram of the STL sheet data of the processed surface before and after patching provided in the embodiments of this application;

[0035] Figure 5 A schematic diagram illustrating the method for calculating the area ratio of initial pores provided in an embodiment of this application;

[0036] Figure 6 This is a schematic diagram illustrating the identification and quantification of different processing damage characteristics provided in the embodiments of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] like Figure 1 As shown in the embodiments of this application, a method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials is provided, including the following steps:

[0039] Step 1: Use a laser confocal microscope to scan the ceramic matrix composite sample to obtain the three-dimensional morphological features of the initial surface and initial pores.

[0040] Step 2: Perform uncooled ultrasonic-assisted grinding on the ceramic matrix composite sample block to obtain a ceramic matrix composite processed block.

[0041] Step 3: Use a laser confocal microscope to scan the ceramic matrix composite material processing block and export the STL sheet data and height contour map of the processing surface.

[0042] Step 4: Based on the height cloud map and the initial pore 3D morphology features, the initial pores of the processed surface STL sheet data are distinguished, eliminated, and patched to make the processed surface STL sheet data converge and be exported as calculable .xyz 3D point cloud data.

[0043] Step 5: Use a three-dimensional roughness measurement system to calculate the true roughness of the machined surface based on the .xyz three-dimensional point cloud data.

[0044] Step 6: After reducing the scanning range using a laser confocal microscope, observe the ceramic matrix composite material processing block, identify processing damage characteristics, and establish a mathematical correlation model between the actual surface roughness of the processed surface and the processing damage characteristics.

[0045] Step 7: Based on the mathematical correlation model, optimize the process parameters of ultrasonic-assisted grinding with the goal of minimizing the actual surface roughness and the processing damage characteristics.

[0046] Specifically, initial porosity refers to the gaps generated during the needle-punching preparation process of ceramic matrix composite samples.

[0047] The laser confocal microscope is model Alicona InfiniteFocus G5.

[0048] In this embodiment, in step one, the ceramic matrix composite sample has dimensions of 30×30×30mm and a porosity of 21.4%. Figure 2 The diagram shows the three-dimensional morphological features of the initial surface and initial pores obtained in step one. Among them, Figure 2 (a) in the figure represents the initial surface STL 3D sheet model. Figure 2 (b) in the image is the initial surface height contour map. Figure 2 (c) in the figure represents the three-dimensional point cloud topography data of the pores.

[0049] For example, in step two, the ultrasonic-assisted grinding process uses an electroplated diamond grinding tool and employs an end-face grinding process.

[0050] The carbon fiber winding direction is consistent on the processed surface of the ceramic matrix composite sample.

[0051] Specifically, in this embodiment, in step two, the ultrasonic-assisted grinding process uses a three-axis grinding machine, model TV80A. The carbon fiber winding angle on the machined surface is 0°.

[0052] For example, in step two, the process parameters of the ultrasonic-assisted grinding process include: grinding speed, grinding feed rate, grinding depth, ultrasonic amplitude, and ultrasonic frequency.

[0053] Specifically, in this embodiment, the range of process parameters for ultrasonic-assisted grinding is set as follows: grinding speed 4000-6000 rpm, grinding feed rate 500-900 mm / min, grinding depth 0.5-1 mm, ultrasonic amplitude 2-4 μm, and ultrasonic frequency 20-27 kHz. Here, grinding speed, grinding feed rate, and grinding depth are referred to as grinding parameters, and ultrasonic amplitude and ultrasonic frequency are referred to as ultrasonic parameters.

[0054] Specifically, in this embodiment, in step three, an Alicona InfiniteFocus G5 laser confocal microscope is used to scan the ceramic matrix composite material processing block at three equidistant locations along the processing path. The first measurement area is 2 mm from the edge where processing begins, and a measurement area is taken every 13 mm. The scanning range of a single location is 4 × 4.8 mm, and the measurement error of the roughness quantification data is ±100 nm. The STL sheet data and height contour map of the processed surface are then exported. Figure 3 As shown, Figure 3 (a) in the image is a height cloud map. Figure 3 (b) in the figure represents the STL sheet data for the machined surface.

[0055] For example, in step three, the height cloud map distinguishes surface features by height abrupt change boundaries, identifies the size and location of initial pores, and provides data support for the identification, removal, and repair of initial pores in step four.

[0056] Specifically, the initial pores typically exhibit abrupt changes in the z-coordinate (sharp edges), a large decrease in height, and are mostly approximately circular or square in shape.

[0057] Specifically, the surface STL sheet data is a surface mesh morphology feature composed of small planes formed by three adjacent points of surface point data, which cannot be calculated.

[0058] For example, in step four, the mending includes:

[0059] Use a point at zero height from the reference plane to stitch up the missing part at the location where the initial pores were removed.

[0060] Specifically, in this embodiment, taking a machined surface with a grinding speed of 6000 rpm, a grinding feed rate of 900 mm / min, a grinding depth of 1 mm, an ultrasonic amplitude of 4 μm, and an ultrasonic frequency of 20 kHz as an example, based on the height contour map and the initial three-dimensional morphological features of the pores, such as... Figure 4 The diagram shows the STL sheet data of the machined surface before and after patching (the upper part is before patching, and the lower part is after patching). CloudCompare is used to distinguish and remove the initial pores in the STL sheet data of the machined surface. Then, at the location where the initial pores were removed, points with a height of zero from the reference plane are used to patch the missing parts, so that the STL sheet data of the machined surface converges and is exported as a computable .xyz 3D point cloud data to reconstruct the surface morphology.

[0061] Specifically, in this embodiment, in step five, the three-dimensional roughness measurement system uses MetMaX 4.0 roughness analysis software to restore the .xyz three-dimensional point cloud data to the true roughness of the machined surface. Table 1 shows the roughness measurement results before and after the initial pore removal and repair:

[0062] Table 1. Roughness measurement results before and after initial pore removal and repair.

[0063]

[0064] As can be seen from Table 1, before the initial pore removal and repair, the roughness of different regions was abnormally high and fluctuated greatly. After the initial pore removal and repair, the roughness approached the normal value, and the range decreased to 0.41 μm, indicating that the removal effect of pores was good.

[0065] Specifically, in this embodiment, in step six, the measurement method of the laser confocal microscope is the same as in step three, except that the scanning range of a single position is reduced to 2×1.6mm.

[0066] For example, in step six, the processing damage features include: processing holes, fiber pull-out, and fiber debonding.

[0067] Specifically, processing damage refers to damage caused by tool wear or macroscopic brittle fracture during processing, such as extrusion, scratching, and weakening of fiber layer bonding properties.

[0068] For example, in step six, quantitative standards for the initial pore area ratio, processing damage area and depth are proposed to quantify the characteristics of initial pores and processing damage.

[0069] like Figure 5 The diagram illustrates the calculation method for the area ratio of initial pores. The quantification standard for the area ratio of initial pores includes: quantifying the initial pores by the ratio of the projected pixels of the removed initial pores on the processing surface to the total scanned pixels.

[0070] like Figure 6 The diagram shown illustrates the identification and quantification of different processing damage characteristics. Figure 6 (a) in the text corresponds to the identification and quantification of the machining hole, which is a continuous, slender, strip-shaped pit; Figure 6 (b) in the figure corresponds to the identification and quantification of fiber pull-out, where fiber pull-out leaves a SiC shell with a height slightly lower than the processing plane; Figure 6 (c) corresponds to the identification and quantification of fiber debonding. Fiber debonding can cause fiber lifting or delamination, which manifests as an increase in height due to fiber delamination. The quantification standards for the processing damage area and depth include: quantifying the processing damage characteristics by the percentage of the damage distribution area affecting the height characteristics to the total area, obtaining the percentage of processing hole area, the percentage of fiber pull-out area, and the percentage of fiber debonding area.

[0071] In this embodiment, the initial porosity and processing damage features are quantified using Photoshop software, and the pixel counting method is used for calculation. The calculation method is: number of defect pixels ÷ total pixels of the scanned image.

[0072] In this embodiment, the influence of different processing damage characteristics on the true roughness of the processed surface is quantified by the Pearson correlation coefficient. The formula for the Pearson correlation coefficient is as follows:

[0073] .

[0074] in, This represents the Pearson correlation coefficient, where n represents the number of data points. and Representing variables respectively and variables The i-th observation, and Representing variables respectively and variables The observed mean, variable Corresponding to the actual roughness of the machined surface, variable Corresponding to each processing damage characteristic.

[0075] Experiments and calculations revealed a strong positive correlation between the proportion of machined hole area and the true surface roughness (r≈0.93), and a strong positive correlation between the proportion of fiber debonding area and the true surface roughness (r≈0.84). This means that the higher the proportions of both the machined hole area and the fiber debonding area, the greater the true surface roughness. The proportion of fiber pull-out area showed a very weak negative correlation with the true surface roughness (r≈-0.09). Therefore, the lowest proportion of fiber pull-out area was observed at a grinding speed of 6000 rpm, a grinding feed rate of 900 mm / min, and a grinding depth of 0.5 mm, and the true surface roughness and other processing damage characteristics were also at relatively low levels.

[0076] Table 2 shows the actual surface roughness, initial pore area ratio, and machining damage characteristics of the machined surface under different grinding parameters:

[0077] Table 2. Actual surface roughness, initial pore area ratio, and machining damage characteristics of the machined surface under different grinding parameters.

[0078]

[0079] Table 2 shows the mathematical correlation between different grinding parameters and the actual surface roughness and processing damage characteristics of the machined surface as follows: Increasing the grinding speed can significantly improve the breaking effect of the initial pores (12.46%), and significantly reduce the proportion of machined hole area (6.79%), fiber pull-out area (1.37%), and fiber debonding area (3.58%), effectively improving the surface forming quality; Decreasing the grinding feed rate has a more significant effect on improving the actual surface roughness of the machined surface than the grinding speed, and significantly reduces the proportion of fiber debonding area to 1.46%, with the main damage changing to fiber pull-out (fiber pull-out area proportion 8.64%); The grinding depth has a weaker effect on the actual surface roughness of the machined surface, but increasing the grinding depth will deteriorate the surface forming quality, and the three processing damage characteristics appear simultaneously, with machined holes being the main feature (machined hole area proportion reaches 8.78%).

[0080] Based on the above analysis, increasing the grinding speed can reduce fiber pull-out damage, while decreasing the grinding feed rate and depth of cut can significantly reduce damage such as machining holes and fiber debonding. Since significance analysis shows that machining holes and fiber debonding are the main causes affecting the true surface roughness, these types of defects have been effectively avoided during the optimization of the true surface roughness. Therefore, damage that is weakly correlated with roughness should be considered first in the parameter optimization for damage reduction. Based on the above analysis and data, the final optimal grinding parameters should be a grinding speed of 6000 rpm, a grinding feed rate of 500 mm / min, and a grinding depth of cut of 0.5 mm. At this point, the proportion of fiber pull-out area can be further reduced, achieving quantification and optimization of low-significance damage.

[0081] Therefore, while increasing the grinding speed, reducing the grinding feed rate and grinding depth can significantly reduce damage formation.

[0082] Similar to Table 2, the ultrasonic parameters (ultrasonic amplitude 2-4 μm, ultrasonic frequency 20-27 kHz) can be adjusted without changing the grinding parameters to obtain the mathematical correlation between different ultrasonic parameters and the actual surface roughness and processing damage characteristics. The results are as follows: within the range of ultrasonic parameter settings, reducing the ultrasonic amplitude and increasing the ultrasonic frequency can significantly reduce damage formation.

[0083] The mathematical correlation between different grinding parameters and the actual surface roughness and damage characteristics of the machined surface, and the mathematical correlation between different ultrasonic parameters and the actual surface roughness and damage characteristics of the machined surface, together constitute a mathematical correlation model.

[0084] For example, in step seven, the optimization process of the process parameters adopts the controlled variable method to obtain the optimal process parameters for each process parameter of ultrasonic assisted grinding.

[0085] Specifically, in this embodiment, in step seven, experiments are conducted using the controlled variable method within the set range of various process parameters for ultrasonic assisted grinding. The optimal process parameters for ultrasonic assisted grinding, with the goal of minimizing surface roughness and machining damage, are: grinding speed 6000 rpm, grinding feed rate 500 mm / min, grinding depth 0.5 mm, ultrasonic amplitude 2 μm, and ultrasonic frequency 27 kHz. When considering only the low surface roughness target, the optimal process parameters are determined to be: grinding speed 6000 rpm, grinding feed rate 900 mm / min, grinding depth 0.5 mm, ultrasonic amplitude 4 μm, and ultrasonic frequency 20 kHz. The difference in surface quality between the two can be distinguished by machining accuracy; the former achieves a machining accuracy of 99%, while the latter only achieves 95.6%.

[0086] This application establishes a mathematical model of the true surface roughness and various processing damage characteristics of the machined surface, obtains the correlation between various processing damages and the true surface roughness, and proposes a new method for optimizing processing parameters that simultaneously considers roughness and processing damage. This improves the accuracy of defect identification and the scientific nature of process parameter optimization for ceramic matrix composite materials. Specifically, when calculating the true surface roughness, it avoids significantly reducing the measurement area to avoid pore interference, thus maximizing the accuracy of the measurement results and reflecting the actual quality of the machined surface.

[0087] By differentiating, removing, and patching the initial pores in the STL sheet data of the processed surface based on height cloud maps and initial pore 3D morphology features, a judgment comparison between the initial surface and the processed surface was established, ensuring the effectiveness and accuracy of pore removal and avoiding excessive removal of surface processing damage features.

[0088] By using a laser confocal microscope, compared to probe-based roughness measurement, the visualization is higher and the scanning area is larger. At the same time, it avoids the surface scratch problems that may be caused by probe-based roughness measurement, and is suitable for undensified ceramic matrix composites.

[0089] By proposing quantitative standards for the proportion of initial pore area, processing damage area and depth, the characteristics of initial pores and processing damage are quantified, providing reliable support for establishing a mathematical correlation model between the true roughness of the processed surface and the characteristics of processing damage, thereby improving the reliability of process parameter optimization.

[0090] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0091] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials, characterized in that, Includes the following steps: Step 1: Use a laser confocal microscope to scan the ceramic matrix composite sample to obtain the three-dimensional morphological features of the initial surface and initial pores; Step 2: Perform uncooled ultrasonic-assisted grinding on the ceramic matrix composite sample block to obtain a processed ceramic matrix composite block; Step 3: Use a laser confocal microscope to scan the ceramic matrix composite material processing block and export the STL sheet data and height contour map of the processing surface; Step 4: Based on the height cloud map and the initial pore 3D morphology features, the initial pores of the processed surface STL sheet data are distinguished, eliminated, and patched to make the processed surface STL sheet data converge and be exported as calculable .xyz 3D point cloud data. Step 5: Calculate the true surface roughness of the machined surface using a 3D roughness measurement system based on the .xyz 3D point cloud data; Step 6: After reducing the scanning range using a laser confocal microscope, observe the ceramic matrix composite material processing block, identify processing damage characteristics, and establish a mathematical correlation model between the actual surface roughness of the processed surface and the processing damage characteristics; Step 7: Based on the mathematical correlation model, optimize the process parameters of ultrasonic-assisted grinding with the goal of minimizing the actual surface roughness and the processing damage characteristics. In step four, the mending includes: Use a point at zero height from the reference plane to repair the missing part at the location where the initial pores were removed; In step six, quantitative standards for the initial pore area ratio, processing damage area and depth are proposed to quantify the characteristics of initial pores and processing damage. The quantification standard for the initial pore area ratio includes: quantifying the initial pores by the ratio of the projected pixels of the initial pores in the processing surface to the total number of scanned pixels; The quantification criteria for the area and depth of processing damage include: quantifying processing damage characteristics by the percentage of the damage distribution area affecting height characteristics to the total area.

2. The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to claim 1, characterized in that, In step two, the ultrasonic-assisted grinding process uses an electroplated diamond grinding tool and employs an end-face grinding process. The carbon fiber winding direction is consistent on the processed surface of the ceramic matrix composite sample.

3. The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to claim 1, characterized in that, In step two, the process parameters for ultrasonic-assisted grinding include: grinding speed, grinding feed rate, grinding depth, ultrasonic amplitude, and ultrasonic frequency.

4. The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to claim 1, characterized in that, In step three, the height cloud map distinguishes surface features through height abrupt change boundaries, identifies the size and location of initial pores, and provides data support for the identification, removal, and repair of initial pores in step four.

5. The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to claim 1, characterized in that, In step six, the processing damage characteristics include: processing holes, fiber pull-out, and fiber debonding.

6. The method for identifying surface defects and optimizing process parameters in ceramic matrix composite materials according to claim 1, characterized in that, In step seven, the optimization of the process parameters adopts the controlled variable method to obtain the optimal process parameters for each process parameter of ultrasonic assisted grinding.

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