Method and system for detecting special-shaped coating of aero-engine based on MEMS-OCT micro-nano probe

Abstract

The invention discloses an aero-engine special-shaped coating detection method and system based on an MEMS-OCT micro-nano probe, and relates to the field of micro-electronic machinery. The method comprises the steps that through parameterized curved surface modeling and self-adaptive path planning, a micro-nano probe is used for covering the geometric surface of the complex coating in a high-precision scanning mode; acquiring a depth resolution reflection signal of the internal structure of the coating through an interference principle, and converting the depth resolution reflection signal into frequency domain information for reconstructing a cross-sectional image; the obtained depth resolution reflection signals are preprocessed; a three-dimensional coating structure is reconstructed through a preset algorithm, and coating thickness distribution is quantified; segmenting an internal defect area of the coating, and quantifying a defect form; and risk assessment and health state classification are carried out by extracting coating thickness and defect features. The MEMS-OCT micro-nano probe is combined with parameterized curved surface modeling and self-adaptive path planning, so that high-precision scanning, three-dimensional imaging and accurate defect quantification of the special-shaped coating of the aero-engine are realized, and the precision, efficiency and reliability of coating detection are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of microelectromechanical systems, and particularly to a method and system for detecting special-shaped coatings of aero-engines based on MEMS-OCT micro-nano probes. Background Art

[0002] With the continuous development of aero-engine technology, the high performance and high reliability requirements of aero-engines make the quality control of coatings in the manufacturing process particularly important. Coatings are important protective layers for many key components in aero-engines. They can not only improve the corrosion resistance, wear resistance, and oxidation resistance of the engine, but also effectively extend the service life of the engine. Therefore, the quality of the coating is directly related to the operation stability and safety of the engine.

[0003] However, the coatings of aero-engine components usually present complex special-shaped geometric structures. The coating thickness distribution, defect types, and their spatial distributions have a profound impact on the performance of the engine. Traditional coating detection methods, such as optical microscopes and scanning electron microscopes, often struggle to meet the high-precision detection requirements of special-shaped coatings. These methods usually can only obtain local information of the coating on a two-dimensional plane, and are complex to operate, time-consuming, and unable to achieve efficient and real-time detection.

[0004] Therefore, to solve the above problems, the present technology proposes a method for detecting special-shaped coatings of aero-engines based on MEMS-OCT micro-nano probes. The aim is to achieve high-precision scanning coverage of the complex coating geometric surface through parametric surface modeling and adaptive path planning; obtain depth-resolved reflection signals of the internal structure of the coating through the interference principle, and reconstruct tomographic images through frequency-domain information; use advanced signal processing and algorithms to reconstruct the three-dimensional structure of the coating, quantify the coating thickness distribution and defect morphology; finally, evaluate the risk of the coating and classify the health status by extracting coating thickness and defect features. This method can provide quantitative analysis of internal defects of the coating and high-precision health status assessment without damaging the coating, significantly improving the efficiency and accuracy of coating detection. Summary of the Invention

[0005] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method and system for detecting special-shaped coatings of aero-engines based on MEMS-OCT micro-nano probes to solve the above technical problems.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for detecting special-shaped coatings of aero-engines based on MEMS-OCT micro-nano probes, including: Through parametric surface modeling and adaptive path planning, for high-precision scanning coverage of the complex coating geometric surface by the micro-nano probe; Obtain the depth-resolved reflection signal of the internal structure of the coating through the interference principle, and convert it into frequency-domain information for reconstructing the tomographic image; Preprocess the obtained depth-resolved reflection signal; Reconstruct the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution; Segment the internal defect area of the coating and quantify the defect morphology; Conduct risk assessment and health status classification by extracting the coating thickness and defect characteristics.

[0007] The present invention is further configured such that, through parametric surface modeling and adaptive path planning, the complex coating geometric surface is scanned and covered, and the steps are as follows: Parametric surface modeling, representing the coating surface as a parametric surface through NURBS surface , where is the normalized parameter used to describe the position of any point on the surface; Adaptive path planning, extracting the local geometric feature Gaussian curvature of the surface through the parametric surface model , defining the objective function , through the Gaussian curvature and the contact force of the weighted sum, dynamically balance the scanning density of the probe for the curvature-sensitive area and the contact pressure control, and the objective function formula is as follows: , where, is the parameter space gradient, is the amplitude of the contact force between the probe and the surface, is the weight factor.

[0008] The present invention is further configured such that, through the interference principle, the depth-resolved reflection signal of the internal structure of the coating is obtained and converted into frequency-domain information for reconstructing the tomographic image, and the steps are as follows: Model the interference signal, and the OCT interference signal intensity is composed of the measurement parameters in the OCT system, and its formula is as follows: , where, is the depth coordinate, representing the distance from the sample surface to the detection point, represents the wave number of light, is the OCT interference signal intensity, represents the light source spectral density, is the reflection coefficient of the reference arm, is the sample surface volatility function, describing the influence of surface micro-fluctuations on the signal intensity, is the frequency-domain modulation function, representing the influence of the modulation frequency on the signal intensity, is the depth position correction factor, reflecting the signal gain and attenuation at different depths, is the optical field modulation function, which controls the relationship between the change in the light source frequency and the sample reflection. is the interference intensity function of the reflected light; Frequency domain demodulation and signal extraction. For the obtained interference signal perform a fast Fourier transform, and suppress noise and spectral leakage through a frequency domain window function to reconstruct the depth-resolved reflection signal: , which is used to accurately reflect the internal structure of the coating.

[0009] The present invention is further configured such that the obtained depth-resolved reflection signal is preprocessed, and noise in the OCT signal is removed by adaptive wavelet threshold denoising and non-local mean filtering to obtain preprocessed multi-angle OCT data .

[0010] The present invention is further configured such that the three-dimensional coating structure is reconstructed by a preset algorithm, the coating thickness distribution is quantified, and three-dimensional image reconstruction is performed according to the preprocessed multi-angle OCT data Interference fields and reflectivity response parameters in spatial tomography are introduced for more accurate surface and internal structure restoration. The formula is: , where is the three-dimensional data of the reconstructed coating, representing the three-dimensional coating structure value at the spatial position , is the preprocessed multi-angle OCT data, representing the reflection intensity signal measured at the depth and the angle , is the radial position in the spatial coordinate, is the Dirac function, is the angle, representing the scanning direction of the probe. The formula for reconstructing the three-dimensional data of the coating constructs the three-dimensional structure of the coating through the combination of projection and back-projection; By integrating the reflectivity along the normal direction, the thickness distribution of the coating is calculated. The formula is: , where is the substrate position and is the surface position.

[0011] The present invention is further configured such that the internal defect area of the coating is segmented and the defect morphology is quantified. The steps are as follows: Construct an energy function , and segment the defect boundary through a minimization process. The formula of the energy function is:

[0012] , where is the total energy function, representing the edge detection energy of the coating, represents the change direction and amplitude of the gray value of the coating, is the gray value of the coating, is the background intensity, is the weight coefficient for controlling the gradient term, is the weight coefficient for controlling the brightness difference term, is the weight coefficient of the defect indication function, is the defect indication function, indicating whether the area in the coating belongs to the defect area, is the coating domain; According to the energy function formula, solve the minimum value of the energy function. When the energy change rate reaches the threshold, terminate the iteration, output the segmentation result, and convert the segmentation result into a binary defect mask , to distinguish defects from the background; Eliminate the noise and holes in the binary defect mask , repair the defect area, and its formula is: , where, is the repaired defect mask. The formula adopts the combined operation of dilation and erosion. The dilation operation expands the defect area and fills small holes. The dilation operation formula is , where, is the dilation operation on the pixel position , is the structural element relative to the pixel position offset, is each element inside the structural element. The erosion operation shrinks the area and removes isolated noise points. The erosion operation formula is , where, is the erosion operation on the pixel position , is the size of the spherical structural element.

[0013] The present invention is further configured such that by extracting the coating thickness and defect characteristics, risk assessment and health status classification are performed. The characteristics include the coating defect profile difference degree, local defect concentration. The coating defect profile difference degree measures the morphological difference of the coating defects, and the local defect concentration measures the density of defects in a certain local area. The quality of the coating is reflected by the spatial structure of the defect distribution.

[0014] The present invention is further configured such that the coating defect profile difference degree, and its calculation formula is: , where, is the coating defect profile difference degree, is the reflectivity of the coating at the point , is the reflectivity of the ideal defect model, is the reference position on the coating surface, is the scale parameter that controls the distance weight between the defect profile and the ideal model; The local defect concentration, and its calculation formula is: , where is the local defect concentration, is the local area of the coating, is the preset threshold of the coating reflectivity to determine the presence or absence of defects, is the scale factor that controls the turning of the control function.

[0015] The present invention is further configured such that, according to the extracted features, a feature vector is constructed, and a support vector machine is used to classify the features, and the risk is evaluated based on the state of the coating.

[0016] The present invention also provides an aircraft engine special-shaped coating detection system based on a MEMS-OCT micro-nano probe, and the system includes: Coating scanning module: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface; Signal acquisition module: Through the interference principle, the depth-resolved reflection signal of the internal structure of the coating is obtained and converted into frequency-domain information for reconstructing tomographic images; Signal preprocessing module: Preprocesses the obtained depth-resolved reflection signal; Quantification of coating thickness distribution module: Reconstructs the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution; Quantification of defect morphology module: Segments the internal defect area of the coating to quantify the defect morphology; Feature extraction and risk assessment module: Through extracting the coating thickness and defect features, risk assessment and health status classification are performed.

[0017] The present invention provides an aircraft engine special-shaped coating detection method and system based on a MEMS-OCT micro-nano probe. The method is used for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface through parametric surface modeling and adaptive path planning; the depth-resolved reflection signal of the internal structure of the coating is obtained through the interference principle and converted into frequency-domain information for reconstructing tomographic images; the obtained depth-resolved reflection signal is preprocessed; the three-dimensional coating structure is reconstructed through a preset algorithm to quantify the coating thickness distribution; the internal defect area of the coating is segmented to quantify the defect morphology; through extracting the coating thickness and defect features, risk assessment and health status classification are performed, and the beneficial effects generated include: High-precision coating scanning and comprehensive coverage: By means of parametric surface modeling and adaptive path planning, the scanning problem of complex coating geometric surfaces is effectively solved. Using the MEMS-OCT micro-nano probe, it can accurately scan and cover the special-shaped coating surfaces of aeroengines, thus realizing comprehensive and efficient detection of the entire coating surface and avoiding the problem that it is difficult to scan complex parts of the coating in traditional methods; High-resolution three-dimensional imaging and acquisition of depth-resolved reflection signals: By using the interference principle to obtain depth-resolved reflection signals and converting them into frequency-domain information for reconstruction, the three-dimensional image of the internal structure of the coating can be accurately reconstructed. This process greatly improves the accuracy of coating defect identification, especially in the coating thickness distribution and the positioning of tiny internal defects of the coating, which is superior to traditional two-dimensional detection methods; Precise quantification of coating thickness and defect morphology: Advanced algorithms are introduced to quantify the coating thickness, and by segmenting and identifying the internal defect areas of the coating, the defect morphology and spatial distribution information can be accurately extracted. This precise quantification and defect classification provide high-quality input for subsequent risk assessment and health status classification, and can comprehensively reflect the quality status of the coating.

[0018] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings. In the drawings: Figure 1 It is a flowchart of a method for detecting special-shaped coatings of aeroengines based on MEMS-OCT micro-nano probes shown in an exemplary embodiment of the present invention; Figure 2 It is a schematic structural diagram of a system for detecting special-shaped coatings of aeroengines based on MEMS-OCT micro-nano probes shown in an exemplary embodiment of the present invention. Detailed Description of the Invention

[0020] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than limiting the protection scope of the present invention.

[0021] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0022] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand. Embodiment 1

[0023] An aviation engine special-shaped coating detection method based on a MEMS-OCT micro-nano probe, as Figure 1 shown, includes: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface; Through the interference principle, the depth-resolved reflection signal of the coating internal structure is obtained and converted into frequency-domain information for reconstructing tomographic images; Preprocess the obtained depth-resolved reflection signal; Reconstruct the three-dimensional coating structure through a preset algorithm and quantify the coating thickness distribution; Segment the internal defect area of the coating and quantify the defect morphology; Through extracting the coating thickness and defect features, perform risk assessment and health status classification.

[0024] The present invention is further configured such that the scanning to cover the complex coating geometric surface through parametric surface modeling and adaptive path planning is carried out as follows: Parametric surface modeling, representing the coating surface as a parametric surface through NURBS surface , where is a normalization parameter. Each point on the coating surface can be described by a two-dimensional parameter space, and this parameter space is normalized with a value range of , ensuring a detailed scan of each part of the surface. Through parametric surface modeling, the geometric features of the coating can be accurately described, ensuring a reasonable scan density of the probe in each area, especially in areas with large curvature changes; Adaptive path planning, through the parametric surface model, extracts the local geometric feature Gaussian curvature of the surface . Gaussian curvature is a scalar representing the degree of surface curvature. Its value at each point describes the bending of the surface at that point. For areas with large curvature, the probe will perform a more intensive scan, defining the objective function , through Gaussian curvature and the contact force The weighted sum of dynamically balances the scan density of the probe in the curvature-sensitive area and the contact pressure control. The objective function formula is as follows: , where is the parameter space gradient, that is, the rate of change of the surface in the direction, calculated from the partial derivatives of the parametric surface , is the contact force amplitude between the probe and the surface, representing the pressure magnitude when the probe contacts the coating surface during scanning, is the weight factor, controlling the relative importance ratio of Gaussian curvature and contact force in the objective function. Adaptive path planning can dynamically adjust the scan density according to the geometric changes of the coating surface, increasing the scan frequency in the curved area and decreasing the scan frequency in the flat area, optimizing the scan efficiency and accuracy. By considering the dynamic balance of contact force and curvature, it avoids damage to the coating surface caused by excessive pressure, and at the same time ensures sufficient scan density in areas with large curvature, improving the detection ability of coating defects.

[0025] The present invention is further configured such that the depth-resolved reflection signal of the internal structure of the coating is obtained through the interference principle and converted into frequency-domain information for reconstructing a tomographic image. The steps are as follows: Model the interference signal. The OCT interference signal intensity is composed of the measurement parameters in the OCT system, reflecting the reflection and interference of light when passing through the sample. The formula is as follows: , where is the depth coordinate, representing the distance from the sample surface to the detection point, with a value range from a few micrometers to a few millimeters, depending on the scanning depth and resolution, represents the wave number of light, between and rad / m, depending on the wavelength of the light source used, is the OCT interference signal intensity, represents the spectral density of the light source, describing the spectral distribution of the light waves emitted by the light source, is the reflection coefficient of the reference arm, describing the reflection intensity of the light rays in the reference arm, is the surface volatility function of the sample, describing the influence of minute surface fluctuations on the signal intensity, is the frequency-domain modulation function, indicating the influence of the modulation frequency on the signal intensity, is the depth position correction factor, reflecting the signal gain and attenuation at different depths, with a value range from 0 to 1, is the light field modulation function, controlling the relationship between the change in the light source frequency and the sample reflection, is the interference intensity function of the reflected light, representing the interference intensity between the sample reflected light and the reference light; Frequency-domain demodulation and signal extraction are performed on the obtained interference signal by performing a fast Fourier transform to convert the time-domain signal into a frequency-domain signal, and suppressing noise and spectral leakage through a frequency-domain window function to reconstruct the depth-resolved reflection signal: , which is used to accurately reflect the internal structure of the coating. By modeling the interference signal and using frequency-domain demodulation techniques, the depth resolution of OCT imaging is improved, thereby accurately detecting the internal structure of the coating. Through the reconstructed depth-resolved reflection signal, the internal structure changes of the coating can be accurately reflected, especially having significant advantages in the positioning and identification of minute defects.

[0026] The present invention is further configured such that the obtained depth-resolved reflection signal is preprocessed, and noise in the OCT signal is removed through adaptive wavelet threshold denoising and non-local mean filtering to obtain preprocessed multi-angle OCT data , improving the image quality. Specifically, the frequency-domain signal is subjected to a discrete wavelet transform to be decomposed into multi-scale coefficients . Noise is suppressed through a non-linear threshold function, and the signal is reconstructed. Then, based on the similarity weight the neighboring pixels are weighted and averaged to obtain .

[0027] The present invention is further configured such that the three-dimensional coating structure is reconstructed through a preset algorithm, and the coating thickness distribution is quantified. Based on the preprocessed multi-angle OCT data a three-dimensional image reconstruction is performed, and the interference field and reflectivity response parameters in spatial tomography are introduced for more accurate surface and internal structure restoration. The formula is: , where, is the three-dimensional data of the reconstructed coating, representing the coating intensity information of each point in the three-dimensional space, is the preprocessed multi-angle OCT data, representing at the depth and the angle The reflected intensity signal measured at the position, is the angle, representing the scanning direction of the probe. Through the combination of projection and back-projection, this formula constructs the three-dimensional structure of the coating, which is used to achieve spatial projection, is the Dirac function, which is used to describe having an infinite value at a certain point and being zero elsewhere, and helps project the reflected signal into the three-dimensional space according to its spatial coordinates, is the radial position in the spatial coordinates, representing the distance in the plane. By combining multi-angle data for reconstruction and calculation, it can handle complex coating geometries and provide comprehensive coating quality information; By integrating the reflectivity along the normal direction, the thickness of the coating is calculated distribution, and its formula is: , where, is the substrate position and is the surface position, represents the reflected intensity of the coating at the position . Using the integration method of the reflectivity along the normal direction, the thickness distribution of the coating can be accurately quantified, especially the change in the coating thickness at different positions, which is crucial for coating quality control.

[0028] The present invention is further configured such that the internal defect area of the segmented coating is quantified for the defect morphology, and the steps are as follows: Construct an energy function , which combines the gradient of the coating gray value, the gray difference, and the indication information of the defect area, and divides the defect boundary through the minimization process. The energy function formula is: , where, is the total energy function, representing the edge detection energy of the coating, represents the change direction and amplitude of the coating gray value. By calculating the change in the gray value, the edges in the coating can be detected, is the gray value of the coating, representing the brightness of each pixel in the image, is the background intensity, representing the gray value of the area outside the coating, and is used to compare with the gray value of the coating itself, is the weight coefficient for controlling the gradient term, is the weight coefficient for controlling the brightness difference term, is the weight coefficient of the defect indication function, controlling the indication intensity of the defect area, is the defect indication function, indicating whether each pixel in the coating belongs to the defect area. The defect area is 1, and other areas are 0, is the coating domain; According to the energy function formula, solve the minimum value of the energy function to obtain the defect boundary in the coating. When the energy change rate reaches the threshold, terminate the iteration, output the segmentation result, and convert the segmentation result into a binary defect mask , where the defect area is 1 and other areas are 0, to distinguish defects from the background; Eliminate the noise and holes in the binary defect mask , and repair the defect area. The formula is: , where is the repaired defect mask. The formula adopts a combined operation of dilation and erosion. Through the combined operation of dilation and erosion, the noise and holes in the binary defect mask can be repaired to obtain a smoother and more continuous defect area. The dilation operation expands the defect area and fills small holes. The dilation operation formula is , where is the dilation operation on the pixel position , is the structural element relative to the pixel position offset, is each element inside the structural element. The erosion operation shrinks the area and removes isolated noise points. The formula is , where is the erosion operation on the pixel position , is the size of the spherical structural element, which is used to control the operation size of dilation and erosion. Through precise segmentation and repair, the defect morphology in the coating, such as cracks and pores, can be better identified, providing accurate data support for subsequent analysis and evaluation.

[0029] The present invention is further configured such that by extracting the coating thickness and defect characteristics, risk assessment and health status classification are performed. The characteristics include the coating defect profile difference degree and the local defect concentration. The coating defect profile difference degree measures the morphological difference of the coating defects, and the local defect concentration measures the density of defects in a certain local area, reflecting the quality of the coating through the spatial structure of the defect distribution.

[0030] The present invention is further configured such that the coating defect profile difference degree has the following calculation formula: , where is the coating defect profile difference degree, is the reflectivity of the coating at the point , is the reflectivity of the ideal defect model, representing the reflection intensity under ideal conditions at this position. Based on the assumed defect-free coating model, is the reference position on the coating surface, is a scale parameter that controls the distance weight between the defect profile and the ideal model. The coating defect profile difference is used to measure the difference between the defect at a certain position in the coating and the ideal defect model. It is used to weight the defect depth. The closer the defect is to the surface, the greater the difference weight. Local defect concentration, and its calculation formula is: where, is the local defect concentration, is the local area of the coating, is the preset threshold of the coating reflectivity, which determines the presence or absence of defects.

[0031] If , it is considered that there is a defect at this position. is the scale factor that controls the turning of the control function. The local defect concentration is used to measure the local defect concentration at a certain position in the coating. Based on the distribution of the reflectivity , the Sigmoid function is used to classify the presence of defects. The local defect concentration combines the relationship between the reflectivity and the preset threshold , and controls the sensitivity of defect judgment; By calculating the defect profile difference and the local defect concentration, the type, morphology and severity of the defects in the coating can be accurately evaluated. By combining the comparison of the reflectivity with the ideal model, the micro-defects in the coating can be effectively identified, and their concentration and depth can be accurately evaluated.

[0032] The present invention is further configured such that, according to the extracted features, a feature vector is constructed, and a support vector machine is used to classify the features, and the risk is evaluated based on the state of the coating. Embodiment 2

[0033] Please refer to Figure 2 , the exemplary aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe includes: Coating scanning module: Through parametric surface modeling and adaptive path planning, it is used for the high-precision scanning of the micro-nano probe to cover the complex coating geometric surface; Signal acquisition module: Through the interference principle, the depth-resolved reflection signal of the coating internal structure is obtained and converted into frequency-domain information for reconstructing the tomographic image; Signal preprocessing module: Preprocess the obtained depth-resolved reflection signal; Quantitative coating thickness distribution module: Reconstruct the three-dimensional coating structure through a preset algorithm and quantify the coating thickness distribution; Quantitative defect morphology module: Segment the internal defect area of the coating and quantify the defect morphology; Feature extraction and risk assessment module: Through extracting the coating thickness and defect features, risk assessment and health status classification are carried out.

[0034] It should be noted that the aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe provided in the above embodiments and the aero-engine special-shaped coating detection method based on the MEMS-OCT micro-nano probe provided in the above embodiments belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be elaborated here. In practical applications, for the aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe provided in the above embodiments, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0036] It should be understood that the term “and / or” in this article is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character “ / ” in this article generally represents an “or” relationship between the associated objects before and after, but it may also represent an “and / or” relationship. The specific meaning can be understood by referring to the context before and after.

[0037] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0038] It should be understood that in various embodiments of this application, the magnitude of the serial numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0039] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0040] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0041] In several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0043] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0044] If the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0045] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probes, characterized in that: include: Through parametric surface modeling and adaptive path planning, micro-nano probes can be used to scan complex coating geometric surfaces with high precision; The depth-resolved reflection signal of the internal structure of the coating is obtained through the interference principle and converted into frequency domain information for reconstructing the tomographic image; Preprocessing the acquired depth-resolved reflection signal; Reconstruct the three-dimensional coating structure through a preset algorithm and quantify the coating thickness distribution; Segment the defect area inside the coating and quantify the defect morphology; Risk assessment and health status classification are performed by extracting coating thickness and defect characteristics.

2. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 1 is characterized in that: Through parametric surface modeling and adaptive path planning, scanning and covering complex coating geometric surfaces, the steps are as follows: Parametric surface modeling, using NURBS surfaces to represent the coating surface as a parametric surface ,in is a normalized parameter used to describe the position of any point on the surface; Adaptive path planning, through parameterized surface model, extracting local geometric features of the surface Gaussian curvature , define the objective function , through Gaussian curvature Contact force The weighted sum of , dynamically balances the scanning density and contact pressure control of the probe on the curvature sensitive area. The objective function formula is as follows: ,in, is the parameter space gradient, is the contact force amplitude between the probe and the surface, is the weight factor.

3. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 1 is characterized in that: The depth-resolved reflection signal of the internal structure of the coating is obtained by interference principle and converted into frequency domain information for reconstructing the tomographic image. The steps are as follows: Modeling the interference signal, OCT interference signal intensity It is composed of the measurement parameters in the OCT system and its formula is as follows: ,in, is the depth coordinate, which indicates the distance from the sample surface to the detection point. represents the wave number of light, is the OCT interference signal intensity, represents the spectral density of the light source, is the reflection coefficient of the reference arm, is the sample surface volatility function, which describes the effect of small surface fluctuations on signal intensity. is the frequency domain modulation function, which indicates the influence of modulation frequency on signal strength. is the depth position correction factor, reflecting the signal gain and attenuation at different depths. is the light field modulation function, which controls the relationship between the change of light source frequency and sample reflection. is the interference intensity function of the reflected light; Frequency domain demodulation and signal extraction, the interference signal obtained Perform a fast Fourier transform and pass it through the frequency domain window function Suppress noise and spectrum leakage and reconstruct depth-resolved reflection signals: , used to accurately reflect the internal structure of the coating.

4. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 3 is characterized in that: The acquired depth-resolved reflection signal is preprocessed, and the noise in the OCT signal is removed by adaptive wavelet threshold denoising and non-local mean filtering to obtain the preprocessed multi-angle OCT data. .

5. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 4 is characterized in that: The three-dimensional coating structure is reconstructed by a preset algorithm, and the coating thickness distribution is quantified based on the pre-processed multi-angle OCT data. To reconstruct three-dimensional images, the interference field and reflectivity response parameters in spatial tomography are introduced for more accurate surface and internal structure recovery. The formula is: ,in, To reconstruct the three-dimensional data of the coating, the spatial position The three-dimensional structure value of the coating at is the preprocessed multi-angle OCT data, indicating the depth and angle The reflected intensity signal measured at is the radial position in spatial coordinates, Dirac function, is the angle, indicating the scanning direction of the probe, The three-dimensional structure of the coating is constructed by combining projection and back-projection; Through the reflectivity Integrate along the normal direction to calculate the coating thickness Distribution, its formula is: ,in, is the base position and The surface position.

6. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 1 is characterized in that: Segment the internal defect area of ​​the coating and quantify the defect morphology. The steps are as follows: Constructing energy function , the defect boundary is segmented by minimization process, and the energy function formula is: ,in, is the total energy function, which represents the edge detection energy of the coating, Indicates the change direction and amplitude of the coating gray value. is the gray value of the coating, is the background intensity, is the weight coefficient for controlling the gradient term, To control the weight coefficient of the brightness difference term, is the weight coefficient of the defect indication function, is a defect indication function, indicating whether the area in the coating is a defect area. is the coating domain; According to the energy function Formula, solve the minimum value of the energy function, terminate the iteration when the energy change rate reaches the threshold, output the segmentation result, and convert the segmentation result into a binary defect mask ,distinguish defects from background; Eliminate binary defect mask The noise and holes in the image are repaired and the defective area is repaired. The formula is: ,in, To repair the defect mask, the formula uses a combination of dilation and corrosion operations. The dilation operation expands the defect area and fills small holes. The dilation operation formula is ,in, For pixel position The expansion operation, Is a structural element Relative to pixel position The offset of For each element inside the structural element, the corrosion operation shrinks the area and removes isolated noise points. The corrosion operation formula is ,in, For pixel position The corrosion operation is the size of the spherical structure element.

7. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 5 or 6, characterized in that: By extracting coating thickness and defect characteristics, risk assessment and health status classification are performed. The characteristics include coating defect profile difference and local defect concentration. The coating defect profile difference measures the morphological difference of coating defects, and the local defect concentration measures the density of defects in a local area. The spatial structure of defect distribution is used to reflect the quality of the coating.

8. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 7 is characterized in that: The coating defect profile difference is calculated as follows: ,in, is the coating defect profile difference, For coating at point The reflectivity at is the reflectivity of the ideal defect model, is the reference position of the coating surface, is the scale parameter, which controls the distance weight between the defect profile and the ideal model; The local defect concentration is calculated as: ,in, is the local defect concentration, is the local area of ​​the coating, Preset thresholds for coating reflectivity to determine the presence or absence of defects, is the scale factor that controls the transition of the function.

9. The method for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probe according to claim 8, characterized in that: Based on the extracted features, feature vectors are constructed, and support vector machines are used to classify the features, and the risks are evaluated based on the status of the coating.

10. An aircraft engine profiled coating detection system based on a MEMS-OCT micro-nano probe, used to implement an aircraft engine profiled coating detection method based on a MEMS-OCT micro-nano probe as described in any one of claims 1 to 9, characterized in that: include: Coating scanning module: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning of complex coating geometric surfaces by micro-nano probes; Signal acquisition module: obtains the depth-resolved reflection signal of the internal structure of the coating through the interference principle and converts it into frequency domain information for reconstructing the tomographic image; Signal preprocessing module: preprocess the acquired depth-resolved reflection signal; Quantitative coating thickness distribution module: reconstructs the three-dimensional coating structure through a preset algorithm and quantifies the coating thickness distribution; Quantification of defect morphology module: segment the internal defect area of ​​the coating and quantify the defect morphology; Feature extraction and risk assessment module: Perform risk assessment and health status classification by extracting coating thickness and defect features.

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