Method and system for measuring morphology parameters of complex structural component, and storage medium

Abstract

The invention relates to a method and system for measuring morphology parameters of a complex structural component, and a storage medium. The method comprises the following steps: calibrating a spectrum confocal displacement sensor, and establishing a mapping relation between a peak wavelength and the position of a measured object; based on the calibrated spectrum confocal displacement sensor, acquiring 3D point cloud data of the surface of the measured object in a scanning mode; performing clustering processing on the 3D point cloud data to obtain classified data after clustering; and performing model fitting on the clustered classification data based on a random sampling consensus algorithm to obtain a plane or space model, and obtaining required structure parameters based on the plane or space model. According to the invention, high-precision dynamic measurement of a measured object with a complex structural profile is realized.

Description

Technical Field

[0001] The invention relates to the technical field of measuring morphological parameters of complex structural components of aircraft engines, and in particular to a method, a storage medium and a system for measuring morphological parameters of complex structural components. Background Art

[0002] Aircraft engine equipment and key components contain a large number of parts and special equipment with complex surface structures. In order to ensure structural reliability, extremely high requirements are often placed on processing accuracy and surface quality. For example, the shape and size of the engine blades are complex. When there are errors or defects in the shape and size of the aircraft engine blades, it will not only directly affect the physical structural strength of the engine itself, but may also cause abnormal working conditions of the engine by affecting the engine's efficiency and surge. Another example is the quality inspection of the tooth surface roughness of the engine gear. If the parameters do not meet the standards, it will not only directly affect the service life of the gears, but also cause poor contact during the gear meshing process, resulting in abnormal vibration and noise conditions during the long-term operation of the engine, thereby affecting the overall life of the engine. Therefore, for the existing production and quality inspection process of aircraft engine equipment and parts, how to effectively solve the measurement and evaluation of key parameters such as precise structural dimensions, aperture depth, wall thickness, etc. of complex surface structural components including curved surfaces and irregular surfaces is of great significance.

[0003] In order to effectively ensure the quality and reliability of processing and manufacturing, it is necessary to use non-destructive testing methods that do not destroy the physical structure and chemical composition of the test unit to effectively control the morphology structure and overall quality. In addition to the traditional human eye visual inspection method, common non-destructive testing methods include non-contact infrared imaging, laser holography, acoustic impedance, acoustic emission detection and other methods. Manual visual coplanarity detection of chip components at the production site is not only prone to missed detection and false detection due to the operator's limited energy and lack of concentration, but also has a slow detection speed, affecting production tasks. Although the accuracy of infrared imaging and current laser holographic detection has been greatly improved compared to manual detection, the detection accuracy for fine structures under small space sizes is still low, and cannot meet the current high-precision detection and measurement needs of the precision component detection industry. In the process of processing the surface structure detection and measurement of micro-sized components, especially in scenarios with high resolution requirements and the need to distinguish and measure the depth information of complex surface structure components, such as the precision machining dimensions, aperture depth, wall thickness and other test scenarios of complex surface structure components containing curved surfaces, irregular surfaces, blade gaps, etc., the measurement accuracy, effectiveness and stability cannot be guaranteed. In order to overcome the problems of low detection efficiency and poor detection accuracy in the measurement of surface structure parameters of existing precision parts, various chip manufacturers and users are trying to find high-precision, batch-scale, high-efficiency high-precision non-destructive detection methods or equipment for complex surface structures. It is also a general trend to solve the problems of surface structure detection and measurement accuracy in a universal way, which is of far-reaching significance.

[0004] In actual engineering, high-precision, batch inspection systems for precision-machined products need to be flexibly connected to the original complete production process (such as parts screening and manufacturing material preparation), and need to be able to adapt to various types of transmission mechanisms and non-standard inspection scenarios of application carriers. How to efficiently and accurately detect multiple areas of interest within the inspection area and measure key parameters such as surface structure size or aperture depth one by one is related to the success of component surface structure inspection and measurement in non-standard application scenarios. Summary of the invention

[0005] To this end, the present invention provides a method, storage medium and system for measuring the morphological parameters of complex structural components, so as to solve the positioning of the detection area of ​​interest and the measurement of the structural parameters in the test scenarios of the precision machining size, aperture depth, wall thickness and other test scenarios of complex surface structural components, and adapt to the application scenarios of batch parts detection under various types of transmission mechanisms, as well as customized transmission carriers such as trays and card slots, so as to improve the accuracy and stability of part surface structure detection, and ultimately improve the quality of parts in system applications.

[0006] In order to solve the above technical problems, the present invention provides a method for measuring the morphological parameters of complex structural components, comprising:

[0007] Calibrate the spectral confocal displacement sensor to establish a mapping relationship between the peak wavelength and the position of the object being measured;

[0008] Based on the calibrated spectral confocal displacement sensor, 3D point cloud data of the measured object surface is obtained by scanning;

[0009] Performing clustering processing on the 3D point cloud data to obtain clustered classification data;

[0010] Based on the random sampling consensus algorithm, model fitting is performed on the clustered classification data to obtain a plane or space model, and based on the plane or space model, the required structural parameters are obtained.

[0011] In one embodiment of the present invention, the spectral confocal displacement sensor is calibrated to establish a mapping relationship between the peak wavelength and the position of the measured object, including:

[0012] A white light source is used to emit complex-color light, and the complex-color light is regarded as a point light source after passing through the first pinhole;

[0013] When the measured object is within the measuring range, the light beam of the light source irradiates the surface of the measured object, and the light containing all the spectral information of the surface of the measured object is reflected;

[0014] The reflected light passes through the dispersion objective lens and the beam splitter prism and reaches the second pinhole. The first pinhole and the second pinhole have a conjugate relationship. Only the wavelength focused on the surface of the measured object will be received by the spectrometer through the second pinhole, and other wavelengths will form a diffuse spot and be blocked.

[0015] The spectrometer receives a spectrum signal including a peak wavelength, processes the spectrum signal collected by the spectrometer, and extracts the peak wavelength of the optical signal;

[0016] When the object under test moves within the measurement range, the peak wavelength changes. By acquiring the peak wavelengths at different positions, a mapping relationship between the peak wavelength and the position of the object under test is established, and the displacement value of the measured point on the object under test is obtained through the change in the peak wavelength.

[0017] In one embodiment of the present invention, obtaining 3D point cloud data of the surface of the object to be measured by scanning includes:

[0018] Using a one-dimensional or two-degree-of-freedom displacement platform, uniformly moving the spectral confocal displacement sensor and acquiring spectral signals in real time;

[0019] The spectral signal is processed, the peak wavelength is extracted, and the height or displacement value of each measurement point is calculated through the mapping relationship to obtain the 3D point cloud data measurement of a line or a plane on the surface structure of the measured object.

[0020] In one embodiment of the present invention, clustering is performed on the 3D point cloud data to obtain clustered classification data, including:

[0021] After removing outliers and / or noise points in the 3D point cloud data, coordinate normalization is performed;

[0022] The clustering radius is set according to the size characteristics of the object to be measured, and each point in the normalized 3D point cloud data is traversed to calculate the Euclidean distance between the point and other points in turn. If the Euclidean distance is within the clustering radius, other points are added to the cluster where the point is located.

[0023] In one embodiment of the present invention, model fitting is performed on the clustered classification data based on a random sampling consensus algorithm to obtain a plane or space model, including:

[0024] Using the clustered classified data as a total point set for model fitting, and randomly selecting a group of points from the total point set as an inner point group;

[0025] Based on the interior point group, an interior point group fitting model is obtained;

[0026] Generate a classifier according to the interior point group fitting model and the set maximum geometric interval, wherein the classifier is used to determine whether a point belongs to an interior point;

[0027] For all the unused points, based on the classifier, whether they are inliers is determined, the number of inliers calculated this time is obtained, and based on the number of inliers, the ratio of the number of inliers to the total point set is obtained;

[0028] Repeat the above steps, randomly reselect the interior point group to fit the model and calculate the interior point ratio, until the interior point ratio is greater than the set threshold or the maximum number of iterations is reached, and then the iteration ends;

[0029] The interior point group fitting model with the largest interior point ratio is selected as the final solution.

[0030] In one embodiment of the present invention, it also includes:

[0031] When the measured object is a hole and the hole depth is measured, the upper and lower surfaces of the target hole are measured respectively using a two-degree-of-freedom platform equipped with a point spectrum measuring instrument to obtain point cloud data on a line on the upper and lower surfaces of the target hole respectively;

[0032] The point cloud data of the upper and lower surfaces are divided into two independent cluster point clouds respectively;

[0033] Randomly select multiple points from the clustered point clouds of the upper and lower surfaces as the inner point groups of the upper and lower surfaces;

[0034] Based on the point groups in the upper and lower surfaces, the straight line model y=kx+b is used for fitting, and converted into a matrix form: XA=Y; the model coefficient A=(k,b) is calculated. -1 , that is, A=(X T X) -1 X T Y, get the fitting straight line of the upper and lower surfaces; according to the fitting straight line of the upper and lower surfaces, get the vertical distance between the fitting straight lines of the upper and lower surfaces, that is, the hole depth data.

[0035] The present invention provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for measuring the morphological parameters of a complex structural component as described in any one of claims 1 to 8 is implemented.

[0036] The present invention provides a system for measuring the morphological parameters of complex structural components of an aero-engine, comprising:

[0037] Spectral confocal displacement sensor, used to collect 3D point cloud data of the object being measured;

[0038] A one-dimensional or two-degree-of-freedom displacement platform, used for moving the spectral confocal displacement sensor to achieve continuous scanning;

[0039] The controller is used to control the displacement platform and the spectral confocal displacement sensor, and cluster the collected 3D point cloud data to obtain clustered classified data, perform model fitting on the clustered classified data based on a random sampling consensus algorithm to obtain a plane or space model, and obtain the required structural parameters based on the plane or space model.

[0040] The above technical solution of the present invention has the following advantages compared with the prior art:

[0041] The present invention discloses a method, storage medium and system for measuring the morphological parameters of complex structural components. The spectral confocal displacement sensor is used to establish a one-to-one mapping relationship between the peak wavelength and the position of the measured object through calibration, so as to achieve accurate measurement and is suitable for high-precision morphological detection of complex structural components. Through clustering processing and random sampling consensus algorithm (RANSAC), a plane or space model can be accurately fitted from point cloud data, effectively eliminating noise interference and improving measurement accuracy.

[0042] The present invention uses a spectral confocal displacement sensor that is not easily blocked, has low requirements on object placement and environment, and has strong adaptability to perform 3D structural surface measurement, and realizes model fitting of a set number of planes through clustering and random sampling consensus algorithms, and cooperates with high-precision point cloud extraction of 3D dynamic scanning to realize high-precision dynamic measurement of objects under complex structural surface measurement; the present invention is faster and more accurate than manual detection, and it is difficult for a monocular or multi-eye 2D lens to realize 3D detection in an environment with space blocking, let alone high-precision measurement; the present invention is faster than contact detection, and will not damage the structure and surface morphology of the object under measurement, and at the same time has strong environmental adaptability and is not affected by ambient light.

[0043] The present invention adopts clustering and random sampling consensus algorithm to perform internal point classification and plane model fitting, which can be well applied in vision, and its accuracy and feasibility have been verified; developers can calibrate and train models for new devices and carry out secondary development.

[0044] The present invention can achieve accurate 3D positioning and model fitting of multiple planes on complex structural surfaces, thereby realizing online detection of the surface morphology of the object under test; it has strong adaptability to the use environment and a wide range of application space; it supports structural defect detection of batch objects under complex test environments including trays with card slots, and realizes accurate and batch detection of objects under non-standard test tooling in complex application scenarios, which facilitates the integration of existing application scenarios while improving the safety and quality inspection requirements of precision machined parts in the processing and manufacturing links. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0046] Figure 1 The present invention is a flow chart of the method for measuring the morphological parameters of complex structural components.

[0047] Figure 2 Schematic diagram of the point scanning spectral confocal displacement sensor.

[0048] Figure 3 Schematic diagram of the measurement range of the point scanning spectral confocal displacement sensor.

[0049] Figure 4 It is a schematic diagram of the structure of the measurement system for the morphological parameters of complex structural components of the present invention.

[0050] Figure 5 Schematic diagram of the 3D data of the aperture of a curved component measured by spectral confocal displacement sensing translation; Figure 5 (a) is a schematic diagram of the classification of interior points of each plane. Figure 5 (b) Schematic diagram of the structural parameters obtained after model fitting. DETAILED DESCRIPTION

[0051] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0052] In the present invention, if directions (up, down, left, right, front and back) are described, it is only for the convenience of describing the technical solution of the present invention, and does not indicate or imply that the technical features referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, it cannot be understood as a limitation of the present invention.

[0053] In the present invention, "several" means one or more, "multiple" means more than two, "greater than", "less than", "exceed" and the like are understood to exclude the number itself; "above", "below", "within" and the like are understood to include the number itself. In the description of the present invention, if there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0054] In the present invention, unless otherwise clearly defined, the words "set", "install", "connect" and the like should be understood in a broad sense, for example, they can be directly connected or indirectly connected through an intermediate medium; they can be fixedly connected or detachably connected or integrally formed; they can be mechanically connected or electrically connected or able to communicate with each other; they can be the internal connection of two elements or the interaction relationship between two elements. Those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0055] Reference Figure 1 As shown, a method for measuring the morphological parameters of a complex structural component of the present invention comprises:

[0056] S1. Calibrate the spectral confocal displacement sensor to establish the mapping relationship between the peak wavelength and the position of the object being measured; obtain the parameters of the spectral camera (effective measurement range and optimal focus). Specifically include:

[0057] A white light source is used to emit complex-color light, and the complex-color light is regarded as a point light source after passing through the first pinhole;

[0058] When the measured object is within the measuring range, the light beam of the light source irradiates the surface of the measured object, and the light containing all the spectral information of the surface of the measured object is reflected;

[0059] The reflected light passes through the dispersion objective lens and the beam splitter prism and reaches the second pinhole. The first pinhole and the second pinhole have a conjugate relationship. Only the wavelength focused on the surface of the measured object will be received by the spectrometer through the second pinhole, and other wavelengths will form a diffuse spot and be blocked.

[0060] The spectrometer receives a spectrum signal including a peak wavelength, processes the spectrum signal collected by the spectrometer, and extracts the peak wavelength of the optical signal;

[0061] When the object under test moves within the measurement range, the peak wavelength changes. By acquiring the peak wavelengths at different positions, a mapping relationship between the peak wavelength and the position of the object under test is established, and the displacement value of the measured point on the object under test is obtained through the change in the peak wavelength.

[0062] It should be noted that the height measurement of a point on a surface structure is achieved through the spectral confocal displacement sensor. The spectral confocal displacement sensor establishes a one-to-one correspondence between the focal distance and the wavelength through optical dispersion. Through displacement scanning, 3D point cloud data that characterizes the surface profile, morphology, and thickness of transparent objects can be detected and obtained, and multiple probe combinations can be used to achieve more dimensional and functional measurements.

[0063] Spectral confocal 3D scanning uses a spectral confocal displacement sensor. The detection principle is as follows Figure 2As shown in the figure, a beam of complex light is emitted by a white light source, which can be approximately regarded as a point light source after passing through the pinhole P. Due to the different refractive indices of light of different wavelengths, after passing through the beam splitter prism and the dispersive objective lens, the light of different wavelengths is focused at different positions on the optical axis, thus forming a series of focused light spots, which is axial dispersion.

[0064] When the object to be measured is within the measurement range, the light containing all the spectral information of the measured surface passes through the measured object without reflection and then passes through the dispersive objective lens and the dichroic prism to reach the pinhole P'. Since the pinholes P and P' have a conjugate relationship, only the wavelength focused on the surface of the object to be measured will be focused at the pinhole P', and thus all will be received by the spectrometer. However, light of other wavelengths cannot be focused at the pinhole P', forming a diffuse spot, which is blocked by the pinhole P'. Very little light is detected by the spectrometer, and the pinhole P' plays the role of a small hole filter. After a series of data processing of the spectral signal collected by the spectrometer, the peak wavelength can be obtained. When the object to be measured moves within the measurement range, the peak wavelength will change. The extracted peak wavelength has a one-to-one correspondence with the position of the object to be measured. The mapping relationship between the peak wavelength and the position of the object to be measured can be obtained through calibration. During measurement, the displacement value of the object to be measured can be obtained through the change of the peak wavelength. The peak wavelength can be obtained by performing a series of data processing on the spectral signals collected by the spectrometer. When the object to be measured moves within the measurement range, the peak wavelength will change. The extracted peak wavelength has a one-to-one correspondence with the position of the object to be measured. The mapping relationship between the peak wavelength and the position of the object to be measured can be obtained through calibration. During measurement, the displacement value of the measured point on the object to be measured can be obtained through the change in the peak wavelength. In the spectral confocal displacement measurement system, light of different wavelengths is focused on different positions on the optical axis after passing through the dispersion lens. The focusing point of light with a short wavelength is close to the dispersion lens, and the focusing point of light with a long wavelength is far from the dispersion lens. Within the working wavelength range, the shortest wavelength λ min With the longest wavelength λ max The distance between the focus points is the measurement range ΔL, which corresponds to the height measurement range of the measured surface structure, such as Figure 3 It can be expressed as:

[0065] ΔL=f(λ max )-f(λ min ).

[0066] S2. Based on the calibrated spectral confocal displacement sensor, obtain 3D point cloud data of the surface of the object to be measured by scanning.

[0067] Specifically, a one-dimensional or two-degree-of-freedom displacement platform is used to uniformly move the spectral confocal displacement sensor and acquire spectral signals in real time;

[0068] The spectral signal is processed, the peak wavelength is extracted, and the height or displacement value of each measurement point is calculated through the mapping relationship to obtain the 3D point cloud data measurement of a line or a plane on the surface structure of the measured object.

[0069] It is understandable that during one-dimensional scanning, the spectral confocal displacement sensor moves along a line to collect 3D point cloud data on the line. During two-dimensional scanning, the spectral confocal displacement sensor moves line by line in a plane to obtain complete surface data.

[0070] S3, clustering the 3D point cloud data to obtain clustered classification data. Specifically, after removing outliers and / or noise points in the 3D point cloud data (such as by using statistical filtering or radius filtering methods), coordinate normalization is performed to facilitate subsequent distance calculation and clustering processing;

[0071] The cluster radius is set according to the size characteristics of the object to be measured (such as the aperture depth), and each point in the normalized 3D point cloud data is traversed, and the Euclidean distance between the point and other points is calculated in turn. If the Euclidean distance is within the cluster radius, the other points are added to the cluster where the point is located. The point cloud data of each cluster is classified into a separate data set, representing different shapes or regions.

[0072] For example, for the point cloud data of a hole, the point clouds of the upper and lower surfaces are classified into two clusters through Euclidean distance clustering, which is convenient for subsequent plane fitting. For the point cloud data of complex surfaces, density clustering is used to distinguish different curvature areas or segmented surfaces.

[0073] S4, performing model fitting on the clustered classified data based on a random sampling consensus algorithm to obtain a plane or space model, and obtaining the required structural parameters based on the plane or space model. The type of model to be fitted can be determined according to the geometric characteristics of the classified data. Specifically, the clustered classified data is used as the total point set for model fitting, and a group of points is randomly selected from the total point set as the inner point group;

[0074] Based on the interior point group, an interior point group fitting model is obtained;

[0075] A classifier is generated based on the interior point group fitting model and the set maximum geometric interval, and the classifier is used to determine whether a point is an interior point; for example, for a plane equation model, all points in the classified data are substituted into the model, and the distance of each point to the model is calculated. If the distance is less than the set distance threshold, the point is considered to be an interior point.

[0076] For all the unused points, based on the classifier, whether they are inliers is determined, the number of inliers calculated this time is obtained, and based on the number of inliers, the ratio of the number of inliers to the total point set is obtained;

[0077] Repeat the above steps, randomly reselect the interior point group to fit the model and calculate the interior point ratio, until the interior point ratio is greater than the set threshold or the maximum number of iterations is reached, and then the iteration ends;

[0078] The interior point group fitting model with the largest interior point ratio is selected as the final solution.

[0079] Exemplarily, when the measured object is a hole and the hole depth is measured, a two-degree-of-freedom platform equipped with a point spectrum measuring instrument is used to measure the upper and lower surfaces (upper surface and bottom surface) of the target hole respectively, and point cloud data on a line of the upper and lower surfaces of the target hole are respectively obtained;

[0080] Reference Figure 5 As shown in (a), the measurement data of the upper and lower surfaces of the hole are a straight line respectively. To obtain the hole depth data, the upper and lower parts of the data must be distinguished first, and clustered using Euclidean distance. According to the actual size characteristics of the upper and lower surfaces of the hole, the cluster radius R is set, the point cloud data is traversed, and the Euclidean distance between the point and other points is calculated. If the distance is less than the cluster radius R, the point is classified into the same cluster. Finally, the point cloud data of the upper and lower surfaces are divided into two independent cluster point clouds.

[0081] On the basis of clustering the upper and lower surface data, a straight line fitting is performed based on a random sampling consensus algorithm, and multiple points (at least 3 points) are randomly selected from the clustered point clouds of the upper and lower surfaces as the inner point groups of the upper and lower surfaces;

[0082] Based on the point groups in the upper and lower surfaces, the straight line model y=kx+b is used for fitting, and converted into a matrix form: XA=Y; the model coefficient A=(k,b) is calculated. -1 , that is, A=(X T X) -1 X T Y, substitute other points into the fitting model, calculate the distance from each point to the straight line model, if the distance is less than the set threshold, the point is identified as an inlier, repeat random sampling and model fitting, record the number of inliers each time, select the fitting model with the largest proportion of inliers as the final result, and obtain the fitting straight lines of the upper and lower surfaces respectively; according to the fitting straight lines of the upper and lower surfaces, obtain the vertical distance between the fitting straight lines of the upper and lower surfaces, i.e. the hole depth data. Figure 5 (b) as shown. Figure 5 The upper curve in (b) is the upper surface fitting model of the surface, and the lower curve is the fitting straight line model of the bottom of the aperture. The vertical distance between the two straight lines is calculated to obtain the hole depth data. For the convenience of analysis, the required structural parameters such as the maximum, minimum and average values ​​of the distance are calculated respectively.

[0083] The above method is used for the inspection of curved structural parts (such as aperture depth and wall thickness). By controlling the displacement platform at a uniform speed, the spectral confocal displacement sensor is used to continuously collect point cloud data in space. The controller collects point cloud data and uses the measurement method of the morphological parameters of complex structural parts to obtain the precise structural model and parameters of each measured plane, calculate other required relative structural parameters, and evaluate the processing quality according to the set tolerance.

[0084] The present invention also provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for measuring the morphological parameters of a complex structural component is implemented.

[0085] The present invention also provides a system for measuring the morphological parameters of complex structural components of an aero-engine, comprising:

[0086] Spectral confocal displacement sensor, used to collect 3D point cloud data of the object being measured;

[0087] A one-dimensional or two-degree-of-freedom displacement platform, used for moving the spectral confocal displacement sensor to achieve continuous scanning;

[0088] The controller is used to control the displacement platform and the spectral confocal displacement sensor, and cluster the collected 3D point cloud data to obtain clustered classified data, perform model fitting on the clustered classified data based on a random sampling consensus algorithm to obtain a plane or space model, and obtain the required structural parameters based on the plane or space model.

[0089] Reference Figure 4 The figure shows the structure diagram of the measurement system for the morphological parameters of complex structural parts. The core part is the controller, which is responsible for the image data processing, statistics and command sending and receiving of the entire system. The controller contains the following submodules:

[0090] Image acquisition communication interface, receiving image data (3D point cloud data) from the spectral confocal displacement sensor;

[0091] Ethernet communication interface, to communicate with external devices or networks for data transmission or remote control;

[0092] The core control unit processes the image data, including cluster classification, model fitting and parameter calculation, and is used to control the working status of the displacement platform and spectral camera;

[0093] Input / output interface, receives input signals from auxiliary equipment such as laser sensors, and outputs the processing results to the display or human-machine interface.

[0094] In addition, the image acquisition component includes a spectral camera (spectral confocal displacement sensor) and a related interface module, which receives a stable power supply provided by the constant voltage source module and transmits data with the image acquisition communication interface of the controller;

[0095] The human-machine interface provides a user interaction interface for setting measurement parameters and displaying real-time status and results;

[0096] The constant voltage source module provides stable power for the spectral camera and other modules, ensuring the stable operation of the system in complex environments;

[0097] The laser sensor is used in conjunction with the spectral camera to perform measurements and provide precise location information;

[0098] The displacement platform is a one-dimensional or two-degree-of-freedom displacement platform, which moves the spectral confocal displacement sensor so that it can continuously scan the object to be measured, covering the entire topographic area. The controller issues a movement instruction to achieve uniform speed or specified path scanning;

[0099] The display shows the measured morphological data, clustering results, fitting model and calculated key parameters (such as surface, pore size, wall thickness, etc.) in real time.

[0100] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] Finally, it should be noted that the above specific implementation methods are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for measuring the morphological parameters of complex structural components, characterized in that: include: Calibrate the spectral confocal displacement sensor to establish a mapping relationship between the peak wavelength and the position of the object being measured; Based on the calibrated spectral confocal displacement sensor, 3D point cloud data of the measured object surface is obtained by scanning; Performing clustering processing on the 3D point cloud data to obtain clustered classification data; Based on the random sampling consensus algorithm, model fitting is performed on the clustered classification data to obtain a plane or space model, and based on the plane or space model, the required structural parameters are obtained.

2. The method for measuring the morphological parameters of a complex structural component according to claim 1, characterized in that: Calibrate the spectral confocal displacement sensor to establish a mapping relationship between the peak wavelength and the position of the object being measured, including: A white light source is used to emit complex-color light, and the complex-color light is regarded as a point light source after passing through the first pinhole; When the measured object is within the measuring range, the light beam of the light source irradiates the surface of the measured object, and the light containing all the spectral information of the surface of the measured object is reflected; The reflected light passes through the dispersion objective lens and the beam splitter prism and reaches the second pinhole. The first pinhole and the second pinhole have a conjugate relationship. Only the wavelength focused on the surface of the measured object will be received by the spectrometer through the second pinhole, and other wavelengths will form a diffuse spot and be blocked. The spectrometer receives a spectrum signal including a peak wavelength, processes the spectrum signal collected by the spectrometer, and extracts the peak wavelength of the optical signal; When the object under test moves within the measurement range, the peak wavelength changes. By acquiring the peak wavelengths at different positions, a mapping relationship between the peak wavelength and the position of the object under test is established, and the displacement value of the measured point on the object under test is obtained through the change in the peak wavelength.

3. The method for measuring the morphological parameters of a complex structural component according to claim 1, characterized in that: The 3D point cloud data of the object surface is obtained by scanning, including: Using a one-dimensional or two-degree-of-freedom displacement platform, uniformly moving the spectral confocal displacement sensor and acquiring spectral signals in real time; The spectral signal is processed, the peak wavelength is extracted, and the height or displacement value of each measurement point is calculated through the mapping relationship to obtain the 3D point cloud data measurement of a line or a plane on the surface structure of the measured object.

4. The method for measuring the morphological parameters of a complex structural component according to claim 1, characterized in that: The 3D point cloud data is clustered to obtain clustered classification data, including: After removing outliers and / or noise points in the 3D point cloud data, coordinate normalization is performed; The clustering radius is set according to the size characteristics of the object to be measured, and each point in the normalized 3D point cloud data is traversed to calculate the Euclidean distance between the point and other points in turn. If the Euclidean distance is within the clustering radius, other points are added to the cluster where the point is located.

5. The method for measuring the morphological parameters of a complex structural component according to claim 3, characterized in that: Performing model fitting on the clustered classified data based on a random sampling consensus algorithm to obtain a plane or space model includes: Using the clustered classified data as a total point set for model fitting, and randomly selecting a group of points from the total point set as an inner point group; Based on the interior point group, an interior point group fitting model is obtained; Generate a classifier according to the interior point group fitting model and the set maximum geometric interval, wherein the classifier is used to determine whether a point belongs to an interior point; For all the unused points, based on the classifier, whether they are inliers is determined, the number of inliers calculated this time is obtained, and based on the number of inliers, the ratio of the number of inliers to the total point set is obtained; Repeat the above steps, randomly reselect the interior point group to fit the model and calculate the interior point ratio, until the interior point ratio is greater than the set threshold or the maximum number of iterations is reached, and then the iteration ends; The interior point group fitting model with the largest interior point ratio is selected as the final solution.

6. The method for measuring the morphological parameters of a complex structural component according to claim 5, characterized in that: Also includes: When the measured object is a hole and the hole depth is measured, the upper and lower surfaces of the target hole are measured respectively using a two-degree-of-freedom platform equipped with a point spectrum measuring instrument to obtain point cloud data on a line on the upper and lower surfaces of the target hole respectively; The point cloud data of the upper and lower surfaces are divided into two independent cluster point clouds respectively; Randomly select multiple points from the clustered point clouds of the upper and lower surfaces as the inner point groups of the upper and lower surfaces; Based on the point groups in the upper and lower surfaces, the straight line model y=kx+b is used for fitting, and converted into a matrix form: XA=Y; the model coefficient A=(k,b) is calculated. -1 , that is, A=(X T X) -1 X T Y, get the fitting straight line of the upper and lower surfaces; according to the fitting straight line of the upper and lower surfaces, get the vertical distance between the fitting straight lines of the upper and lower surfaces, that is, the hole depth data.

7. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for measuring the morphological parameters of a complex structural component as described in any one of claims 1 to 8 is implemented.

8. A system for measuring the morphological parameters of complex structural parts of an aircraft engine, characterized in that: include: Spectral confocal displacement sensor, used to collect 3D point cloud data of the object being measured; A one-dimensional or two-degree-of-freedom displacement platform, used for moving the spectral confocal displacement sensor to achieve continuous scanning; The controller is used to control the displacement platform and the spectral confocal displacement sensor, and cluster the collected 3D point cloud data to obtain clustered classified data, perform model fitting on the clustered classified data based on a random sampling consensus algorithm to obtain a plane or space model, and obtain the required structural parameters based on the plane or space model.

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