Coating thickness measuring method and system based on ultrasonic waves

Abstract

The invention discloses an ultrasonic-based coating thickness measurement method and system, and the method comprises the steps: recognizing a to-be-measured workpiece structure, obtaining surface curvature data, generating initial coating thickness detection points in an area with large curvature change, carrying out the distance measurement of the points through an ultrasonic probe, carrying out the time domain-frequency domain combined analysis of echo signals, and carrying out the measurement of the initial coating thickness. And obtaining a thickness matrix containing the coating thickness value, the confidence coefficient and the neighborhood correlation parameters, dynamically updating the position of the detection point by means of a clustering algorithm according to the thickness matrix, and repeating the operation until the termination condition of the space coverage rate difference or the number of the detection points is met. The method effectively improves the accuracy and efficiency of coating thickness measurement through the operations of detection point determination based on the workpiece structure, ultrasonic measurement and echo data analysis, dynamic detection point updating and the like, and is especially suitable for coating detection of complex workpieces.

Description

Technical Field

[0001] The present invention relates to the technical field of coating thickness measurement, and in particular to an ultrasonic-based coating thickness measurement method and system. Background Art

[0002] In the field of industrial production and quality inspection, accurate measurement of coating thickness is crucial to ensure product quality, performance and service life. Take the automobile wheel hub in automobile manufacturing as an example. Its surface coating not only affects the appearance, but also affects key performance such as corrosion resistance and wear resistance.

[0003] At present, traditional coating thickness measurement methods have many shortcomings. Some methods use full-volume measurement methods. Although they can obtain comprehensive data, they are extremely inefficient and time-consuming and labor-intensive in large-scale production inspection scenarios, and it is difficult to meet the production rhythm requirements. Although other methods introduce the idea of ​​detection point measurement to improve efficiency, there is a lack of scientific basis for the determination of detection points. Most of them are selected based on experience or fixed positions, which cannot accurately match the complex structural characteristics of the workpiece and the law of coating thickness changes, resulting in poor measurement results.

[0004] With the increasing complexity of workpiece structures, such as the emergence of parts with irregular surfaces and special structural features, and the continuous improvement of coating quality requirements, the defects of existing coating thickness measurement technology in measurement efficiency and accuracy have become increasingly prominent, and a coating thickness measurement method that can balance efficiency and accuracy is urgently needed. Summary of the invention

[0005] In order to solve at least one of the above-mentioned technical problems, the present invention provides a coating thickness measurement method and system based on ultrasound.

[0006] In a first aspect, the present invention provides a coating thickness measurement method based on ultrasound, the method comprising:

[0007] S100, acquiring surface curvature data based on a structure recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient;

[0008] S200, generating an initial coating thickness detection point set in an area where a curvature change gradient exceeds a preset threshold value according to the surface curvature data;

[0009] S300, using an ultrasonic probe to measure the distance of the initial coating thickness detection point to obtain echo signal data of each detection point;

[0010] S400, performing a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence, and neighborhood correlation parameters;

[0011] S500, dynamically updating the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set;

[0012] S600, repeat steps S300-S500 until one of the following termination conditions is met:

[0013] a) The difference in spatial coverage of the detection point set between two consecutive iterations is less than 5%;

[0014] b) The number of detection points reaches the preset surface complexity correlation threshold.

[0015] Preferably, the S400 includes the following sub-steps:

[0016] S410, performing wavelet threshold noise reduction on the ultrasonic echo signal;

[0017] S420, using the Teager energy operator to perform wave peak detection on the ultrasonic echo signal to identify the direct wave front time and the interface reflection wave front time; calculating the coating thickness base value according to the direct wave front time, the interface reflection wave front time and the preset sound velocity parameter;

[0018] S430, perform FFT transformation on the signal in the reflected wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the coating thickness base value according to the thickness value trimming model. The thickness value trimming model is expressed as:

[0019] ,

[0020] In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient;

[0021] S440, calculating the confidence level of the coating thickness base value by the signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude;

[0022] S450, calculating the neighborhood association parameters between the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient;

[0023] S460, constructing a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

[0024] Preferably, the clustering algorithm in step S500 adopts an improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points, and the adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

[0025] In a second aspect, the present invention further provides a coating thickness measurement system based on ultrasound, the system comprising:

[0026] A structural curvature analysis module, used for acquiring surface curvature data based on the structural recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient;

[0027] An initial coating thickness detection point generation module, used to generate an initial coating thickness detection point set in an area where the curvature change gradient exceeds a preset threshold value according to the surface curvature data;

[0028] An ultrasonic distance measurement module, used to measure the distance of the initial coating thickness detection points using an ultrasonic probe to obtain echo signal data of each detection point;

[0029] The echo data processing and matrix construction module performs a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence and neighborhood correlation parameters;

[0030] A detection point optimization module dynamically updates the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set;

[0031] The iterative control module is used to repeatedly execute the ultrasonic ranging module, the echo data processing and matrix construction module and the detection point optimization module until one of the following termination conditions is met: the difference in spatial coverage of the detection point set of two consecutive iterations is less than 5%; the number of detection points reaches a preset surface complexity association threshold.

[0032] Preferably, the echo data processing and matrix construction module comprises:

[0033] A signal noise reduction unit, used for performing wavelet threshold noise reduction on the ultrasonic echo signal;

[0034] The peak detection and thickness calculation unit is used to detect the peak of the ultrasonic echo signal using the Teager energy operator to identify the front time of the direct wave and the front time of the interface reflection wave; the basic value of the coating thickness is calculated according to the front time of the direct wave, the front time of the interface reflection wave and the preset sound velocity parameters;

[0035] The thickness value refinement unit is used to perform FFT transformation on the signal in the reflection wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the basic value of the coating thickness according to the thickness value trimming model. The thickness value trimming model is expressed as:

[0036] ,

[0037] In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient;

[0038] A confidence evaluation unit, used for calculating the confidence of the coating thickness base value by means of a signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude;

[0039] Neighborhood parameter calculation unit, used to calculate the neighborhood association parameters between the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient;

[0040] The matrix integration construction unit is used to construct a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

[0041] Preferably, the clustering algorithm in the detection point optimization module adopts an improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points. The adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

[0042] In a third aspect, the present invention further provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0043] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, wherein the computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation thereof.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1) The present invention obtains surface curvature data based on workpiece structure identification to determine the initial coating thickness detection points, combines ultrasonic ranging with echo data processing to construct a thickness matrix, and uses a clustering algorithm to dynamically update the detection points. First, the detection points are determined based on the surface curvature, which can accurately focus on the area where the coating thickness is prone to change, thereby improving the measurement pertinence and accuracy; secondly, the echo data is processed in multiple dimensions including confidence calculation to ensure the reliability of the measurement data; further, the clustering algorithm can perform cluster analysis on the detection points based on the measurement confidence and neighborhood association parameters, screen out detection points with low reliability and move them to reliable areas, avoid invalid measurements, and achieve an optimized layout of the detection points; further, the mechanism of dynamically updating the detection points optimizes the detection process, significantly improves the efficiency of coating thickness measurement, and is particularly suitable for complex workpieces such as automobile wheels, and has good application value and competitive advantages in the field of industrial coating detection.

[0046] 2) The present invention adopts an improved DBSCAN algorithm to adaptively adjust the density parameters according to the thickness gradient of adjacent detection points. On the one hand, it can accurately adapt to the coating thickness distribution characteristics of complex workpieces such as automobile wheels. In places with large thickness gradients, the density parameters are dynamically adjusted to make clustering more refined, accurately capture thickness change areas, and improve measurement accuracy; on the other hand, adaptive adjustment avoids over-crowding or over-sparse clustering under fixed parameters, optimizes the layout of detection points, reduces invalid detection points, and improves measurement efficiency, especially in industrial detection scenarios with complex surfaces and uneven coating thickness, effectively enhancing measurement reliability and flexibility, and having significant technical advantages and application value.

[0047] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0049] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0050] Figure 1A schematic flow chart of a coating thickness measurement method based on ultrasound provided in an embodiment of the present invention;

[0051] Figure 2 A schematic flow chart of sub-steps of step S400 of a coating thickness measurement method based on ultrasound provided in an embodiment of the present invention;

[0052] Figure 3 A schematic structural diagram of an ultrasonic-based coating thickness measurement system provided in an embodiment of the present invention;

[0053] Figure 4 The embodiment of the present invention provides Figure 3 A schematic diagram of the structure of the submodules of the echo data processing and matrix construction module 400. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0056] Traditional coating thickness measurement methods have problems such as low efficiency and unscientific detection point determination, which are difficult to meet the measurement needs of complex workpieces and high-quality coatings. The existing measurement ideas based on workpiece structure recognition also need to be improved.

[0057] See also Figure 1 , Figure 1 The present invention provides a schematic flow chart of a coating thickness measurement method based on ultrasound. Figure 1 As shown, the method includes:

[0058] S100, acquiring surface curvature data based on a structure recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient;

[0059] Use a laser scanner or a 3D scanner to scan the workpiece to obtain its surface point cloud data; calculate the normal vector of each point in the point cloud data, and then calculate the surface radius of curvature and the curvature gradient. The principal curvature method or Gaussian curvature method can be used for curvature analysis; the curvature radius and curvature gradient data are stored in the database. For example, near the edge of the hub and the bolt hole, due to the large shape change and obvious curvature gradient, the surface curvature data of these areas are recorded to prepare for the subsequent determination of the inspection points.

[0060] S200, generating an initial coating thickness detection point set in an area where a curvature change gradient exceeds a preset threshold value according to the surface curvature data;

[0061] According to the coating characteristics and application requirements of the workpiece, a preset threshold value of the curvature change gradient is set; the curvature data is traversed to identify the area where the curvature change gradient exceeds the preset threshold value, and it is marked as the initial coating thickness detection point; all qualified detection points are summarized to form an initial coating thickness detection point set. In one possible embodiment, a preset threshold value of the curvature change gradient is set, such as 0.8. In the area where the curvature change gradient of the hub surface exceeds the threshold value, the initial coating thickness detection point is generated using a grid-based method. In the hub edge area, a detection point is determined every 8 mm to ensure that there are a sufficient number of detection points in areas with complex shapes and large curvature changes to improve measurement accuracy.

[0062] S300, using an ultrasonic probe to measure the distance of the initial coating thickness detection point to obtain echo signal data of each detection point;

[0063] Select a suitable ultrasonic probe to ensure that its frequency and sensitivity meet the coating measurement requirements; at each initial coating thickness detection point, transmit an ultrasonic signal and record the echo signal; collect the echo signal of each detection point to ensure data integrity. In this implementation, an ultrasonic probe with a frequency of 5MHz and a beam angle of 30° is selected, which is suitable for coating measurement of curved surface structures such as automobile wheels. The ultrasonic probe is connected to a high-precision ultrasonic thickness gauge, and at each initial coating thickness detection point, the probe is placed perpendicular to the wheel surface for measurement. To reduce errors, each point is measured 3 times, and the echo signal data obtained from each measurement is recorded, and the average value is taken as the final echo signal data of the detection point.

[0064] S400, performing a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence, and neighborhood correlation parameters;

[0065] See also Figure 2 , Figure 2 A schematic flow chart of the sub-steps of step S400 of a coating thickness measurement method based on ultrasound provided in an embodiment of the present invention. Figure 2 As shown:

[0066] Preferably, S400 includes the following sub-steps:

[0067] S410, performing wavelet threshold noise reduction on the ultrasonic echo signal;

[0068] The sym8 wavelet basis function is selected to perform wavelet transform on the echo signal, and the soft threshold method is used to determine the threshold according to the signal characteristics. After wavelet threshold noise reduction processing, the noise in the signal is removed, the signal quality is improved, and a guarantee is provided for subsequent accurate analysis.

[0069] S420, using the Teager energy operator to perform wave peak detection on the ultrasonic echo signal to identify the direct wave front time and the interface reflection wave front time; calculating the coating thickness base value according to the direct wave front time, the interface reflection wave front time and the preset sound velocity parameter;

[0070] Use the Teager energy operator to detect the peak of the echo signal after noise reduction and identify the front time of the direct wave and the interface reflection wave front time The speed of ultrasound in the coating material is known to be , according to the formula Calculate the base value of coating thickness. , ,but .

[0071] S430, perform FFT transformation on the signal in the reflected wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the coating thickness base value according to the thickness value trimming model. The thickness value trimming model is expressed as:

[0072] ,

[0073] In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient;

[0074] Perform fast Fourier transform (FFT) on the signal in the reflected wave time window and calculate the energy spectrum integral value of the preset characteristic frequency band (2-4MHz) . By measuring the standard sample, the reference energy value of the standard sample is obtained. , the dispersion coefficient of the coating material is known , trim the model according to the thickness value The coating thickness base value is corrected. In this embodiment, , ,but .

[0075] S440, calculating the confidence level of the coating thickness base value by the signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude;

[0076] Use an ultrasonic thickness gauge to collect the direct wave signal amplitude at each initial coating thickness detection point on the automobile wheel hub and the reflected wave signal amplitude According to the formula Calculate signal attenuation rate For example, at a certain detection point, =12, =5, then: Before the measurement, the standard sample with the same coating material and process as the automobile wheel hub is measured multiple times under the calibration conditions to determine the ideal attenuation rate. According to the formula Calculating confidence .Will =12, =5Substitute: If the calculated , corrected to 1; if , corrected to 0. The final confidence of this detection point The value is .

[0077] Using the signal attenuation rate to evaluate the measurement reliability and calculate the confidence level can more accurately reflect the reliability of the measurement results of the detection points, provide a more accurate basis for the subsequent construction of the thickness matrix and dynamic update of the detection points, and improve the accuracy and reliability of the automobile wheel hub coating thickness measurement.

[0078] S450, calculating the neighborhood association parameters between the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient;

[0079] Calculate the neighborhood association parameters between the current detection point and the adjacent points. Assume that the thickness standard deviation of the current point is , standard deviation of thickness of adjacent points , spatial correlation coefficient ρ = 0.7, according to the formula The neighborhood correlation parameter matrix is ​​calculated as .

[0080] S460, constructing a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

[0081] The thickness matrix is ​​constructed based on the coating thickness value, confidence level and neighborhood association parameters. For example, for a certain detection point, the coating thickness value is 6.272 mm and the confidence level is , the neighborhood correlation parameter matrix is , then the thickness matrix of the detection point is ].

[0082] S500, dynamically updating the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set;

[0083] The DBSCAN clustering algorithm is used to input multiple thickness matrices. The algorithm performs cluster analysis on the detection points according to the measurement confidence and neighborhood association parameters. For detection points with confidence lower than 0.4 and abnormal neighborhood association parameters, they are moved to areas with high confidence and reasonable neighborhood association parameters, such as moving 3mm, to form an optimized detection point set.

[0084] S600, repeat steps S300-S500 until one of the following termination conditions is met:

[0085] The difference in spatial coverage of the detection point set between two consecutive iterations is less than 5%;

[0086] The number of detection points reaches the preset surface complexity correlation threshold.

[0087] Repeat steps S3-S5, and calculate the difference in spatial coverage of the detection point set and the number of detection points after each iteration. The spatial coverage of the detection point set in the first iteration is 75%, and the second is 78%, with a difference of 3%. When the difference in spatial coverage of the detection point set in two consecutive iterations is less than 5%, or the number of detection points reaches a threshold preset according to the complexity of the wheel hub surface (such as 50 detection points), the iteration is stopped and the measurement of the coating thickness on the surface of the automobile wheel hub is completed.

[0088] In this embodiment, surface curvature data is obtained based on workpiece structure recognition to determine the initial coating thickness detection points, and a thickness matrix is ​​constructed by combining ultrasonic ranging and echo data processing, and a clustering algorithm is used to dynamically update the detection points. First, the detection points are determined based on the surface curvature, which can accurately focus on the area where the coating thickness is prone to change, thereby improving the measurement pertinence and accuracy; secondly, the echo data is processed in multiple dimensions including confidence calculation to ensure the reliability of the measurement data; further, the clustering algorithm can perform cluster analysis on the detection points according to the measurement confidence and neighborhood association parameters, screen out detection points with low reliability and move them to reliable areas, avoid invalid measurements, and achieve an optimized layout of the detection points; further, the mechanism of dynamically updating the detection points optimizes the detection process, significantly improves the efficiency of coating thickness measurement, and is particularly suitable for complex workpieces such as automobile wheels, and has good application value and competitive advantages in the field of industrial coating detection.

[0089] Preferably, the clustering algorithm in step S500 adopts an improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points, and the adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

[0090] After completing step S460 to construct the thickness matrix, the thickness matrix of each initial coating thickness detection point has been obtained, including information such as coating thickness value, confidence, and neighborhood association parameters. Taking a set of detection points on a car wheel hub as an example, assuming that there are 10 detection points in total, the thickness matrix information is as follows: Test point number Coating thickness value (mm) Confidence neighborhood correlation parameter matrix 1 5.8 0.9 ; 2 6.0 0.85 ; ... ... ... ...; 10 5.6 0.92 ;

[0091] Set initial density parameters , curvature sensitivity factor . Calculate the average thickness of the current area , add up the coating thickness values ​​of the 10 test points and divide by 10, assuming that the result is 5.8mm.

[0092] For test point 1 and test point 2, the coating thickness value of test point 1 is 5.8mm, and the coating thickness value of test point 2 is 6.0mm, so the thickness difference between adjacent points is 0.2mm. According to the formula Calculate the density parameter corresponding to detection point 1 Similarly, calculate the thickness difference between other adjacent detection points in turn, and calculate the corresponding density parameters according to the formula .

[0093] The thickness matrix of each detection point and the corresponding adaptively adjusted density parameters Input into the improved DBSCAN algorithm. Taking detection point 1 as an example, the algorithm takes detection point 1 as the core point. Search for other points in the neighborhood with a radius of . Assuming that detection points 2 and 3 are found in the neighborhood, and the confidence of these three points is higher than the set threshold (such as 0.8), and the neighborhood association parameters also meet certain conditions, they are divided into a cluster. For detection points with confidence lower than the threshold or abnormal neighborhood association parameters, such as the confidence of detection point 4 is 0.75, which is lower than 0.8, the algorithm marks it as a noise point and moves it to an area with high confidence and reasonable neighborhood association parameters according to the position of reliable detection points in the surrounding clusters. Assuming the moving distance is 2mm, an optimized detection point set is formed.

[0094] After completing the cluster analysis and detection point adjustment, proceed to the subsequent steps, such as repeating steps S3-S5 (i.e., performing ultrasonic ranging, echo signal processing, thickness matrix construction, and dynamic updating of detection points again) until the termination condition is met (such as the difference in spatial coverage of the detection point set for two consecutive iterations is less than 5%, or the number of detection points reaches a preset threshold based on the complexity of the wheel hub surface), and the measurement of the coating thickness on the surface of the automobile wheel hub is completed.

[0095] In this embodiment, an improved DBSCAN algorithm is used to adaptively adjust the density parameters according to the thickness gradient of adjacent detection points. On the one hand, it can accurately adapt to the coating thickness distribution characteristics of complex workpieces such as automobile wheels. In places with large thickness gradients, the density parameters are dynamically adjusted to make clustering more refined, accurately capture thickness change areas, and improve measurement accuracy; on the other hand, adaptive adjustment avoids over-crowding or over-sparse clustering under fixed parameters, optimizes the layout of detection points, reduces invalid detection points, and improves measurement efficiency, especially in industrial detection scenarios with complex surfaces and uneven coating thickness, effectively enhancing measurement reliability and flexibility, and having significant technical advantages and application value.

[0096] In summary, the method provided in this embodiment can at least achieve the following effects:

[0097] 1) The present invention obtains surface curvature data based on workpiece structure identification to determine the initial coating thickness detection points, combines ultrasonic ranging with echo data processing to construct a thickness matrix, and uses a clustering algorithm to dynamically update the detection points. First, the detection points are determined based on the surface curvature, which can accurately focus on the area where the coating thickness is prone to change, thereby improving the measurement pertinence and accuracy; secondly, the echo data is processed in multiple dimensions including confidence calculation to ensure the reliability of the measurement data; further, the clustering algorithm can perform cluster analysis on the detection points based on the measurement confidence and neighborhood association parameters, screen out detection points with low reliability and move them to reliable areas, avoid invalid measurements, and achieve an optimized layout of the detection points; further, the mechanism of dynamically updating the detection points optimizes the detection process, significantly improves the efficiency of coating thickness measurement, and is particularly suitable for complex workpieces such as automobile wheels, and has good application value and competitive advantages in the field of industrial coating detection.

[0098] 2) The present invention adopts an improved DBSCAN algorithm to adaptively adjust the density parameters according to the thickness gradient of adjacent detection points. On the one hand, it can accurately adapt to the coating thickness distribution characteristics of complex workpieces such as automobile wheels. In places with large thickness gradients, the density parameters are dynamically adjusted to make clustering more refined, accurately capture thickness change areas, and improve measurement accuracy; on the other hand, adaptive adjustment avoids over-crowding or over-sparse clustering under fixed parameters, optimizes the layout of detection points, reduces invalid detection points, and improves measurement efficiency, especially in industrial detection scenarios with complex surfaces and uneven coating thickness, effectively enhancing measurement reliability and flexibility, and having significant technical advantages and application value.

[0099] See also Figure 3 In one embodiment, a coating thickness measurement system based on ultrasound is also provided, the system comprising:

[0100] The structural curvature analysis module 100 is used to obtain surface curvature data based on the structural recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient;

[0101] An initial coating thickness detection point generation module 200 is used to generate an initial coating thickness detection point set in an area where the curvature change gradient exceeds a preset threshold value according to the surface curvature data;

[0102] The ultrasonic distance measurement module 300 is used to measure the distance of the initial coating thickness detection point using an ultrasonic probe to obtain echo signal data of each detection point;

[0103] The echo data processing and matrix construction module 400 performs a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence and neighborhood correlation parameters;

[0104] The detection point optimization module 500 dynamically updates the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set;

[0105] The iterative control module 600 is used to repeatedly execute the ultrasonic ranging module, the echo data processing and matrix construction module and the detection point optimization module until one of the following termination conditions is met: the difference in spatial coverage of the detection point set of two consecutive iterations is less than 5%; the number of detection points reaches a preset surface complexity association threshold.

[0106] See also Figure 4 , Figure 4 The embodiment of the present invention provides Figure 3 Schematic diagram of the structure of the submodules of the echo data processing and matrix construction module 400:

[0107] Preferably, the echo data processing and matrix construction module 400 includes:

[0108] A signal denoising unit 410 is used to perform wavelet threshold denoising on the ultrasonic echo signal;

[0109] The peak detection and thickness preliminary calculation unit 420 is used to perform peak detection on the ultrasonic echo signal using the Teager energy operator to identify the direct wave front time and the interface reflection wave front time; and calculate the coating thickness base value according to the direct wave front time, the interface reflection wave front time and the preset sound velocity parameter;

[0110] The thickness value refinement unit 430 is used to perform FFT transformation on the signal in the reflection wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the coating thickness base value according to the thickness value trimming model. The thickness value trimming model is expressed as:

[0111] ,

[0112] In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient;

[0113] A confidence evaluation unit 440 is used to calculate the confidence of the coating thickness base value by using the signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude;

[0114] Neighborhood parameter calculation unit 450, used to calculate the neighborhood association parameters of the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient;

[0115] The matrix integration construction unit 460 is used to construct a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

[0116] Preferably, the clustering algorithm in the detection point optimization module 500 adopts an improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points. The adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

[0117] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0118] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any possible implementation manner.

[0119] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any possible implementation manner.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. Those skilled in the art can also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, refer to the records of other embodiments.

Claims

1. A coating thickness measurement method based on ultrasound, characterized in that: The method comprises: S100, acquiring surface curvature data based on a structure recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient; S200, generating an initial coating thickness detection point set in an area where a curvature change gradient exceeds a preset threshold value according to the surface curvature data; S300, using an ultrasonic probe to measure the distance of the initial coating thickness detection point to obtain echo signal data of each detection point; S400, performing a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence, and neighborhood correlation parameters; S500, dynamically updating the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set; S600, repeat steps S300-S500 until one of the following termination conditions is met: a) The difference in spatial coverage of the detection point set between two consecutive iterations is less than 5%; b) The number of detection points reaches the preset surface complexity correlation threshold.

2. The method for measuring coating thickness based on ultrasound according to claim 1, characterized in that: The S400 includes the following sub-steps: S410, performing wavelet threshold noise reduction on the ultrasonic echo signal; S420, using the Teager energy operator to perform wave peak detection on the ultrasonic echo signal to identify the direct wave front time and the interface reflection wave front time; calculating the coating thickness base value according to the direct wave front time, the interface reflection wave front time and the preset sound velocity parameter; S430, perform FFT transformation on the signal in the reflected wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the coating thickness base value according to the thickness value trimming model. The thickness value trimming model is expressed as: ， In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient; S440, calculating the confidence level of the coating thickness base value by the signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude; S450, calculating the neighborhood association parameters between the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient; S460, constructing a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

3. The method for measuring coating thickness based on ultrasound according to claim 1, characterized in that: The clustering algorithm in step S500 adopts an improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points. The adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

4. A coating thickness measurement system based on ultrasound, characterized in that: The system comprises: A structural curvature analysis module, used for acquiring surface curvature data based on the structural recognition result of the workpiece to be measured, wherein the surface curvature data includes a curvature radius and a curvature change gradient; An initial coating thickness detection point generation module, used to generate an initial coating thickness detection point set in an area where the curvature change gradient exceeds a preset threshold value according to the surface curvature data; An ultrasonic distance measurement module, used to measure the distance of the initial coating thickness detection points using an ultrasonic probe to obtain echo signal data of each detection point; The echo data processing and matrix construction module performs a time domain-frequency domain joint analysis on the echo signal data to generate a thickness matrix including coating thickness values, measurement confidence and neighborhood correlation parameters; A detection point optimization module dynamically updates the detection point positions through a clustering algorithm according to the measurement confidence and neighborhood association parameters in the thickness matrix to form an optimized detection point set; The iterative control module is used to repeatedly execute the ultrasonic ranging module, the echo data processing and matrix construction module and the detection point optimization module until one of the following termination conditions is met: the difference in spatial coverage of the detection point set of two consecutive iterations is less than 5%; the number of detection points reaches a preset surface complexity association threshold.

5. The ultrasonic-based coating thickness measurement system according to claim 4, characterized in that: The echo data processing and matrix construction module includes: A signal noise reduction unit, used for performing wavelet threshold noise reduction on the ultrasonic echo signal; The peak detection and thickness calculation unit is used to detect the peak of the ultrasonic echo signal using the Teager energy operator to identify the front time of the direct wave and the front time of the interface reflection wave; the basic value of the coating thickness is calculated according to the front time of the direct wave, the front time of the interface reflection wave and the preset sound velocity parameters; The thickness value refinement unit is used to perform FFT transformation on the signal in the reflection wave time window, calculate the energy spectrum integral value of the preset characteristic frequency band, and correct the basic value of the coating thickness according to the thickness value trimming model. The thickness value trimming model is expressed as: ， In the formula, is the corrected coating thickness value, is the basic value of coating thickness, is the energy spectrum integral value, is the reference energy value of the standard sample, is the material dispersion coefficient; A confidence evaluation unit, used for calculating the confidence of the coating thickness base value by means of a signal attenuation coefficient of the ultrasonic echo signal, wherein the signal attenuation coefficient is positively correlated with the echo amplitude; Neighborhood parameter calculation unit, used to calculate the neighborhood association parameters between the current detection point and the adjacent points , where is the standard deviation of thickness at the current point, is the standard deviation of thickness at adjacent points, ρ is the spatial correlation coefficient; The matrix integration construction unit is used to construct a thickness matrix according to the coating thickness value, the confidence of the coating thickness base value and the neighborhood association parameter.

6. The ultrasonic-based coating thickness measurement system according to claim 4, characterized in that: The clustering algorithm in the detection point optimization module adopts the improved DBSCAN algorithm, and its density parameter ε is adaptively adjusted according to the thickness gradient of adjacent detection points. The adjustment formula is: , where is the initial density parameter, is the curvature sensitivity factor, is the thickness difference between adjacent points, is the average thickness of the current area.

7. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store computer program codes, wherein the computer program codes include computer instructions. When the processor executes the computer instructions, the electronic device executes the ultrasonic-based coating thickness measurement method according to any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the ultrasonic-based coating thickness measurement method according to any one of claims 1 to 3.

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