A method and system for detecting coating uniformity based on lining cloth

Abstract

The application discloses a kind of based on lining cloth cloth coating uniformity detection method and system, it is related to coating detection technical field, the coating uniformity detection method based on lining cloth cloth includes the following steps: using ultrasonic detection equipment to scan cloth coating, obtain the ultrasonic response data of ultrasonic wave in cloth coating;Based on ultrasonic response data, establish ultrasonic wave propagation model, and the material properties of lining cloth cloth are combined to optimize ultrasonic wave propagation model;According to the point operation of ultrasonic wave propagation characteristics, point data is converted into point cloud data, and according to point cloud data fitting generation uniformity representation curve;The curvature variation trend of uniformity representation curve is analyzed, and the uniformity of cloth coating is evaluated according to curvature variation trend.The present application can effectively provide intuitive and quantitative evaluation of coating uniformity, and provide important reference for optimizing coating process and identifying uneven area, so as to improve coating quality and process efficiency.

Description

Technical Field

[0001] This invention relates to the field of coating inspection technology, and more specifically, to a method and system for detecting the uniformity of coatings on lining fabric. Background Technology

[0002] Interlining fabric is a type of fabric used in garment making. It is typically a lightweight, soft fabric used to enhance the comfort and texture of clothing. Interlining fabric is commonly used to make the inner lining of garments such as shirts, suits, and dresses to increase the garment's structure and stability. In the manufacturing process, the interlining fabric is cut into the same shape as the main fabric and sewn together with it to form the internal structure of the garment.

[0003] Lining fabric coating is a treatment method that adds a special coating to the surface of the lining fabric. This coating can change the surface properties of the lining fabric, such as increasing water resistance, stain resistance, and antibacterial properties. In some specific applications, ultrasonic equipment can be used for the preparation and testing of lining fabric coatings. Ultrasonic technology can be used to detect coating uniformity, adhesion testing, and coating thickness measurement. By using ultrasonic equipment, quality control and performance evaluation of lining fabric coatings can be achieved, ensuring the stability and reliability of the coating.

[0004] However, the accuracy of ultrasonic testing is highly dependent on the accurate simulation of the propagation speed and attenuation behavior of ultrasonic waves in materials. Since the propagation behavior of ultrasonic waves varies significantly in different materials, it is difficult to accurately explain these differences if the propagation behavior of ultrasonic waves cannot be simulated and analyzed, which will reduce the reliability of the test results.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] In view of the problems in related technologies, the present invention proposes a method and system for detecting the coating uniformity of lining fabric, so as to overcome the above-mentioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, a method for detecting the coating uniformity of an interlining fabric is provided, the method comprising the following steps:

[0009] S1. Use an ultrasonic testing device to scan the fabric coating and obtain ultrasonic response data in the fabric coating.

[0010] S2. Establish an ultrasonic propagation model based on ultrasonic response data, and optimize the ultrasonic propagation model by combining the material properties of the lining fabric.

[0011] S3. Perform point placement operation based on the ultrasonic propagation characteristics, convert the point placement data into point cloud data, and generate a uniformity characterization curve based on the point cloud data.

[0012] S4. Analyze the curvature change trend of the uniformity characterization curve, and evaluate the uniformity of the fabric coating based on the curvature change trend.

[0013] Preferably, the process of establishing an ultrasonic propagation model based on ultrasonic response data and optimizing the ultrasonic propagation model by combining the material properties of the lining fabric includes the following steps:

[0014] S21. Collect the material properties of the lining fabric, including material thickness, material density and material absorption coefficient;

[0015] S22. Extract ultrasonic response data to establish an ultrasonic propagation model in the fabric coating, and simulate and analyze the ultrasonic propagation characteristics of ultrasonic waves.

[0016] S23. Evaluate the influence of the material thickness, material density, and material absorption coefficient of the lining fabric on the ultrasonic wave propagation characteristics, and optimize the ultrasonic wave propagation model based on the degree of influence.

[0017] Preferably, the extraction of ultrasonic response data to establish an ultrasonic propagation model in the fabric coating, and the simulation and analysis of the ultrasonic propagation characteristics include the following steps:

[0018] S221. Perform continuous wavelet transform on the ultrasonic signal to correct abrupt changes in the ultrasonic response data;

[0019] S222. Based on the corrected ultrasonic response data, establish a time-domain finite-difference ultrasonic propagation model, and input the material properties of the lining fabric into the ultrasonic propagation model as boundary and initial conditions.

[0020] S223. Determine the time step and spatial step of the ultrasonic propagation model, and simulate and analyze the propagation behavior of ultrasonic waves in the fabric coating by adjusting the ultrasonic source position and frequency of the ultrasonic propagation model.

[0021] S224. Analyze the propagation path, propagation speed and attenuation characteristics of ultrasound waves based on propagation behavior, and fuse them to generate ultrasound propagation characteristics.

[0022] Preferably, performing continuous wavelet transform on the ultrasonic signal to correct abrupt changes in the ultrasonic response data includes the following steps:

[0023] S2211. The wavelet function is obtained by scaling and translating the preset basic wavelet, and the continuous wavelet transform form is determined based on the wavelet function.

[0024] S2212. Extract the ultrasonic signal generated by the ultrasonic testing equipment, and use inner product operation to calculate the similarity between the local features of the ultrasonic signal and the wavelet function.

[0025] S2213. In the process of calculating the similarity between local features and wavelet functions, the scale variable of continuous wavelet transform is specified, and the center frequency under each scale variable is generated.

[0026] S2214. Reconstruct the ultrasonic signal using wavelet coefficients in the continuous wavelet transform form, and determine the ultrasonic abrupt change region based on the reconstructed ultrasonic signal.

[0027] S2215. Mark the corresponding position of the ultrasonic mutation area in the ultrasonic data and correct the mutation data at the corresponding position.

[0028] Preferably, marking the corresponding location of the ultrasonic abrupt change region in the ultrasonic data and correcting the abrupt change data at the corresponding location includes the following steps:

[0029] S22151. Determine the filtering coefficients of the wavelet coefficients, and use the filtering coefficients to filter the wavelet coefficients in the ultrasonic change region to generate the filtered time spectrum.

[0030] S22152. Remove the filtered time spectrum from the original time spectrum of the ultrasonic signal to generate an ultrasonic time spectrum estimate, and perform an inverse transform on the ultrasonic time spectrum estimate to generate an ultrasonic signal estimate.

[0031] S22153. Remove the ultrasonic signal estimate from the ultrasonic signal to generate an effective signal estimate, and perform high-pass filtering on the effective signal estimate to generate a suppressed signal.

[0032] S22154. Replace the ultrasonic signal with the suppression signal, mark the mutation data corresponding to the ultrasonic mutation area in the ultrasonic data, and use a smoothing algorithm to correct the mutation data.

[0033] Preferably, the process of performing a point-spotting operation based on the ultrasonic wave propagation characteristics, converting the point-spotting data into point cloud data, and fitting a uniformity characterization curve based on the point cloud data includes the following steps:

[0034] S31. Based on the ultrasonic propagation characteristics, uniformly distribute the dots on the fabric coating surface to generate the dot distribution data of the fabric coating.

[0035] S32. Obtain the three-dimensional coordinates of each point location in the point data, and import the three-dimensional coordinates of each point location into the point cloud processing software to generate point cloud data.

[0036] S33. Preprocess the point cloud data and use a curve fitting algorithm to generate a uniformity characterization curve representing the uniformity of the fabric coating.

[0037] Preferably, the preprocessing of point cloud data and the generation of a uniformity characterization curve representing the uniformity of the fabric coating using a curve fitting algorithm include the following steps:

[0038] S331. Divide the point cloud data into blocks, and compress the block-based point cloud data according to features to generate deployment points;

[0039] S332. Calculate the node vector and spline basis function in the current direction based on the deployment point, and calculate the first control point based on the node vector and spline basis function.

[0040] S333. Use the node insertion algorithm to insert deployment points in another direction, and recalculate the node vector and spline basis function in the other direction. At the same time, calculate the second control point based on the node vector and spline basis function.

[0041] S334. A control point matrix is ​​formed based on the first control point and the second control point, and a uniformity characterization curve is constructed using a system of linear equations.

[0042] S335. Adjust the weighting factors of the first control point and the second control point to achieve local adjustment of the uniformity characterization curve.

[0043] Preferably, analyzing the curvature variation trend of the uniformity characterization curve and evaluating the uniformity of the fabric coating based on the curvature variation trend includes the following steps:

[0044] S41. Differentiate the uniformity characterization curve, calculate the first and second derivatives, and calculate the curvature characteristics of the uniformity characterization curve based on the first and second derivatives.

[0045] S42. Analyze the trend of curvature characteristics as they change in different regions of the fabric coating, and compare the trend of curvature characteristics with the preset range of change.

[0046] S43. If the curvature characteristic change trend is less than the preset change range, it indicates that the fabric coating uniformity in this area is high. If the curvature characteristic change trend is greater than the preset change range, it indicates that the fabric coating uniformity in this area is low, and this area is marked as a uniformity abrupt change area.

[0047] Preferably, the expression for the wavelet coefficients in the continuous wavelet transform is:

[0048] ;

[0049] Represents the wavelet coefficients in the continuous wavelet transform;

[0050] a This represents the scaling parameter used in scaling and translation processing of the basic wavelet;

[0051] b This represents the translation parameter used in scaling and translating the basic wavelet;

[0052] Represents the wavelet function;

[0053] The conjugate function of the wavelet function;

[0054] f The frequency parameter represents the wavelet coefficients;

[0055] dt This represents the time step in the continuous wavelet transform;

[0056] t Represents a time variable.

[0057] According to another system of the present invention, a coating uniformity detection system based on lining fabric is also provided, which includes a data acquisition module, a propagation model establishment module, a curve fitting module and a uniformity detection module.

[0058] The data acquisition module is connected sequentially to the propagation model establishment module, the curve fitting module, and the uniformity detection module.

[0059] The data acquisition module is used to scan the fabric coating using ultrasonic testing equipment to acquire ultrasonic response data in the fabric coating.

[0060] The propagation model building module is used to build an ultrasonic propagation model based on ultrasonic response data and optimize the ultrasonic propagation model by combining the material properties of the lining fabric.

[0061] The curve fitting module is used to perform point placement operations based on the propagation characteristics of ultrasonic waves, convert the point placement data into point cloud data, and generate a uniformity characterization curve based on the point cloud data.

[0062] The uniformity detection module is used to analyze the curvature change trend of the uniformity characterization curve and evaluate the uniformity of the fabric coating based on the curvature change trend.

[0063] The beneficial effects of this invention are as follows:

[0064] 1. This invention uses an ultrasonic propagation model to more accurately predict the propagation path and speed of ultrasonic waves in lining fabric, thereby improving the accuracy of coating uniformity detection. At the same time, by considering the material properties of the lining fabric, the ultrasonic propagation model can have better robustness and adaptability. Since different types of lining fabric may have different acoustic characteristics, the ultrasonic propagation model, by incorporating these properties, can accurately describe the propagation behavior of ultrasonic waves under different conditions, thereby enhancing the reliability and applicability of the detection method.

[0065] 2. This invention performs point-spotting operations based on the propagation characteristics of ultrasonic waves, enabling the rapid and accurate acquisition of a large number of data points on the fabric surface. The point-spotting data is converted into point cloud data, and a uniformity characterization curve is generated by fitting the point cloud data. This allows for a more accurate characterization of the coating uniformity and a more detailed description of the fabric surface morphology and coating thickness distribution. Consequently, it enables a better assessment of the complex morphology and nonlinear changes of the coating surface, improving the accuracy and reliability of the detection results.

[0066] 3. By analyzing the curvature variation trend of the uniformity characterization curve, this invention can intuitively evaluate the uniformity of the fabric coating. The curvature variation trend can reveal the uniformity variation of the coating at different locations, effectively providing an intuitive and quantitative assessment of the coating uniformity. It also provides an important reference for optimizing the coating process and identifying non-uniform areas, thereby improving coating quality and process efficiency. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a flowchart of a coating uniformity detection method based on a lining fabric according to an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram of a coating uniformity detection system based on lining fabric according to an embodiment of the present invention.

[0070] In the picture:

[0071] 1. Data acquisition module; 2. Propagation model establishment module; 3. Curve fitting module; 4. Uniformity detection module. Detailed Implementation

[0072] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0073] According to an embodiment of the present invention, a method and system for detecting the coating uniformity of a lining fabric are provided.

[0074] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the coating uniformity detection method based on lining fabric according to an embodiment of the present invention includes the following steps:

[0075] S1. Use an ultrasonic testing device to scan the fabric coating and obtain ultrasonic response data in the fabric coating.

[0076] It should be noted that scanning the fabric coating with ultrasonic testing equipment to obtain ultrasonic response data in the fabric coating includes the following steps: ensuring that the ultrasonic testing equipment is in a proper working state, including probe calibration and equipment settings adjustment; setting scanning parameters such as ultrasonic frequency, beam angle, and probe position to ensure a comprehensive and effective scan of the fabric coating; scanning the fabric coating surface with the ultrasonic probe, usually in a grid or linear pattern, recording the ultrasonic signal response during the scan, and monitoring the scan coverage and quality in real time.

[0077] It should be noted that ultrasonic response data includes ultrasonic signal amplitude, ultrasonic signal time delay, ultrasonic signal spectral characteristics, and ultrasonic signal waveform.

[0078] S2. Establish an ultrasonic propagation model based on ultrasonic response data, and optimize the ultrasonic propagation model by combining the material properties of the lining fabric.

[0079] The process of establishing an ultrasonic propagation model based on ultrasonic response data and optimizing the ultrasonic propagation model by combining the material properties of the lining fabric includes the following steps:

[0080] S21. Collect the material properties of the lining fabric, including material thickness, material density, and material absorption coefficient.

[0081] It should be noted that the thickness of the lining fabric refers to its dimension in the vertical direction. For ultrasonic testing, thickness is a key parameter because the propagation speed of ultrasonic waves in different materials depends on the material's density and elastic modulus, which in turn affects the propagation time and amplitude of the ultrasonic signal.

[0082] Material density refers to the mass contained in a unit volume. For ultrasonic testing, material density directly affects the propagation speed of ultrasonic waves in the material. Generally, the higher the density, the faster the ultrasonic waves propagate.

[0083] The absorption coefficient of a material is a parameter that measures the degree to which a material absorbs ultrasonic energy. A high absorption coefficient means that the material absorbs ultrasonic energy more strongly, resulting in faster energy attenuation of the ultrasonic waves as they propagate through the material. In ultrasonic testing, understanding the absorption coefficient of a material helps to assess the attenuation of the ultrasonic signal, and thus infer the material's properties and potential defects.

[0084] S22. Extract ultrasonic response data to establish an ultrasonic propagation model in the fabric coating, and simulate and analyze the ultrasonic propagation characteristics of ultrasonic waves.

[0085] The process of extracting ultrasonic response data to establish an ultrasonic propagation model in the fabric coating and simulating and analyzing the ultrasonic propagation characteristics includes the following steps:

[0086] S221. Perform continuous wavelet transform on the ultrasonic signal to correct abrupt changes in the ultrasonic response data.

[0087] The process of performing continuous wavelet transform on ultrasonic signals to correct abrupt changes in ultrasonic response data includes the following steps:

[0088] S2211. The preset basic wavelet is scaled and translated to obtain the wavelet function, and the continuous wavelet transform form is determined based on the wavelet function.

[0089] It should be noted that the wavelet function is selected according to the application requirements and signal characteristics. Different wavelet functions are suitable for different types of signal analysis. For example, the Morlet wavelet is suitable for frequency analysis, while the Haar wavelet is suitable for edge detection. The selected wavelet function is scaled and translated to generate wavelet functions within a continuous range. The scaling operation controls the scale of the wavelet function, and the translation operation controls the position of the wavelet function in time or space.

[0090] S2212. Extract the ultrasonic signal generated by the ultrasonic testing equipment, and use inner product operation to calculate the similarity between the local features of the ultrasonic signal and the wavelet function.

[0091] It should be noted that calculating the similarity between local features of an ultrasonic signal and a wavelet function using inner product operations includes the following steps:

[0092] For each scale and translation of the wavelet function, calculate its inner product with the local region of the original signal. The inner product represents the degree of similarity between the signal and the wavelet function at a given time or scale. Based on the calculation result of the inner product, obtain the degree of similarity between the signal and the wavelet function at different scales and different times. A higher inner product value indicates that the signal is more similar to the wavelet function at that time or scale, and vice versa.

[0093] S2213. In the process of calculating the similarity between local features and wavelet functions, the scale variable of continuous wavelet transform is specified, and the center frequency under each scale variable is generated.

[0094] S2214. Reconstruct the ultrasonic signal using wavelet coefficients in the continuous wavelet transform form, and determine the ultrasonic abrupt change region based on the reconstructed ultrasonic signal.

[0095] The expression for the wavelet coefficients in the continuous wavelet transform is:

[0096] ;

[0097] Represents the wavelet coefficients in the continuous wavelet transform;

[0098] a This represents the scaling parameter used in scaling and translation processing of the basic wavelet;

[0099] b This represents the translation parameter used in scaling and translating the basic wavelet;

[0100] Represents the wavelet function;

[0101] The conjugate function of the wavelet function;

[0102] f The frequency parameter represents the wavelet coefficients;

[0103] dt This represents the time step in the continuous wavelet transform;

[0104] t Represents a time variable.

[0105] S2215. Mark the corresponding position of the ultrasonic mutation area in the ultrasonic data and correct the mutation data at the corresponding position.

[0106] The process of marking the corresponding locations of ultrasonic abrupt change regions in the ultrasonic data and correcting the abrupt change data at those locations includes the following steps:

[0107] S22151. Determine the filtering coefficients of the wavelet coefficients, and use the filtering coefficients to filter the wavelet coefficients in the ultrasonic change region to generate the filtered time spectrum.

[0108] It should be noted that determining the wavelet coefficients and using them to filter the wavelet coefficients in the ultrasonic abrupt change region to generate the filtered time spectrum involves: determining the filter coefficients using a selected wavelet function and scale. The filter coefficients are typically obtained by transforming and adjusting the wavelet function in the frequency domain to extract or suppress specific frequency components of the signal; filtering the wavelet coefficients in the ultrasonic signal using the determined filter coefficients, which involves convolving the wavelet coefficients with the filter coefficients to achieve filtering operations in both the frequency and time domains; after filtering, the filtered wavelet coefficients are obtained, and these filtered wavelet coefficients are used to reconstruct the filtered time spectrum for analyzing the characteristic changes of the signal at different frequencies and times.

[0109] S22152. Remove the filtered time spectrum from the original time spectrum of the ultrasonic signal to generate an ultrasonic time spectrum estimate, and perform an inverse transform on the ultrasonic time spectrum estimate to generate an ultrasonic signal estimate.

[0110] It should be noted that the inverse transform processing of the ultrasonic time-frequency spectrum estimation involves inverse filtering of the estimation results using an inverse wavelet transform algorithm. The purpose of the inverse filtering is to convert the signal represented in the time-frequency domain back to the time-domain representation of the original signal based on the selected wavelet function, scale, and wavelet coefficients.

[0111] S22153. Remove the ultrasonic signal estimate from the ultrasonic signal to generate an effective signal estimate, and perform high-pass filtering on the effective signal estimate to generate a suppressed signal.

[0112] It should be noted that the process of removing the ultrasonic signal estimate from the ultrasonic signal to generate an effective signal estimate, and then performing high-pass filtering to suppress the effective signal estimate and generate the suppressed signal, includes: obtaining the ultrasonic signal estimate from the original ultrasonic signal; using the obtained ultrasonic signal estimate to generate an effective signal estimate through signal feature extraction technology; performing high-pass filtering on the effective signal estimate to suppress low-frequency components and remove baseline drift or other low-frequency noise; the high-pass filtering can use digital filters, including Butterworth filters, etc.; after high-pass filtering, the suppressed signal is produced.

[0113] S22154. Replace the ultrasonic signal with the suppression signal, mark the mutation data corresponding to the ultrasonic mutation area in the ultrasonic data, and use a smoothing algorithm to correct the mutation data.

[0114] It should be noted that correcting abrupt data using smoothing algorithms involves: selecting a smoothing algorithm and setting appropriate parameters, such as window size and weighting coefficients, based on the characteristics of the abrupt data and processing requirements. These parameters need to be adjusted according to the data characteristics and the desired smoothing effect. Smoothing algorithms include moving average, weighted moving average, and Loess smoothing. The selected smoothing algorithm is then used to process the abrupt data, including sliding a window across the data, calculating the average or weighted average of the data within the window, and using the smoothed value as the corrected data. Finally, the data before and after smoothing are compared to evaluate the effectiveness of the smoothing algorithm.

[0115] S222. Based on the corrected ultrasonic response data, establish a time-domain finite-difference ultrasonic propagation model, and input the material properties of the lining fabric into the ultrasonic propagation model as boundary and initial conditions.

[0116] It should be noted that the finite-difference time-domain method is a commonly used numerical method for simulating the propagation of waves in a medium. The ultrasonic propagation model can use the finite-difference time-domain method to describe the propagation characteristics of ultrasonic waves in different media, as well as their interaction with different structures in the medium.

[0117] The finite-difference time-domain ultrasonic propagation model includes the following key factors:

[0118] Properties of the medium: The medium through which ultrasound propagates usually has properties such as isotropy or anisotropy, and its parameters such as density and sound velocity will affect the propagation of the wave.

[0119] Excitation of the wave source: Ultrasonic propagation models usually need to consider the excitation method of the wave source, such as pulse excitation, continuous frequency excitation, etc. The type and characteristics of the wave source will affect the accuracy and applicability of the simulation results.

[0120] Boundary conditions: At the boundary of the simulated medium, appropriate boundary conditions need to be considered to ensure that the reflection and refraction of waves at the boundary conform to the laws of physics.

[0121] Numerical grid: The finite-difference time-domain method divides the medium into discrete grid points and describes the propagation behavior of waves at different grid points through difference techniques. The grid density affects the accuracy of the simulation results and the computational efficiency.

[0122] Analysis of simulation results: The ultrasonic propagation results obtained by finite-difference time-domain simulation are in the form of waveform diagrams, sound pressure distribution diagrams, energy propagation diagrams, etc. These results need to be analyzed to obtain information about the properties and structure of the medium.

[0123] S223. Determine the time step and spatial step of the ultrasonic propagation model, and simulate and analyze the propagation behavior of ultrasonic waves in the fabric coating by adjusting the ultrasonic source position and frequency of the ultrasonic propagation model.

[0124] S224. Analyze the propagation path, propagation speed and attenuation characteristics of ultrasound waves based on propagation behavior, and fuse them to generate ultrasound propagation characteristics.

[0125] It should be noted that the propagation path of ultrasound in a medium depends on the geometry of the medium and the location of the wave source. Generally, ultrasound propagates in a straight line, but when it encounters a medium interface, inhomogeneity, or scattering body, refraction, reflection, and scattering occur, thus changing the propagation path. The complexity of the propagation path affects the propagation time and energy distribution of ultrasound in the medium.

[0126] The propagation speed of ultrasound in a medium depends on the physical properties of the medium, such as its density and elastic modulus. In a homogeneous isotropic medium, the propagation speed of ultrasound can be calculated using the elastic modulus and density of the medium.

[0127] Ultrasonic waves attenuate as they propagate through a medium, primarily due to factors such as absorption, scattering, and the length of the propagation path. This attenuation causes the ultrasonic energy to gradually weaken, affecting the intensity and clarity of the ultrasonic signal. The attenuation coefficient describes the rate of attenuation of ultrasonic waves during propagation through a medium and is typically related to factors such as the properties of the medium, its frequency, and the propagation distance.

[0128] S23. Evaluate the influence of the material thickness, material density, and material absorption coefficient of the lining fabric on the ultrasonic wave propagation characteristics, and optimize the ultrasonic wave propagation model based on the degree of influence.

[0129] It should be noted that the evaluation of the influence of the material thickness, material density, and material absorption coefficient of the lining fabric on the ultrasonic propagation characteristics, and the optimization of the ultrasonic propagation model based on the degree of influence, includes: changing the thickness of the lining fabric in the ultrasonic propagation model to simulate the propagation behavior of ultrasonic waves at different thicknesses, observing the changes in ultrasonic propagation path, propagation time, signal attenuation, and other characteristics, and evaluating the degree of influence of material thickness on propagation characteristics.

[0130] In the ultrasonic propagation model, the density of the lining fabric is changed to simulate the propagation behavior of ultrasonic waves at different densities. The changes in propagation speed, reflection and refraction, and signal attenuation are observed to evaluate the degree of influence of material density on propagation characteristics. Increasing density may lead to a decrease in propagation speed and changes in reflection and refraction.

[0131] Adjusting the material absorption coefficient in the ultrasonic propagation model simulates the propagation behavior of ultrasonic waves under different absorption coefficients. Observe the effects of signal attenuation rate, energy loss, and propagation distance, and evaluate the degree of influence of the material absorption coefficient on propagation characteristics. Increasing the absorption coefficient may lead to faster signal attenuation and increased energy loss.

[0132] S3. Perform point placement operation based on the ultrasonic propagation characteristics, convert the point placement data into point cloud data, and generate a uniformity characterization curve based on the point cloud data.

[0133] The process of performing point placement based on the propagation characteristics of ultrasonic waves, converting the point placement data into point cloud data, and then fitting a uniformity characterization curve based on the point cloud data includes the following steps:

[0134] S31. Based on the ultrasonic propagation characteristics, uniformly distribute the ultrasonic dots on the surface of the fabric coating to generate the dot distribution data of the fabric coating.

[0135] S32. Obtain the three-dimensional coordinates of each point location in the point data, and import the three-dimensional coordinates of each point location into the point cloud processing software to generate point cloud data.

[0136] It should be noted that obtaining the 3D coordinates of each point location in the point data and importing these coordinates into the point cloud processing software to generate point cloud data includes: extracting the 3D coordinate information of each point location from the point data by reading data files, querying databases, or calling APIs to ensure accurate coordinate information for each point; converting the extracted 3D coordinate data into a format suitable for import into the point cloud processing software (supported formats include PLY, XYZ, and LAS, so the coordinate data needs to be converted to the corresponding format); importing the converted 3D coordinate data into the point cloud processing software; generating point cloud data using the imported 3D coordinate data by creating point cloud objects and adding the coordinate data; and then performing point cloud processing and editing as needed, such as filtering, registration, and reconstruction.

[0137] S33. Preprocess the point cloud data and use a curve fitting algorithm to generate a uniformity characterization curve representing the uniformity of the fabric coating.

[0138] The preprocessing of point cloud data and the generation of a uniformity characterization curve representing the uniformity of the fabric coating using a curve fitting algorithm include the following steps:

[0139] S331. Divide the point cloud data into blocks, and compress the block-based point cloud data according to features to generate deployment points;

[0140] It should be noted that the process of segmenting point cloud data and compressing the segmented point cloud data according to features to generate deployment points includes: dividing the entire point cloud dataset into multiple blocks or regions using spatial partitioning methods, such as dividing the point cloud data into cubic grids or spherical regions, or dividing it according to specific attributes of the point cloud data; extracting features from the point cloud data within each block using deep learning models or methods based on geometric shapes; compressing the extracted features to reduce the amount of data while retaining key information. Compression methods include dimensionality reduction techniques (such as principal component analysis, t-SNE, etc.), feature selection (such as based on information gain or variance analysis, etc.), and quantization methods; and generating points for deployment or storage based on the compressed features. These points can be compressed feature vectors or representative points from the original point cloud data. The purpose of generating deployment points is to compress the original point cloud data into a smaller representation for easier storage, transmission, and processing.

[0141] S332. Calculate the node vector and spline basis function in the current direction based on the deployment point, and calculate the first control point based on the node vector and spline basis function.

[0142] It should be noted that the calculation of the node vector and spline basis function in the current direction based on the deployment points, and the inverse calculation of the first control point based on the node vector and spline basis function, includes: calculating the node vector in the current direction based on the data generated by the deployment points. The node vector is usually determined based on the position and density of the deployment points, and is either evenly distributed or dynamically determined based on data characteristics; calculating the spline basis function using the node vector. The spline basis function is a basic component describing the shape of the spline curve. Common spline basis functions include B-splines, N-splines, etc.; calculating the node value of each deployment point on the node vector based on the node vector and spline basis function in the current direction; and calculating the first control point by determining the control point used to fit the spline curve based on the node vector, spline basis function, and information of the deployment points.

[0143] It should be noted that the specific method for back-calculating the first control point can be the least squares method, curve fitting algorithm, or optimization algorithm. During the solution process, constraints should be considered to ensure that the generated spline curve meets specific requirements.

[0144] S333. Use the node insertion algorithm to insert deployment points in another direction, and recalculate the node vector and spline basis function in the other direction. At the same time, calculate the second control point based on the node vector and spline basis function.

[0145] S334. A control point matrix is ​​formed based on the first control point and the second control point, and a uniformity characterization curve is constructed using a system of linear equations.

[0146] S335. Adjust the weighting factors of the first control point and the second control point to achieve local adjustment of the uniformity characterization curve.

[0147] It should be noted that adjusting the weighting factors of the first and second control points to achieve local adjustment of the uniformity characterization curve includes: determining the region requiring local adjustment; selecting appropriate weighting factors for the region to be adjusted (the weighting factor can be a scalar value, a vector, or a matrix) to adjust the weights of the control points or the positions of the nodes; using linear interpolation and weighted averaging based on the selected weighting factors to adjust the control points or nodes within the specified region, ensuring that the adjusted control points or nodes can smoothly transition into the surrounding curve portion; after adjusting the control points or nodes, updating the uniformity characterization curve, including resolving the parameterized representation of the curve and regenerating the curve based on the updated control points or nodes, ensuring that the shape of the curve is improved within the adjustment region.

[0148] S4. Analyze the curvature change trend of the uniformity characterization curve, and evaluate the uniformity of the fabric coating based on the curvature change trend.

[0149] The analysis of the curvature variation trend of the uniformity characterization curve and the evaluation of the uniformity of the fabric coating based on the curvature variation trend include the following steps:

[0150] S41. Differentiate the uniformity characterization curve, calculate the first and second derivatives, and calculate the curvature characteristics of the uniformity characterization curve based on the first and second derivatives.

[0151] It should be noted that the first derivative represents the slope of the curve at each point and is calculated using methods such as numerical differentiation, analytical differentiation, or the finite difference method; the second derivative represents the curvature of the curve, describing the change in the degree of curvature of the curve, and is also calculated using numerical differentiation or analytical differentiation; numerical differentiation estimates the value of the derivative by using the values ​​of the function near a certain point. It is based on the definition of the derivative and uses the values ​​of the function at that point and in its vicinity to approximate the slope of the derivative.

[0152] It should be noted that numerical differentiation methods include forward differencing, backward differencing, and central differencing. Forward differencing uses the values ​​of the function at a point and at a small step in the vicinity of that point to estimate the derivative; backward differencing uses the values ​​of the function at a point and at a point before a small step in the vicinity of that point to estimate the derivative; and central differencing uses the values ​​of the function at a point and at two small steps before and after that point to estimate the derivative, and generally has higher accuracy.

[0153] It should be noted that the formula for calculating the curvature characteristics of the uniformity characterization curve based on the first and second derivatives is as follows:

[0154] ;

[0155] In the formula, k Indicates curvature;

[0156] This represents the first derivative of the curve at point x;

[0157] This represents the second derivative of the curve at point x.

[0158] S42. Analyze the trend of curvature characteristics as they change in different regions of the fabric coating, and compare the trend of curvature characteristics with the preset range of change.

[0159] S43. If the curvature characteristic change trend is less than the preset change range, it indicates that the fabric coating uniformity in this area is high. If the curvature characteristic change trend is greater than the preset change range, it indicates that the fabric coating uniformity in this area is low, and this area is marked as a uniformity abrupt change area.

[0160] According to another aspect of the present invention, a coating uniformity detection system based on lining fabric is also provided. The coating uniformity detection system based on lining fabric includes a data acquisition module 1, a propagation model establishment module 2, a curve fitting module 3, and a uniformity detection module 4.

[0161] The data acquisition module 1 is connected in sequence to the propagation model establishment module 2, the curve fitting module 3, and the uniformity detection module 4.

[0162] Data acquisition module 1 is used to scan the fabric coating with an ultrasonic testing device to acquire ultrasonic response data in the fabric coating;

[0163] Module 2 for establishing the propagation model is used to establish an ultrasonic propagation model based on ultrasonic response data and to optimize the ultrasonic propagation model by combining the material properties of the lining fabric.

[0164] The curve fitting module 3 is used to perform point placement operations based on the ultrasonic propagation characteristics, convert the point placement data into point cloud data, and fit and generate a uniformity characterization curve based on the point cloud data.

[0165] The uniformity detection module 4 is used to analyze the curvature change trend of the uniformity characterization curve and evaluate the uniformity of the fabric coating based on the curvature change trend.

[0166] In summary, by utilizing the above-mentioned technical solution of this invention, the ultrasonic propagation model can more accurately predict the propagation path and speed of ultrasonic waves in the lining fabric, thereby improving the accuracy of coating uniformity detection. Furthermore, by considering the material properties of the lining fabric, the ultrasonic propagation model can possess better robustness and adaptability. Since different types of lining fabrics may have different acoustic characteristics, combining these properties allows the ultrasonic propagation model to accurately describe the propagation behavior of ultrasonic waves under various conditions, thus enhancing the reliability and applicability of the detection method. This invention performs point-based operations based on the ultrasonic propagation characteristics, enabling the rapid and accurate acquisition of a large number of data points on the fabric surface. By converting the data into point cloud data and simultaneously fitting a uniformity characterization curve based on the point cloud data, the uniformity of the coating can be characterized more accurately, and the morphology of the fabric surface and the distribution of coating thickness can be described more precisely. This allows for a better assessment of the complex morphology and nonlinear changes of the coating surface, improving the accuracy and reliability of the detection results. Furthermore, by analyzing the curvature variation trend of the uniformity characterization curve, this invention can intuitively assess the uniformity of the fabric coating. The curvature variation trend reveals the uniformity changes of the coating at different locations, effectively providing an intuitive and quantitative assessment of coating uniformity. This also provides important reference for optimizing coating processes and identifying non-uniform areas, thereby improving coating quality and process efficiency.

[0167] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting the coating uniformity of a lining fabric, characterized in that, The method for detecting the coating uniformity of the lining fabric includes the following steps: S1. Use an ultrasonic testing device to scan the fabric coating and obtain ultrasonic response data in the fabric coating. S2. Establish an ultrasonic propagation model based on ultrasonic response data, and optimize the ultrasonic propagation model by combining the material properties of the lining fabric. S3. Perform point placement operation based on the ultrasonic propagation characteristics, convert the point placement data into point cloud data, and generate a uniformity characterization curve based on the point cloud data. S4. Analyze the curvature change trend of the uniformity characterization curve, and evaluate the uniformity of the fabric coating based on the curvature change trend. The process of establishing an ultrasonic propagation model based on ultrasonic response data and optimizing the ultrasonic propagation model by combining the material properties of the lining fabric includes the following steps: S21. Collect the material properties of the lining fabric, including material thickness, material density and material absorption coefficient; S22. Extract ultrasonic response data to establish an ultrasonic propagation model in the fabric coating, and simulate and analyze the ultrasonic propagation characteristics of ultrasonic waves. S23. Evaluate the influence of the material thickness, material density, and material absorption coefficient of the lining fabric on the ultrasonic wave propagation characteristics, and optimize the ultrasonic wave propagation model based on the degree of influence. The step of performing a point placement operation based on the ultrasonic propagation characteristics, converting the point placement data into point cloud data, and fitting a uniformity characterization curve based on the point cloud data includes the following steps: S31. Based on the ultrasonic propagation characteristics, uniformly distribute the dots on the fabric coating surface to generate the dot distribution data of the fabric coating. S32. Obtain the three-dimensional coordinates of each point location in the point data, and import the three-dimensional coordinates of each point location into the point cloud processing software to generate point cloud data. S33. Preprocess the point cloud data and use a curve fitting algorithm to generate a uniformity characterization curve representing the uniformity of the fabric coating. The analysis of the curvature variation trend of the uniformity characterization curve, and the evaluation of the uniformity of the fabric coating based on the curvature variation trend, includes the following steps: S41. Differentiate the uniformity characterization curve, calculate the first and second derivatives, and calculate the curvature characteristics of the uniformity characterization curve based on the first and second derivatives. S42. Analyze the trend of curvature characteristics as they change in different regions of the fabric coating, and compare the trend of curvature characteristics with the preset range of change. S43. If the curvature characteristic change trend is less than the preset change range, it indicates that the fabric coating uniformity in this area is high. If the curvature characteristic change trend is greater than the preset change range, it indicates that the fabric coating uniformity in this area is low, and this area is marked as a uniformity abrupt change area.

2. The method for detecting the coating uniformity of a lining fabric according to claim 1, characterized in that, The process of extracting ultrasonic response data to establish an ultrasonic propagation model in the fabric coating and simulating and analyzing the ultrasonic propagation characteristics includes the following steps: S221. Perform continuous wavelet transform on the ultrasonic signal to correct abrupt changes in the ultrasonic response data; S222. Based on the corrected ultrasonic response data, establish a time-domain finite-difference ultrasonic propagation model, and input the material properties of the lining fabric into the ultrasonic propagation model as boundary and initial conditions. S223. Determine the time step and spatial step of the ultrasonic propagation model, and simulate and analyze the propagation behavior of ultrasonic waves in the fabric coating by adjusting the ultrasonic source position and frequency of the ultrasonic propagation model. S224. Analyze the propagation path, propagation speed and attenuation characteristics of ultrasound waves based on propagation behavior, and fuse them to generate ultrasound propagation characteristics.

3. The method for detecting the coating uniformity of a lining fabric according to claim 2, characterized in that, The step of performing continuous wavelet transform on the ultrasonic signal to correct abrupt changes in the ultrasonic response data includes the following steps: S2211. The wavelet function is obtained by scaling and translating the preset basic wavelet, and the continuous wavelet transform form is determined based on the wavelet function. S2212. Extract the ultrasonic signal generated by the ultrasonic testing equipment, and use inner product operation to calculate the similarity between the local features of the ultrasonic signal and the wavelet function. S2213. In the process of calculating the similarity between local features and wavelet functions, the scale variable of continuous wavelet transform is specified, and the center frequency under each scale variable is generated. S2214. Reconstruct the ultrasonic signal using wavelet coefficients in the continuous wavelet transform form, and determine the ultrasonic abrupt change region based on the reconstructed ultrasonic signal. S2215. Mark the corresponding position of the ultrasonic mutation area in the ultrasonic data and correct the mutation data at the corresponding position.

4. The method for detecting the coating uniformity of a lining fabric according to claim 3, characterized in that, The step of marking the corresponding position in the ultrasonic data to the ultrasonic mutation region and correcting the mutation data at the corresponding position includes the following steps: S22151. Determine the filtering coefficients of the wavelet coefficients, and use the filtering coefficients to filter the wavelet coefficients in the ultrasonic change region to generate the filtered time spectrum. S22152. Remove the filtered time spectrum from the original time spectrum of the ultrasonic signal to generate an ultrasonic time spectrum estimate, and perform an inverse transform on the ultrasonic time spectrum estimate to generate an ultrasonic signal estimate. S22153. Remove the ultrasonic signal estimate from the ultrasonic signal to generate an effective signal estimate, and perform high-pass filtering on the effective signal estimate to generate a suppressed signal. S22154. Replace the ultrasonic signal with the suppression signal, mark the mutation data corresponding to the ultrasonic mutation area in the ultrasonic data, and use a smoothing algorithm to correct the mutation data.

5. The method for detecting the coating uniformity of a lining fabric according to claim 1, characterized in that, The preprocessing of point cloud data and the generation of a uniformity characterization curve representing the uniformity of the fabric coating using a curve fitting algorithm include the following steps: S331. Divide the point cloud data into blocks, and compress the block-based point cloud data according to features to generate deployment points; S332. Calculate the node vector and spline basis function in the current direction based on the deployment point, and calculate the first control point based on the node vector and spline basis function. S333. Use the node insertion algorithm to insert deployment points in another direction, and recalculate the node vector and spline basis function in the other direction. At the same time, calculate the second control point based on the node vector and spline basis function. S334. A control point matrix is ​​formed based on the first control point and the second control point, and a uniformity characterization curve is constructed using a system of linear equations. S335. Adjust the weighting factors of the first control point and the second control point to achieve local adjustment of the uniformity characterization curve.

6. The method for detecting the coating uniformity of a lining fabric according to claim 2, characterized in that, The expression for the wavelet coefficients in the continuous wavelet transform is as follows: ; Represents the wavelet coefficients in the continuous wavelet transform; a This represents the scaling parameter used in scaling and translation processing of the basic wavelet; b This represents the translation parameter used in scaling and translating the basic wavelet; Represents the wavelet function; The conjugate function of the wavelet function; f The frequency parameter represents the wavelet coefficients; dt This represents the time step in the continuous wavelet transform; t Represents a time variable.

7. A coating uniformity detection system based on interlining fabric, used to implement the coating uniformity detection method based on interlining fabric as described in any one of claims 1-6, characterized in that, The coating uniformity detection system based on lining fabric includes a data acquisition module, a propagation model establishment module, a curve fitting module, and a uniformity detection module. The data acquisition module is sequentially connected to the propagation model establishment module, the curve fitting module, and the uniformity detection module. The data acquisition module is used to scan the fabric coating using an ultrasonic testing device to acquire ultrasonic response data in the fabric coating. The propagation model establishment module is used to establish an ultrasonic propagation model based on ultrasonic response data, and to optimize the ultrasonic propagation model by combining the material properties of the lining fabric. The curve fitting module is used to perform a point placement operation based on the ultrasonic propagation characteristics, convert the point placement data into point cloud data, and generate a uniformity characterization curve based on the point cloud data. The uniformity detection module is used to analyze the curvature change trend of the uniformity characterization curve and evaluate the uniformity of the fabric coating based on the curvature change trend.

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