A method of locating colloidal defects

Abstract

This invention discloses a method for locating defects in colloids. The invention includes: scanning a calibration part using two line laser profile scanners at the end effector of a robot; stitching the data from the left and right sides together to obtain measured profile data; comparing the measured profile data with the actual profile data of the calibration part to obtain a comparison result; adjusting the angle of the line laser profile scanners based on the comparison result; during adhesive application, the line laser profile scanners scan the colloid, stitching the data from the left and right sides together; calculating the curvature value of each discrete point; when the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point; acquiring the colloid height and width from the stitched data; comparing the colloid height and width with a preset adhesive application standard to determine whether the colloid has a volumetric defect. This invention solves the problems of high misjudgment rate and inaccurate defect location in related technologies.

Description

Technical Field

[0001] This invention relates to the field of scanning and positioning technology, and more particularly to a method for locating colloidal defects. Background Technology

[0002] Adhesive application is a crucial step in automobile manufacturing, and its quality directly affects the safety and comfort of the vehicle. Adhesive bonding not only provides excellent sealing but also high strength. With advancements in bonding technology and materials, adhesive bonding accounts for an increasingly larger proportion of automotive applications.

[0003] In current glue coating inspection methods, traditional manual inspection has been gradually phased out. Due to the high flexibility, high precision, and high efficiency of industrial robots, their application in the manufacturing field is receiving increasing attention.

[0004] With continuous technological advancements, adhesive application inspection has shifted from traditional manual inspection to visual inspection. Currently, published patents include: CN115963113A, which uses a single line laser profile scanner fixedly mounted on the end effector of a robot to determine adhesive application quality and defect types based on deep learning algorithms; CN115601353A, which obtains single-frame adhesive point cloud data through a line laser profile scanner, stitches them together to form overall adhesive profile data, and then performs defect detection; CN115106260A, which mounts four line laser profile scanners on the end effector of a robot to calibrate and divide the adhesive application path before inspection, and calculates the offset between the glue gun and the template in real time during operation for adhesive application inspection; and CN116930079A, which filters image noise through intensity filtering and other steps to improve data quality and reduce interference from the scanning data of the four line laser profile scanners.

[0005] In the existing technology, there is no effective solution to the technical problems of excessively high misjudgment rate of adhesive coating defects and inaccurate defect location. Summary of the Invention

[0006] The main objective of this application is to provide a method for locating adhesive defects, so as to at least solve the problem of excessively high misjudgment rate of adhesive defects in related technologies.

[0007] To achieve the above objectives, according to one aspect of this application, a method for locating colloidal defects is provided. The method includes: scanning a calibration part using two line laser profile scanners at the end of a robot; stitching the data from the left and right sides together to obtain measured profile data; comparing the measured profile data with the actual profile data of the calibration part to obtain a comparison result; adjusting the angle of the line laser profile scanner based on the comparison result; wherein the data scanned by the line laser profile scanner consists of discrete points, the left data is the data scanned by the line laser profile scanner located on the left, and the right data is the data scanned by the line laser profile scanner located on the right; during adhesive application, the line laser profile scanner scans the adhesive, and after stitching the left and right data together, calculating the curvature value of each discrete point; when the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point; determining the defect area based on the suspected defect points; fitting the defect area line using the global least squares method; when the distance from the suspected defect point to the defect area line is greater than the defect threshold, the suspected defect point is identified as a real defect point; obtaining the adhesive height and width from the stitched data; comparing the adhesive height and width with a preset adhesive application standard to determine whether the adhesive has a volume defect.

[0008] Optionally, the left and right data are fitted with straight lines using the global least squares method to obtain the first left straight line and the first right straight line. The intersection point and the included angle of the first left straight line and the first right straight line are calculated. Rotation is performed with the intersection point as the axis and the included angle as the rotation angle. The lowest point in the left and right data is obtained respectively. The distance between the two lowest points is calculated to obtain the translation distance. The right data is translated to the left based on the translation distance.

[0009] Optionally, the intersection point of the first left line and the first right line is calculated using the following formula: ,in, The intersection point The parameters are the equation of the first line on the left. The parameters are the equation of the second line on the left. The parameters of the equation of the third line on the left. The parameters are the equation of the first line on the right. The parameters are the equation of the second line on the right. Let be the equation parameters of the third line on the right; calculate the angle between the first left line and the first right line using the following formula. ,in, It represents the angle between two straight lines.

[0010] Optionally, calculate the distance between the left and right sides. ,in, The distance between the first left line and the first right line is denoted as , where . The coordinates of the lowest point among the data points on the left. These are the coordinates of the lowest point among the data points on the right.

[0011] Optionally, a discrete point is selected as the curvature point to be calculated, and then two discrete points are selected to the left and right of the curvature point to be calculated. The curvature of the curvature point to be calculated is obtained by the three-point circumcircle method.

[0012] Optionally, the mean curvature and variance of curvature are calculated based on the curvature of all discrete points; the dynamic threshold is calculated using the following formula: ,in, For dynamic thresholds, The mean curvature, The curvature variance is used to compare the curvature of all discrete points with a dynamic threshold. When the curvature of a discrete point is greater than the dynamic threshold, the discrete point is identified as a suspected defect point.

[0013] Optionally, a first preset number of discrete points are selected on both the left and right sides of the suspected defect point as first region points, and the first region points and the suspected defect point are determined as the defect region; a second preset number of discrete points are selected on both the left and right sides of the defect region as second region points, and the defect region line is obtained by fitting a straight line equation based on the second region points; the distance from the suspected defect point to the fitted straight line equation is calculated, and when the distance is greater than the defect threshold, the suspected defect point is the real defect point.

[0014] Optionally, the spliced ​​data is divided into two equal parts, left and right, along its axis of symmetry, to obtain left and right end data. A second left-side straight line and a second right-side straight line are then fitted using the overall least squares method. The end data consists of a third preset number of discrete points at the edges of all data points. The second left-side straight line is extended to the adhesive surface, and the two nearest left adhesive surface data points to the extended second left-side straight line are obtained. The intersection of the straight line connecting the left adhesive surface data points and the second left-side straight line is determined as the left boundary. The second right-side straight line is extended to the adhesive surface, and the two nearest right adhesive surface data points to the extended second right-side straight line are obtained. The intersection of the straight line connecting the right adhesive surface data points and the second right-side straight line is determined as the right boundary. The distance between the left and right boundaries is calculated to obtain the colloid width.

[0015] Optionally, the highest discrete point in the vertical direction in the spliced ​​data is obtained to get the highest point; the left boundary and the right boundary are connected to obtain the bottom straight line; the distance from the highest point to the bottom straight line is calculated to obtain the colloid height.

[0016] This application employs the following steps: Two line laser profile scanners at the end effector of a robot scan a calibration part to obtain measured profile data. The measured profile data is compared with the actual profile data of the calibration part to obtain a comparison result. The angle of the line laser profile scanner is adjusted based on the comparison result. The data scanned by the line laser profile scanner consists of discrete points. During adhesive application, the line laser profile scanner scans and calculates the curvature value of each discrete point. When the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point. Based on the suspected defect points, a defect area is determined. The defect area line is fitted using the global least squares method. When the distance from the suspected defect point to the defect area line is greater than the defect threshold, the suspected defect point is determined as a real defect point. This solves the problems of excessively high misjudgment rate and inaccurate defect location in related technologies, thereby achieving accurate defect judgment and location. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for locating colloidal defects according to an embodiment of this application;

[0019] Figure 2 A schematic diagram of a line laser contour scanner scan;

[0020] Figure 3 This is a schematic diagram of the calibration component of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0025] This embodiment provides a method for locating colloidal defects that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart of a method for locating colloidal defects according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0027] Step S101: The calibration part is scanned by two line laser profile scanners at the end of the robot. The data on the left side and the data on the right side are stitched together to obtain the measured profile data. The measured profile data is compared with the actual profile data of the calibration part to obtain the comparison result. The angle of the line laser profile scanner is adjusted based on the comparison result. The data scanned by the line laser profile scanner consists of discrete points. The data on the left side is the data scanned by the line laser profile scanner located on the left side, and the data on the right side is the data scanned by the line laser profile scanner located on the right side.

[0028] Specifically, refer to Figure 2Two line laser profile scanners are mounted at a fixed angle at the end of the robot. This fixture can adjust the position and attitude angle of the line laser profile scanners in space to ensure that the same position of the colloid is scanned. During measurement, the colloid measurement height is set. When in use, the two line laser profile scanners will scan the colloid synchronously along the colloid coating motion trajectory to obtain point cloud data of the colloid surface. The point cloud data is the discrete points scanned. The rotation angle of the line laser profile scanners is 20°~30°, the distance between the two line laser profile scanners is 100mm~120mm, the measurement height is 150mm~170mm, and the robot scanning speed is not less than 200mm / s. Figure 2 The image shown is merely an illustration of the scanning position of the line laser profile scanner.

[0029] Reference Figure 3 As shown, a calibration part is to be made here. The contour dimension calibration part is composed of three shapes: rectangle, trapezoid and triangle, and is divided into four parts. The bottom of the first three parts is a rectangle, the top of the first part and the second part is a trapezoid, and the size of the upper part of the trapezoid in the second part is smaller than the size of the upper part of the trapezoid in the first part. The top of the third part is a triangle, and the fourth part is the rectangular part that connects these three parts.

[0030] The data scanned on the left and right sides during scanning represents the common area scanned by two scanners. Due to differences in orientation and angle, the left and right data must be stitched together to form complete data. After stitching, the measured contour data is compared with the actual contour data of the calibration part, and then the position and angle of the line laser contour scanner are adjusted.

[0031] The specific operation process is as follows: First, a fixing fixture is installed at the end of the robot to fix the simulated glue gun. The robot's pose is adjusted using a teach pendant to align the simulated glue gun with the top of the triangle on the calibration part, ensuring that the line laser is perpendicular to the calibration part for measurement. By collecting and analyzing the width and height values ​​of different shaped parts of the contour dimension calibration part, it is ensured that the two line laser contour scans scan the same position of the glue. If they are not at the same position, the position and attitude angle of the line laser contour scanner in space are adjusted. Second, the common part of the two data points is selected and a straight line is fitted using the global least squares method. The intersection point of these two lines is calculated, and the right data is rotated around the intersection point by an angle equal to the angle between the two lines, so that the right data is aligned with the left data. At the same time, the lowest point of the two data points is found, and the translation vector when the Euclidean distance between the two lowest points is zero is calculated. The right data is then translated as a whole, thus completing the data splicing.

[0032] In step S102, when applying adhesive, the linear laser profile scanner scans the adhesive, stitches the data on the left side with the data on the right side, and calculates the curvature value of each discrete point. When the curvature value exceeds the dynamic threshold, the discrete point is identified as a suspected defect point.

[0033] Specifically, by first identifying suspected defect points through the curvature of discrete points, a preliminary screening is performed, which solves the technical problem of high error rate in the existing technology. This preliminary screening provides a basis for determining the actual defect points and improves the accuracy of subsequent judgments.

[0034] Step S103: Determine the defect area based on the suspected defect point, and fit the defect area line by the global least squares method. When the distance from the suspected defect point to the defect area line is greater than the defect threshold, the suspected defect point is determined to be the real defect point.

[0035] Specifically, defining the defect area helps to expand the calculation range, and the discrete points are analyzed again using the overall least squares method, which more accurately determines the defect points and their locations.

[0036] Step S104: Obtain the height and width of the colloid after splicing, compare the height and width of the colloid with the preset standard for applying the adhesive, and determine whether the colloid has defects in volume.

[0037] Specifically, since the adhesive may have not only surface defects but also volume defects (too large or too small) during the coating process, it is necessary to compare the data with the preset coating standards.

[0038] In one optional embodiment, the left and right data are fitted with straight lines using the global least squares method to obtain a first left straight line and a first right straight line. The intersection point and the included angle of the first left straight line and the first right straight line are calculated. The data are rotated with the intersection point as the axis and the included angle as the rotation angle. The lowest point in the left and right data is obtained respectively. The distance between the two lowest points is calculated to obtain the translation distance. The right data is translated to the left based on the translation distance.

[0039] Specifically, even after adjusting the scanner's position, subsequent scans may still have slight deviations. Adjusting the angle and translation data in this way to stitch the data together ensures accuracy. This method is also used for subsequent glue application. Accurate scan data is a necessary foundation for identifying defects.

[0040] After the calibration parts are assembled, calculate each feature parameter and compare it with the actual measured value: First, if there is a difference between the top trapezoidal dimension of the assembled calibration part and the actual measured dimension, further adjustments are needed. Rotate the right-side data around its lowest point by an angle ranging from -0.5° to 0.5°. Second, translate the right-side data. The translation vector is the difference between the width of the bottom rectangle of the calibration part and the actual measurement, generally ranging from -0.2 mm to 0.2 mm. Adjust the splicing parameter settings to complete the calibration and splicing of the contour scan data.

[0041] In one optional embodiment, the intersection point of the first left line and the first right line is calculated using the following formula: ,in, The intersection point The parameters are the equation of the first line on the left. The parameters are the equation of the second line on the left. The parameters of the equation of the third line on the left. The parameters are the equation of the first line on the right. The parameters are the equation of the second line on the right. Let be the equation parameters of the third line on the right; calculate the angle between the first left line and the first right line using the following formula. ,in, It represents the angle between two straight lines.

[0042] Specifically, before calculating the intersection point and rotation angle, a straight line needs to be fitted to the left and right data. The method for fitting the straight line is the commonly used overall least squares method, and the straight line is fitted according to the formula: ,in, The parameters of the first straight line equation are... The parameters of the second line equation are... The parameters of the third line equation are... Let be the distance from the fitted point to the line. Using the covariance matrix, solve for the parameters of the first, second, and third line equations. The covariance matrix is: ,in, , This represents the mean of the data in the x-direction. This represents the mean of the data in the y-direction. and C represents the decentralized coordinates, and C represents the covariance matrix;

[0043] In one alternative embodiment, the distance between the left and right sides is calculated. ,in, The distance between the first left line and the first right line is denoted as , where . The coordinates of the lowest point among the data points on the left. These are the coordinates of the lowest point among the data points on the right.

[0044] In one alternative embodiment, a discrete point is selected as the curvature point to be calculated, and then two discrete points are selected to the left and right of the curvature point to be calculated. The curvature of the curvature point to be calculated is obtained by the three-point circumcircle method.

[0045] Specifically, the three-point circumcircle method is a commonly used method for calculating curvature. , , In this equation, a, b, and c represent the three side lengths of the triangle, ∠B represents the angle of the midpoint B of the triangle, R represents the radius of the circumcircle of the triangle, and κ represents the curvature.

[0046] In one optional embodiment, the mean curvature and the variance of curvature are calculated based on the curvature of all discrete points; a dynamic threshold is calculated using the following formula: ,in, For dynamic thresholds, The mean curvature, The curvature variance is used to compare the curvature of all discrete points with a dynamic threshold. When the curvature of a discrete point is greater than the dynamic threshold, the discrete point is identified as a suspected defect point.

[0047] Specifically, the dynamic threshold changes continuously with the overall curvature data. If the overall curvature data is large, the threshold will also increase. This dynamic threshold increases the accuracy of data judgment and avoids the increased judgment error caused by a fixed threshold.

[0048] In one optional embodiment, a first preset number of discrete points are selected on the left and right sides of the suspected defect point as first region points, and the first region points and the suspected defect point are determined as a defect region; a second preset number of discrete points are selected on the left and right sides of the defect region as second region points, and a straight line equation of the defect region is obtained based on the second region points; the distance from the suspected defect point to the fitted straight line equation is calculated, and when the distance is greater than the defect threshold, the suspected defect point is the real defect point.

[0049] Specifically, to ensure accurate identification of defects, a first preset number of points are selected around the suspected defect point. The area encompassed by these points is defined as the defect region. The selection can be divided into left / right or front / back portions based on the suspected defect point's location, with a first preset number of points selected in each portion. After determining the suspected defect region, points are selected again using the same method with a second preset number. These selected points are then used for fitting a straight line using the global least squares method. Since the curvature of surface defects varies significantly from its surroundings, the distance from the suspected defect point to the fitted straight line is used to determine whether the suspected defect point is a true defect point.

[0050] In one optional embodiment, the spliced ​​data is divided into two equal parts, left and right, along its axis of symmetry, to obtain left-side end data and right-side end data. A second left-side straight line and a second right-side straight line are then fitted using the global least squares method to the left-side end data and right-side end data. The end data consists of a third preset number of discrete points representing the edges of all data points. The second left-side straight line is extended to the adhesive surface, and the two nearest left adhesive surface data points to the extended second left-side straight line are obtained. The intersection of the straight line connecting the left adhesive surface data points and the second left-side straight line is determined as the left boundary. The second right-side straight line is extended to the adhesive surface, and the two nearest right adhesive surface data points to the extended second right-side straight line are obtained. The intersection of the straight line connecting the right adhesive surface data points and the second right-side straight line is determined as the right boundary. The distance between the left and right boundaries is calculated to obtain the colloid width.

[0051] Specifically, the scanned data includes colloid profile measurement data and the profile measurement data of the bottom adhesive surface, which is typically glass. Data from both ends of the colloid are selected, and the overall least squares method is used to fit them to a second left straight line and a second right straight line, respectively. The second left straight line is extended to the glass data, and the two closest glass data points are found. The intersection of these two points with the second left straight line is calculated; this intersection point serves as the left boundary of the colloid. The same process is applied to the right straight line to find the right intersection point, thus obtaining the right boundary of the colloid. The distance between the two intersection points is used as the detected value of the colloid width.

[0052] In one optional embodiment, the highest discrete point in the vertical direction in the spliced ​​data is obtained to obtain the highest point; the left boundary and the right boundary are connected to obtain the bottom straight line; the distance from the highest point to the bottom straight line is calculated to obtain the colloid height.

[0053] Specifically, the highest point in the vertical direction of the spliced ​​data is found, and the distance between the highest point and the line connecting the two intersection points is used as the detection value of the colloid height. By comparing it with the preset standard for colloid application, it can be determined whether the current colloid is a defective colloid, thus completing the development of the colloid contour scanning calibration and defect location method.

[0054] This application employs the following steps: Two line laser profile scanners at the end effector of a robot scan a calibration part to obtain measured profile data. The measured profile data is compared with the actual profile data of the calibration part to obtain a comparison result. The angle of the line laser profile scanner is adjusted based on the comparison result. The data scanned by the line laser profile scanner consists of discrete points. During adhesive application, the line laser profile scanner scans and calculates the curvature value of each discrete point. When the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point. Based on the suspected defect points, a defect area is determined. The defect area line is fitted using the global least squares method. When the distance from the suspected defect point to the defect area line is greater than the defect threshold, the suspected defect point is determined as a real defect point. This solves the problems of excessively high misjudgment rate and inaccurate defect location in related technologies, thereby achieving accurate defect judgment and location.

[0055] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0060] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0061] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0062] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0063] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for locating colloidal defects, characterized in that, include: The calibration piece is scanned by two line laser profile scanners at the end of the robot. The data from the left side and the data from the right side are stitched together to obtain the measured profile data. The measured profile data is compared with the actual profile data of the calibration piece to obtain a comparison result. The angle of the line laser profile scanner is adjusted based on the comparison result. The data scanned by the line laser profile scanner consists of discrete points. The data from the left side is the data scanned by the line laser profile scanner located on the left side, and the data from the right side is the data scanned by the line laser profile scanner located on the right side. During the application of adhesive, the line laser profile scanner scans the adhesive, and after splicing the data from the left and right sides, it calculates the curvature value of each discrete point. When the curvature value exceeds the dynamic threshold, the discrete point is determined to be a suspected defect point. Based on the suspected defect points, the defect region is determined, and the defect region line is fitted by the global least squares method. When the distance from the suspected defect point to the defect region line is greater than the defect threshold, the suspected defect point is determined to be the actual defect point. The height and width of the colloid after splicing are obtained, and the height and width of the colloid are compared with the preset standard for applying glue to determine whether the colloid has defects in volume. A first preset number of discrete points are selected on both sides of the suspected defect point as first region points, and the first region points and the suspected defect point are determined as a defect region; a second preset number of discrete points are selected on both sides of the defect region as second region points, and a straight line equation of the defect region is obtained based on the second region points; the distance from the suspected defect point to the fitted straight line equation is calculated, and when the distance is greater than the defect threshold, the suspected defect point is a real defect point.

2. The method according to claim 1, characterized in that, Concatenate the data on the left and the data on the right, including: The left and right data are fitted with straight lines using the global least squares method to obtain the first left straight line and the first right straight line. The intersection point and the included angle of the first left straight line and the first right straight line are calculated. Rotate with the intersection point as the axis and the included angle as the rotation angle; The lowest point in the left-side data and the right-side data are obtained respectively. The distance between the two lowest points is calculated to obtain the translation distance. The right-side data is then translated to the left based on the translation distance.

3. The method according to claim 2, characterized in that, The left and right data are fitted with straight lines using the global least squares method to obtain a first left straight line and a first right straight line. The intersection point and the included angle of the first left straight line and the first right straight line are calculated, including: Calculate the intersection point of the first left line and the first right line using the following formula: ,in, The intersection point The parameters are the equation of the first line on the left. The parameters are the equation of the second line on the left. The parameters of the equation of the third line on the left. The parameters are the equation of the first line on the right. The parameters are the equation of the second line on the right. The parameters are the equation of the third line on the right. Calculate the angle between the first left line and the first right line using the following formula: ,in, It represents the angle between two straight lines.

4. The method according to claim 2, characterized in that, The lowest point is obtained from the left-side data and the right-side data respectively. The distance between the two lowest points is calculated to obtain the translation distance. The right-side data is then translated to the left based on the translation distance, including: Calculate the distance between the left and right sides. ,in, The distance between the first left line and the first right line is denoted as , where . The coordinates of the lowest point among the data points on the left are... The coordinates of the lowest point among the data points on the right are given.

5. The method according to claim 1, characterized in that, During adhesive application, the line laser profile scanner scans the adhesive, stitches the data from the left and right sides together, and calculates the curvature value of each discrete point. When the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point, including: Select a discrete point as the curvature point to be calculated, and then select one discrete point to the left and one to the right of the curvature point to be calculated. Use the three-point circumcircle method to calculate the curvature of the curvature point to be calculated.

6. The method according to claim 1, characterized in that, During adhesive application, the line laser profile scanner scans the adhesive, stitches the data from the left and right sides together, and calculates the curvature value of each discrete point. When the curvature value exceeds a dynamic threshold, the discrete point is identified as a suspected defect point, including: Calculate the mean curvature and the variance of curvature based on the curvature of all the discrete points. The dynamic threshold is calculated using the following formula: ,in, The dynamic threshold, The mean curvature, The curvature variance; The curvature of all the discrete points is compared with the dynamic threshold. When the curvature of a discrete point is greater than the dynamic threshold, the discrete point is determined to be a suspected defect point.

7. The method according to claim 1, characterized in that, The height and width of the colloid after splicing are obtained. These are then compared with a preset adhesive application standard to determine if the colloid has any volume defects, including: The spliced ​​data is divided into two parts, left and right, on the same axis of symmetry. The left end data and the right end data are obtained. The left end data and the right end data are fitted with the overall least squares method to obtain the second left line and the second right line. The end data is composed of the third preset number of discrete points on the edge of all data points. Extend the second left straight line to the adhesive surface, and obtain the two left adhesive surface data points that are closest to the extended second left straight line. The intersection of the straight line connecting the left adhesive surface data points and the second left straight line is determined as the left boundary. Extend the second right-side straight line to the adhesive surface, and obtain the two right-side adhesive surface data points closest to the extended second right-side straight line. The intersection of the straight line connecting the right-side adhesive surface data points and the second right-side straight line is determined as the right-side boundary. The distance between the left and right boundaries is calculated to obtain the width of the colloid.

8. The method according to claim 7, characterized in that, The height and width of the colloid after splicing are obtained. These are then compared with a preset adhesive application standard to determine if the colloid has any volume defects, including: The highest discrete point in the vertical direction is obtained from the spliced ​​data; Connect the left boundary and the right boundary to obtain the bottom straight line; The height of the colloid is obtained by calculating the distance from the highest point to the bottom straight line.

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