Defective product detection method and system for laser cutting antenna line type

Abstract

The invention relates to the technical field of antenna detection, in particular to a laser cutting antenna line type defective product detection method and system, and the method comprises the steps: obtaining processing batch information, building a coiled material motion coordinate system, and storing target antenna parameters and reference information of a dispensing region; acquiring image data of the antenna, and aligning the image data with the reference information in the coiled material motion coordinate system; extracting an antenna boundary according to the image data, and calculating a geometric deviation based on the antenna boundary and the reference information; obtaining a defect coordinate position according to the geometric deviation, and comparing the dispensing area with the geometric deviation to judge the severity of the defect; mapping the coordinate position to a coiled material motion coordinate system to obtain bad position data, and executing differentiation processing according to the severity; and storing the bad position data and the processing batch information corresponding to the differentiation processing, and generating a detection result. According to the invention, the antenna boundary and geometric deviation are analyzed through the image data and the reference information, defects in the antenna cutting process are identified, and the detection accuracy is improved.

Description

Technical Field

[0001] This invention relates to the field of antenna testing technology, and in particular to a method and system for detecting defective products in laser-cut antenna profiles. Background Technology

[0002] RFID tag antennas are core components in RFID technology used for receiving and transmitting signals, typically composed of a composite of metal materials (such as aluminum foil) and a substrate (such as cloth). During production, the antenna shape needs to be precisely processed using laser cutting technology, and special attention must be paid to the chip adhesive dispensing area during the cutting process to avoid affecting subsequent chip installation and performance. Laser cutting technology is widely used in antenna manufacturing due to its high precision and controllability. By irradiating the composite material with a laser beam, it can be precisely cut into the required antenna shape while maintaining high cutting quality. However, defective products may be generated during laser cutting. These defects include shape deviations, splatter, and other flaws, which may affect the normal use of the antenna, especially the chip adhesive dispensing area, potentially causing the chip to fail to install correctly.

[0003] Current RFID tag antenna production typically employs laser cutting technology to process the antenna shape. However, traditional laser cutting has several drawbacks. Shape deviations or defects may occur during the cutting process, stemming from factors such as unstable laser power control and uneven cutting speed. Protecting the adhesive-coated area also presents challenges; inaccurate cutting can damage this area, affecting chip installation. Currently, most production lines rely on manual inspection or simple image recognition systems to check antenna cutting quality. These methods usually only allow for inspection after cutting and depend on manual judgment. Manual inspection is not only inefficient but also prone to missed detections and misjudgments, failing to promptly identify and address defective products. While existing image processing technologies can be used for antenna shape detection, their accuracy and speed remain limited, and they cannot accurately classify the types of defects occurring during the cutting process.

[0004] Therefore, there is a need for a defect detection method and system for laser-cut antenna lines to improve the accuracy of detection during the cutting process. Summary of the Invention

[0005] In view of at least one of the above technical problems, the present invention provides a defect detection method and system for laser-cut antenna lines. The method uses image data and reference information alignment, antenna boundary extraction and geometric deviation calculation to identify and locate defects that occur during the cutting process, and performs differentiated processing according to the severity of the defects, thereby improving detection accuracy, reducing manual intervention and optimizing the production process.

[0006] This invention provides a method for detecting defective products in laser-cut antenna profiles, comprising the following steps: S10: Obtain processing batch information, establish a roll material motion coordinate system, and store target antenna parameters and reference information for the dispensing area; S20: Acquire image data of the antenna after cutting, correct the image data, and align the image data with the reference information in the roll motion coordinate system; S30: Extract the antenna boundary based on the image data, and calculate the geometric deviation based on the antenna boundary and the reference information; S40: Obtain the coordinate position of the defect in the image data based on the geometric deviation, compare the dispensing area with the geometric deviation, and determine the severity of the defect; S50: Map the coordinate position to the roll material motion coordinate system to obtain defect location data, and perform differentiated processing according to the severity, including repair processing or marking processing; S60: Store the defect location data and the processing batch information corresponding to the differential processing, and generate the detection result.

[0007] In some embodiments of the present invention, step S20 includes: The image data of the antenna after cutting is acquired, and the image data is segmented and stitched together during the continuous movement of the roll material to obtain a complete antenna image; Geometric and brightness corrections are performed on the image data to eliminate distortions and unevenness caused by deformation of the imaging device or roll material, thereby obtaining a corrected image; Preset positioning marks or feature points are identified in the calibrated image and used as a reference to register the calibrated image with the reference information, so that the calibrated image and the roll material motion coordinate system are established to correspond to each other, and the image data and the reference information are aligned in the roll material motion coordinate system.

[0008] In some embodiments of the present invention, step S30, extracting the antenna boundary based on the image data, includes: The acquired image data is preprocessed to suppress background noise and enhance the contrast between the antenna area and the substrate; Threshold segmentation is performed based on the preprocessed image data, and candidate boundaries for the antenna are determined by combining the analysis of the roll material's motion direction. The candidate boundaries are refined by repairing breaks, removing isolated points, and deleting unclosed pseudo-contours to form continuous and complete antenna boundaries.

[0009] In some embodiments of the present invention, step S30, calculating the geometric deviation based on the antenna boundary and the reference information, includes: The linewidth distribution curve is calculated along the extension direction of the antenna and compared with the target antenna parameters in the reference information to obtain the linewidth deviation; The interval length and the included angle between adjacent boundary segments are obtained based on the antenna boundary. The continuity of the antenna boundary is detected based on the interval length to generate a breakage deviation. The orientation of the antenna boundary is detected based on the included angle to generate an orientation deviation. The geometric deviation is generated by combining the linewidth deviation, the breakage deviation, and the orientation deviation.

[0010] In some embodiments of the present invention, step S40, determining the severity of the defect, includes: Enhance the edge information of the image data and remove low-frequency noise, extract boundaries and feature points; The boundary line of the antenna is extracted based on the edge information and the boundary, the feature points are detected and matched with the boundary line to obtain the coordinate position; Defects are classified into defect categories, and different impact performances are assigned to different defect categories. The impact degree of the defect in the signal transmission path is calculated based on the impact performance. The severity of the defect is determined based on the defect category and the impact level. The severity of the defect is divided into minor defects and major defects.

[0011] In some embodiments of the present invention, step S40 further includes determining the severity of the defect in the dispensing area: Calculate the masking area, shape change, and relative position of the defect located within the dispensing area with respect to the boundary of the dispensing area; Based on the pressure difference caused by the shape change detection defect to the fluid flow, the pressure difference, the obstruction area, and the preset flow coefficient are multiplied to obtain the fluid resistance value. The dispensing path length is obtained based on the relative position, and the mass impact value is obtained by multiplying the obstruction area by the pressure difference and dividing by the dispensing path length. The fluid resistance value and the mass impact value are compared with preset thresholds respectively. When both exceed the preset thresholds, the defect in the dispensing area is determined to be a major defect.

[0012] In some embodiments of the present invention, step S50, obtaining defect location data, includes: The pixel coordinates in the image data are mapped to the real-time displacement parameters of the roll material movement, thus establishing a mapping relationship between the pixel points and the roll material movement coordinate system. Based on the mapping relationship, the coordinate position of the defect is converted into the coordinates of the defective point in the roll material motion coordinate system; The coordinates of the defective points are recorded as defective location data, and a correspondence between the defective points and the processing batch information is established in the roll material movement path.

[0013] In some embodiments of the present invention, in step S50, differential processing is performed according to the severity, including repair processing or marking processing: For defects identified as minor defects, repair procedures are performed, including cleaning or repairing the adhesive application area and the antenna. For defects identified as major defects, a marking process is performed, marking and storing the coordinate position corresponding to the major defect, waiting for subsequent operators to perform corresponding operations.

[0014] In some embodiments of the present invention, the process parameters of laser cutting are dynamically adjusted based on the detection results to optimize the subsequent processing quality.

[0015] The present invention also provides a defect detection system for laser-cut antenna lines, comprising: Storage module: Obtains processing batch information, establishes a roll material motion coordinate system, and stores target antenna parameters and reference information for the dispensing area; Correction and alignment module: acquires image data of the antenna after cutting, corrects the image data, and aligns the image data with the reference information in the roll motion coordinate system; Deviation calculation module: Extracts antenna boundaries based on the image data, and calculates geometric deviations based on the antenna boundaries and the reference information; Defect judgment module: Based on the geometric deviation, the coordinate position of the defect in the image data is obtained, and the dispensing area is compared with the geometric deviation to determine the severity of the defect; Difference processing module: Maps the coordinate position to the roll material motion coordinate system to obtain defect location data, and performs differential processing according to the severity, including repair processing or marking processing; Result generation module: Stores the defect location data and the processing batch information corresponding to the differential processing, and generates the detection results.

[0016] The beneficial effects of this invention are as follows: Through precise image correction and antenna boundary extraction technology, this invention can effectively improve the accuracy of defect detection, especially when detecting minute and complex defects, avoiding missed detections and misjudgments, and ensuring consistent product quality; by detecting defects in real time and processing them differently according to their severity, the process can be adjusted immediately during production, improving production efficiency; it achieves automatic defect detection and processing, significantly reducing labor costs and the risk of human error; by accurately detecting defects in and around the dispensing area, this invention can effectively protect the integrity of the chip dispensing location, avoiding damage to the dispensing area from laser cutting, thereby ensuring proper chip installation and improving the reliability of RFID tags. Attached Figure Description

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

[0018] Figure 1 This is a schematic diagram of the steps in the defect detection method for laser-cut antenna lines in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the process of aligning image data and reference information in an embodiment of the present invention; Figure 3 This is a schematic diagram of the process for extracting antenna boundaries based on image data in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for calculating geometric deviations based on antenna boundaries and reference information in an embodiment of the present invention; Figure 5 This is a schematic diagram of the process for determining the severity of a defect in an embodiment of the present invention; Figure 6 This is a flowchart illustrating the process of determining the severity of defects in the dispensing area in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0022] This invention provides a method such as Figures 1 to 6 The defect detection method for laser-cut antenna profiles shown includes the following steps: S10: Obtain batch processing information, including the target shape and size of the antenna and the specific location of the chip dispensing area, which can be obtained through design documents or preset standard parameters; establish a roll material motion coordinate system, which is a coordinate system established during roll material processing to accurately locate the cutting position, and it is coordinated with the operating system of the laser cutting machine; store the target antenna parameters and reference information of the dispensing area, such as the antenna geometry, size requirements, dispensing position, etc.

[0023] S20: Use a camera or industrial camera to photograph the antenna after cutting to obtain image data of the antenna after cutting. The image data may have geometric distortion and uneven brightness due to factors such as the camera's viewing angle, lighting, or roll material movement. Therefore, it is necessary to perform geometric correction and brightness correction on these image data to eliminate distortion and unevenness. Geometric correction can be performed using a calibration plate or specific reference points to ensure that the image data is consistent with the actual antenna position. Align the image data and reference information in the roll material movement coordinate system. Image processing algorithms can be used to determine the correct position and orientation of the cut image, thereby ensuring that the image data accurately matches the geometric information of the target antenna.

[0024] S30: Extract the antenna boundary from the image data, and calculate the geometric deviation based on the antenna boundary and reference information; prioritizing background noise suppression and contrast enhancement of the antenna region in the image, the antenna boundary is extracted using an edge detection algorithm (such as the Canny algorithm), and the geometric deviation of the antenna is calculated based on the extracted antenna boundary and the target antenna parameters. The calculation includes: the linewidth deviation of the antenna boundary, the spacing length, the angle between adjacent boundary line segments, etc., thereby generating the geometric deviation of the antenna. These deviations will help with subsequent defect analysis and processing.

[0025] S40: Obtain the coordinate position of the defect in the image data based on the geometric deviation, compare the dispensing area with the geometric deviation, and determine the severity of the defect; for the defect, the system determines whether the defect affects the dispensing position by comparing it with the dispensing area. When determining the severity of the defect, it will also evaluate its potential impact on the chip bonding performance based on the type, size and location of the defect.

[0026] S50: Map the coordinate position to the roll material motion coordinate system to obtain defect location data, and perform differentiated processing according to the severity, including repair processing or marking processing; S60: Store the defect location data and the corresponding processing batch information for differentiated processing, generate inspection results, and track the handling of defective products containing defects based on this data in the subsequent production process.

[0027] This embodiment compares and correlates geometric deviations with the dispensing area to comprehensively assess the potential impact of defects on electrical performance and process reliability. This allows defects to be classified into different levels, their severity to be assessed, and differentiated processing to be performed based on defect type. This effectively improves the accuracy of defect detection during the cutting process, increases detection efficiency, significantly reduces errors from manual judgment, and ensures the consistency and reliability of product quality.

[0028] In some embodiments of the present invention, such as Figure 2 As shown, step S20 includes: After the antenna is cut, image data is acquired and stitched together in segments during the continuous movement of the roll material to obtain a complete antenna image. To achieve accurate antenna defect detection, a high-resolution industrial camera or other imaging device is used to photograph the antenna after cutting. In this process, considering the continuous movement of the roll material during cutting, the image data is usually acquired in segments. During each acquisition process, due to the movement of the roll material, there may be partial overlap of images. Therefore, these images need to be acquired in segments, and all images are stitched together into a complete antenna image using a stitching algorithm.

[0029] Geometric and brightness corrections are performed on the image data to eliminate distortions and unevenness caused by the deformation of the imaging device or roll material, resulting in a corrected image. First, a geometric correction method is used to correct geometric distortions in the image data caused by the movement of the device or roll material through a calibration plate or specific reference points. In addition, brightness correction is also essential. Through homogenization processing, brightness differences in the image data caused by uneven illumination are eliminated, ensuring that the corrected image has consistent illumination conditions and improving the accuracy of subsequent image processing.

[0030] Pre-defined positioning markers or feature points are identified in the calibrated image. These markers or feature points are pre-defined during antenna design and are usually located at key positions of the antenna. Using these as references, the calibrated image is registered with the reference information to establish a correspondence between the calibrated image and the roll motion coordinate system. This process not only ensures the accurate alignment of the image data but also lays a solid foundation for subsequent antenna boundary extraction and defect detection.

[0031] In some embodiments of the present invention, such as Figure 3 As shown, in step S30, the antenna boundary is extracted based on the image data, including: The acquired image data is preprocessed. The antenna image may be affected by background noise, lighting changes or other factors, so image preprocessing is required to improve the accuracy of subsequent processing. Background noise is suppressed by removing random noise in the image through filtering algorithms (such as median filtering or Gaussian filtering). The contrast between the antenna area and the substrate is enhanced to make the antenna edge clearer and easier to identify. Contrast enhancement usually uses local contrast enhancement algorithms or histogram equalization methods to make the difference between the antenna area and the background substrate more obvious.

[0032] Threshold segmentation is performed on the preprocessed image data to separate the antenna region from the background. The threshold segmentation can use an adaptive threshold algorithm, which automatically adjusts the threshold according to different image features, making the difference between the antenna and the background more obvious. The preliminary outline of the antenna is extracted from the image, providing a basis for subsequent boundary refinement. During the continuous movement of the roll material in the cutting process, the shape of the antenna may be tilted or deformed in the image. By combining the analysis of the roll material movement direction, the candidate boundary of the antenna is determined, which effectively reduces the impact of the instability of the roll material movement and ensures the correctness of the antenna outline in the cutting image.

[0033] Boundary refinement processing is performed on candidate boundaries, including repairing broken parts of the boundaries, filling in breaks caused by uneven cutting or image acquisition problems, removing isolated points which are usually noise or erroneous boundary markers that do not belong to the actual boundaries of the antenna, and deleting unclosed pseudo-contours to ensure that the boundaries are continuous and closed, forming continuous and complete antenna boundaries, making the antenna boundaries more accurate and suitable for subsequent geometric deviation calculations and defect analysis.

[0034] In some embodiments of the present invention, such as Figure 4 As shown, in step S30, the geometric deviation is calculated based on the antenna boundary and reference information, including: The linewidth distribution curve is calculated along the antenna's extension direction and compared with the target antenna parameters in the reference information to obtain the linewidth deviation. The antenna linewidth refers to the width of the antenna boundary at various locations, which is crucial to the antenna's performance. To calculate the linewidth distribution curve, we first determine a series of sampling points along the antenna's extension direction, extract the antenna width at these points based on image data, and obtain a linewidth distribution curve along the antenna's extension direction by calculating the width of each sampling point. This curve is then compared with the target antenna parameters in the reference information. The target antenna parameters typically include standard linewidth requirements and allowable deviation ranges. Through comparison, the linewidth deviation is obtained, which reflects the difference between the actual cut width of the antenna and the target width.

[0035] Based on the antenna boundary, the interval length and the angle between adjacent boundary segments are obtained. The continuity of the antenna boundary is detected based on the interval length, generating a breakage deviation. The orientation of the antenna boundary is detected based on the angle, generating an orientation deviation. The interval length refers to the distance between two adjacent cutting points in the antenna boundary, while the angle refers to the angle between two adjacent boundary segments. The system can perform the following two detections: First, the interval length determines the continuity of the antenna boundary; if the interval length is large or a break occurs, it indicates a problem in the cutting process, thus generating a breakage deviation. Second, the angle determines the orientation of the antenna boundary, i.e., whether the antenna cutting direction is consistent with the design requirements. Through the analysis of these angles, an orientation deviation can be generated, reflecting the error in the antenna orientation.

[0036] Geometric deviation is generated by comprehensively considering linewidth deviation, breakage deviation, and orientation deviation. Geometric deviation is the overall difference between the actual cut shape of the antenna and the target shape. It can fully reflect the shape error that exists in the antenna cutting process and provide a basis for subsequent defect analysis and processing.

[0037] In some embodiments of the present invention, such as Figure 5 As shown, in step S40, determining the severity of the defect includes: Image data may be blurred or noisy due to shooting environment, equipment limitations, or roll movement. Edge enhancement algorithms (such as Sobel operator, Canny edge detection, etc.) can enhance the edge information of image data, and low-pass filtering (such as Gaussian filtering or mean filtering) can be used to remove low-frequency noise in the image, preserve antenna boundaries and other important features, reduce misjudgments caused by noise, and extract boundaries and feature points.

[0038] The antenna boundary line is extracted based on edge information and boundary conditions. Feature points are detected and matched with the boundary line to obtain their coordinate positions. The antenna boundary is the core information for defect detection, and feature points are usually located at key positions of the antenna, such as cutting start points, corner points, or connection points. Through image processing algorithms, combined with edge information and geometric models, the complete boundary of the antenna is extracted, and the positions of feature points are marked, ensuring the accurate positioning of feature points in the image and providing a foundation for subsequent defect classification and impact assessment.

[0039] Defects are categorized and different impact performances are assigned to different defect categories. The impact degree of the defect in the signal transmission path is calculated based on the impact performance. According to the shape and location of the defect, it is divided into different defect categories, such as minor defects, serious defects, missing parts or overcuts, etc. Each defect category will be analyzed according to its impact on antenna performance. Minor defects may only affect the appearance of the antenna, while serious defects may affect the signal transmission capability of the antenna. The impact degree of the defect is obtained by quantifying the interference that the antenna defect may cause to the signal transmission path. According to the type, size and impact of the defect on signal propagation, the degree of impact of the defect on signal attenuation, distortion or signal quality is calculated.

[0040] The severity of a defect is determined based on its category and impact. Among them, the severity of defects is divided into minor defects and major defects. Minor defects usually have little impact on antenna performance and will not significantly reduce signal transmission quality, while major defects may directly lead to the loss of antenna function or a serious decline in signal quality.

[0041] Based on the above embodiments, such as Figure 6 As shown, step S40 also includes further determining the severity of defects in the dispensing area: Calculate the occupancy area, shape changes, and relative position of defects within the dispensing area to the boundary of the dispensing area. During the laser cutting process of the antenna, some defects may appear in the dispensing area, which may affect the subsequent chip mounting and dispensing process. A detailed analysis of defects within the dispensing area is required, calculating the occupancy area (i.e., the area covered by the defect), analyzing the relative position of the defect to the boundary of the dispensing area, and the shape changes of the defect. Shape changes of defects may lead to uneven distribution within the dispensing area, affecting fluid flow in that area.

[0042] Based on the pressure difference caused by the defect in the shape change detection, that is, the pressure difference between the defect location and the normal flow conditions, the fluid resistance value is obtained by multiplying the pressure difference, the obstruction area and the preset flow coefficient.

[0043] When fluid flows through a pipe, its pressure decreases due to flow resistance. Defects increase this resistance, leading to a greater pressure differential, which reflects the impact of the defect on fluid flow. The obstruction area is the region occupied by the defect, representing the degree to which it occupies physical space for fluid flow. If a defect exists, it affects the flow path, causing uneven flow or partial obstruction. The larger the obstruction area, the greater the degree of flow obstruction, thus affecting flow efficiency. The flow coefficient is a coefficient related to fluid properties, defect geometry, and flow conditions, reflecting the flow resistance under specific conditions. For example, in pipe flow, the flow coefficient relates to pipe roughness and fluid viscosity. For the dispensing area containing a defect, the flow coefficient describes the combined effect of defect shape, material properties, and fluid viscosity on fluid flow. Therefore, multiplying these three physical quantities during dispensing yields a fluid resistance value that comprehensively assesses the impact of defects on fluid flow in the dispensing area.

[0044] The dispensing path length obtained based on relative position refers to the length of the fluid transmission path from the dispensing equipment to the dispensing area. The longer the path length, the greater the restriction on fluid flow, and the more significant the impact of defects on fluid flow. The quality impact value is obtained by multiplying the obstruction area by the pressure difference and dividing by the dispensing path length. The quality of the dispensing process depends not only on the uniform distribution of the fluid, but also on factors such as the fluid flow path and velocity. In particular, defects in the dispensing area may alter the fluid flow characteristics, such as causing uneven flow, excessive concentration, or dispersion, thereby affecting the dispensing effect. The quality impact value comprehensively considers the geometry and location of defects and their influence on the fluid flow path. It can quantify the interference of defects on fluid flow and link this interference to the dispensing quality, ensuring uniform fluid distribution during the dispensing process and avoiding quality problems caused by defects.

[0045] The fluid resistance value and the quality impact value are compared with preset thresholds. When both exceed the preset thresholds, the defect in the dispensing area is judged as a major defect. Major defects may cause dispensing failure or affect chip installation, so they need to be marked or excluded.

[0046] In some embodiments of the present invention, step S50, obtaining defect location data, includes: By mapping the pixel coordinates in the image data to the real-time displacement parameters of the roll material movement, a mapping relationship between the pixel points and the roll material movement coordinate system is established. Since the image data is acquired by a camera device, the coordinates of each pixel in the image correspond to the camera's viewing angle and the position of the image plane. However, the actual antenna cutting process is carried out on a continuously moving roll material, and there is a difference between the image coordinates and the actual roll material movement coordinates. Therefore, it is necessary to establish a mapping relationship between the image coordinates and the roll material movement coordinate system through real-time displacement parameters. The real-time displacement parameters are usually provided by the roll material movement control system, and they represent the actual displacement of the roll material at each moment. Through the mapping relationship, the pixel coordinates in the image can be converted into the actual roll material movement coordinates, ensuring that the defect position in the image is consistent with the position in the actual process.

[0047] Based on the mapping relationship, the coordinate position of the defect is converted into the coordinate position of the defective point in the roll material motion coordinate system. By combining the defect coordinates in the image data with the motion displacement parameters of the roll material, the defect position in the image coordinate system can be mapped to the actual position in the roll material motion coordinate system, ensuring that the actual position of the defect can be accurately matched with other information in the production process (such as processing batch, process parameters, etc.).

[0048] The coordinates of defective points are recorded as defect location data, and a correspondence is established between the defect location data and the processing batch information in the movement path of the roll material. The defect location data not only includes the precise location of the defect on the roll material, but also includes additional information such as the type and severity of the defect. By recording the coordinates of defective points, these defective products can be tracked in real time during subsequent processing and quality control, ensuring that subsequent processing steps can accurately locate and handle defective products. Each processing batch corresponds to a section of the roll material cutting process. The status of each position of the roll material during its movement (whether it is a defective product, the type of defect, etc.) needs to be associated with the production record of that batch. The location information of defective products can be matched with the specific processing batch, ensuring that the traceability and handling of defective products are more efficient and accurate throughout the entire production process.

[0049] Based on the above embodiments, in step S50, differentiated processing is performed according to the severity, including repair processing or marking processing: For defects classified as minor, repair procedures are performed, including cleaning or repairing the dispensing area and the antenna. When assessing the severity of a defect, minor defects typically refer to those that have minimal impact on the antenna and dispensing area and will not significantly affect the dispensing process. These defects may include tiny droplets, minor cutting deviations, etc. In such cases, repair procedures are executed, such as cleaning the dispensing area with a wiping tool or slightly repairing the antenna surface defect. This ensures the cleanliness of the dispensing area and the normal shape of the antenna, preventing minor defects from affecting subsequent steps. The repair process may specifically include: wiping and cleaning, using specialized tools (such as a clean cloth, cleaning agent, etc.) to wipe the dispensing area to remove droplets or contaminants; and surface repair, for small cutting deviations or unevenness, using repair tools to fine-tune the antenna surface to restore it to a normal state. This allows for quick problem-solving without affecting production progress. Wiping or cleaning the dispensing area ensures dispensing quality and does not affect subsequent processing.

[0050] For defects identified as major defects, they are marked and stored, awaiting subsequent actions by operators. Major defects typically refer to those that significantly affect antenna functionality or the dispensing area, potentially causing incorrect chip installation or severe signal transmission disruptions. These major defects cannot be resolved through simple repairs, therefore they need to be marked and processed. Based on the severity of the defect, its coordinates are recorded as defect location data and marked through the production management system to prevent it from entering the next process. This ensures that subsequent operators can identify and take appropriate action, improving the consistency and reliability of the final product.

[0051] In some embodiments of the present invention, the current laser cutting process parameters are analyzed based on the detection results, especially those directly affecting cutting accuracy, cutting quality, and antenna shape, such as laser power, cutting speed, focal length, and cutting frequency. These laser cutting process parameters are dynamically adjusted to optimize subsequent processing quality. If the detection results indicate over-burning or incomplete cutting during the cutting process, the laser power is reduced to avoid over-melting or uneven cutting. If defects frequently appear in rapidly cutting areas, the cutting speed is appropriately reduced to make the laser cutting process more stable and reduce errors. If uneven cutting lines or over-cutting are detected, the focal length is adjusted according to the actual situation of the cutting area to obtain a more precise cutting effect. In some cases, adjustments to the cutting frequency or wavelength can also affect cutting quality; these parameters can be optimized based on real-time detection feedback. By flexibly adjusting process parameters based on real-time detection results, subsequent processing quality can be optimized, improving production efficiency, cutting accuracy, and product consistency.

[0052] The present invention also provides a defect detection system for laser-cut antenna lines, comprising: Storage module: Obtains processing batch information, establishes a roll material motion coordinate system, and stores target antenna parameters and reference information for the dispensing area; Correction and alignment module: acquires image data of the antenna after cutting, corrects the image data, and aligns the image data with the reference information in the roll motion coordinate system; Deviation calculation module: Extracts antenna boundaries from image data and calculates geometric deviations based on antenna boundaries and reference information; Defect judgment module: Obtain the coordinate position of the defect in the image data based on the geometric deviation, compare the dispensing area with the geometric deviation, and judge the severity of the defect; Difference processing module: Maps coordinate positions to the roll material motion coordinate system to obtain defect location data, and performs differential processing based on the severity, including repair processing or marking processing; Result generation module: Stores defect location data and corresponding processing batch information for differentiated processing, and generates test results.

[0053] The detection system described above in this invention can effectively realize a method for detecting defective products of laser-cut antenna lines, and the technical effects it can achieve are as described in the above embodiments, and will not be repeated here.

[0054] Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to this invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting defective products in laser-cut antenna profiles, characterized in that, Includes the following steps: S10: Obtain processing batch information, establish a roll material motion coordinate system, and store target antenna parameters and reference information for the dispensing area; S20: Acquire image data of the antenna after cutting, correct the image data, and align the image data with the reference information in the roll motion coordinate system; S30: Extract the antenna boundary based on the image data, and calculate the geometric deviation based on the antenna boundary and the reference information; S40: Obtain the coordinate position of the defect in the image data based on the geometric deviation, compare the dispensing area with the geometric deviation, and determine the severity of the defect; S50: Map the coordinate position to the roll material motion coordinate system to obtain defect location data, and perform differentiated processing according to the severity, including repair processing or marking processing; S60: Store the defect location data and the processing batch information corresponding to the differential processing, and generate the detection result.

2. The defect detection method for laser-cut antenna profiles according to claim 1, characterized in that, Step S20 includes: The image data of the antenna after cutting is acquired, and the image data is segmented and stitched together during the continuous movement of the roll material to obtain a complete antenna image; Geometric and brightness corrections are performed on the image data to eliminate distortions and unevenness caused by deformation of the imaging device or roll material, thereby obtaining a corrected image; Preset positioning marks or feature points are identified in the calibrated image and used as a reference to register the calibrated image with the reference information, so that the calibrated image and the roll material motion coordinate system are established to correspond to each other, and the image data and the reference information are aligned in the roll material motion coordinate system.

3. The defect detection method for laser-cut antenna profiles according to claim 1, characterized in that, In step S30, the antenna boundary is extracted based on the image data, including: The acquired image data is preprocessed to suppress background noise and enhance the contrast between the antenna area and the substrate; Threshold segmentation is performed based on the preprocessed image data, and candidate boundaries for the antenna are determined by combining the analysis of the roll material's motion direction. The candidate boundaries are refined by repairing breaks, removing isolated points, and deleting unclosed pseudo-contours to form continuous and complete antenna boundaries.

4. The defect detection method for laser-cut antenna profiles according to claim 1, characterized in that, In step S30, the geometric deviation is calculated based on the antenna boundary and the reference information, including: The linewidth distribution curve is calculated along the extension direction of the antenna and compared with the target antenna parameters in the reference information to obtain the linewidth deviation; The interval length and the included angle between adjacent boundary segments are obtained based on the antenna boundary. The continuity of the antenna boundary is detected based on the interval length to generate a breakage deviation. The orientation of the antenna boundary is detected based on the included angle to generate an orientation deviation. The geometric deviation is generated by combining the linewidth deviation, the breakage deviation, and the orientation deviation.

5. The defect detection method for laser-cut antenna profiles according to claim 1, characterized in that, In step S40, the severity of the defect is determined, including: Enhance the edge information of the image data and remove low-frequency noise, extract boundaries and feature points; The boundary line of the antenna is extracted based on the edge information and the boundary, the feature points are detected and matched with the boundary line to obtain the coordinate position; Defects are classified into defect categories, and different impact performances are assigned to different defect categories. The impact degree of the defect in the signal transmission path is calculated based on the impact performance. The severity of the defect is determined based on the defect category and the impact level. The severity of the defect is divided into minor defects and major defects.

6. The defect detection method for laser-cut antenna profiles according to claim 5, characterized in that, Step S40 further includes determining the severity of the defect in the dispensing area: Calculate the masking area, shape change, and relative position of the defect located within the dispensing area with respect to the boundary of the dispensing area; Based on the pressure difference caused by the shape change detection defect to the fluid flow, the pressure difference, the obstruction area, and the preset flow coefficient are multiplied to obtain the fluid resistance value. The dispensing path length is obtained based on the relative position, and the mass impact value is obtained by multiplying the obstruction area by the pressure difference and dividing by the dispensing path length. The fluid resistance value and the mass impact value are compared with preset thresholds respectively. When both exceed the preset thresholds, the defect in the dispensing area is determined to be a major defect.

7. The defect detection method for laser-cut antenna profiles according to claim 1, characterized in that, In step S50, defect location data is obtained, including: The pixel coordinates in the image data are mapped to the real-time displacement parameters of the roll material movement, thus establishing a mapping relationship between the pixel points and the roll material movement coordinate system. Based on the mapping relationship, the coordinate position of the defect is converted into the coordinates of the defective point in the roll material motion coordinate system; The coordinates of the defective points are recorded as defective location data, and a correspondence between the defective points and the processing batch information is established in the roll material movement path.

8. The defect detection method for laser-cut antenna profiles according to claim 6, characterized in that, In step S50, differentiated processing is performed based on the severity level, including repair processing or marking processing: For defects identified as minor defects, repair procedures are performed, including cleaning or repairing the adhesive application area and the antenna. For defects identified as major defects, a marking process is performed, marking and storing the coordinate position corresponding to the major defect, waiting for subsequent operators to perform corresponding operations.

9. The method for detecting defective products of laser-cut antenna profiles according to any one of claims 1 to 8, characterized in that, Also includes the S70: Based on the test results, the laser cutting process parameters are dynamically adjusted to optimize the subsequent processing quality.

10. A defect detection system for laser-cut antenna lines, characterized in that, include: Storage module: Obtains processing batch information, establishes a roll material motion coordinate system, and stores target antenna parameters and reference information for the dispensing area; Correction and alignment module: acquires image data of the antenna after cutting, corrects the image data, and aligns the image data with the reference information in the roll motion coordinate system; Deviation calculation module: Extracts antenna boundaries based on the image data, and calculates geometric deviations based on the antenna boundaries and the reference information; Defect judgment module: Based on the geometric deviation, the coordinate position of the defect in the image data is obtained, and the dispensing area is compared with the geometric deviation to determine the severity of the defect; Difference processing module: Maps the coordinate position to the roll material motion coordinate system to obtain defect location data, and performs differential processing according to the severity, including repair processing or marking processing; Result generation module: Stores the defect location data and the processing batch information corresponding to the differential processing, and generates the detection results.

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