Full-automatic conductive adhesive 3D detection method and device

Abstract

The application is suitable for the technical field of conductive glue, and provides a full-automatic conductive glue 3D detection method and device, detection requirement information is acquired, task parameters are configured for a 3D scanning module and a motion control module according to the detection requirement information, the motion control module is driven based on the task parameters to drive the 3D scanning module to move relative to the conductive glue, point cloud data of the conductive glue is collected through the 3D scanning module, 3D model reconstruction is performed on the conductive glue according to the point cloud data, the 3D model of the conductive glue is obtained, the 3D model is compared according to a standard template established in advance, so that the conductive glue is analyzed and processed in multiple projects, quantitative detection information of the conductive glue is generated, and the problem of low efficiency of manual visual inspection of the conductive glue is solved.

Description

Technical Field

[0001] This invention relates to the field of conductive adhesive testing technology, and in particular to a fully automated 3D testing method and apparatus for conductive adhesives. Background Technology

[0002] In the electronics manufacturing industry, conductive adhesives are widely used for bonding and conducting electricity in components such as circuit boards and displays. Their quality directly affects the reliability and performance of products. Current technologies primarily rely on manual visual inspection or two-dimensional image detection for conductive adhesive testing, which suffers from low efficiency, high subjectivity, and difficulty in detecting three-dimensional parameters such as adhesive thickness and shape. For example, uneven coating, thickness deviations, or the presence of bubbles in the conductive adhesive can lead to short circuits or connection failures, but traditional methods cannot fully quantify these three-dimensional characteristics. Furthermore, with the miniaturization and increasing density of electronic products, the precision requirements for conductive adhesives are constantly increasing, necessitating a fully automated, high-precision 3D inspection solution. Therefore, the purpose of this invention is to provide a fully automated 3D inspection device for conductive adhesives, capable of automating the detection of the three-dimensional morphology, thickness, and defects of conductive adhesives, thereby improving production quality and efficiency. Summary of the Invention

[0003] This invention provides a fully automated 3D inspection method and apparatus for conductive adhesives to solve the problem of low efficiency in manual visual inspection of conductive adhesives.

[0004] In a first aspect, embodiments of this application provide a fully automated 3D inspection method for conductive adhesives, including: Obtain the detection requirement information and configure task parameters for the 3D scanning module and motion control module according to the detection requirement information; Based on the task parameters, the motion control module drives the 3D scanning module to move relative to the conductive adhesive, and the 3D scanning module collects the point cloud data of the conductive adhesive. The conductive adhesive is reconstructed into a 3D model based on the point cloud data to obtain the 3D model of the conductive adhesive. The 3D model is compared with a pre-established standard template to perform multi-item analysis and processing on the conductive adhesive, generating quantitative detection information of the conductive adhesive.

[0005] Secondly, embodiments of this application provide a fully automatic conductive adhesive 3D inspection device, including: a 3D scanning module, a motion control module, and a processing module; The processing module is electrically connected to the 3D scanning module and the motion control module respectively. The processing module is used to send control signals to the 3D scanning module and the motion control module to execute the fully automatic conductive adhesive 3D detection method according to any one of claims 1-7. The 3D scanning module is mounted on the motion control module, and the motion control module is used to drive the 3D scanning module to move.

[0006] The aforementioned fully automated 3D inspection method and apparatus for conductive adhesives provides a solution that achieves fully automated inspection, reduces manual intervention, improves inspection efficiency, lowers labor costs, and can quickly complete the inspection of conductive adhesives, adapting to the needs of large-scale production. Parameters are configured according to inspection requirements, accurate point cloud data is collected, and a high-precision 3D model is reconstructed. By comparing with a standard template, multi-item analysis of the conductive adhesive, such as thickness and defect area, can be performed, generating quantitative inspection information to ensure the accuracy of the inspection results. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram of a fully automatic conductive adhesive 3D detection device according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a fully automated conductive adhesive 3D detection method according to an embodiment of the present invention; Figure 3 yes Figure 2 A schematic diagram of the implementation process of step S10; Figure 4 yes Figure 2 A schematic diagram of the implementation process of step S20; Figure 5 yes Figure 2 A schematic diagram of the implementation process of step S30. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. It should also be understood that, as used in this specification and the appended claims, the term "and / or" refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0011] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0012] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0013] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0014] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0015] This application proposes a fully automated 3D inspection method and apparatus for conductive adhesives. By automating the inspection process, manual intervention is reduced, inspection efficiency is improved, and labor costs are lowered. The method can quickly complete the inspection of conductive adhesives, adapting to the needs of large-scale production. Parameters are configured according to inspection requirements, accurate point cloud data is collected, and a high-precision 3D model is reconstructed. By comparing with a standard template, multi-item analysis of the conductive adhesive, such as thickness and defect area, can be performed, generating quantitative inspection information to ensure the accuracy of the inspection results.

[0016] In one embodiment, such as Figure 2 As shown, a fully automated 3D inspection method for conductive adhesive is provided, which is applied to... Figure 1 Taking the fully automated conductive adhesive 3D inspection device as an example, the following steps are included: S10: Obtain detection requirement information and configure task parameters for the 3D scanning module and motion control module according to the detection requirement information; S20: Based on the task parameters, drive the motion control module to move the 3D scanning module relative to the conductive adhesive, and collect point cloud data of the conductive adhesive through the 3D scanning module; S30: Reconstruct the 3D model of the conductive adhesive based on the point cloud data to obtain the 3D model of the conductive adhesive; S40: The 3D model is compared with a pre-established standard template to perform multi-item analysis and processing on the conductive adhesive, generating quantitative detection information of the conductive adhesive.

[0017] In this embodiment, the height information of the conductive adhesive under test and the detection accuracy requirements are obtained, and the two are used together as the detection requirement information. The height information can be obtained through previous measurement of the conductive adhesive or known specifications, while the detection accuracy requirements are determined according to actual detection needs and product standards.

[0018] The scanning position of the 3D scanning module is analyzed based on the height information of the conductive adhesive to determine the scanning position of the 3D scanning module relative to the conductive adhesive. Then, based on this scanning position, the motion control module is assigned corresponding task parameters, such as the distance, speed, and direction of movement, to ensure that the motion control module can drive the 3D scanning module to accurately reach the appropriate scanning position.

[0019] Based on the required detection accuracy, the scanning mode of the 3D scanning module is analyzed to obtain the expected scanning mode, such as high-resolution scanning mode or fast scanning mode. Then, according to the expected scanning mode, the corresponding task parameters, such as scanning frequency and exposure time, are deployed to the 3D scanning module.

[0020] Different conductive adhesive heights and detection accuracy requirements will affect the scanning position and method. By acquiring this information and configuring the corresponding task parameters, the 3D scanning module and motion control module can work in the most suitable state during the subsequent detection process, thereby improving the accuracy and efficiency of the detection.

[0021] Based on the task parameters, the motion control module drives the 3D scanning module to the pre-planned scanning position. During this process, the attitude stability of the 3D scanning module is monitored in real time to obtain attitude stability characteristics. Based on the attitude stability characteristics, the motion control module is optimized for motion control to ensure that the 3D scanning module accurately reaches the specified scanning position.

[0022] Based on the task parameters, the 3D scanning module performs ambient light illumination and 3D morphology scanning on the conductive adhesive in a pre-planned expected scanning mode, and collects the raw scanning data. The raw scanning data is then transformed into point cloud data of the conductive adhesive, which contains the three-dimensional coordinate information of each point on the surface of the conductive adhesive.

[0023] Accurate scanning position and stable scanning posture are the foundation for acquiring high-quality point cloud data. Through real-time monitoring and feedback optimization, it can be ensured that the 3D scanning module scans in the appropriate position and posture. The collected point cloud data is the basis for subsequent 3D model reconstruction. Only by acquiring accurate and complete point cloud data can a 3D model that conforms to the actual situation be constructed.

[0024] The collected point cloud data is analyzed in terms of overall spatial form to obtain the overall distribution information of the conductive adhesive, understand the approximate shape and position of the conductive adhesive in space, select the corresponding representative sampling standard based on the overall distribution information, perform average spatial division and representative point selection on the point cloud data to obtain representative point data, so as to reduce the amount of data and retain key information, and map the representative point data to the pre-constructed three-dimensional coordinate space to generate a basic three-dimensional model.

[0025] The covariance matrix of adjacent representative points is calculated for each representative point in the basic 3D model to obtain the feature vectors between each representative point, which are used to describe the local geometric features of the point cloud data. Anomalies are evaluated for each space in the basic 3D model based on the feature vectors. According to the anomaly evaluation results, corresponding space filling strategies are deployed for each space in the basic 3D model. According to the space filling strategies, corresponding data analysis is performed on the point cloud data. Information is restored for each space in the basic 3D model, and finally, a 3D model of conductive adhesive is obtained.

[0026] Point cloud data is a discrete set of points. By reconstructing a 3D model, these discrete point sets can be transformed into a continuous three-dimensional model, which can more intuitively show the actual morphology of the conductive adhesive. During the reconstruction process, operations such as representative point selection, anomaly assessment, and space filling can improve the accuracy and completeness of the model, providing a reliable foundation for subsequent comparative analysis.

[0027] The 3D model is compared with a pre-established standard template, which is usually established based on the design requirements of conductive adhesive or the characteristics of qualified products. The conductive adhesive is analyzed for multiple items such as maximum thickness, minimum thickness, average thickness, and defect area. By comparing the 3D model with the standard template, the specific values ​​of these parameters are calculated.

[0028] The numerical values ​​of various parameters obtained from the analysis are used as quantitative detection information for the conductive adhesive. The quantitative detection information is judged according to the preset threshold. If the judgment result meets the preset threshold, the conductive adhesive is judged as a qualified product; if the judgment result does not meet the preset threshold, the conductive adhesive is judged as a non-qualified product.

[0029] By comparing 3D models with standard templates and performing multi-item analysis, the quality and performance of conductive adhesives can be accurately evaluated. The generated quantitative detection information can intuitively reflect whether the various indicators of conductive adhesives meet the requirements, facilitating product quality control and screening. Based on preset thresholds, qualified and unqualified products can be quickly distinguished, improving production efficiency and product quality.

[0030] In one embodiment, such as Figure 3 As shown, step S10 involves obtaining detection requirement information and configuring task parameters for the 3D scanning module and motion control module based on the detection requirement information. This specifically includes the following steps: S11: Obtain the height information and detection accuracy requirements of the conductive adhesive being tested, and use them together as the detection requirement information; S12: Analyze the scanning position of the 3D scanning module based on the height information to obtain the scanning position of the 3D scanning module for the conductive adhesive, and deploy corresponding task parameters for the motion control module based on the scanning position; S13: Analyze the scanning mode of the 3D scanning module according to the detection accuracy requirements to obtain the expected scanning mode, and deploy corresponding task parameters to the 3D scanning module according to the expected scanning mode.

[0031] S11: Obtain the height information and detection accuracy requirements of the conductive adhesive being tested, and use them together as the detection requirement information. If the specifications of the conductive adhesive are known, its height data can be obtained directly from the product design documents or production records. If the specifications are unknown, some measuring tools can be used for measurement, such as high-precision calipers, laser rangefinders, etc. For batch testing, some samples can be randomly selected for measurement to obtain the approximate height range.

[0032] Communicate with the stakeholders who require the testing (such as the production department and quality control department) to clarify the accuracy requirements for various indicators of the conductive adhesive (such as thickness and shape). For example, if a thickness measurement accuracy of ±0.01mm is required, refer to relevant industry standards or product standards to determine the specific values ​​for the testing accuracy.

[0033] Based on the height information of the conductive adhesive, combined with the working principle and effective scanning range of the 3D scanning module, the optimal scanning position of the 3D scanning module relative to the conductive adhesive is determined. For example, for some laser scanning modules, it is necessary to ensure that they maintain a suitable distance from the surface of the conductive adhesive to ensure that all parts of the conductive adhesive can be scanned completely and clearly. Considering the placement method and position of the conductive adhesive, the specific scanning position coordinates (x, y, z) of the 3D scanning module in three-dimensional space are planned.

[0034] The determined scanning position coordinates are converted into instructions that the motion control module can recognize. The motion control module usually includes a displacement platform and a multi-axis robot. Based on the scanning position coordinates, parameters such as the moving distance and direction of the displacement platform and the rotation angle of the multi-axis robot are set. At the same time, parameters such as the motion speed and acceleration of the motion control module also need to be set to ensure that the 3D scanning module can reach the scanning position smoothly and accurately.

[0035] If high detection accuracy is required, a high-resolution scanning mode should be selected. In high-resolution scanning mode, the 3D scanning module will increase the number of scanning points and reduce the scanning interval to obtain more detailed information about the conductive adhesive surface. If detection speed is required and the accuracy requirement is relatively low, a fast scanning mode can be selected. In fast scanning mode, the number of scanning points will be reduced and the scanning speed will be increased.

[0036] Based on the expected scanning mode, set the relevant parameters of the 3D scanning module. For example, for laser scanning sensors, set the laser emission frequency and scanning angle range; for illumination sources, set parameters such as light intensity and color to adapt to different scanning modes.

[0037] The height of the conductive adhesive affects the scanning position and scanning effect of the 3D scanning module. Conductive adhesive of different heights requires the 3D scanning module to scan at different positions to ensure that the surface information of the conductive adhesive can be completely acquired. The required detection accuracy determines the scanning mode and scanning parameters of the 3D scanning module. Different accuracy requirements require different scanning modes and parameter settings to meet the accuracy of the detection.

[0038] Only when the 3D scanning module is in the appropriate scanning position can a comprehensive and accurate scan of the conductive adhesive be guaranteed. If the scanning position is inaccurate, some areas may not be scanned or the scan data may be inaccurate, affecting subsequent 3D model reconstruction and detection results. The task of the motion control module is to accurately move the 3D scanning module to the scanning position. By reasonably deploying the task parameters of the motion control module, it can be ensured that the 3D scanning module can reach the designated position according to the predetermined path and speed, improving the efficiency and accuracy of detection.

[0039] Different detection accuracy requirements necessitate different scanning modes. Selecting the appropriate scanning mode can improve detection efficiency while meeting the accuracy requirements. If the selected scanning mode is inappropriate, it may lead to insufficient detection accuracy or excessive detection time. By deploying the corresponding task parameters to the 3D scanning module, the 3D scanning module can achieve the best scanning effect in the selected scanning mode, obtain high-quality scanning data, and provide a reliable foundation for subsequent analysis and processing.

[0040] In one embodiment, such as Figure 4 As shown, in step S20, the motion control module is driven to move the 3D scanning module relative to the conductive adhesive based on the task parameters, and the point cloud data of the conductive adhesive is collected through the 3D scanning module. Specifically, this includes the following steps: S21: Based on the task parameters, drive the motion control module to move the 3D scanning module to the pre-planned scanning position; S22: Based on the task parameters, drive the 3D scanning module to perform ambient light irradiation and 3D morphology scanning on the conductive adhesive in a pre-planned expected scanning mode to collect raw scanning data; S23: Perform a three-dimensional coordinate transformation on the original scan data to generate point cloud data of the conductive adhesive.

[0041] In this embodiment, the motion control module reads the previously configured task parameters from the system. These parameters include the coordinate information of the scanning position, the movement speed, acceleration, etc. For example, if the scanning position is a specific point (x, y, z) in three-dimensional space, the motion control module will convert it into instructions that it can recognize and execute.

[0042] Based on the scanning position and the current position of the 3D scanning module, the motion control module plans a reasonable motion path, which involves the coordinated movement of multiple axes, such as the linear motion of the displacement platform and the rotational motion of the multi-axis robot. During the movement, the motion control module drives the 3D scanning module to move towards the target scanning position according to the set speed and acceleration.

[0043] During the movement of the 3D scanning module, its posture stability is monitored in real time. Posture information is obtained by posture sensors (such as gyroscopes) installed on the 3D scanning module to obtain posture stability characteristics. If a deviation in posture is detected, the motion control module will optimize based on these characteristics and fine-tune the motion parameters to ensure that the 3D scanning module accurately reaches the specified scanning position.

[0044] Accurate scanning position is the foundation for obtaining high-quality scanning data. Only when the 3D scanning module reaches the pre-planned scanning position can the scanning of the conductive adhesive cover the area to be detected, avoiding some areas not being scanned or being scanned inaccurately due to position deviation. Reasonable path planning and attitude control can reduce the movement time of the 3D scanning module and unnecessary adjustments, thereby improving the efficiency of the entire detection process.

[0045] The 3D scanning module is set to the expected scanning mode based on the previously configured task parameters. For example, if high detection accuracy is required, the high-resolution scanning mode is selected; if it is necessary to quickly obtain general information, the fast scanning mode is selected.

[0046] The lighting source in the 3D scanning module illuminates the conductive adhesive with ambient light according to the set parameters. The intensity, angle, and color of the lighting are adjusted according to the scanning mode and the characteristics of the conductive adhesive to ensure that the surface of the conductive adhesive can be uniformly illuminated, which is convenient for subsequent 3D morphology scanning.

[0047] The laser scanning sensor performs 3D morphological scanning on the conductive adhesive according to the set scanning frequency and scanning range. The laser scanning sensor emits a laser beam to the surface of the conductive adhesive, and obtains the distance information of each point on the surface of the conductive adhesive by measuring information such as the time or angle of the laser reflection, thereby collecting the original scanning data.

[0048] Different scanning modes can meet different requirements for detection accuracy and efficiency. High-resolution scanning mode can acquire more detailed information about the conductive adhesive surface, but the scanning speed is relatively slow; fast scanning mode can acquire general morphological information in a shorter time, which is suitable for preliminary detection or scenarios with high detection speed requirements. Appropriate ambient light can enhance the contrast of the conductive adhesive surface, enabling the laser scanning sensor to acquire surface information more accurately and improve the quality of the raw scanning data.

[0049] The acquired raw scanning data undergoes preliminary processing, including noise removal and filtering, to improve data quality. For example, median filtering is used to remove random noise points from the data. Based on the relationship between the coordinate system of the laser scanning sensor and the actual three-dimensional spatial coordinate system, the distance information in the raw scanning data is converted into three-dimensional coordinate information. This usually involves transformation operations such as rotation and translation. The relative coordinates measured by the sensor are converted into actual three-dimensional spatial coordinates. The three-dimensional coordinate information after coordinate transformation is combined into point cloud data, where each point represents a position on the conductive adhesive surface. The point cloud data contains the three-dimensional geometric information of the conductive adhesive surface.

[0050] Raw scan data is usually in the form of sensor measurements, which is not convenient for direct subsequent analysis and processing. By transforming the raw scan data into a unified point cloud data format through 3D coordinate transformation, it is easier to reconstruct and analyze the 3D model. Point cloud data is a common form of 3D data representation and has wide applications in computer graphics and 3D modeling. After converting the raw scan data into point cloud data, it is easier to perform 3D model reconstruction, feature extraction and other operations, providing a foundation for the detection and analysis of conductive adhesives.

[0051] In one embodiment, 3D topography scanning is performed by laser scanning to acquire raw scan data, wherein the resolution of the laser scanning is not less than 5 μm.

[0052] In one embodiment, a data acquisition card pre-installed in the 3D scanning module is used in conjunction with laser scanning to synchronously acquire raw scanning data, wherein the sampling rate of the data acquisition card is not less than 100KPS.

[0053] Ensure the laser scanning sensor in the 3D scanning module is working properly, set the scanning parameters such as scanning range and scanning frequency, place the conductive adhesive at a suitable detection position, and adjust the distance and angle between the laser scanning sensor and the conductive adhesive to ensure a comprehensive and clear scan of the conductive adhesive.

[0054] The laser scanning sensor emits a laser beam onto the surface of the conductive adhesive. Using principles such as time-of-flight or triangulation, it measures the time or angle from the laser's emission to its reflection back to the sensor, thereby calculating the distance information of various points on the conductive adhesive surface. Since the resolution of laser scanning is no less than 5μm, this means that the sensor can distinguish minute features and changes on the conductive adhesive surface and acquire high-precision raw scanning data.

[0055] The data acquisition card is pre-installed in the 3D scanning module and connected to the laser scanning sensor. The sampling rate of the data acquisition card is set to be no less than 100KPS (100,000 samples per second) to ensure that the raw scanning data generated during the laser scanning process can be acquired quickly and accurately.

[0056] During laser scanning, the data acquisition card synchronously acquires the electrical signals output by the laser scanning sensor according to the set sampling rate and converts them into digital signals for storage. This ensures that the acquired data is complete and accurate, and avoids data loss or distortion.

[0057] The laser scanning resolution is no less than 5μm, enabling the technology to detect very fine defects, protrusions, or depressions on the surface of conductive adhesives, thus improving the accuracy and reliability of the inspection and helping to discover potential quality problems. The data acquisition card has a sampling rate of no less than 100KPS, which can quickly acquire a large amount of raw scanning data, ensuring the timeliness and completeness of data acquisition. This makes the entire inspection process more efficient, shortens the inspection time, and improves production efficiency.

[0058] In one embodiment, during the process of the motion control module driving the 3D scanning module to a pre-planned scanning position, the attitude stability of the 3D scanning module is monitored in real time to obtain attitude stability characteristics, and the motion control of the motion control module is optimized based on the attitude stability characteristics until the motion control module moves the 3D scanning module to the designated scanning position.

[0059] Attitude sensors, such as gyroscopes and accelerometers, are installed on the 3D scanning module. These sensors can measure the attitude information of the 3D scanning module in real time, including tilt angle, rotation angle, acceleration, etc. The sensors will continuously collect data at a certain sampling frequency and transmit this data to the monitoring system.

[0060] After receiving the data collected by the sensors, the monitoring system processes and analyzes it. Through specific algorithms, it extracts features that reflect the posture stability of the 3D scanning module from the raw data, such as the amplitude of posture fluctuations and the rate of change. These features can quantitatively describe the posture stability of the 3D scanning module during movement.

[0061] The attitude stability features are compared with the preset attitude stability standards. If the attitude stability features exceed the standard range, it indicates that the attitude of the 3D scanning module is unstable. At this time, the monitoring system will generate a corresponding feedback signal.

[0062] After receiving the feedback signal, the motion control module adjusts the motion control parameters accordingly. For example, if it finds that the 3D scanning module is tilted too much in a certain direction, the motion control module will adjust the motion speed or acceleration of the corresponding axis to correct the posture of the 3D scanning module.

[0063] During the movement of the 3D scanning module, the above monitoring and feedback optimization process is repeated continuously. After each adjustment, the posture stability characteristics are monitored again, and the motion control parameters are further adjusted according to the new characteristics until the posture of the 3D scanning module is stable within the standard range and moves accurately to the specified scanning position.

[0064] The attitude stability of the 3D scanning module directly affects the quality of the acquired point cloud data. If the attitude of the 3D scanning module is unstable during the scanning process, it will lead to problems such as deviation and deformation in the acquired point cloud data. For example, attitude tilt may cause the surface shape of the conductive adhesive obtained by scanning to be distorted, affecting the accuracy of subsequent 3D model reconstruction. By monitoring and optimizing the attitude in real time, it can be ensured that the 3D scanning module scans under a stable attitude, thereby improving the accuracy of the acquired point cloud data.

[0065] Accurate point cloud data is the foundation for 3D inspection of conductive adhesives. Unstable posture may lead to incomplete scanning of some areas or inaccurate scanning data, which will affect the results of multi-item analysis and processing of conductive adhesives. By ensuring the posture stability of the 3D scanning module, the detection error caused by posture problems can be reduced, the reliability of the detection results can be improved, and the ability to accurately determine whether the conductive adhesive is qualified can be ensured.

[0066] During the motion control module's movement of the 3D scanning module, it is affected by various factors, such as mechanical vibration and external interference, which cause changes in the 3D scanning module's posture. The real-time monitoring and feedback optimization mechanism can detect and correct these posture changes in a timely manner, enabling the 3D scanning module to adapt to complex motion environments and ensuring that it can accurately reach the designated scanning position and perform stable scanning under different conditions.

[0067] In one embodiment, such as Figure 5 As shown, step S30, which involves reconstructing a 3D model of the conductive adhesive based on the point cloud data to obtain a 3D model of the conductive adhesive, specifically includes the following steps: S31: Analyze the overall spatial form of the point cloud data to obtain the overall distribution information of the conductive adhesive; S32: Select the corresponding representative sampling standard according to the overall distribution information, and perform average spatial division and representative point selection on the point cloud data according to the representative sampling standard to obtain representative point data; S33: Map the representative point data to a pre-constructed three-dimensional coordinate space to generate a basic three-dimensional model, and calculate the covariance matrix of adjacent representative points on the basic three-dimensional model to obtain the feature vector between representative points. S34: Based on the feature vector, perform anomaly assessment on each space of the basic 3D model, and deploy corresponding space filling strategies for each space of the basic 3D model according to the anomaly assessment results; S35: Perform corresponding data analysis on the point cloud data according to the space filling strategy to restore information in various spaces of the basic 3D model and obtain a 3D model of the conductive adhesive.

[0068] In this embodiment, the original point cloud data is denoised to remove outliers caused by scanning errors or environmental interference. Statistical filtering, radius filtering, and other methods can be used. By setting appropriate thresholds, points that do not conform to the normal distribution are removed. The boundary of the point cloud data in three-dimensional space is calculated, and the maximum and minimum values ​​of the conductive adhesive on the X, Y, and Z coordinate axes are determined, thereby obtaining the approximate range of the conductive adhesive in space. The density distribution of the point cloud data is analyzed to understand the point cloud density of different parts of the conductive adhesive surface. For example, histograms are used to count the number of point clouds in different regions to understand the undulations and complexity of the conductive adhesive surface.

[0069] Point cloud data typically contains a large number of points. Directly processing this data would consume a lot of computing resources and time. By analyzing its overall spatial form, we can quickly understand the approximate distribution of conductive adhesive, providing basic information for subsequent sampling and modeling, and improving processing efficiency.

[0070] Based on the overall distribution information of the conductive adhesive, if the point cloud distribution is relatively uniform, a uniform sampling standard can be selected; if the point cloud is denser in some areas and sparser in others, an adaptive sampling standard can be used. According to the selected sampling standard, the three-dimensional space containing the point cloud data is divided into several small cubic or cuboid grids. The size of the grid can be adjusted according to the sampling accuracy and the point cloud distribution. Within each grid, one or more representative points are selected as representative points. The centroid, center point, or other characteristic points of the point cloud within the grid can be selected as representative points, and these representative points are combined to form representative point data.

[0071] The original point cloud data has data redundancy. By selecting an appropriate representative sampling standard for average spatial division and representative point selection, the amount of data can be reduced while retaining the main feature information of the conductive adhesive. This can reduce the complexity of subsequent processing and ensure the accuracy of the model.

[0072] Each point in the representative point data is mapped to a pre-constructed 3D coordinate space according to its 3D coordinates to form a basic 3D model. This model is a preliminary framework composed of discrete representative points. For each representative point in the basic 3D model, its neighboring representative points are selected, and the covariance matrix between these neighboring points and the point is calculated. The covariance matrix reflects the spatial distribution relationship and correlation between neighboring points. The covariance matrix is ​​decomposed into eigenvalues ​​to obtain its eigenvectors. The eigenvectors describe the main directions and trends of change between neighboring points and can reflect the local geometric features of the conductive adhesive surface.

[0073] Mapping representative point data to a three-dimensional coordinate space generates a basic three-dimensional model, providing a preliminary framework for subsequent analysis. The calculation of the covariance matrix and eigenvectors can extract the local geometric features of the conductive adhesive surface, which helps to discover surface changes and anomalies, providing a basis for subsequent anomaly assessment and space filling.

[0074] Based on the direction and magnitude of the feature vectors, anomalies in various parts of the space in the basic 3D modeling are determined. For example, if the feature vector of a certain area changes drastically, it indicates that there are surface defects or discontinuities in that area, and it is marked as an abnormal area. For different anomalies, corresponding space filling strategies are formulated. For small holes or missing areas, interpolation can be used to fill them; for larger defect areas, more complex reconstruction is required by combining information from surrounding points.

[0075] In the process of basic 3D modeling, there may be some holes, missing or abnormal areas. Anomaly assessment can identify these problematic areas, and based on the assessment results, corresponding space filling strategies can be deployed to repair and improve these areas, thereby enhancing the integrity and accuracy of the model.

[0076] Based on the spatial filling strategy, the original point cloud data is further analyzed and processed. For example, for interpolation filling, the coordinates and attribute values ​​of missing points are calculated based on the coordinates and attribute information of surrounding points. The filled point cloud data is then integrated with the basic 3D model to restore information from the basic 3D model, making it more complete and accurate. The final result is a 3D model that accurately reflects the true shape and structure of the conductive adhesive.

[0077] By performing data analysis on the original point cloud data based on a space-filling strategy, information in missing or abnormal areas can be restored, making the basic 3D modeling more complete and accurate. Ultimately, a 3D model that can truly reflect the shape and structure of the conductive adhesive is obtained, providing a reliable foundation for subsequent detection and analysis.

[0078] In one embodiment, multiple detection locations are set, and point cloud data collected from multiple detection locations are combined to perform 3D modeling and reconstruction of the conductive adhesive.

[0079] For conductive adhesives with regular shapes, the detection positions can be set according to a certain grid distribution to ensure that the surface of the conductive adhesive is fully covered. For conductive adhesives with complex shapes, the detection positions need to be set in a targeted manner according to their specific shape and areas where defects may exist.

[0080] The 3D scanning module is moved to various preset detection positions by a motion control module (such as a displacement platform and a multi-axis robot). The motion control module can precisely control the position and orientation of the 3D scanning module to ensure accurate scanning at each detection position.

[0081] At each detection location, a 3D scanning module (such as a laser scanning sensor) scans the conductive adhesive and collects point cloud data of the conductive adhesive surface at that location. The point cloud data contains the three-dimensional coordinate information of each point on the conductive adhesive surface. The point cloud data of each detection location is collected synchronously using a data acquisition card and stored in the processing module. The high sampling rate of the data acquisition card ensures that the collected data is complete and accurate.

[0082] The processing module fuses point cloud data collected from multiple detection locations. Since data from different locations may have inconsistent coordinate systems, coordinate transformation and registration are required to unify all point cloud data into a single coordinate system. Based on this data fusion, the processing module uses the point cloud data to reconstruct a 3D model. Through a specific algorithm, discrete point cloud data are connected into a continuous surface, generating a 3D model of the conductive adhesive, thus achieving 3D modeling and restoration of the conductive adhesive.

[0083] Point cloud data collected from multiple detection locations can more comprehensively reflect the true shape and surface features of the conductive adhesive, reducing scanning blind spots and thus improving the accuracy and precision of 3D modeling. It can detect defects and features in various parts of the conductive adhesive, including problems hidden in complex shapes or structures, improving the comprehensiveness and reliability of detection. For conductive adhesives with complex shapes, by reasonably setting the detection locations, it is possible to effectively obtain their complete surface information, achieve accurate 3D modeling and restoration, and meet the detection needs of conductive adhesives with different shapes.

[0084] In one embodiment, step S40, which involves reconstructing a 3D model of the conductive adhesive based on the point cloud data to obtain a 3D model of the conductive adhesive, specifically includes the following steps: The 3D model is compared with a pre-established standard template to analyze the maximum, minimum, and average thickness of the conductive adhesive, as well as the defect area, to obtain quantitative detection information of the conductive adhesive.

[0085] In one embodiment, the quantitative detection information is judged according to a preset threshold. If the judgment result meets the preset threshold, the conductive adhesive is judged as a qualified product; if the judgment result does not meet the preset threshold, the conductive adhesive is judged as a non-qualified product.

[0086] In one embodiment, when the conductive adhesive is determined to be a non-conforming product, the quantitative detection information is analyzed to obtain defect location information, and the quantitative detection information, the determination result, and the defect location information are combined to generate a test report for the conductive adhesive.

[0087] In one embodiment, the test reports of conductive adhesive are temporarily stored in a database, and feature statistics are performed on each test report temporarily stored in the database to obtain the defect trend characteristics of the conductive adhesive in this production batch. In the subsequent 3D model reconstruction of conductive adhesive, attention weights are allocated to each spatial part of the conductive adhesive based on the defect trend characteristics.

[0088] Once the conductive adhesive is determined to be a non-conforming product, the quantitative test information is analyzed in depth. By comparing the 3D model with the standard template, the types of defects in the conductive adhesive are identified, such as holes, cracks, and uneven thickness. The specific locations of these defects on the conductive adhesive are then determined, and defect location information is obtained.

[0089] The quantitative test information (such as size and shape deviation data), judgment results (non-conforming products) and defect location information are integrated together to generate a test report for conductive adhesive according to a preset report template. The report includes a detailed description of the defect, location diagrams, etc., so as to clearly present the quality problems of the conductive adhesive.

[0090] The generated conductive adhesive test reports are stored in a database for easy retrieval and analysis. The database can categorize and manage test reports according to information such as production batch and testing time. It also performs feature statistics on test reports from the same production batch temporarily stored in the database. The statistics include the frequency of different types of defects and the distribution of defects on the conductive adhesive, thus revealing the defect trend characteristics of that production batch of conductive adhesive.

[0091] In the subsequent 3D model reconstruction of the conductive adhesive, different attention weights are assigned to each spatial part of the conductive adhesive based on the statistically obtained defect trend characteristics. For example, higher attention weights are assigned to parts that frequently have defects, and more attention is paid to the data processing and analysis of these parts during the model reconstruction process.

[0092] The generated test report records the quality status and defect information of the conductive adhesive in detail, which facilitates quality traceability and analysis for manufacturers. By locating and analyzing defects, potential problems in the production process can be identified, and targeted improvement measures can be taken to improve product quality.

[0093] Statistical analysis of the features of inspection reports in the database can reflect the defect trends of conductive adhesives in production batches. Based on these trend characteristics, production processes can be adjusted and optimized to reduce defects, improve production efficiency and product qualification rate. In 3D model reconstruction, attention weights can be assigned according to defect trend characteristics, enabling the reconstructed model to focus more on areas prone to defects, improving the accuracy and reliability of the model, and further enhancing the precision and effectiveness of inspection.

[0094] In one embodiment, a fully automatic conductive adhesive 3D detection device is provided, which corresponds one-to-one with the fully automatic conductive adhesive 3D detection method in the above embodiment, and includes: a 3D scanning module, a motion control module, and a processing module; The processing module is electrically connected to the 3D scanning module and the motion control module respectively. The processing module is used to send control signals to the 3D scanning module and the motion control module to execute the fully automatic conductive adhesive 3D detection method according to any one of the first aspects. The 3D scanning module is mounted on the motion control module, and the motion control module is used to drive the 3D scanning module to move.

[0095] Specifically, the processing module first receives external input information about the height of the conductive adhesive and detection accuracy requirements. Based on this information, the processing module performs internal algorithm calculations to configure corresponding task parameters for the 3D scanning module and the motion control module. For example, it determines the scanning position parameters of the 3D scanning module based on the height of the conductive adhesive and converts them into motion commands that the motion control module can recognize; and it determines the scanning mode and related parameters of the 3D scanning module based on the detection accuracy requirements.

[0096] The processing module sends the configured task parameters to the 3D scanning module and the motion control module in the form of control signals. These control signals include instructions such as start, stop, motion trajectory, and scanning mode to ensure that the two modules work according to the predetermined tasks.

[0097] The processing module receives the raw scanning data collected by the 3D scanning module, performs 3D coordinate transformation to generate point cloud data, then reconstructs the 3D model based on the point cloud data, compares the reconstructed 3D model with a pre-established standard template, performs multi-item analysis and processing on the conductive adhesive, generates quantitative detection information of the conductive adhesive, and determines whether the conductive adhesive is qualified based on a preset threshold.

[0098] After receiving the control signal from the processing module, the 3D scanning module prepares according to the preset scanning mode. For example, it adjusts the intensity, angle, and color of the illumination source to ensure that the conductive adhesive surface is uniformly illuminated, creating favorable conditions for 3D morphology scanning. According to the scanning frequency and scanning range set by the processing module, the laser scanning sensor in the 3D scanning module emits a laser beam to the conductive adhesive surface. By measuring information such as the time or angle of the laser reflection, it collects the distance information of various points on the conductive adhesive surface to obtain the raw scanning data. The collected raw scanning data is then transmitted to the processing module for subsequent processing and analysis.

[0099] The system receives control signals from the processing module and plans the motion path of the 3D scanning module based on the scanning position information contained therein. For example, it determines the moving distance, speed, and direction of the displacement platform, as well as the rotation angle of the multi-axis robot. Following the planned motion path, the system moves the 3D scanning module accurately to the pre-planned scanning position. During the movement, the system monitors the posture stability of the 3D scanning module in real time. If any posture deviation is detected, the system adjusts the motion parameters based on the feedback signal from the processing module to ensure that the 3D scanning module reaches the designated position stably and accurately.

[0100] As the core of the entire testing device, the processing module is responsible for the overall planning and coordinated control of the testing process. By acquiring testing requirement information and configuring task parameters, the 3D scanning module and motion control module can operate according to actual needs, ensuring the accuracy and efficiency of the testing process. The processing module has powerful data processing capabilities, enabling it to process and analyze the collected raw data, generate a 3D model of the conductive adhesive and quantitative testing information, providing a basis for determining whether the conductive adhesive is qualified.

[0101] The main function of the 3D scanning module is to perform 3D morphological scanning of conductive adhesives, collect point cloud data of the conductive adhesive surface, and meet different detection accuracy requirements through different scanning modes and parameter settings, providing basic data for subsequent 3D model reconstruction and analysis. The 3D scanning module can adjust the scanning mode and parameters according to the control signal of the processing module to adapt to the detection needs of different types and specifications of conductive adhesives.

[0102] The function of the motion control module is to accurately move the 3D scanning module to the predetermined scanning position, ensuring that the 3D scanning module can perform a comprehensive and accurate scan of the conductive adhesive. By monitoring and adjusting the posture stability of the 3D scanning module in real time, the quality of the scanned data can be improved. Reasonable motion planning and execution enable the 3D scanning module to reach the scanning position quickly and accurately, reducing detection time and improving the efficiency of the entire detection process.

[0103] In one embodiment, the 3D scanning module includes a laser scanning sensor and an illumination source.

[0104] In one embodiment, the motion control module includes a displacement platform and a multi-axis robot.

[0105] Upon receiving the start signal from the processing module, the laser scanning sensor performs self-checks and initialization to ensure that all its parameters are in normal working order. For example, it checks whether parameters such as laser emission power and scanning angle range meet preset values. Following the scanning frequency and range set by the processing module, the laser scanning sensor emits a laser beam onto the conductive adhesive surface. After the laser beam hits the surface, some light is reflected back. The sensor calculates the distance from each point on the conductive adhesive surface to the sensor by measuring the time of reflection (time-of-flight method) or angle (triangulation method). The measured distance information is converted into electrical signals and digitized to form raw scanning data. This data is then transmitted to the processing module for subsequent 3D coordinate transformation and analysis.

[0106] Based on the control signals from the processing module, the intensity, color, and illumination angle of the illumination source are adjusted. For example, if the conductive adhesive surface is dark, the illumination intensity needs to be increased; if the scanning requires specific color contrast, the color of the light source will be adjusted. When the laser scanning sensor is scanning, the illumination source provides uniform and suitable lighting conditions for the conductive adhesive surface to enhance the laser reflection effect and improve the quality of the scanning data.

[0107] Laser scanning is characterized by high precision and high speed, and can accurately acquire the three-dimensional morphology information of the conductive adhesive surface. By measuring the time or angle of laser reflection, the shape of the conductive adhesive surface can be converted into digital distance data, providing a foundation for subsequent 3D model reconstruction. Appropriate lighting conditions are crucial for laser scanning. Uniform lighting can reduce the influence of shadows and reflections, enabling the laser scanning sensor to more accurately measure the reflected light from the conductive adhesive surface, thereby improving the accuracy and reliability of the scanning data.

[0108] The displacement platform receives control signals sent by the processing module, which include information such as the distance, speed and direction of movement. According to the received instructions, the displacement platform moves in a straight line in the horizontal or vertical direction to move the 3D scanning module to the designated position. During the movement, the displacement platform will move smoothly according to the set speed and acceleration to avoid affecting the scanning effect due to movement that is too fast or too slow.

[0109] The displacement platform is equipped with a position sensor, which can monitor its own position in real time and feed the position information back to the processing module. If the position of the displacement platform is found to deviate from the preset position, the processing module will adjust the control signal in time to correct the movement of the displacement platform.

[0110] Based on the scanning position information provided by the processing module, the multi-axis robot plans its own motion path. The multi-axis robot can achieve multiple degrees of freedom of movement, such as rotation and tilting, to adjust the posture of the 3D scanning module so that it can scan the conductive adhesive from different angles. During the movement, the multi-axis robot adjusts the movement of its various axes in real time according to the position of the displacement platform and the posture requirements of the 3D scanning module to ensure that the 3D scanning module is always in the optimal scanning posture.

[0111] The multi-axis robot works in collaboration with the displacement platform to complete the positioning and attitude adjustment tasks of the 3D scanning module. For example, while the displacement platform moves in a straight line, the multi-axis robot can rotate, enabling the 3D scanning module to cover all areas of the conductive adhesive.

[0112] The displacement platform enables the 3D scanning module to move linearly in both horizontal and vertical directions, providing basic position adjustment functions for scanning. Through precise position control, it ensures that the 3D scanning module accurately reaches the predetermined scanning position, improving scanning accuracy. The multi-axis robot's multiple degrees of freedom allow the 3D scanning module to scan the conductive adhesive from different angles, expanding the scanning range and improving the comprehensiveness of the scan. At the same time, the multi-axis robot can flexibly adjust the posture of the 3D scanning module according to the shape of the conductive adhesive and the detection requirements, ensuring the acquisition of high-quality scanning data.

[0113] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the embodiments provided in this application, it should be understood that the disclosed apparatus / devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A fully automated 3D inspection method for conductive adhesive, characterized in that, include: Obtain the detection requirement information and configure task parameters for the 3D scanning module and motion control module according to the detection requirement information; Based on the task parameters, the motion control module drives the 3D scanning module to move relative to the conductive adhesive, and the 3D scanning module collects the point cloud data of the conductive adhesive. The conductive adhesive is reconstructed into a 3D model based on the point cloud data to obtain the 3D model of the conductive adhesive. The 3D model is compared with a pre-established standard template to perform multi-item analysis and processing on the conductive adhesive, generating quantitative detection information of the conductive adhesive.

2. The fully automated 3D inspection method for conductive adhesive as described in claim 1, characterized in that, The steps of obtaining detection requirement information and configuring task parameters for the 3D scanning module and motion control module based on the detection requirement information include: Obtain the height information of the conductive adhesive being tested and the required testing accuracy, and use them together as the testing requirement information; The scanning position of the 3D scanning module is analyzed based on the height information to obtain the scanning position of the 3D scanning module for the conductive adhesive, and corresponding task parameters are deployed for the motion control module based on the scanning position. The scanning mode of the 3D scanning module is analyzed according to the detection accuracy requirements to obtain the expected scanning mode, and the corresponding task parameters are deployed to the 3D scanning module according to the expected scanning mode.

3. The fully automated 3D inspection method for conductive adhesive as described in claim 2, characterized in that, The steps of driving the motion control module to move the 3D scanning module relative to the conductive adhesive based on the task parameters, and acquiring point cloud data of the conductive adhesive through the 3D scanning module include: Based on the task parameters, the motion control module drives the 3D scanning module to move to the pre-planned scanning position; Based on the task parameters, the 3D scanning module is driven to perform ambient light irradiation and 3D morphology scanning on the conductive adhesive in a pre-planned expected scanning mode to acquire raw scanning data. The original scan data is transformed into three-dimensional coordinates to generate point cloud data of the conductive adhesive.

4. The fully automated 3D inspection method for conductive adhesive as described in claim 2, characterized in that, 3D topography scanning is performed using laser scanning to acquire raw scan data, wherein the resolution of the laser scanning is not less than 5μm; the raw scan data is synchronously acquired using a data acquisition card pre-installed in the 3D scanning module in conjunction with the laser scanning, wherein the sampling rate of the data acquisition card is not less than 100KPS.

5. The fully automated 3D inspection method for conductive adhesive as described in claim 3, characterized in that, During the process of the motion control module driving the 3D scanning module to the pre-planned scanning position, the attitude stability of the 3D scanning module is monitored in real time to obtain attitude stability characteristics. Based on the attitude stability characteristics, the motion control module is optimized for motion control until the motion control module moves the 3D scanning module to the designated scanning position.

6. The fully automated 3D inspection method for conductive adhesive as described in claim 1, characterized in that, The steps for reconstructing a 3D model of the conductive adhesive based on the point cloud data to obtain the 3D model of the conductive adhesive include: The point cloud data is analyzed in its overall spatial form to obtain the overall distribution information of the conductive adhesive; Based on the overall distribution information, a corresponding representative sampling standard is selected to perform average spatial division and representative point selection on the point cloud data according to the representative sampling standard, so as to obtain representative point data. The representative point data is mapped to a pre-constructed three-dimensional coordinate space to generate a basic three-dimensional model. The covariance matrix of adjacent representative points is calculated for each representative point on the basic three-dimensional model to obtain the feature vector between each representative point. Anomalies are assessed in each space of the basic 3D model based on the feature vectors, and corresponding space-filling strategies are deployed in each space of the basic 3D model according to the anomaly assessment results. According to the space filling strategy, the point cloud data is analyzed in a corresponding form to restore the information of each space in the basic 3D model and obtain the 3D model of the conductive adhesive.

7. The fully automated 3D inspection method for conductive adhesive as described in claim 1, characterized in that, The steps of comparing the 3D model with a pre-established standard template to perform multi-item analysis and processing of the conductive adhesive and generate quantitative detection information of the conductive adhesive include: The 3D model is compared with a pre-established standard template to analyze the maximum, minimum, and average thickness of the conductive adhesive, as well as the defect area, to obtain quantitative detection information of the conductive adhesive.

8. The fully automated 3D inspection method for conductive adhesive as described in claim 7, characterized in that, The quantitative detection information is judged according to a preset threshold. If the judgment result meets the preset threshold, the conductive adhesive is judged as a qualified product. If the judgment result does not meet the preset threshold, the conductive adhesive is judged as a non-qualified product.

9. The fully automated 3D inspection method for conductive adhesive as described in claim 8, characterized in that, When the conductive adhesive is determined to be a non-conforming product, the quantitative detection information is analyzed to obtain defect location information. The quantitative detection information, the judgment result, and the defect location information are combined to generate a test report for the conductive adhesive. The test report for the conductive adhesive is temporarily stored in a database, and feature statistics are performed on each test report temporarily stored in the database to obtain the defect trend characteristics of the conductive adhesive in the same production batch. In the subsequent 3D model reconstruction of the conductive adhesive, attention weights are allocated to each spatial part of the conductive adhesive based on the defect trend characteristics.

10. A fully automatic conductive adhesive 3D inspection device, characterized in that, include: 3D scanning module, motion control module, processing module; The processing module is electrically connected to the 3D scanning module and the motion control module respectively. The processing module is used to send control signals to the 3D scanning module and the motion control module to execute the fully automatic conductive adhesive 3D detection method according to any one of claims 1-7. The 3D scanning module is mounted on the motion control module, and the motion control module is used to drive the 3D scanning module to move. The 3D scanning module includes a laser scanning sensor and an illumination source, while the motion control module includes a displacement platform and a multi-axis robot.

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