Automatic detection method and equipment for coating test panel

Abstract

The invention provides an automatic detection method and equipment for a coating test panel, and the method comprises the following steps: controlling a material taking assembly to take the test panel from a stock bin through a robot, and positioning the test panel on a test board; a differential detection process is executed according to the type of the test plate, and the detection process comprises the steps that if the test plate is a standard sample plate, detection assemblies are switched according to a preset sequence to conduct film thickness detection, gloss detection, color detection and particle detection; if the test panel is a daily panel, identifying a basic two-dimensional code preset on the back of the daily panel through a code scanning assembly to analyze the detection item combination, and dynamically scheduling a corresponding detection assembly to execute detection based on an analysis result; and a unique two-dimensional code is generated based on the detection result, the unique two-dimensional code is printed to the back of the test plate through a code spraying assembly, and the test plate is sorted to the corresponding discharging bin according to the detection result. The detection precision can be improved, and quality data can be traced in the whole process.

Description

Technical Field

[0001] This application relates to the field of coating test panel testing technology, specifically to an automated testing method and equipment for coating test panels. Background Technology

[0002] In the field of coating test panel quality inspection, the traditional method of relying on manual visual inspection has significant drawbacks: inspectors need to use handheld color charts, gloss meters, and other equipment to sample and test each item, and the inspection of a single test panel takes more than 5 minutes. Moreover, the difference in judgment between different personnel on indicators such as stone chip peeling area and salt spray corrosion density can be more than 20% (such as the difference in judgment between 3% and 5% peeling area), resulting in low data reliability. At the same time, the paper record method causes the test results to be disconnected from the physical identity of the test panel, making it impossible to achieve quality traceability (such as the inability to link the historical salt spray corrosion data of a certain batch of test panels).

[0003] To alleviate the efficiency and consistency issues associated with manual inspection, semi-automatic or single-function automated inspection solutions have emerged in the industry. These solutions deploy gloss meters, colorimeters, or simple vision modules at fixed workstations, supplemented by barcode scanners to record test plate numbers. However, these existing automation improvement solutions still suffer from the following systemic shortcomings: Limited functionality: Equipment such as gloss meters or colorimeters only support single-item testing. The benchmark testing of standard samples and the batch testing of daily samples need to be carried out separately, resulting in low equipment utilization. Lack of dynamic scheduling: Although the semi-automatic system introduces barcode recognition, the switching of detection components relies on manual instructions, which takes a long time and cannot optimize the sequence according to task priority (such as prioritizing the scheduling of detection items with short processing time). Data silos: The test results are not linked to the physical test panel and are not connected to the cloud-based quality system, resulting in a break in the data traceability chain.

[0004] In view of this, the present invention proposes an automated testing method and equipment for coating test panels that integrates robot dynamic scheduling, multi-index collaborative detection, and quality data closed loop, which can effectively improve testing efficiency. Summary of the Invention

[0005] To address the problems in current coating test panel testing, such as low equipment utilization due to isolated functional modules, insufficient testing efficiency due to lack of dynamic scheduling, and the disconnect between quality data and physical carriers affecting traceability and closed-loop testing, this invention provides an automated coating test panel testing method and equipment to solve the aforementioned technical deficiencies.

[0006] In a first aspect, the present invention proposes an automated testing method for coating test panels, comprising the following steps: S1. The robot controls the material handling component to extract the test plate from the silo and position the test plate on the test table; S2. Perform differentiated testing procedures based on the test plate type. The testing procedures include: If the test panel is a standard sample, the detection components are switched in the preset order to perform film thickness detection, gloss detection, color detection and particle detection; If the test board is a regular board, the basic QR code pre-installed on the back of the regular board is identified by the scanning component to parse the combination of detection items. Based on the parsing result, the corresponding detection component is dynamically scheduled to perform at least one of the following detections: a. Film thickness detection, gloss detection, color detection, and particle detection; b. Quantitative detection of stone impact defects: The surface image of the daily board is acquired by CCD component, and the U-Net neural network is used to segment the outline of the peeling area and calculate the area ratio. c. Adhesion defect analysis: Extract Fourier descriptor features of the peeling edge of the coating on the daily board and compare the similarity with the benchmark template; d. Salt spray corrosion level determination: Count the number of corrosion points per unit area of ​​the daily-use board and calculate the distribution density; S3. Generate a unique QR code based on the test results, print the unique QR code on the back of the test plate using a coding component, and sort the test plates to the corresponding unloading bins according to the test results.

[0007] More preferably, in step S2, when performing the quantitative detection of stone impact defects, the following sub-steps are included: b1. The robot moves the CCD component to the top of the test platform, triggering the coaxial light source to turn on the dark field lighting mode. b2. Acquire daily board surface images and transmit them to the host computer; b3. The host computer calls the pre-trained U-Net model to segment the peeling area in the daily board surface image and generate a binary contour map. b4. Calculate the proportion of the peeling area based on the binary contour map, and determine the defect level according to the proportion value to obtain the detection result.

[0008] More preferably, in step S2, when performing adhesion defect analysis, the following sub-steps are included: c1. The robot moves the CCD component to the top of the test platform, switches the CCD component to bright field illumination mode, and acquires images of the stripped area. c2. The Canny operator is used to perform edge detection on the peeled area image, and a gradient threshold is set to obtain a continuous coordinate sequence of the coating peeled edge; c3. Perform a discrete Fourier transform on the continuous coordinate sequence to generate a Fourier descriptor sequence; c4. Based on the Fourier descriptor sequence, calculate the Euclidean distance with the reference template. By determining whether the Euclidean distance is greater than or equal to the distance threshold, determine whether the adhesion has failed and obtain the detection result.

[0009] More preferably, in step S2, when performing the salt spray corrosion level determination, the following sub-steps are included: d1. The robot moves the CCD component to the top of the test platform, switches the CCD component to bright field illumination mode, and collects multiple sets of surface images along the length of the daily board at preset intervals. d2. Perform morphological opening operation on each group of surface images to separate the adhesion regions and identify the contours of independent corrosion points that meet the requirement that the area is greater than the preset area threshold. d3. Based on the independent corrosion point profiles, count the number of effective corrosion points within the standard detection area, and then calculate the distribution density; d4. Classify the corrosion level according to the distribution density and obtain the test results.

[0010] Preferably, in step S1, the robot-controlled material handling component extracts the test plate from the hopper and positions the test plate on the test table, specifically including the following sub-steps: S11. A vacuum suction cup material handling component is installed at the robot end via an electro-coupled quick-change interface; S12. Select the standard sample material silo or the daily sample material silo according to the type of test plate; S13. Start the pneumatic lifting mechanism at the bottom of the standard sample silo or daily sample silo to vertically lift the bottom test plate in the silo to a set height, so that the top test plate is in the material picking position. S14. After the material handling component adsorbs the top test plate, the robot moves out of the hopper in a horizontal direction; S15. Precisely position the test plate on the right-angle positioning edge of the test bench.

[0011] Preferably, in step S2, before performing the detection, an environmental parameter compensation step is also included: The environmental parameters around the test bench are monitored in real time using temperature and humidity sensors. When the ambient temperature deviates from the standard value, the film thickness measurement value is linearly compensated according to the thermal expansion coefficient α of the substrate. The compensation formula is: Δd=d×α×ΔT, where Δd is the thickness compensation amount, d is the measured film thickness value, α is the thermal expansion coefficient of the substrate, and ΔT is the difference between the current ambient temperature and the standard value. When the ambient humidity exceeds the preset relative humidity value, the intensity of the drying airflow inside the gloss meter probe is increased; When the ambient light intensity fluctuates above the preset light intensity, the colorimeter's light shield is triggered and the built-in calibration light source is activated.

[0012] Preferably, in step S2, the testing process for the standard sample includes: S211. The robot switches to the gloss meter component via a quick-change interface and performs gloss measurement of the test plate surface based on preset angle parameters. S212. The robot switches to the colorimeter component, collects the chromaticity data of the test panel in the CIE L*a*b* color space, and compares it with the configurable color difference threshold to determine the result. S213. The robot switches to the CCD component, turns on the coaxial light source and acquires surface images. It identifies the surface particle outlines of the surface images through image segmentation algorithms, calculates the particle size distribution density, and marks particle defects when the particle size exceeds the threshold or the distribution density exceeds the standard. S214. The robot switches to the film thickness gauge component to measure the coating thickness on the test plate surface. If the film thickness value deviates from the preset standard value, the film thickness is marked as abnormal.

[0013] Preferably, in step S2, the corresponding detection component is dynamically scheduled based on the parsing results, including the following sub-steps: S221. The scanning component reads the basic QR code on the back of the daily board and extracts the daily board ID and the detection item code. S222. Query the preset rule base according to the detection item code to generate a detection sequence instruction; the detection sequence instruction includes the detection component type, detection order and parameter configuration; S223. The robot switches the detection components sequentially through the quick-change interface according to the detection sequence instructions, and monitors the switching status of the components in real time. If the switching time is longer than the preset time, an alarm is triggered and a fault code is recorded.

[0014] Preferably, in step S3, a unique QR code is generated based on the detection result, and the unique QR code is printed onto the back of the test plate using a coding component. The test plates are then sorted to the corresponding unloading bins according to the detection results. This specifically includes the following sub-steps: S31. The industrial control computer receives the test result data, binds it with the test board ID and the test timestamp, and generates a unique QR code containing the following fields: test board material and size, test result value, BASF Digilab system traceability link, and encrypted verification code. S32. The robot transports the test panel to the coding station. The UV coding machine, under the adjustment of the multi-degree-of-freedom bracket, prints the QR code on the back of the test panel at an incident angle of 30°-60°. S33. Based on the defect type in the test results, sort the test panels to the standard sample OK warehouse, standard sample NG warehouse, or daily sample warehouse.

[0015] Secondly, the present invention proposes an automated testing device for coating test panels, used to implement any of the above-mentioned automated testing methods for coating test panels, comprising: The robot has a vacuum suction cup material handling component installed at its end via an electrically coupled quick-change interface; The feeding hopper includes a standard sample hopper and a daily sample hopper. The bottom of the feeding hopper is equipped with a pneumatic lifting mechanism. The testing component library includes film thickness gauge components, gloss meter components, colorimeter components, and CCD components. The CCD components are equipped with a coaxial light source and a bright / dark field switching controller. The test bench is equipped with a servo-driven precision transfer platform and an adjustable right-angle positioning edge. The QR code scanning module is located on the entrance side of the test platform; The inkjet printing module is equipped with a multi-degree-of-freedom adjustable bracket, with the printhead facing the test bench outlet side. The material unloading hopper includes the standard sample OK hopper, the standard sample NG hopper, and the daily sample hopper; The controller connects the robot, the detection component library, the barcode scanning module, and the inkjet printing module, and has the following built-in modules: The rule parsing module is used to decode the basic QR code on the daily board and generate a detection sequence; The dynamic scheduling module is used to control the robot to switch detection components sequentially. The defect analysis module is used to perform stone chip peeling segmentation, adhesion profile comparison, and salt spray density statistics. The data encryption module is used to bind the test results and the test plate ID to generate a unique QR code.

[0016] Compared with the prior art, the beneficial results of the present invention are as follows: (1) Significantly improved testing efficiency: The robot, combined with the quick-change interface, enables instant switching of testing components and uses the pneumatic lifting mechanism for continuous material supply, significantly reducing the cycle time of a single piece and meeting the needs of continuous production; standard samples and daily samples share the same test bench, avoiding separate line operations and greatly improving equipment utilization.

[0017] (2) Defect judgment is objective and quantifiable: The area of ​​stone chipping is segmented at the pixel level by a deep learning model, and the area ratio is calculated accurately; Fourier descriptors are used to compare the morphology of the adhesion peeling edge, and the similarity judgment is reliable; the salt spray corrosion density is based on the distribution density of corrosion points, realizing the digital classification of corrosion level, which can eliminate the subjective error of manual visual inspection.

[0018] (3) Dynamic combination of detection items: The QR code on the back of the daily board carries the code of the detection item. The system generates personalized detection sequences in real time through the rule parsing module, avoiding the waste of resources caused by the fixed menu; adding new detection items only requires expanding the code in the rule base, and the function can be expanded without hardware modification.

[0019] (4) Quality data is traceable throughout the process: The test results, test plate identity, timestamp, and material information are encrypted and written into a unique QR code, and simultaneously uploaded to the quality traceability system to form a closed loop of "test plate-data"; when an abnormality occurs, the specific batch and testing process can be quickly located, greatly improving traceability efficiency.

[0020] (5) The equipment occupies a small area and has a flexible layout: the whole machine has a compact structure and the quick-change interface integrates multiple detection components into the same robot end, which significantly reduces the space required by traditional rotary tables or multi-station slides; the right-angle positioning edge and the precision transfer platform are compatible with various specifications of test plates, and no mechanical adjustment is required for changing the type, which is highly adaptable.

[0021] (6) Low operation and maintenance cost and quick replication: All testing components adopt a modular sub-panel design, which is convenient to disassemble and assemble; the control system has reserved standard interfaces, and when the production line is replicated later, only parameters need to be imported for batch deployment, which greatly shortens the debugging cycle and significantly reduces maintenance costs. Attached Figure Description

[0022] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments, taken with reference to the accompanying drawings: Figure 1 This is a flowchart of the automated testing method for coating test panels according to the present invention; Figure 2 This is a schematic diagram of the adhesion detection positioning calibration plate according to the present invention; Figure 3 This is a schematic diagram of the coating peeling feature proportions according to the present invention; Figure 4 This is a schematic diagram of the quantitative analysis of localized corrosion according to the present invention; Figure 5 This is a comparative diagram of multi-dimensional corrosion assessment according to the present invention; Figure 6 This is a top view of the physical layout of the automated testing equipment according to the present invention; Figure 7 A schematic diagram of the material handling assembly structure of the present invention is shown; Figure 8 A schematic diagram of the gloss meter assembly structure of the present invention is shown; Figure 9 A schematic diagram of the colorimeter assembly structure of the present invention is shown; Figure 10 A schematic diagram of the CCD component structure of the present invention is shown; Figure 11 A schematic diagram of the film thickness gauge assembly structure of the present invention is shown.

[0023] Reference numerals: 1. Robot; 2. Material handling component; 21. Instrument mounting mechanism of the material handling component; 22. Quick-change tool tray of the material handling component; 23. Tool placement mechanism of the material handling component; 24. Suction cup; 31. Standard sample hopper; 32. Daily sample hopper; 41. Gloss meter component; 411. Instrument mounting mechanism of the gloss meter component; 412. Quick-change tool tray of the gloss meter component; 413. Tool placement mechanism of the gloss meter component; 42. Colorimeter component; 421. Instrument mounting mechanism of the colorimeter component; 422. Quick-change tool tray of the colorimeter component; 423. 43. Tool placement mechanism; 431. CCD component; 432. CCD component quick-change tool tray; 433. CCD component tool placement mechanism; 434. Lens; 435. Coaxial light source; 436. Camera; 44. Film thickness gauge component; 441. Film thickness gauge component instrument mounting mechanism; 442. Film thickness gauge component quick-change tool tray; 443. Film thickness gauge component tool placement mechanism; 5. Test stand; 6. Barcode scanning module; 7. Inkjet coding module; 81. Standard sample OK compartment; 82. Standard sample NG compartment; 83. Daily sample compartment. Detailed Implementation

[0024] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] This invention proposes an automated testing method for coating test panels. Figure 1 A flowchart of the automated testing method for coating test panels of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps: S1. The robot-controlled material handling component extracts a coating test panel from the silo and positions the test panel on the test platform. Preferably, the coating test panel is a paint test panel. The specific steps include the following: S11. A vacuum suction cup material handling component is installed at the robot end via an electro-coupled quick-change interface; S12. Select the standard sample material silo or the daily sample material silo according to the type of test plate; S13. Start the pneumatic lifting mechanism at the bottom of the standard sample silo or daily sample silo to vertically lift the bottom test plate in the silo to a set height, so that the top test plate is in the material picking position. S14. After the material handling component adsorbs the top test plate, the robot moves out of the hopper in a horizontal direction; S15. Precisely position the test board on the right-angle positioning edge of the test bench. During positioning, ensure the back of the test board is facing upwards so that the barcode scanning component can read the QR code.

[0027] In a specific embodiment, the six-axis industrial robot first installs a vacuum suction cup-type material handling assembly via an electro-coupled quick-change interface on the end flange. The main quick-change interface plate is fixed to the robot's sixth axis, while the auxiliary plate is integrated into the back of the material handling assembly, ensuring that the air path and signal can be used immediately after a single plug-in connection, without the need for additional manual intervention.

[0028] When the system identifies a standard sample (master board) for the current batch, the robot automatically turns to the standard sample hopper; if it is a daily sample, it turns to the daily sample hopper. Both hoppers adopt a "right-angle double-sided positioning + bottom pneumatic lifting" structure: a cylinder-driven lifting mechanism is arranged under the bottom plate of the hopper, which can push the entire stack of test boards upwards, ensuring that the top layer of test boards is always at the same picking height, thus guaranteeing the robot's repeatability and positioning accuracy. The side walls of the hoppers are equipped with adjustable baffles, compatible with various specifications of aluminum or tinplate such as 150×100mm, 200×100mm, and 90×190mm, and the right-angle double-sided positioning prevents the test boards from shifting horizontally during the lifting process.

[0029] Once the robot reaches above the hopper, the material handling assembly descends, and the vacuum suction cup contacts the upper surface of the test plate. The vacuum generator instantly creates negative pressure, causing the suction cup to firmly adhere to the test plate. The robot then smoothly moves horizontally out of the hopper, avoiding collisions with the hopper walls. The entire material handling process is completed with the safety door interlocked. If the door is accidentally opened, the robot immediately stops to prevent personal injury.

[0030] The robot moves the test plate to the entrance of the test platform. The test platform consists of a precision transfer platform driven by a servo motor and an adjustable right-angle positioning edge. The transfer platform surface is embedded with low-friction ball bearings to reduce the sliding resistance of the test plate. The right-angle positioning edge achieves two-stage positioning of "forward push + side push" through a micro cylinder, ensuring that the long and short sides of the test plate are simultaneously in contact with the reference surface. After positioning is completed, the transfer platform descends to the testing plane and enters the subsequent testing process.

[0031] Through the above steps, the robot can automatically pick up and accurately position standard samples and daily samples within the same cycle time, without human intervention, laying a stable foundation for subsequent differential testing.

[0032] Continue to refer to Figure 1 The automated testing method for coating test panels provided by this invention further includes the following steps: S2. Perform differentiated testing procedures based on the test plate type. The testing procedures include: If the test panel is a standard sample, the detection components are switched in the preset order to perform film thickness detection, gloss detection, color detection and particle detection; If the test board is a regular board, the basic QR code pre-installed on the back of the regular board is identified by the scanning component to parse the combination of detection items. Based on the parsing result, the corresponding detection component is dynamically scheduled to perform at least one of the following detections: a. Gloss detection, color detection, particle detection, and film thickness detection; b. Quantitative detection of stone impact defects: The surface image of the daily board is acquired by CCD component, and the U-Net neural network is used to segment the outline of the peeling area and calculate the area ratio. c. Adhesion defect analysis: Extract Fourier descriptor features of the peeling edge of the coating on the daily board and compare the similarity with the benchmark template; d. Salt spray corrosion level determination: Count the number of corrosion points per unit area of ​​the daily board and calculate the distribution density.

[0033] Before performing the detection in step S2, an environmental parameter compensation step is also included: The environmental parameters around the test bench are monitored in real time using temperature and humidity sensors. When the ambient temperature deviates from the standard value (e.g., 23℃), the film thickness measurement value is linearly compensated according to the thermal expansion coefficient α of the substrate. The compensation formula is: Δd=d×α×ΔT, where Δd is the thickness compensation amount, d is the measured film thickness value, α is the thermal expansion coefficient of the substrate, and ΔT is the difference between the current ambient temperature and the standard value. When the ambient humidity exceeds the preset relative humidity value (e.g., 60%RH), the intensity of the drying airflow inside the gloss meter probe is increased, such as to 1.5 L / min. When the ambient light intensity fluctuates more than the preset light intensity (e.g., ±50 lux), the colorimeter's light shield is triggered and the built-in calibration light source is activated. All compensation operation logs are written into the final report along with the test results.

[0034] In this embodiment, when performing gloss detection, color detection, particle detection, and film thickness detection (the execution process is exactly the same for standard samples and daily samples, except that for daily samples, the detection sequence needs to be inserted based on dynamic scheduling), the following sub-steps are included: a1. Gloss Measurement Execution: The robot switches to the BYK gloss meter assembly via a quick-change interface; the gloss meter probe is vertically aligned with the test plate surface (e.g., spacing 50±0.1mm), and the gloss is measured at a 60° incident angle according to GB / T 9754 standard (other preset angles are also possible, such as 20°, 85°, etc.): (1) Perform three repeated measurements (with an interval of 0.5s) and take the average value as the final value; (2) If the fluctuation value is greater than 2 GU, the automatic retest mechanism is triggered (up to 3 retests). (3) The measured values ​​are uploaded to the industrial control computer database in real time.

[0035] a2. Color detection execution: The robot switches to the BYK color difference meter component; collect the chromaticity data on the surface of the test panel under the D65 standard light source, and output the CIE L*a*b* value (accuracy ΔE ≤ 0.1). Compare the determination result with the configurable color difference threshold. Specifically, it can be to compare the color difference ΔE with the preset standard color plate. If ΔE ≤ 1.0, it is determined as qualified; if 1.0 < ΔE ≤ 2.0, mark a slight chromaticity deviation; if ΔE > 2.0, mark a serious color difference (mark NG).

[0036] a3. Particle detection preparation: The robot switches to the CCD component and turns on the coaxial light source bright field mode (illuminance 1500 ± 100 lux); adjust the lens focal length to make the imaging resolution reach 0.05 mm / pixel, and collect the surface image.

[0037] a4. Particle defect quantification: Separate the particle area through morphological top-hat transformation; calculate the particle size distribution density: identify the particle contour with a diameter > 50 μm; count the number of particles in a unit area (1 cm ,

[0039] , , , , , , ,

[0040] , , , ) If the particle density > 5 particles / cm<00000​​​​​​​​​​​​​​​​​​​​

[0041] Where R represents the percentage value. If the percentage value R ≥ 5%, the daily board is judged to have serious defects.

[0042] Figure 2 A schematic diagram of the adhesion detection positioning calibration plate of the present invention is shown. Figure 3 A schematic diagram showing the coating peeling characteristics of the present invention is provided; in conjunction with reference to... Figure 1 , Figure 2 and Figure 3 When performing adhesion defect analysis, the following sub-steps are included: c1. The robot moves the CCD component to the top of the test platform and aligns it with the marked area on the test plate (e.g., ...). Figure 2 (The green box in the image) Switch the CCD component to bright field illumination mode (5500K color temperature) and strip the image area at a resolution of 0.05mm / pixel. Figure 3 scale parameters); c2. Use the Canny operator to perform edge detection on the peeled area image, set the gradient threshold (preferably 50), and obtain a continuous coordinate sequence of the coating peeled edge; c3. Perform a discrete Fourier transform on the continuous coordinate sequence to generate a Fourier descriptor sequence; c4. Based on the Fourier descriptor sequence, calculate the Euclidean distance with the reference template. By determining whether the Euclidean distance is greater than or equal to the distance threshold, determine whether the adhesion has failed and obtain the detection result.

[0043] Figure 4 A schematic diagram of the local corrosion quantitative analysis of the present invention is shown. Figure 5 This invention illustrates a comparative diagram of multi-dimensional corrosion assessment, in conjunction with reference. Figure 1 , Figure 4 and Figure 5 When performing salt spray corrosion level determination, the following sub-steps are included: d1. The robot moves the CCD component above the test platform, switches the CCD component to bright field illumination mode, and acquires multiple sets of surface images along the length of the test board at preset intervals (e.g., 10±0.5mm interval). Figure 5 (Multi-region comparison) d2. Perform morphological opening operations on each group of surface images to separate adherent regions and identify those with areas greater than a preset area threshold (see reference). Figure 5 The green frame can actually be set to 25cm. 2 The independent corrosion point profile; d3. Based on the independent corrosion point profiles, count the number of effective corrosion points within the standard detection area, and then calculate the distribution density ρ. d4. Classify the corrosion level according to the distribution density ρ and obtain the test results. For example, if ρ < 5, the daily board is determined to be G0 level and needs to be sorted to the standard sample OK warehouse; if 5 ≤ ρ ≤ 20, the daily board is determined to be G1 level and needs to be sorted to the daily board warehouse; if ρ is greater than 20, the daily board is determined to be G2 level and needs to be sorted to the standard sample NG warehouse.

[0044] In a specific embodiment, the testing process for the standard sample includes: S211. The robot switches to the gloss meter component via a quick-change interface and performs a gloss measurement of the test plate surface based on preset angle parameters (e.g., preset angle parameters are 20°, 60° or 85°). S212. The robot switches to the colorimeter component, collects the chromaticity data of the test panel in the CIE L*a*b* color space, and compares it with the configurable color difference threshold to determine the result. S213. The robot switches to the CCD component, turns on the coaxial light source and acquires surface images. It identifies the surface particle outlines of the surface images through image segmentation algorithms, calculates the particle size distribution density, and marks particle defects when the particle size exceeds the threshold or the distribution density exceeds the standard. S214. The robot switches to the film thickness gauge component and uses a magnetic induction / eddy current dual-mode probe to measure the thickness of the coating on the test plate surface and obtain film thickness distribution data. If the film thickness value deviates from the preset standard value, the film thickness is marked as abnormal.

[0045] The gloss meter, colorimeter, and film thickness gauge components are connected to the data cable via electrical pins of a quick-change interface and employ a hierarchical communication protocol: the gloss meter uses the RS485-Modbus protocol (transmission delay ≤10ms); the colorimeter uses the Modbus-TCP protocol (transmission delay ≤5ms); and the film thickness gauge component uses the EtherCAT protocol (transmission delay ≤1ms).

[0046] Dynamically scheduling the corresponding detection components based on the parsing results includes the following sub-steps: S221. The scanning component reads the basic QR code on the back of the daily board and extracts the daily board ID and the detection item code. S222. Query the preset rule base according to the detection item code to generate a detection sequence instruction; the detection sequence instruction includes the detection component type, detection order and parameter configuration; S223. The robot switches the detection components sequentially through the quick-change interface according to the detection sequence instructions, and monitors the switching status of the components in real time. If the switching time is longer than the preset time (e.g., 1 second), an alarm is triggered and a fault code is recorded.

[0047] The automated testing method for coating test panels further includes an automatic calibration and status monitoring mechanism for the testing components: Before each testing task begins, the robot controls a standard calibration plate to be placed on the test bench, and sequentially switches each testing component to perform a self-calibration process; among them, the gloss meter component performs deviation correction by measuring the known gloss value (e.g., 100 GU) of the calibration plate; the colorimeter component calibrates the L*a*b* value of the standard color plate and calculates the color difference offset; if ΔE≥0.3, soft calibration is triggered; the CCD component performs distortion correction and pixel calibration by capturing standard grid images to ensure that the imaging resolution is stable at 0.05mm / pixel; the film thickness gauge component performs zero-point calibration by measuring a standard sheet with a known thickness (e.g., 100μm); if the measurement deviation exceeds ±1μm, the probe pressure is automatically adjusted; all calibration data are uploaded to the industrial control computer in real time and recorded in the log; if calibration fails, the testing is paused and an alarm is triggered to ensure that the testing data is reliable and traceable throughout the process.

[0048] Continue to refer to Figure 1 The automated testing method for coating test panels provided by this invention further includes the following steps: S3. Generate a unique QR code based on the test results, and print the unique QR code onto the back of the test panel using a coding component. Then, sort the test panels to the corresponding unloading bins according to the test results. This includes the following sub-steps: S31. The industrial control computer receives the test result data, binds it with the test board ID and the test timestamp, and generates a unique QR code containing the following fields: test board material and size, test result value, BASF Digilab system traceability link (BASF Digital Laboratory Platform), and encrypted verification code. S32. The robot transports the test panel to the coding station. The UV coding machine, under the adjustment of the multi-degree-of-freedom bracket, prints the QR code on the back of the test panel at an incident angle of 30°-60°. S33. Based on the defect type in the test results, sort the test panels to the standard sample OK warehouse, standard sample NG warehouse, or daily sample warehouse.

[0049] All three hoppers feature a bottom-detachable drawer structure, capable of stacking no fewer than 200 test plates at a time. Once the drawer is in place, photoelectric sensors confirm the full status and prompt manual or AGV personnel to change the hopper. The sorting path is programmed offline by the robot to ensure no collisions with the hopper entrance; simultaneously, the equipment's safety interlock door remains locked until the robot completely exits the hopper area.

[0050] The automated testing method for coating test panels further includes an intelligent learning and optimization mechanism based on edge computing and cloud collaboration: the system automatically starts a model optimization cycle after every 1000 tests. The specific steps are as follows: the industrial control computer extracts historical test data (including images, spectral data, and measurements) and transmits it to the cloud training platform in encryption; the cloud training platform uses an incremental learning algorithm to update the defect recognition model—for stone chip defect segmentation, the U-Net model convolution kernel weights are optimized using new samples to improve the recognition sensitivity of small-area peeling (<0.5%); for adhesion analysis, the Fourier descriptor template library is expanded by adding peeling contour samples, and a dynamic Euclidean distance threshold (initial threshold ±3σ adaptively adjusted) is adopted; the optimized model is digitally signed and sent to each terminal device. After receiving the new model, the robot automatically enters the verification mode: three test panels are randomly selected from the standard sample library for comparative testing. If the new model's detection results are more than 98% consistent with the traditional method, it is applied; otherwise, it is rolled back to the previous version and a technical intervention warning is triggered, realizing the continuous self-evolution of the detection algorithm.

[0051] Secondly, this invention proposes an automated testing device for coating test panels, used to implement any of the above methods. Figure 6 A top view of the physical layout of the automated testing equipment of the present invention is shown, as follows: Figure 6 As shown, the automated testing equipment includes: Robot 1, wherein a vacuum suction cup material handling component 2 is installed at the end of the robot via an electrically coupled quick-change interface; The feeding hopper includes a standard sample hopper 31 and a daily plate hopper 32. The bottom of the feeding hopper is equipped with a pneumatic lifting mechanism. The testing component library includes a gloss meter component 41, a colorimeter component 42, a CCD component 43, and a film thickness gauge component 44. Among them, the CCD component 43 is equipped with a coaxial light source and a bright field / dark field switching controller. Test bench 5 is equipped with a servo-driven precision transfer platform and an adjustable right-angle positioning edge; The scanning module 6 is located on the entrance side of the test platform; The inkjet printing module 7 is equipped with a multi-degree-of-freedom adjustable bracket, with the printhead facing the test bench outlet side; The material unloading hopper includes the standard sample OK hopper 81, the standard sample NG hopper 82, and the daily sample hopper 83; The controller connects to robot 1, the detection component library, the barcode scanning module 6, and the inkjet printing module 7, and has the following built-in modules: The rule parsing module is used to decode the basic QR code on the daily board and generate a detection sequence; The dynamic scheduling module is used to control the robot to switch detection components sequentially. The defect analysis module is used to perform stone chip peeling segmentation, adhesion profile comparison, and salt spray density statistics. The data encryption module is used to bind the test results and the test plate ID to generate a unique QR code.

[0052] The automated coating test panel testing equipment proposed in this invention is also equipped with: a real-time quality closed-loop control mechanism based on multi-sensor data fusion: when the result of any test item exceeds the threshold, the system automatically triggers a re-inspection protocol—the robot moves the test panel to the re-inspection station, where a high-resolution CCD component (pixel accuracy 0.01mm) performs local area multispectral scanning (including visible and near-infrared bands), while simultaneously fusing multi-point redundant measurement data from the film thickness gauge (e.g., increasing to 9-point grid measurement); the industrial control computer uses a time series analysis algorithm to weight the initial inspection and re-inspection data (e.g., initial inspection weight 0.4, re-inspection weight 0.6). If the fused data still exceeds the limit, a quality anomaly report is immediately generated and synchronized to the MES system, automatically suspending the testing of subsequent test panels in the same batch; at the same time, the system dynamically adjusts the detection parameters: for batches with abnormal color differences, the colorimeter measurement points are automatically increased from 3 to 5; for batches with high particle defect frequency, the ultra-high resolution mode of the CCD component (0.02mm / pixel) is activated and Gaussian filtering noise reduction is performed to achieve adaptive optimization of detection accuracy and efficiency.

[0053] In addition, the automated coating test panel testing equipment proposed in this invention also integrates temperature, humidity, and ambient light sensors for real-time monitoring of environmental parameters around the test bench. Before performing high-precision optical testing (such as gloss and color testing), the industrial control computer calls a preset compensation algorithm model: when the ambient temperature deviates from the standard 23℃, linear compensation is performed on the film thickness measurement value based on the material's thermal expansion coefficient; when the ambient humidity exceeds 60%RH, the drying airflow intensity inside the gloss meter probe is automatically increased to 1.5L / min to prevent water vapor condensation from affecting the measurement; when the ambient light intensity fluctuates greater than ±50lux, the colorimeter automatically triggers the light shield and starts the built-in calibration light source to ensure that the colorimetric data acquisition is not interfered with. All compensation operation logs, along with environmental data, are written into the final test report to ensure that the testing conditions are traceable throughout the process.

[0054] Figure 7 A schematic diagram of the material handling component structure of the present invention is shown, as follows: Figure 7As shown, the material handling assembly 2 includes: an instrument mounting mechanism 21, a quick-change tool tray 22, a tool placement mechanism 23, and suction cups 24. The instrument mounting mechanism 21 is fixed to the robot's end flange with high-strength bolts. The quick-change tool tray 22 is an electromagnetic locking sub-disc with a coupling response time ≤0.1 seconds with the robot's main disk, and its surface is engraved with radial anti-misalignment grooves. The tool placement mechanism 23 includes a bidirectional spring buffer (10mm stroke), providing a vertical shock absorption efficiency >85% and a horizontal anti-deviation accuracy of ±0.1mm when the assembly returns to its original position. The suction cups 24 include two silicone suction cups with a 3mm orifice diameter. This material handling assembly has a built-in pressure sensor (range 0-1MPa) that automatically adjusts the negative pressure according to the test plate size.

[0055] Figure 8 A schematic diagram of the gloss meter assembly structure of the present invention is shown, as follows: Figure 8 As shown, the gloss meter assembly 41 integrates a quick-change interface system, including: an instrument mounting mechanism 411, a quick-change tool tray 412, and a tool placement mechanism 413. The instrument mounting mechanism 411 secures the gloss meter body with a high-rigidity support column, achieving a positioning repeatability of ±0.05mm. The quick-change tool tray 412, acting as a secondary tray, is electromagnetically coupled to the robot's main tray, with a docking response time ≤0.1 seconds. The tool placement mechanism 413 is a spring-buffered positioning slot that cushions impact forces during assembly retraction.

[0056] Figure 9 A schematic diagram of the colorimeter component structure of the present invention is shown, as follows: Figure 9 As shown, the colorimeter component 42 integrates a quick-change interface system, including: an instrument mounting mechanism 421 for the colorimeter component, a quick-change tool tray 422 for the colorimeter component, and a tool placement mechanism 423 for the colorimeter component. The instrument mounting mechanism 421 of the colorimeter component fixes the colorimeter body with a high-rigidity support column, with a repeatability error ≤ ±0.05mm. The quick-change tool tray 422 of the colorimeter component, as a secondary tray, is coupled to the robot's main tray via an electromagnetic locking device, with a response time ≤ 0.1 seconds and a current signal confirmation status (5-24VDC). The tool placement mechanism 423 of the colorimeter component is a positioning slot with spring buffer, with a maximum buffer stroke of 10mm.

[0057] Figure 10 A schematic diagram of the CCD component structure of the present invention is shown, as follows. Figure 10As shown, the CCD component 43 includes: an instrument mounting mechanism 431, a quick-change tool tray 432, a tool placement mechanism 433, a lens 434, a coaxial light source 435, and a camera 436. The camera 436 is equipped with a Sony IMX535 global shutter sensor, vertically mounted in the center of the top plate, and electromagnetically coupled to the robot's main disk via the quick-change tool tray 432, with a response time ≤0.1 seconds. The instrument mounting mechanism 431 uses a high-rigidity aluminum alloy frame to ensure a camera repeatability accuracy of ±0.05mm. The lens 434 is equipped with a telecentric optical system with a working distance of 50±0.1mm. The coaxial light source 435 is located in front of the lens, covering a wavelength range of 400-700nm, with an illuminance of 2000±100lux. The tool placement mechanism 433 incorporates a Shore A 60A silicone buffer pad with a maximum buffer stroke of 8mm, providing a shock absorption efficiency >90% when the component returns to its original position.

[0058] Figure 11 A schematic diagram of the film thickness gauge assembly structure of the present invention is shown, as follows: Figure 11 As shown, the thickness gauge assembly 44 includes: an instrument mounting mechanism 441, a quick-change tool tray 442, and a tool placement mechanism 443. The instrument mounting mechanism 441 secures the thickness gauge body (model: ElektroPhysik MiniTest 740) to a high-rigidity stainless steel bracket, achieving a repeatability of ±0.05mm. The thickness gauge body incorporates a piezoelectric sensor, allowing real-time adjustment of the probe contact pressure to 0.5±0.1N. The quick-change tool tray 442 is electromagnetically coupled to the robot's main disk, with a response time ≤0.1 seconds. The tool placement mechanism 443 is a spring-buffered positioning slot with a maximum buffer stroke of 10mm. The thickness gauge assembly 44 also includes a dual-mode probe that automatically switches between magnetic induction and eddy current modes, with a switching response ≤0.2 seconds and a measurement accuracy of ±1μm. The film thickness gauge assembly 44 also includes a cross-shaped positioning bracket to achieve a five-point measurement distribution (center + four corners, spacing ≥ 20 mm).

[0059] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. An automated testing method for coating test panels, characterized in that, Includes the following steps: S1. The robot controls the material handling component to extract the test plate from the silo and position the test plate on the test table; S2. Perform a differentiated testing process based on the test plate type. The testing process includes: If the test plate is a standard sample, the detection components are switched in a preset order to perform film thickness detection, gloss detection, color detection and particle detection; If the test board is a daily board, the basic QR code pre-installed on the back of the daily board is identified by the scanning component to parse the combination of detection items, and the corresponding detection component is dynamically scheduled to perform at least one of the following detections based on the parsing result: a. Film thickness detection, gloss detection, color detection, and particle detection; b. Quantitative detection of stone impact defects: The surface image of the daily board is acquired by the CCD component, and the U-Net neural network is used to segment the outline of the peeling area and calculate the area ratio. c. Adhesion defect analysis: Extract the Fourier descriptor features of the peeling edge of the coating on the daily board and compare the similarity with the reference template; d. Salt spray corrosion level determination: Count the number of corrosion points per unit area of ​​the daily-use board and calculate the distribution density; S3. Generate a unique QR code based on the test results, print the unique QR code onto the back of the test plate using a coding component, and sort the test plates to the corresponding unloading bins according to the test results.

2. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, when performing the quantitative detection of stone impact defects, the following sub-steps are included: b1. The robot moves the CCD component to the top of the test platform, triggering the coaxial light source to turn on the dark field illumination mode; b2. Acquire the surface image of the daily board and transmit it to the host computer; b3. The host computer calls the pre-trained U-Net model to segment the peeling area of ​​the daily board surface image and generate a binarized contour map. b4. Calculate the peeling area ratio based on the binarized contour map, and determine the defect level according to the ratio value to obtain the detection result.

3. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, when performing adhesion defect analysis, the following sub-steps are included: c1. The robot moves the CCD component to the top of the test platform, switches the CCD component to bright field illumination mode, and acquires images of the stripped area. c2. The Canny operator is used to perform edge detection on the image of the stripped area, and a gradient threshold is set to obtain a continuous coordinate sequence of the coating stripped edge; c3. Perform a discrete Fourier transform on the continuous coordinate sequence to generate a Fourier descriptor sequence; c4. Based on the Fourier descriptor sequence, calculate the Euclidean distance with the reference template, and determine whether the adhesion has failed by judging whether the Euclidean distance is greater than or equal to the distance threshold, and obtain the detection result.

4. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, when performing the salt spray corrosion level determination, the following sub-steps are included: d1. The robot moves the CCD component to the top of the test platform, switches the CCD component to bright field illumination mode, and acquires multiple sets of surface images at preset intervals along the length of the daily board. d2. Perform morphological opening operation on each group of surface images to separate the adhesion regions and identify the contours of independent corrosion points that meet the requirement that the area is greater than the preset area threshold. d3. Based on the independent corrosion point contours, count the number of effective corrosion points within the standard detection area, and then calculate the distribution density; d4. Classify the corrosion level according to the distribution density and obtain the test results.

5. The automated testing method for coating test panels according to claim 1, characterized in that, In step S1, the robot-controlled material handling component extracts the test plate from the hopper and positions the test plate on the test table, specifically including the following sub-steps: S11. A vacuum suction cup material handling component is installed at the robot end via an electro-coupled quick-change interface; S12. Select the standard sample material silo or the daily sample material silo according to the type of test plate; S13. Start the pneumatic lifting mechanism at the bottom of the standard sample silo or daily sample silo to vertically lift the bottom test plate in the silo to a set height, so that the top test plate is in the material picking position. S14. After the material handling component adsorbs the top test plate, the robot moves out of the hopper in a horizontal direction; S15. Precisely position the test plate on the right-angle positioning edge of the test bench.

6. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, before performing the detection, an environmental parameter compensation step is also included: The environmental parameters around the test bench are monitored in real time using temperature and humidity sensors. When the ambient temperature deviates from the standard value, the film thickness measurement value is linearly compensated according to the thermal expansion coefficient α of the substrate. The compensation formula is: Δd=d×α×ΔT, where Δd is the thickness compensation amount, d is the measured film thickness value, α is the thermal expansion coefficient of the substrate, and ΔT is the difference between the current ambient temperature and the standard value. When the ambient humidity exceeds the preset relative humidity value, the intensity of the drying airflow inside the gloss meter probe is increased; When the ambient light intensity fluctuates above the preset light intensity, the colorimeter's light shield is triggered and the built-in calibration light source is activated.

7. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, the testing process for the standard sample includes: S211. The robot switches to the gloss meter component via a quick-change interface and performs gloss measurement of the test plate surface based on preset angle parameters. S212. The robot switches to the colorimeter component, collects the chromaticity data of the test plate in the CIE L*a*b* color space, and compares the result with the configurable color difference threshold. S213. The robot switches to the CCD component, turns on the coaxial light source and acquires surface images. It identifies the surface particle outlines of the surface images through image segmentation algorithms, calculates the particle size distribution density, and marks particle defects when the particle size exceeds the threshold or the distribution density exceeds the standard. S214. The robot switches to the film thickness gauge component to measure the coating thickness on the test plate surface. If the film thickness value deviates from the preset standard value, the film thickness is marked as abnormal.

8. The automated testing method for coating test panels according to claim 1, characterized in that, In step S2, the corresponding detection components are dynamically scheduled based on the parsing results, including the following sub-steps: S221. The scanning component reads the basic QR code on the back of the daily board and extracts the daily board ID and the detection item code. S222. Query the preset rule base according to the detection item code to generate a detection sequence instruction; the detection sequence instruction includes the detection component type, detection order and parameter configuration; S223. The robot switches the detection components sequentially through the quick-switch interface according to the detection sequence instructions, and monitors the switching status of the components in real time. If the switching time is longer than the preset time, an alarm is triggered and a fault code is recorded.

9. The automated testing method for coating test panels according to claim 1, characterized in that, In step S3, a unique QR code is generated based on the detection result, and the unique QR code is printed onto the back of the test plate using a coding component. The test plates are then sorted into the corresponding unloading bins according to the detection results. This process includes the following sub-steps: S31. The industrial control computer receives the test result data, binds it with the test board ID and the test timestamp, and generates a unique QR code containing the following fields: test board material and size, test result value, BASF Digilab system traceability link, and encrypted verification code. S32. The robot transports the test plate to the coding station, and the UV coding machine, under the adjustment of the multi-degree-of-freedom bracket, prints a QR code on the back of the test plate at an incident angle of 30°-60°. S33. Based on the defect type in the test results, sort the test boards to the standard sample OK warehouse, standard sample NG warehouse, or daily board warehouse.

10. An automated testing device for coating test panels, used to implement the automated testing method for coating test panels according to any one of claims 1-9, characterized in that, include: The robot has a vacuum suction cup material handling component installed at its end via an electrically coupled quick-change interface; The feeding hopper includes a standard sample hopper and a daily sample hopper, and the bottom of the feeding hopper is equipped with a pneumatic lifting mechanism; The detection component library includes a film thickness gauge component, a gloss meter component, a colorimeter component, and a CCD component. The CCD component is equipped with a coaxial light source and a bright / dark field switching controller. The test bench is equipped with a servo-driven precision transplanting platform and an adjustable right-angle positioning edge. The scanning module is located on the entrance side of the test platform; The inkjet printing module is equipped with a multi-degree-of-freedom adjustable bracket, and the printhead faces the outlet side of the test bench. The material unloading hopper includes a standard sample OK hopper, a standard sample NG hopper, and a daily sample hopper; A controller, which connects to the robot, the detection component library, the barcode scanning module, and the inkjet coding module, and the controller has the following built-in modules: The rule parsing module is used to decode the basic QR code of the daily board and generate a detection sequence; A dynamic scheduling module is used to control the robot to switch detection components sequentially; The defect analysis module is used to perform stone chip peeling segmentation, adhesion profile comparison, and salt spray density statistics. The data encryption module is used to bind the test results and the test plate ID to generate a unique QR code.

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