Container coating thickness online detection system and method

Abstract

The invention discloses a container coating thickness online detection system and method, and the system comprises a non-contact thickness measurement unit which is used for transmitting modulation light to the surface of a container in real time according to the infrared light thermal modulation principle to excite a coating to generate a thermal wave signal, and determining the coating thickness through detecting the thermal wave reflection delay; the multi-mode executing mechanism is used for selecting a robot or a truss to carry the thickness measuring unit according to the specification of the container, and receiving a control instruction to drive the thickness measuring unit to move to a target detection point of the container according to a preset path; the three-dimensional positioning system is used for detecting the position deviation of the container entering the measuring station and feeding back the space coordinates of the container to the control system; and the control system is used for generating a motion path instruction according to the size of the container and a preset sampling rule, and receiving the coating thickness data of the thickness gauge in real time to generate a detection report, so that the precision, intelligence and high efficiency of container coating detection are realized.

Description

Technical Field

[0001] This invention relates to the field of automated inspection technology, and in particular to an online inspection system and method for container coating thickness. Background Art

[0002] As core equipment in logistics and transportation, shipping containers are exposed to harsh environments such as high humidity and salt spray corrosion in the ocean for extended periods. The corrosion resistance of their surface coating directly determines their service life. Coating thickness is a key quality indicator that must be strictly controlled to ensure uniform coverage and eliminate defects such as missed areas or excessive thickness. Traditional manual sampling methods for coating thickness inspection suffer from low efficiency, insufficient accuracy, and the inability to provide real-time feedback and adjustments, making it difficult to meet the high-efficiency, precise, and real-time quality control requirements of modern production lines. Summary of the Invention

[0003] The purpose of this invention is to provide an online detection system and method for container coating thickness, which solves the technical defects caused by manual inspection.

[0004] This invention provides an online coating thickness detection system for containers, comprising: The non-contact thickness measurement unit is used to emit modulated light to the surface of the container in real time to excite the coating to generate a thermal wave signal based on the principle of infrared photothermal modulation, and to determine the coating thickness by detecting the thermal wave reflection delay. A multimodal actuator is used to select a robot or truss to carry the thickness measuring unit according to the container specifications, and to receive control commands to drive the thickness measuring unit to move to the target inspection point of the container along a preset path; The three-dimensional positioning system includes an optoelectronic sensor array and a 3D vision system, which are used to detect the positional deviation of the container as it enters the measurement station and to feed back the spatial coordinates of the container to the control system. The control system is communicatively connected to the thickness measuring unit, the multimodal actuator, and the three-dimensional positioning system. It is used to generate motion path instructions based on the container size and preset sampling rules, and to receive coating thickness data from the thickness gauge in real time to generate an inspection report.

[0005] Preferably, the non-contact thickness measurement unit includes: A modulation light source unit is used to emit modulated light of a preset frequency to irradiate the surface of the container coating to excite heat waves to conduct to the substrate; An infrared detection unit is used to receive infrared thermal radiation signals reflected by the substrate and record the time delay from excitation to return of the thermal wave. The thickness analysis unit is used to calculate the coating thickness d based on the thermal wave signal delay time Δt and the preset thermal conduction model, satisfying the relationship: d=k * Δt+C, where k is the slope associated with the thermal conductivity of the container and C is the calibration constant; Thermal wave loss compensation unit: For high-thickness coatings, the thickness calculation results are corrected according to the attenuation amplitude of the thermal wave signal.

[0006] This invention also provides an online method for detecting the coating thickness of containers, applicable to the aforementioned online container coating thickness detection system, the method comprising: System initialization: Integrate the detection probe into the end of the actuator, start the control system, and calibrate the measurement accuracy of the detection probe; Container positioning: The container to be tested is sent to the measurement station by a conveyor to trigger a positioning signal to position the container, and the spatial coordinates of the container are obtained by 3D vision or scanning to adjust the posture of the detection probe. Path planning: Based on the container dimensions and preset sampling rules, measurement points are extracted, and the motion path of the actuator is dynamically generated; Dynamic detection: The actuator moves along the motion path to move the detection probe to the target detection point on the container, performs fixed-point detection based on infrared photothermal method, outputs a detection report and uploads it to the factory MES system simultaneously.

[0007] Preferably, the method further includes: acquiring real-time measurement data and comparing it with a preset thickness threshold, triggering data feedback and anomaly handling based on the comparison result, and further including: When the real-time measurement data exceeds the preset thickness threshold, the detection points that exceed the preset thickness threshold are marked. If N consecutive marker points are detected to exceed the preset thickness threshold, an alarm signal is triggered and the position coordinates of the consecutive out-of-tolerance detection points are recorded. The position coordinates are used for subsequent re-inspection or automatic re-testing.

[0008] Preferably, the step of sending the container to be tested to the measurement station via a conveying device to trigger a positioning signal to position the container, and adjusting the attitude of the detection probe by scanning to obtain the spatial coordinates of the container, includes: Coarse positioning is achieved by detecting the arrival signal of the container through an array of photoelectric sensors arranged along the conveyor track; After obtaining the spatial coordinates of the container side using a 3D vision system or laser scanning, the rotation angles of each axis on the detection probe are calculated using inverse kinematics based on the spatial coordinate information and a preset detection path planning algorithm. The posture of the detection probe is then precisely adjusted so that the detection probe and the container surface to be inspected maintain the target angle. The 3D vision system acquires point cloud data of the container side using structured light projection or binocular vision principles. After point cloud processing algorithms, feature points of the container are extracted, and the spatial coordinates of the container side are calculated. The laser scanning system emits laser beams using a rotating lidar, measures distances based on the time-of-flight method, constructs a three-dimensional contour of the container side, and obtains accurate spatial coordinate information.

[0009] Preferably, the step of extracting measurement points based on the container size and preset sampling rules includes: Obtain the container's length, width, height, relevant location information, and sampling parameters; Establish a three-dimensional coordinate system for the container with any corner as the origin, the length direction as the X-axis, the width direction as the Y-axis, and the height direction as the Z-axis, and mark the key areas. For the weld area, sampling points are generated at high density intervals along the weld direction, and additional sampling points are added at weld intersections; For edge areas, sampling points are generated at medium-density intervals along the edge line of the container, and additional sampling points are added at edge corners; For regular areas, generate uniform grid sampling points at low-density intervals in non-critical areas to ensure that the transition zone around the critical areas is covered. Sampling points are extracted from all areas of the container surface to generate measurement points, thereby ensuring that the measurement points fully cover the key areas on the sides of the container.

[0010] Preferably, the motion path of the dynamically generated actuator includes: The width of the container is divided into multiple sub-blocks, and measurement points are covered in each sub-block according to the reciprocating motion and fixed-point sampling is carried out. Based on the path optimization strategy, high-density measurement point areas are prioritized for coverage, and the global shortest path is calculated to balance the movement distance within and between blocks. Continuous movement in non-critical areas triggers stop measurement operations, while accelerating measurements in regular areas; The system uses a vision system or sensor feedback to confirm whether the measurement points are covered. If any omissions are detected, compensation measurement points are dynamically inserted, and the subsequent path is adjusted based on the actual measurement results.

[0011] The present invention also provides an electronic device, comprising: The memory is used to store the processing program; A processor, which executes the processing program to implement the online detection method for container coating thickness as described in the embodiments of the invention.

[0012] The present invention also provides a readable storage medium storing a processing program, which, when executed by a processor, implements the online detection method for container coating thickness as described in the embodiments of the invention.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The online coating thickness detection system and method for containers provided by this invention achieves precise, intelligent, and efficient container coating detection. While ensuring coating quality, improving production efficiency, and reducing operating costs, it provides data support for coating process optimization, demonstrating significant economic value and industry application potential. It directly calculates thickness through thermal wave reflection delay, achieving millisecond-level response to meet the high-speed detection requirements of production lines and improve detection efficiency. It automatically selects robots or gantry based on container dimensions, balancing flexibility and stability. The preset path generated by the control system covers key areas (such as welds and edges), and combined with a dynamic compensation mechanism, it avoids missed detections, ensuring comprehensive inspection. A photoelectric sensor array is also included. Rapidly triggers coarse positioning; the 3D vision system accurately acquires the spatial coordinates of the container with a positioning accuracy of ±1mm, correcting mechanical conveying deviations. Through inverse kinematics calculations, the angle of the detection probe is dynamically adjusted to ensure the probe is perpendicular to the surface being measured, avoiding thickness measurement errors caused by tilting. Thickness data is compared with preset thresholds in real time; continuous deviations trigger alarms and record coordinates, supporting automatic generation of re-inspection paths. The control system uniformly coordinates thickness measurement, positioning, and execution mechanisms, achieving full automation of the "detection-analysis-reporting" process and reducing manual intervention. Detection data is uploaded to the MES system in real time, automatically generating standardized reports containing thickness distribution, deviation statistics, and trend analysis, conforming to industrial quality inspection standards. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an online coating thickness detection system for containers according to an embodiment of the present invention; Figure 2 This is a schematic diagram showing the distribution of measurement points on the container in an embodiment of the present invention; Figure 3 This is a schematic diagram showing the measurement positions of the measurement points distributed on the corrugated plate in an embodiment of the present invention; Figure 4 This is a schematic diagram of the steps of an online detection method for container coating thickness in an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, not all embodiments. 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.

[0016] The term "comprising" and its variations as used herein are open-ended inclusion, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0017] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0018] It should be noted that the terms "a" and "a plurality of" used in this application disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0019] like Figure 1 As shown, this embodiment of the invention provides an online coating thickness detection system for containers, comprising: The non-contact thickness measurement unit is used to emit modulated light onto the container surface in real time based on the principle of infrared photothermal modulation to excite the coating to generate a thermal wave signal, and to determine the coating thickness by detecting the thermal wave reflection delay. This non-contact thickness measurement unit can be an online thickness gauge, employing non-contact measurement technology and the principle of infrared photothermal measurement, enabling high-precision, fast, and accurate measurement of coating thickness. The advanced non-contact measurement technology ensures a measurement accuracy of <2%. A multimodal actuator is used to select either a robot or a truss to carry the thickness measuring unit according to the container specifications. It receives control commands and moves the thickness measuring unit along a preset path to the target inspection point on the container. This replaces traditional manual contact measurement by using a non-contact thickness gauge in conjunction with a robotic arm and a truss. Depending on the container specifications and standard thickness inspection requirements, it can be used with either a robot or a truss to perform targeted sampling measurements on the sides and top of the container.

[0020] The three-dimensional positioning system includes an optoelectronic sensor array and a 3D vision system, which are used to detect the positional deviation of the container as it enters the measurement station and to feed back the spatial coordinates of the container to the control system. The control system, communicatively connected to the thickness measuring unit, multimodal actuator, and 3D positioning system, generates motion path instructions based on container dimensions and preset sampling rules, and receives coating thickness data from the thickness gauge in real time to generate an inspection report. Integrated into the production line control system, the control system communicates with PLCs, robots, and other controllers, receives measurement instructions, and provides feedback on measurement results. It supports industrial bus communication protocols such as Modbus TCP, facilitating integration with the upper-level MES system. A display and operation interface, equipped with an industrial-grade IPC controller and a capacitive touchscreen display, provides an intuitive operating interface and real-time data display.

[0021] The non-contact thickness measurement unit used in this embodiment is based on the principle of infrared photothermal modulation. Employing non-contact measurement technology, it can measure coating thickness with high precision and speed. It can emit modulated light to the container surface in real time to excite the coating to generate a thermal wave signal, and determine the coating thickness by detecting the thermal wave reflection delay. Compared with traditional manual contact measurement, both efficiency and accuracy are significantly improved. The multimodal actuator can select a robot or truss to mount the thickness measurement unit according to the container specifications. It can perform fixed-point sampling measurements on the sides and top of the container according to the standard thickness inspection requirements, adapting to the inspection needs of containers of different specifications. The photoelectric sensor array and 3D vision system in the three-dimensional positioning system can detect the positional deviation of the container entering the measurement station and feed back its spatial coordinates, providing positional assurance for accurate measurement. The control system is integrated into the production line control system, enabling communication with PLCs, robots, and other controllers. It receives measurement commands and feeds back measurement results, and also supports industrial bus communication protocols such as ModbusTCP, facilitating interface with the upper-level MES system and improving the automation and informatization level of the entire production line. Equipped with an industrial-grade IPC controller and a capacitive touch screen display, it provides an intuitive operating interface and real-time data display, facilitating operation and monitoring by operators. This technology utilizes LED-modulated light to excite thermal waves. When a detection probe illuminates a coating / deposit / film layer, the substrate emits infrared thermal radiation. The thicker the coating, the slower the thermal wave signal feedback and the greater the thermal wave loss. The system scans point-by-point or performs surface measurements to reflect the two-dimensional distribution of the coating thickness. Coating thickness, hardness, porosity, defects, etc., are directly and closely related to the thermal wave signal delay. This technology significantly improves the measurement efficiency and accuracy of container coating thickness, enabling real-time monitoring and adjustment, reducing defect rates, and improving overall production quality. It is unaffected by ambient temperature, workpiece color, workpiece temperature, vibration, humidity, dust, and other factors, maintaining stable measurement performance in various complex environments. It supports continuous real-time online measurement without interruption, suitable for the high-efficiency inspection needs of production lines. The system's ease of integration and high adaptability make it widely applicable. This system is highly adaptable: it supports coating measurement on various material surfaces, including metals, plastics, and rubber, with a wide range of applications; it is easy to integrate: both the system hardware and software are designed with production line compatibility in mind, facilitating integration with existing automated production lines.

[0022] In one embodiment, the non-contact thickness measurement unit includes: The modulation light source unit is used to emit modulated light of a preset frequency to irradiate the surface of the container coating to excite heat waves to conduct to the substrate. By controlling the frequency of the modulated light, heat waves with specific characteristics can be accurately excited, providing a stable and repeatable excitation signal for subsequent thickness measurement based on heat waves, ensuring the accuracy and consistency of the measurement.

[0023] The infrared detection unit is used to receive the infrared thermal radiation signal reflected by the substrate and record the time delay from the excitation to the return of the thermal wave. This unit can sensitively detect the infrared thermal radiation reflected back by the thermal wave and accurately record the time delay, providing key measurement data for subsequent calculation of coating thickness. Its accurate capture of thermal radiation signal is an important prerequisite for accurate measurement of coating thickness. The thickness analysis unit is used to calculate the coating thickness d based on the thermal wave signal delay time Δt and the preset heat conduction model, satisfying the relationship: d=k*Δt+C, where k is the slope associated with the thermal conductivity of the container and C is the calibration constant. This calculation method utilizes the established physical model and the measured time delay to quickly and efficiently calculate the coating thickness, realize the quantitative evaluation of the coating thickness, and provide a direct basis for quality control. Thermal wave loss compensation unit: For high-thickness coatings, the thickness calculation results are corrected based on the attenuation amplitude of the thermal wave signal. Since thermal waves will experience significant loss when propagating in high-thickness coatings, this unit corrects the thickness calculation results by analyzing the attenuation amplitude of the thermal wave signal. This allows the measurement system to still provide accurate measurement values ​​when dealing with high-thickness coatings, broadening the applicability of the measurement system and improving its versatility and reliability.

[0024] To achieve accurate analysis of multi-layer coating thickness and avoid misjudgments caused by signal penetration limitations of a single sensor, and to support online monitoring of the curing degree of complex processes such as two-component coatings, this system adds an ultrasonic probe to the non-contact thickness measurement unit. High frequencies are used for thin layers, low frequencies for thick layers, and X-ray fluorescence spectrometry is used to detect coating composition. The infrared detection unit, ultrasonic unit, and spectrometer work collaboratively, with data acquisition triggered synchronously. The layered coating analysis algorithm includes: 1) Signal feature extraction: Infrared signal: Extracting the thermal wave attenuation coefficient to reflect surface thickness; Ultrasonic signal: Analyzing the echo time difference to calculate the interface depth of each layer; Spectral signal: Identifying the coating material type, such as epoxy / polyester. 2) Fusion model: Establishing a physical model of the layered coating, inputting multi-sensor data, and fitting the thickness and material parameters of each layer using nonlinear least squares method. Example formula: d1=k1 * Δt 红外 +C1, d2=k2 * Δt 超声 -d1, where d1 is the surface layer thickness, d2 is the bottom layer thickness, and k1 and k2 are calibration coefficients. For multi-layer anti-corrosion coatings such as primer + intermediate coat + topcoat, the thickness of each layer is analyzed to determine whether it meets the process requirements; uneven coating mixing or incorrect materials are identified through component analysis.

[0025] like Figure 4 As shown, this embodiment of the invention provides an online detection method for container coating thickness, applicable to the aforementioned online container coating thickness detection system. The method includes: System Initialization: The detection probe is integrated into the end effector of the actuator. The control system is started and the measurement accuracy of the detection probe is calibrated, ensuring the accuracy of the detection data from the source and laying the foundation for reliable coating thickness detection in the future. Option 1: The detection probe is used with a robot. After the container reaches the designated position, sampling measurements are performed on the sides of the container. The two sides of the container are the largest coated surfaces, requiring a measurement station with a width of approximately 2.5 meters. Option 2: Used with a truss, sampling measurements are performed on the sides of the container, requiring a measurement station with a width of approximately 1.5 meters. The operating steps are as follows: 1. System Preparation and Startup: 1.1 Integrate the detection probe with the robot actuator, ensuring the probe is fixed to the robot end effector and the signal connection is completed. 1.2 Start the control system and coating thickness detector, calibrate the probe measurement accuracy, and confirm that the robot's motion trajectory error is ≤ ±0.5mm. 1.3 Based on the target container size and measurement requirements, preset the sampling rules for side sampling detection in the control system, such as random distribution or equidistant distribution.

[0026] Container Positioning: The container to be tested is transported to the measurement station via a conveyor to trigger a positioning signal and locate the container. The spatial coordinates of the container are obtained through 3D vision or scanning, and the attitude of the detection probe is adjusted accordingly. This process ensures the accuracy of the relative position and attitude between the detection probe and the container, enabling precise detection to correspond to the target position of the container, thereby improving the reliability and consistency of the detection results. 2. Container Positioning and Attitude Adjustment: 2.1 The container to be tested enters the measurement station via a conveyor, and a positioning signal is triggered by a photoelectric sensor to ensure the container stops at the designated position with a positioning accuracy ≤ ±2mm. 2.2 The robot, carrying the detection probe, moves to the starting point on the side of the container, and the spatial coordinates of the side of the container are confirmed through a 3D vision system or laser scanning.

[0027] Path planning: Measurement points are extracted based on the container dimensions and preset sampling rules, and the motion path of the actuator is dynamically generated. Targeted path planning avoids unnecessary movement, enabling the actuator to efficiently drive the detection probe to each target detection point, saving detection time, improving overall detection efficiency, and meeting the speed requirements of online detection; 3. Sampling measurement path planning and execution: 3.1 Based on preset sampling rules, the robot motion path is automatically generated in the control system to ensure that the measurement points cover key areas on the side of the container, such as welds and edges, in a two-dimensional motion path, both vertically and horizontally.

[0028] Dynamic Detection: The actuator moves along a motion path, driving the detection probe to the target detection point on the container. Fixed-point detection is performed using infrared photothermal methods, and a detection report is output and simultaneously uploaded to the factory's MES system. This allows the detection data to be integrated into the factory's production management process in a timely manner, facilitating real-time access to coating thickness information for management personnel, quality monitoring, and decision analysis, thus contributing to the informatization and intelligent management of the production process. 3.2 The robot moves along the planned path, and the probe uses non-contact methods such as ultrasonic / laser or contact methods such as electromagnetic induction to sequentially collect coating thickness data at selected detection points. 3.3 During the measurement process, the robot's lateral movement range is controlled within a 2.5-meter width to adapt to a compact measurement station layout. Furthermore, the measurement results are transmitted to the control system in real time, facilitating timely adjustment of process parameters and improving production efficiency. The film thickness data at the measured points is used to infer whether the spray gun application is qualified. If the film thickness is too thin, spray gun parameters such as air pressure and nozzle are adjusted immediately to increase the flow rate of the sprayed paint to meet the film thickness requirements.

[0029] In one embodiment, the online detection method for container coating thickness provided by this invention further includes: acquiring real-time measurement data and comparing it with a preset thickness threshold, triggering data feedback and performing anomaly handling based on the comparison result, and further including: When the real-time measurement data exceeds the preset thickness threshold, the detection points that exceed the preset thickness threshold are marked. If N consecutive marker points are detected to exceed the preset thickness threshold, an alarm signal is triggered and the position coordinates of the consecutive out-of-tolerance detection points are recorded. The position coordinates are used for subsequent re-inspection or automatic re-testing.

[0030] This embodiment employs the following data feedback and anomaly handling: real-time measurement data is transmitted to the control terminal, and the system automatically compares the measurement data with a preset thickness threshold and marks out-of-tolerance detection points; if a preset number of detection points exceed the tolerance, such as 3, an alarm signal is triggered, and the location coordinates of the out-of-tolerance points are recorded for manual re-inspection or automatic re-testing by a robot. In one embodiment, the online coating thickness detection method for containers provided by this invention further includes: Inspection Completion and Reset: After completing all sampling measurements, the robot is controlled to return to the initial standby position and the probe is released to a safe state; an inspection report is output, which includes a thickness distribution map, out-of-tolerance point statistics and measurement timestamps, and the inspection report is simultaneously uploaded to the factory MES system; System maintenance and footprint optimization: The measurement station is designed as a modular structure, with a 2.5-meter space reserved in the width direction. This space integrates the robot activity area, probe calibration platform and emergency passage; the accuracy of the robot guide rail and probe sensors is regularly calibrated to ensure the long-term stability of the system.

[0031] The specific measurement method is as follows: Measurement station setup steps: Set up a coating thickness measurement station at the completion station of the automated production line. When the container to be measured arrives at the completion station, position the container by photoelectric positioning switch according to the preset program. Wet film / dry film measurement selection steps: Depending on actual needs, choose to perform the test in the wet film state so as to adjust the spraying process in time; or choose to perform the test in the dry film measurement stage to monitor the total film thickness process qualification rate; the dry film thickness is set according to customer requirements, the wet film thickness is converted into dry film by the volume solids content, and the amount of paint applied is adjusted by controlling the air pressure and flow rate of the spraying. Fixed-point sampling test steps: uniformly select 10 or more measurement points on the surface of the container to be tested, and perform fixed-point measurements on the convex, concave and inclined surfaces of the container surface to ensure that the measurement results are comprehensive. For example, to effectively reflect coating thickness, representative locations are selected: the door end, front end, right side, left side, and top panel are chosen as measurement point locations. These locations can better reflect the overall coating condition of the container. (See [reference]). Figure 2 As shown.

[0032] Avoid corners principle: Avoid measuring points near corners, as the coating thickness at corners may differ from other areas due to the coating process. Avoiding corners will allow the measurement results to more accurately reflect the film thickness in normal areas.

[0033] Determining measurement points for corrugated sheets: For corrugated sheets, the center point of each of the three faces on the sheet is selected as the measurement location. This effectively covers the special structural area of ​​the corrugated sheet, ensuring comprehensive measurement. See [link / reference]. Figure 3 As shown.

[0034] Error control: A precise photoelectric positioning sensor switch is set up to control the positioning error of the workpiece within 1cm each time, thereby improving measurement accuracy.

[0035] In one embodiment, the step of sending the container to be tested to the measurement station via a conveying device to trigger a positioning signal to locate the container, and adjusting the attitude of the detection probe by scanning to obtain the spatial coordinates of the container, includes: Coarse positioning is achieved by detecting the arrival signal of the container through an array of photoelectric sensors arranged along the conveyor track; After obtaining the spatial coordinates of the container side using a 3D vision system or laser scanning, the rotation angles of each axis on the detection probe are calculated using inverse kinematics based on the spatial coordinate information and a preset detection path planning algorithm. The posture of the detection probe is then precisely adjusted so that the detection probe and the container surface to be inspected maintain the target angle. The 3D vision system acquires point cloud data of the container's side using structured light projection or binocular vision principles. After point cloud processing algorithms, feature points of the container are extracted, and the spatial coordinates of the container's side are calculated. The laser scanning system uses a rotating lidar to emit laser beams and measures distance based on the time-of-flight method to construct a three-dimensional contour of the container's side, obtaining precise spatial coordinate information. An array of photoelectric sensors along the conveyor track detects the container's arrival signal for coarse positioning, quickly sensing whether the container has reached the measurement station and providing a start signal for subsequent precise inspection, ensuring the timely initiation of the inspection process and improving inspection efficiency. The 3D vision system or laser scanning acquires the spatial coordinates of the container's side, accurately determining the container's position and shape information, laying the foundation for precise adjustment of the inspection probe's attitude, ensuring accurate correspondence between the inspection probe and the container's surface to be inspected, and improving measurement accuracy. Based on the acquired spatial coordinate information and a preset inspection path planning algorithm, the rotation angles of each axis connected to the inspection probe are calculated using inverse kinematics, allowing for precise adjustment of the inspection probe's attitude to maintain the target angle between the inspection probe and the container's surface to be inspected. This precise attitude adjustment ensures that the detection probe measures at the optimal position and angle, minimizing measurement errors and improving accuracy. The 3D vision system acquires point cloud data of the container's side through structured light projection or binocular vision principles, extracts feature points using point cloud processing algorithms, and calculates spatial coordinates. This method is highly adaptable to containers of different shapes and materials, especially suitable for containers with rich surface textures or complex shapes, effectively extracting their features and achieving accurate measurement. Laser scanning uses a rotating lidar to emit a laser beam, and constructs a three-dimensional contour based on the time-of-flight method to obtain precise spatial coordinate information. It features high precision and high resolution, providing reliable data support for container inspection tasks requiring high-precision measurements, and is particularly suitable for applications with stringent measurement accuracy requirements.

[0036] In one embodiment, the step of extracting measurement points based on the container's dimensions and preset sampling rules includes: Obtain the container's length, width, height, relevant location information, and sampling parameters; Establish a three-dimensional coordinate system for the container with any corner as the origin, the length direction as the X-axis, the width direction as the Y-axis, and the height direction as the Z-axis, and mark the key areas. For the weld area, sampling points are generated at high density intervals along the weld direction, and additional sampling points are added at weld intersections; For edge areas, sampling points are generated at medium-density intervals along the edge line of the container, and additional sampling points are added at edge corners; For regular areas, generate uniform grid sampling points at low-density intervals in non-critical areas to ensure that the transition zone around the critical areas is covered. Sampling points are extracted from all areas of the container surface to generate measurement points, ensuring comprehensive coverage of key areas on the container's sides. Different sampling densities and special treatments, such as adding sampling points at weld intersections and edge corners, are used to generate sampling points. The final extracted measurement points comprehensively cover key areas on the container's sides, ensuring thorough inspection of the container's surface condition and making the measurement results more representative, effectively reflecting the overall coating thickness of the container surface. High-density sampling is used along the weld direction in the weld area because the coating quality at welds can be affected by the welding process, easily leading to uneven thickness; high-density sampling allows for more detailed inspection of this area. Additional sampling points are added at weld intersections to further capture the coating condition at complex structures. Similarly, sampling is performed at medium-density intervals along the edge line in the edge area, with additional sampling points at corners. Considering that edge areas may experience greater stress during use and are more susceptible to coating damage, this sampling strategy helps to accurately detect the coating thickness of these critical areas, providing more precise data support for quality assessment. For regular areas, uniform grid sampling points are generated at low-density intervals in non-critical areas. This ensures that the transition zone around critical areas is covered while avoiding over-sampling in non-critical areas. While ensuring detection quality, it effectively controls the number of measurement points, improves detection efficiency, reduces detection costs, and achieves a balance between detection efficiency and economy. The entire measurement point extraction process is based on container dimensions, preset sampling rules, and a clear coordinate system and area division. It has a clear logic and standardized operating procedures, which is conducive to the standardization of the detection process. This allows different operators or different detection equipment to extract measurement points according to the same rules, ensuring the consistency and comparability of detection results, and facilitating subsequent quality control and data analysis.

[0037] In one embodiment, the motion path of the dynamically generated actuator includes: The container width is divided into multiple sub-blocks, and within each sub-block, measurement points are covered by a reciprocating motion and fixed-point sampling is performed. This partitioned reciprocating motion method allows the actuator to measure the container surface in an orderly manner, avoiding the time waste caused by disordered motion and effectively improving the measurement efficiency.

[0038] Based on the path optimization strategy, high-density measurement point areas are prioritized for coverage, and the global shortest path is calculated to balance the movement distance within and between blocks. Prioritizing high-density measurement point areas allows for focused and rapid completion of key area detection. Calculating the global shortest path reduces the idle travel of the actuator between different areas, further improving overall detection efficiency.

[0039] The system continuously moves through non-critical areas, triggering stop-and-measure operations, while accelerating measurements in regular areas. This strategy of using different movement and measurement speeds for different areas ensures comprehensive inspection while rationally allocating measurement time, helping to complete the measurement of the entire container surface in the shortest possible time.

[0040] The system uses a vision system or sensors to confirm whether measurement points are covered. If any omissions are detected, compensation measurement points are dynamically inserted, and the subsequent path is adjusted based on the actual measurement results. This mechanism ensures that all predetermined measurement points on the container surface are detected, avoiding incomplete data due to missed measurement points and thus guaranteeing the reliability of the inspection quality. Adjusting the subsequent path based on actual measurement results allows the inspection process to be flexible and adaptable to different situations. For example, if an abnormal coating thickness is found in a certain area, it may be necessary to add measurement points near that area. Dynamically adjusting the path can meet this need, further improving the adaptability of the inspection to complex situations and ensuring the accuracy of the inspection results.

[0041] Based on the same concept, an electronic device is also provided in some embodiments of this application. This electronic device includes a memory and a processor, wherein the memory stores a processing program, and the processor executes the processing program according to instructions. When the processor executes the processing program, the online container coating thickness detection method described in the foregoing embodiments is realized.

[0042] In some embodiments of this application, a readable storage medium is also provided, which can be a non-volatile readable storage medium or a volatile readable storage medium. This readable storage medium stores instructions that, when executed on a computer, cause an electronic device containing this readable storage medium to perform the aforementioned online container coating thickness detection method.

[0043] It is understood that, for the aforementioned online detection methods for container coating thickness, if all are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0044] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0045] The program code for executing the technical solutions disclosed in this application can be written in any combination of one or more programming languages. These programming languages ​​include object-oriented programming languages—such as Python and C++—and conventional procedural programming languages—such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. An online detection system for container coating thickness, characterized in that, include: The non-contact thickness measurement unit is used to emit modulated light to the surface of the container in real time to excite the coating to generate a thermal wave signal based on the principle of infrared photothermal modulation, and to determine the coating thickness by detecting the thermal wave reflection delay. A multimodal actuator is used to select a robot or truss to carry the thickness measuring unit according to the container specifications, and to receive control commands to drive the thickness measuring unit to move to the target inspection point of the container along a preset path; The three-dimensional positioning system includes an optoelectronic sensor array and a 3D vision system, which are used to detect the positional deviation of the container as it enters the measurement station and to feed back the spatial coordinates of the container to the control system. The control system is communicatively connected to the thickness measuring unit, the multimodal actuator, and the three-dimensional positioning system. It is used to generate motion path instructions based on the container size and preset sampling rules, and to receive coating thickness data from the thickness gauge in real time to generate an inspection report.

2. The online container coating thickness detection system as described in claim 1, characterized in that, The non-contact thickness measurement unit includes: A modulation light source unit is used to emit modulated light of a preset frequency to irradiate the surface of the container coating to excite heat waves to conduct to the substrate; An infrared detection unit is used to receive infrared thermal radiation signals reflected by the substrate and record the time delay from excitation to return of the thermal wave. The thickness analysis unit is used to calculate the coating thickness d based on the thermal wave signal delay time Δt and the preset heat conduction model, satisfying the relationship: d=k*Δt+C, where k is the slope related to the thermal conductivity of the container and C is the calibration constant; Thermal wave loss compensation unit: For high-thickness coatings, the thickness calculation results are corrected according to the attenuation amplitude of the thermal wave signal.

3. A method for online detection of container coating thickness, characterized in that, The method applicable to the online container coating thickness detection system as described in claim 1 or 2 includes: System initialization: Integrate the detection probe into the end of the actuator, start the control system, and calibrate the measurement accuracy of the detection probe; Container positioning: The container to be tested is sent to the measurement station by a conveyor to trigger a positioning signal to position the container, and the spatial coordinates of the container are obtained by 3D vision or scanning to adjust the posture of the detection probe. Path planning: Based on the container dimensions and preset sampling rules, measurement points are extracted, and the motion path of the actuator is dynamically generated; Dynamic detection: The actuator moves along the motion path to move the detection probe to the target detection point on the container, performs fixed-point detection based on infrared photothermal method, outputs a detection report and uploads it to the factory MES system simultaneously.

4. The online detection method for container coating thickness as described in claim 3, characterized in that, Also includes: The system acquires real-time measurement data and compares it with a preset thickness threshold. Based on the comparison result, it triggers data feedback and performs anomaly handling, further including: When the real-time measurement data exceeds the preset thickness threshold, the detection points that exceed the preset thickness threshold are marked. If N consecutive marker points are detected to exceed the preset thickness threshold, an alarm signal is triggered and the position coordinates of the consecutive out-of-tolerance detection points are recorded. The position coordinates are used for subsequent re-inspection or automatic re-testing.

5. The online detection method for container coating thickness as described in claim 3, characterized in that, The process of delivering the container to be tested to the measurement station via a conveying device to trigger a positioning signal for positioning the container, and adjusting the attitude of the detection probe by scanning to obtain the spatial coordinates of the container, includes: Coarse positioning is achieved by detecting the arrival signal of the container through an array of photoelectric sensors arranged along the conveyor track; After obtaining the spatial coordinates of the container side using a 3D vision system or laser scanning, the rotation angles of each axis on the detection probe are calculated using inverse kinematics based on the spatial coordinate information and a preset detection path planning algorithm. The posture of the detection probe is then precisely adjusted so that the detection probe and the container surface to be inspected maintain the target angle. The 3D vision system acquires point cloud data of the container side using structured light projection or binocular vision principles. After point cloud processing algorithms, feature points of the container are extracted, and the spatial coordinates of the container side are calculated. The laser scanning system emits laser beams using a rotating lidar, measures distances based on the time-of-flight method, constructs a three-dimensional contour of the container side, and obtains accurate spatial coordinate information.

6. The online detection method for container coating thickness as described in claim 3, characterized in that, The extraction of measurement points based on the container dimensions and preset sampling rules includes: Obtain the container's length, width, height, relevant location information, and sampling parameters; Establish a three-dimensional coordinate system for the container with any corner as the origin, the length direction as the X-axis, the width direction as the Y-axis, and the height direction as the Z-axis, and mark the key areas. For the weld area, sampling points are generated at high density intervals along the weld direction, and additional sampling points are added at weld intersections; For edge areas, sampling points are generated at medium-density intervals along the edge line of the container, and additional sampling points are added at edge corners; For regular areas, generate uniform grid sampling points at low-density intervals in non-critical areas to ensure that the transition zone around the critical areas is covered. Sampling points are extracted from all areas of the container surface to generate measurement points, thereby ensuring that the measurement points fully cover the key areas on the sides of the container.

7. The online detection method for container coating thickness as described in claim 3, characterized in that, The motion path of the dynamically generated actuator includes: The width of the container is divided into multiple sub-blocks, and measurement points are covered in each sub-block according to the reciprocating motion and fixed-point sampling is carried out. Based on the path optimization strategy, high-density measurement point areas are prioritized for coverage, and the global shortest path is calculated to balance the movement distance within and between blocks. Continuous movement in non-critical areas triggers stop measurement operations, while accelerating measurements in regular areas; The system uses a vision system or sensor feedback to confirm whether the measurement points are covered. If any omissions are detected, compensation measurement points are dynamically inserted, and the subsequent path is adjusted based on the actual measurement results.

8. An electronic device, characterized in that, include: The memory is used to store the processing program; A processor, which, when executing the processing program, implements the online detection method for container coating thickness as described in any one of claims 3-7.

9. A readable storage medium, characterized in that, The readable storage medium stores a processing program, which, when executed by a processor, implements the online detection method for container coating thickness as described in claims 3-7.

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