HPL full-automatic board loading and unloading production line with automatic control system

Abstract

The invention discloses an HPL full-automatic board loading and unloading production line with an automatic control system, and relates to the technical field of wood processing machinery, and the HPL full-automatic board loading and unloading production line comprises a board positioning device based on machine vision and used for identifying position information of boards with different thicknesses and materials; the adjusting mechanism is used for adjusting the grabbing force and position of the grabbing device according to the position information; the control unit is used for analyzing the plate type data and automatically setting operation parameters of the adjusting mechanism according to plate characteristics; and the automatic detection module detects the operation state in the production process and feeds back the operation state to the control unit for dynamically adjusting the control system, and the problem of adaptability of the automatic control system to plates with different thicknesses and materials on the HPL full-automatic plate loading and unloading production line can be solved.

Description

Technical Field

[0001] The invention relates to the technical field of wood processing machinery, in particular to an HPL full-automatic loading and unloading board production line with an automatic control system. Background Art

[0002] In the traditional HPL board production process, the loading and unloading operations of the boards usually rely on manual or semi-automatic equipment. These traditional methods often have problems such as low efficiency, inaccurate positioning, and easy damage to the boards. Manual operation is not only slow, but also limited by the skills and experience of the operator, making it difficult to ensure the consistency and stability of production. Although semi-automatic equipment can improve efficiency to a certain extent, it has poor adaptability when handling boards of different thicknesses and materials, and it is difficult to meet complex and changing production needs. Summary of the invention

[0003] To solve the above technical problems, the present invention provides an HPL fully automatic loading and unloading production line with an automatic control system, which can solve the problem of the adaptability of the automatic control system to panels of different thicknesses and materials on the HPL fully automatic loading and unloading production line.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: A fully automatic HPL board loading and unloading production line with an automatic control system, including a board positioning device based on machine vision for identifying the position information of boards of different thicknesses and materials; An adjustment mechanism, for adjusting the gripping force and position of the gripping device according to the position information; A control unit for analyzing the plate type data and automatically setting the operating parameters of the adjustment mechanism according to the plate characteristics; The automatic detection module detects the operating status during the production process and feeds back to the control unit for dynamic adjustment of the control system.

[0005] Preferably, the steps of the plate positioning device based on machine vision specifically include: Collect plate images; Using a preset algorithm to identify the edge of the plate in the image to obtain the plate outline; Analyze the length, width and position angle of the plate according to the outer contour; Determine the position information corresponding to the material and thickness of the plate; A control signal including position information is sent to the adjustment mechanism.

[0006] Preferably, the step of adjusting the gripping device by the adjustment mechanism specifically includes: Adjust the mobile platform to the correct grasping position according to the received position information; Analyze the sheet thickness to adjust the gripper pressure range setting; Adjust the size of the fixture opening to match the outermost size of the plate; Verify positioning accuracy and make fine adjustments until the preset position error range is reached; After positioning, the sheet is automatically picked up and moved to the subsequent process.

[0007] Preferably, the steps of the control unit analyzing and setting the adjustment mechanism specifically include: Separate and process the image to obtain the type and material feature information of the plate; Corresponding search for empirical model data input in advance or acquired through real-time learning; Identify the standard gripping force and gripping strategy configuration required for the material type; Automatically adjust parameters such as force level, movement speed, and acceleration and deceleration ratios; Updated operating commands are transmitted to the adjustment machinery to ensure precise movement.

[0008] Preferably, the steps of automatic detection module detection and feedback specifically include: Track and monitor the continuous movement trajectory and position of the plate; Continuously monitor system pressure changes and the working conditions of the gripping mechanism; Record the plate placement time series and mobile trolley positioning data; Determine whether the production line is synchronized and within the normal range; Based on the results, corresponding correction instructions are given to the control core.

[0009] Preferably, the plate positioning device based on machine vision specifically includes: The acquisition device captures an image of the target plate area; The image preprocessing algorithm performs brightness and contrast adjustments on the acquired images; Use edge detection technology to obtain the plate contour and extract position information; Combine image analysis technology to determine the thickness and material of the plate.

[0010] Preferably, when the machine vision-based plate positioning device acquisition device captures the image of the target plate area, it also includes: Set multiple regions of interest (ROIs) within the target plate area; Determine whether any target plate is located in each ROI: If a plate exists, plate positioning is performed; If it does not exist, an alarm signal is issued; The formula for setting the gripping force is based on the thickness of the plate: the gripping force is equal to the square of the smallest plate size divided by 35; The threshold value of the ROI is ensured by the plate edge detection to ensure that the plate completely covers the entire ROI.

[0011] Preferably, when setting the condition of the plate in the region of interest ROI by the plate positioning device based on machine vision, it also includes: Calculate the coverage ratio P (%) of the plate in the set region of interest ROI; If P is less than 70%, reposition and acquire the image again; Set the plate position offset D, and issue an adjustment warning when | D | is greater than 3 mm; Judgment basis: If the value of D falls outside the range of 5 to +5 mm, the adjustment mechanism needs to be recalibrated.

[0012] Preferably, after the process of calculating the coverage ratio of the plate in the set region of interest ROI by the plate positioning device based on machine vision includes: The detection coverage ratio P is higher than 85%; Execute the precise measurement procedure, otherwise repeat the positioning step, conditional formula: P>= 85%, perform further analysis; otherwise reposition; For each measurement result, check whether there are three consecutive cases where the coverage ratio is too low; If this exception occurs, the positioning mechanism is locked and the exception is reported. The condition is: the ratio P is lower than the threshold at least M times in N consecutive checks, and the device is locked, where M represents 3 and N represents 5, and the settings of N and M are determined by the control center.

[0013] Preferably, the operations involved in the locking and positioning mechanism process also include: Record the number of abnormal coverage ratio events X and update the record; Determine whether the maximum number of abnormalities L is exceeded. The formula condition is X>L. If the condition is met, the system will automatically shut down for inspection and maintenance. In each cycle, the frequency F of abnormal occurrence is counted and sent to the control system before being forwarded to the maintenance team; The system determines whether the work intensity needs to be further reduced based on the abnormal frequency. If F is greater than 25 times / minute, the control system slows down the production rate.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention adjusts the working pressure of the cylinder on the gripping claw by analyzing the thickness of the plate, ensuring that the plate can be firmly grasped without being deformed or damaged due to excessive pressure. If the pressure is too low, it is not enough to fix the plate, which may cause the plate to fall or shift during the grasping process; on the contrary, if it is set too high, it may damage the material. For example, when the system detects a steel plate with a thickness of 4mm, it will automatically adjust the pressure within an appropriate range. In addition, the opening of the clamps on both sides can be adjusted to suit plates of different sizes, so that sufficient coverage and support are guaranteed while being as close as possible to the edge of the plate without compressing it, ensuring that the grip is both stable and flexible.

[0015] 2. The present invention can determine the position information corresponding to the actual material and thickness of the plate. Here, it means that the actual measurement results are compared with the database or other pre-established standard data, and the best answer is found through matching to determine the specific position of the plate and its material and thickness parameter values. For example, different position coordinate adjustment schemes are formulated according to the different materials and the thickness of the HPL plate. After the positioning is completed, the relevant information will be integrated and compiled into a control signal to be transmitted to the robot or automated mechanical device responsible for moving the HPL plate in the production line. That is to say, the control center will finally obtain a set of complete data including position, size parameters, material, plate type information, etc., and after conversion and format adaptation, it will be transmitted as a command content to the corresponding mechanism actuator (such as a transport trolley) to adjust its motion amplitude and position, so that the latter can complete the predetermined task objectives more accurately and efficiently.

[0016] 3. The present invention calculates the coverage ratio P (%) of the plate in the set region of interest ROI; if P is less than 70% and the plate position offset | D | is greater than 3 mm, an adjustment warning is issued, repositioning and calibration are performed, and the frequency of abnormal occurrence F is counted in each cycle, and this frequency is first sent to the control system and then forwarded to the maintenance team; the system determines whether the work intensity needs to be further reduced based on the abnormal frequency. If F is greater than 25 times / minute, the control system slows down the production rate to ensure the correct placement of the plate, reduce the number of defective products, and avoid long-term shutdown and resetting through timely adjustment, thereby maintaining high-efficiency operation of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0018] Figure 1 It is a modular schematic diagram of a HPL fully automatic loading and unloading panel production line with an automatic control system of the present invention; Figure 2It is a flow chart of the plate positioning device and adjustment mechanism based on machine vision in the present invention; Figure 3 It is a schematic diagram of a plate positioning device based on machine vision and a process of setting multiple regions of interest ROI in the present invention; Figure 4 It is a schematic diagram of the process of setting the coverage ratio within the region of interest ROI in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-4 The key features of the HPL fully automatic loading and unloading board production line with an automatic control system of the present invention mainly include a board positioning device based on machine vision, an adjustment mechanism, a control unit and an automatic detection module.

[0021] First of all, the core of this automatic production line is a plate positioning device based on machine vision, which uses a series of image sensing and data processing technologies to identify different plates and determine their specific positions, thereby realizing automatic plate positioning. For example, in this positioning stage, when the production line starts to operate, after a batch of HPL plates are placed (such as HPL plates of different materials with different thickness specifications of 5mm, 10mm and 15mm), the machine vision positioning system will transmit the captured images to the control console and analyze and process them to identify the material, specifications, etc. of each plate, and mark and identify the position through a precise coordinate system. This function can help the production line quickly distinguish between different types of plates and determine the appropriate gripping position in subsequent gripping operations.

[0022] Secondly, after confirming the precise coordinates of the board, the control console will send instructions to the adjustment mechanism to adjust the force and position parameters of the gripping device so that it can be accurately grasped in place, complete a grasp and transport it to the set position. Taking two cases with different 10mm thick materials as an example, one material is ordinary cellulose board and the other is a harder high-density board (for example: a board with special treatment to increase rigidity), the control unit adjusts the control parameters according to the characteristics of the two types of board materials, such as hardness, toughness and other different physical properties, and sends instructions to the actuator accordingly, so that the robot arm grabs the board with appropriate force. For example, when grabbing hard high-density boards, a larger force is required to ensure stability and safety.

[0023] Then, in order to automatically set the operating parameters according to the actual situation, the control unit will use a variety of mathematical algorithms to evaluate and compare the various parameters of different material plates during the entire assembly process, and continuously adjust according to the actual test results to meet the needs of different materials. In this link, if different materials and thicknesses of plates need to be processed at the same time, the control unit can use the pre-established model to judge and match the best operation plan, and pass the instructions to the front-end workstations in real time for execution, so that the whole set of equipment can operate in a coordinated manner, improve the efficiency and accuracy of plate picking and placing operations, and ultimately achieve the goal of automatic production.

[0024] Finally, the production line must also have a certain self-calibration capability and emergency response strategy for abnormal situations during operation. Therefore, the system is equipped with a series of devices for monitoring and detecting the operation of the production line - automated inspection units, which continuously monitor and analyze various key performance indicators such as speed, vibration, energy consumption, etc. Once a problem or non-compliance occurs, it will be immediately reported to the superior department or the entire operation process will be stopped for further inspection and maintenance to ensure that all equipment is stable and within a controllable range.

[0025] In summary, the automatic control system on the HPL automatic production line uses a variety of strategies and methods to achieve a highly integrated board loading and unloading process for different thicknesses and types of wood, and can adaptively optimize according to actual conditions, effectively improving production capacity and reducing the probability of errors. In addition, the use of a large number of advanced sensors and intelligent computing technology also ensures that the entire system is more stable, safe and reliable. This solution not only significantly improves productivity, but also greatly reduces the risk of safety accidents caused by human factors.

[0026] Next, the specific steps of the plate positioning device based on machine vision are described.

[0027] First, the process of collecting plate images refers to the process of converting the information on the plate surface into image data by the camera or imaging device. For example, on a fully automatic unloading line, when a piece of HPL (high-pressure decorative panel) plate needs to be moved, a high-precision industrial camera will automatically take one or more pictures of the plate at a specific time.

[0028] Subsequently, a preset algorithm is applied to identify the edge of the plate in the image to obtain the plate outline. In specific usage scenarios, the detection of the "outline" can be achieved by using an image processing algorithm based on edge detection, such as Hough transform, Canny operator, Sobel edge detection algorithm and other technologies; for example, using image segmentation and contour tracking technology, the boundary information in the HPL plate image can be identified through software.

[0029] Based on the obtained shape contour, the length, width and position angle of the plate relative to the handling equipment and other geometric feature data will be further calculated. For example, if the above edge contour is a standard rectangle, the program will directly calculate the maximum distance between the four vertices as its longest side, and the second largest value is its widest side.

[0030] In order to execute the next step more accurately, the system also needs to determine the position information corresponding to the actual material and thickness of the board. This means using a database or other pre-established standard data to compare the actual measurement results, and find the best answer through matching, and then determine the specific position of the board and its material and thickness parameter values, for example, according to the different materials and thickness of the HPL board, different position coordinate adjustment plans are formulated.

[0031] Finally, after the positioning is completed, the relevant information will be integrated and compiled into control signals and transmitted to the robot or automated mechanical device responsible for moving the HPL board in the production line. In other words, the control center will finally obtain a set of complete data including position, size parameters, material, board type information, etc., and after conversion and format adaptation, it will be transmitted as instructions to the corresponding mechanism actuator (such as a transport trolley) to adjust its movement amplitude and position, so that the latter can complete the predetermined task objectives more accurately and efficiently.

[0032] Next, describe "the specific steps of the adjustment mechanism to adjust the gripping device".

[0033] First, after receiving the position data from the control system, the mobile platform is adjusted by controlling the electric drive device to ensure that it stops accurately at the appropriate position next to the plate so that the gripping device can accurately grasp the plate without deviation. For example, the control system sends information indicating that a plate placed at position 5 needs to be picked up and placed at the unloading port; the mobile platform that receives this information immediately moves along the set track to the corresponding starting position to prepare for gripping.

[0034] Analyzing the thickness of the plate to adjust the working pressure of the cylinder on the gripper is a key step, because if the pressure is too low, it is not enough to fix the plate, which may cause the plate to fall or shift during the gripping process; on the contrary, if it is set too high, it may damage the material. For example, when the system detects a steel plate as thick as 4mm, it will automatically adjust the pressure within an appropriate range to ensure that the plate can be firmly grasped without being deformed or damaged due to excessive pressure.

[0035] In addition, the opening of the clamps on both sides needs to be adjusted to suit plates of different sizes, so that sufficient coverage and support are guaranteed while being as close as possible without pressing the edge of the plate, ensuring stable and flexible gripping. For example, the length and width of the plate to be processed is 100cm×120cm. Before starting the gripping work, the adjustment mechanism will correspondingly expand the length of the clamp to adapt to this size to ensure that the plate is completely contained between the two clamps.

[0036] Furthermore, the visual sensor collects and processes the difference between the actual stop position of the mobile platform and the target point in real time and feeds it back to the central system, which is then combined with methods such as precise motor speed regulation to ensure the precise realization of the final specified placement position and orientation. For example, after the initial positioning is completed, the controller commands the execution unit to make further subtle movements based on the deviation value to make the loading point closer to the pre-given standard until it is within the allowable ±1mm tolerance range.

[0037] Finally, the servo motor is used to accurately and stably grasp the plate, and the mechanical structure guides the plate to move it down to the next working condition to wait for subsequent processing. For example, once it is confirmed that all conditions meet the requirements, the system can drive the equipment to perform the final automated loading and unloading process, that is, the grasped product is transferred and placed in the next operation area according to production needs.

[0038] Next, describe "the specific steps of the control unit analyzing and setting the adjustment mechanism."

[0039] First, the control system uses a high-definition camera to obtain images of the panels on the production line. This process involves computer vision technology, which parses the type and material properties of the panels from the image. For example, on the panel conveyor belt, the system captures an image of a long wooden panel. Then, by identifying the image, the control system determines that the panel is walnut and has unique texture characteristics.

[0040] Then, based on the identified information, the system searches the pre-entered database or the empirical rule base established through machine learning technology to find the best practice cases and parameter sets that match the material type of the board. For example, after confirming that the above board is walnut, the system calls the operational experience parameter table for walnut materials.

[0041] In this step, the control unit further determines the standard gripping force and strategy settings required for the board to avoid damage based on the material type and characteristics previously identified. Continuing with the above example, the system will find the most suitable pressure range required for the wood and the optimal movement path mode.

[0042] Then, based on the previous analysis results and the recommended settings in the best practice solution, the control unit automatically adjusts the details of the robot arm's movements, including the arm strength when grasping the object, the speed of the mechanical structure during the entire transportation process, and the acceleration or deceleration ratio of the movement. For the aforementioned wooden panel situation, the control logic may include setting a "light touch" force level, a "constant and smooth" movement mode, and a moderately slow acceleration process to ensure smooth and precise loading and unloading.

[0043] Finally, after completing a series of necessary calibrations, the updated instructions are converted into operation instructions that can be directly executed by the robot, and the communication module sends them to the corresponding drive device to ensure the accurate completion of the given task action. In other words, after completing the parameter setting and adjustment, the system sends this information to the specific execution component - the robot arm, so that it can accurately execute according to the given instructions, such as achieving safe and effective loading and unloading of the walnut board.

[0044] Next, the "specific steps of detection and feedback by the automatic detection module" are described.

[0045] The first step is to track and monitor the continuous movement trajectory and position information of the plate on the conveyor belt or production line. For example, during the production line operation, the route of each plate is tracked by a camera or optical sensor to ensure that the plate can move along the established route without deviation.

[0046] The second step is to continuously monitor the changes in the system's working parameters, especially the system's pressure and the working condition of the gripping mechanism. This step is usually achieved through sensor monitoring. For example, in the HPL production line, pressure sensors are used to check whether the pressure is within the normal range, and components such as travel switches are used to confirm that the clamp can accurately grasp the board without any risk of loosening.

[0047] The third step is to record the timestamp when the plates are put down and the time and spatial coordinates when the mobile cart carries them to a specific location. For example, every time the mobile cart transports a batch of plates to the point before the next process, the time, minute and second of this event is stored in the log file, which is convenient for future audit analysis and problem tracing, and also helps to make the timing of subsequent automated actions more precise.

[0048] The fourth step is to evaluate whether the entire manufacturing process is running synchronously and within the reasonable range of allowable deviations. In actual operation scenarios, the processing time difference or distance difference between different links will be compared to determine whether there are abnormal fluctuations in the production line to ensure that production quality and efficiency meet standards.

[0049] Finally, after obtaining all the necessary information, adjustment suggestions are made based on the above inspection results and update instructions are submitted to the equipment main controller to correct deviations or prevent potential problems from occurring in a timely manner, thereby ensuring an efficient and smooth production process.

[0050] These steps ensure that the automated production line can make real-time decisions based on actual monitoring data to optimize the entire processing process without human intervention.

[0051] Next, to describe the “specifics of the plate positioning device based on machine vision”, what needs to be understood first is how the positioning system works step by step to accurately determine the position and properties of the plate.

[0052] In the first stage, a high-precision camera or other image acquisition device is used to acquire an image of the target sheet placed on a conveyor line or workstation. This image capture action is very important because it is the cornerstone of subsequent analysis.

[0053] For example, in a practical application scenario, suppose we have an HPL (high-pressure laminate) board used to make high-end cabinets. When this board passes through the production line, a high-resolution CCD camera installed on its path will be triggered and capture a clear image of the board for further analysis.

[0054] After image acquisition comes the second step of image preprocessing. At this stage, software algorithms are used to improve the quality of the raw image for better processing. The preprocessing step usually involves adjusting the contrast and brightness of the image to make it more suitable for recognition by the operating machine and image processing software, ensuring that any slight changes in the surface of the board can be recognized.

[0055] In this example, the system adjusts the brightness of the HPL board image received from the camera in the first step to highlight the texture details of the material itself and reduce the shadow problem caused by ambient light. This helps to reduce false recognition caused by insufficient lighting in the next step of edge detection.

[0056] The next step is to use edge detection algorithms to identify edges and corners that are sensitive to changes in brightness in the image to determine the contour boundaries of the object. This contour information will then be further used to generate specific numerical data associated with the actual position. The key to this stage is to accurately capture every detail of the sheet metal contour.

[0057] For example, continuing with the scenario in the previous example, once the system has completed pre-processing tasks such as contrast and exposure correction, it will continue to use a software package designed specifically for finding edges to perform detection and extraction, thereby obtaining the exact coordinate location of the board boundary.

[0058] Finally, in the last process of the entire machine vision pipeline, all previously acquired data is combined for in-depth analysis and further determination of the target. This link may include many factors such as color differences or special material identification, etc., and make corresponding decisions to determine whether the current board meets the production line standards or requires special processing.

[0059] The above is an example of how to use a set of highly accurate visual sensors and related software technologies to identify and locate the key processes and implementation methods of the plate in the actual application of automated production. This integrated visual guidance solution greatly improves product quality while also saving a lot of cost investment and time consumption, and the benefits are very obvious.

[0060] Next, describe "when the machine vision-based plate positioning device acquisition device captures an image of the target plate area."

[0061] This process mainly includes several consecutive steps: 1. First, the machine vision equipment is set to monitor a specific target area - the so-called "target sheet area".

[0062] 2. After capturing the image of the area, the software automatically sets "multiple regions of interest" (ROIs) in the image to refine the detection range. Each ROI represents a part of the image, and there may be a single or multiple plates that need to be processed.

[0063] 3. Next, the system will determine whether any of the plates to be processed have entered any of the regions of interest (ROI).

[0064] - If a sheet does enter any of these areas, the system proceeds with precise sheet positioning.

[0065] - On the contrary, if the plate is not detected in the ROI, the system will trigger an alarm mechanism.

[0066] 4. The gripping action of the plate is determined by its thickness, according to a specially designed formula: "gripping force = square of the smallest plate size / 35". This ensures that no matter how thin or thick the plate is, the force can be applied accurately to achieve a stable and safe gripping operation.

[0067] 5. Before the actual grabbing is performed, a special edge recognition algorithm is used for further verification: the next step of the operation is only started after it is confirmed that the sheet has completely covered the corresponding area of ​​interest.

[0068] For example, consider a process where HPL panels are continuously supplied on a production line: suppose that at a certain moment, a machine vision-based camera captures an overall image containing the HPL panels to be processed; then the algorithm defines multiple ROIs to help with positioning. Assuming that one of the ROIs does contain the panel to be processed, the next step will involve accurately determining its position; and if no objects are detected in the other defined ROIs, an alarm signal will be issued to remind the operator to check whether there is a problem. In addition, for the monitored panels, it is necessary to adjust the appropriate clamping or suction gripping force, and control it according to the calculation formula proposed above (for example, if the minimum panel width is 8cm, the force should be set to 64 / 35 ≈ 1.8 approximately Newtons); at the same time, it is confirmed that it completely fills the corresponding area of ​​interest before the movement process can be officially started. In this way, the overall operation can be effectively managed and optimized, making the entire HPL loading and unloading panel assembly line operation more intelligent and efficient.

[0069] Next, the process of “setting the situation of the plate in the region of interest ROI by the plate positioning device based on machine vision” involves several important steps of accuracy and quality assurance.

[0070] The first is image capture and preprocessing: after the plate is placed, the machine vision-based system will take an image of the area where the plate is located; in order to eliminate interference information and reduce the source of error, the image needs to be preprocessed, such as brightness uniformity, denoising, and clarity enhancement. Take a 1024×1024 pixel photo as an example. During the shooting process, it may be blurred or uneven in brightness due to ambient light or other physical factors; the image contrast can be enhanced to a more appropriate level through image processing software, and denoising and other processing can be performed to improve the analysis accuracy; then, the region of interest (ROI), that is, the actual detection range, is selected from the image, for example, the 1024x200 pixel range above the plate is used as the ROI area for subsequent analysis.

[0071] Then enter the feature detection step, that is, to judge the existence status and relative position of the plate in the ROI, and then calculate the proportion of the plate in the area. If the ROI is 1024x200 pixels, assuming that the plate coverage rate is 66.7%, it means that the number of effective pixels in this area is 1024x200*66.7%=about 135647 pixels, and the total number of pixels corresponding to the entire area is 1024x200=204800 pixels; at this time, the plate coverage ratio is less than the standard value of 70%, so the control system instructs the adjustment component to move so that the plate is within the appropriate range, and triggers the re-shooting mechanism to confirm the change result.

[0072] Next comes the position deviation calculation and judgment processing. Based on the calibrated point cloud or template, the deviation value D between the center of the target object and the expected target point is calculated, and its absolute value is judged. If | D | = 3.2 mm is greater than the specified tolerance + 3mm or -3mm, an early warning is triggered, indicating that the mechanical system needs to be calibrated again to ensure processing accuracy. When using the above image as an example to illustrate the process, it was found that: the system concluded through algorithm processing that the center was offset to the left, deviating 3.96 mm to the left relative to the reference point; this offset value exceeded the set threshold of 3 mm, so an alarm was issued to prompt the staff to make corresponding modifications to the calibration equipment.

[0073] Next, the process details of "after the process of calculating the coverage ratio of the plate in the set region of interest ROI by the plate positioning device based on machine vision" are described. First, the detected data must be processed to confirm that the coverage ratio P is higher than 85%, that is, the system will evaluate whether the ratio of the plate covering the set area reaches a predetermined minimum ratio threshold. This process is usually determined by comparing the ratio of the actual detected plate to the ROI area; for example, in a given application, 85% is set as the standard value, and the system calculates that the current ratio is 92%, then the system will accept this value and proceed to the next step.

[0074] Once the coverage ratio exceeds the preset 85% threshold, more in-depth precision measurement and data analysis will begin. This step is to achieve high-precision measurement and confirm the accuracy of positioning through more refined edge and position corrections of the plate. Assuming that the 92% coverage ratio in the previous example is judged to be accurate by the system, further adjustments will be made to ensure its accuracy and enter the accuracy inspection step.

[0075] Next, a monitoring mechanism for continuous coverage ratio checks is required. During the positioning process, the current coverage ratio value will be recorded after each check, and the coverage ratio of the last five checks will be continuously observed to see if the coverage ratio has been below the threshold for three times. For example, in a certain operation case, the coverage ratios of the last five times are as follows: 1) 65%; 2) 88%; 3) 79%; 4) 57%; 5) 60%. In this case, the system will trigger the detection criteria of the abnormal mechanism and respond.

[0076] Once no less than M=3 of the N=5 consecutive measurements show that the coverage ratio is too low, the plate processing process will be stopped and an abnormality will be reported to prompt the staff to check. For example, in the above example, since the coverage ratio in two consecutive inspections was lower than the minimum limit of 85%, and the other inspection also failed to meet the standard, the total number accumulated exceeded 3 times, which means that the defined conditions in the above mechanism are met. At this time, in order to ensure that the subsequent process does not fail or interrupt the operation process due to product quality problems, the control system will issue an instruction to suspend the plate positioning and lock the equipment until manual intervention. This process helps to detect possible technical defects in a timely manner and avoid the production of unqualified products. These specific detection processes and mechanisms work together to ensure that the automated loading and unloading line can work accurately and stably while also detecting possible defects in a timely manner.

[0077] Next, the description of "the operations involved in the locking positioning mechanism process also includes: Record the number of abnormal coverage ratio events X and update the record; Determine whether the maximum number of abnormalities L is exceeded. The formula condition is X>L. If the condition is met, the system will automatically shut down for inspection and maintenance. In each cycle, the frequency F of abnormal occurrence is counted and sent to the control system before being forwarded to the maintenance team; The system determines whether the work intensity needs to be further reduced based on the abnormal frequency. If F is greater than 25 times / minute, the control system slows down the production rate. In the implementation of a locking positioning mechanism with an automatic control system on this HPL fully automatic loading and unloading panel production line, multiple steps are involved to ensure the working stability and quality of the production line.

[0078] The first step is to "record the number of abnormal coverage ratio events X and update the record", that is, the system detects any abnormal coverage ratio that occurs during the production line (for example, a certain plate is not installed correctly, resulting in insufficient coverage), and when this happens, it will increment the cumulative number of events X and save the value of this change.

[0079] For example, if there were 15 instances of panels not being fully seated in one hour, this would be counted as 15 incidents.

[0080] The second step is to "determine whether it exceeds the predetermined maximum allowable number of abnormalities L. If the condition X>L is met, the system will automatically shut down for maintenance", which means that once the number of abnormal events exceeds the prescribed maximum allowable number (assuming it is set to 20 times), the system will execute a shutdown action to prevent possible damage and require technical personnel to intervene for maintenance.

[0081] If in the previous example, the system has recorded more than 20 abnormal events within the specified time (23 times in this example), the control system will automatically shut down for safety inspection according to this logic.

[0082] This is followed by the third step, "calculating the proportion of total abnormal coverage within each fixed time interval (such as within an hour or the time range for the completion of loading of a batch of products) and generating the corresponding frequency count F". This step not only records the actual frequency of occurrence but also generates the frequency and sends these frequency values ​​to the control system. The control system then sends the abnormal frequency to the relevant maintenance department to ensure that they can receive the alarm and take action.

[0083] Assuming that 24 incorrect coverage events occur within a continuous hour, the system will send abnormal coverage occurrence data, such as an average of 0.6 anomalies per minute (24 problems / 40 minutes = 0.6 / min), to the relevant maintenance personnel.

[0084] Finally, the control system processes the reported data to determine the production line's work rhythm: "Based on the actual statistical abnormal frequency F, the rule in this step will instruct the control system's operating software to automatically adjust and reduce production efficiency if F is greater than 25 times per minute." Based on the previously assumed error coverage frequency of 0.6, since there are only 0.6 errors per minute, which is lower than the requirement of 25, the current efficiency level of the production line does not need to be changed, and production can continue as planned. However, if 30 errors occur per minute (such as due to other reasons), the control system software will adjust the parameters to reduce production to prevent the failure from happening again, and perform appropriate equipment tuning to solve this problem.

[0085] The entire mechanism is designed to ensure that when production efficiency is low or quality defects exist, problems are discovered and resolved in a timely manner through monitoring and self-protection. The integration of these processes enables efficient operation while reducing the probability of failure and loss.

[0086] The "HPL fully automatic loading and unloading production line with an automatic control system" of the present invention includes core parts such as a plate positioning device based on machine vision, an adjustment mechanism, a control unit and an automatic detection module. During the process of processing the plates, it can automatically control and process plates of different specifications and properties, solving the specific technical problems faced by the automation system when processing plates of various thicknesses and materials.

[0087] Specifically, the first step is to use a machine vision-based plate positioning device, which can capture the specific information of the plate to be processed, such as position and type, before starting any processing action. This step is key to ensure accurate processing and prevent damage caused by positioning errors.

[0088] After that, the adjustment mechanism receives information from the positioning device and dynamically controls the force and angle according to the actual thickness of the plate and the material characteristics it exhibits. This adaptability allows the equipment to be applied to a variety of plate types without manual intervention.

[0089] Then, through the intelligent processing of the control unit, the system can make the best operation decision based on the data analysis results of the specific plate type, automatically set the working parameters of the gripping mechanism such as suction cups or robotic arms, and further ensure the processing quality and efficiency.

[0090] Finally, in order to monitor the operation status of the entire production line in real time and make timely corrections when necessary, the present invention is equipped with an automatic detection module. This detection mechanism can not only monitor the overall status of the production line, but also transmit relevant data back to the main controller in a timely manner so that it can adjust the parameter settings of each control instruction according to the actual situation. This constitutes a closed-loop control system, which effectively improves the stability, adaptability and work efficiency of the overall system. Therefore, this set of automated control solutions has well solved the challenges of adaptability to plates of different thicknesses and materials.

[0091] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0092] The above description is only a preferred embodiment of the present invention and is not intended to be a further limitation of the present invention. All equivalent changes made using the contents of the present specification and drawings are within the protection scope of the present invention.

Claims

1. A fully automatic HPL board loading and unloading production line with an automatic control system, characterized in that: include: Plate positioning device based on machine vision, used to identify the position information of plates of different thicknesses and materials; An adjustment mechanism, for adjusting the gripping force and position of the gripping device according to the position information; A control unit for analyzing the plate type data and automatically setting the operating parameters of the adjustment mechanism according to the plate characteristics; The automatic detection module detects the operating status during the production process and feeds back to the control unit for dynamic adjustment of the control system.

2. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 1 is characterized in that: The steps of the plate positioning device based on machine vision specifically include: Collect plate images; Using a preset algorithm to identify the edge of the plate in the image to obtain the plate outline; Analyze the length, width and position angle of the plate according to the outer contour; Determine the position information corresponding to the material and thickness of the plate; A control signal including position information is sent to the adjustment mechanism.

3. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 1 is characterized in that: The steps of adjusting the gripping device by the adjusting mechanism specifically include: Adjust the mobile platform to the correct grasping position according to the received position information; Analyze the sheet thickness to adjust the gripper pressure range setting; Adjust the size of the fixture opening to match the outermost size of the plate; Verify positioning accuracy and make fine adjustments until the preset position error range is reached; After positioning, the sheet is automatically picked up and moved to the subsequent process.

4. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 1 is characterized in that: The steps of the control unit analyzing and setting the adjustment mechanism specifically include: Separate and process the image to obtain the type and material feature information of the plate; Corresponding search for empirical model data input in advance or acquired through real-time learning; Identify the standard gripping force and gripping strategy configuration required for the material type; Automatically adjust parameters such as force level, movement speed, and acceleration and deceleration ratios; Updated operating commands are transmitted to the adjustment machinery to ensure precise movement.

5. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 1 is characterized in that: The steps of automatic detection module detection and feedback specifically include: Track and monitor the continuous movement trajectory and position of the plate; Continuously monitor system pressure changes and the working conditions of the gripping mechanism; Record the plate placement time series and mobile trolley positioning data; Determine whether the production line is synchronized and within the normal range; Based on the results, corresponding correction instructions are given to the control core.

6. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 1 is characterized in that: The plate positioning device based on machine vision specifically includes: The acquisition device captures an image of the target plate area; The image preprocessing algorithm performs brightness and contrast adjustments on the acquired images; Use edge detection technology to obtain the plate contour and extract position information; Combine image analysis technology to determine the thickness and material of the plate.

7. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 6 is characterized in that: When the machine vision-based plate positioning device acquisition device captures an image of the target plate area, it also includes: Set multiple regions of interest (ROIs) within the target plate area; Determine whether any target plate is located in each ROI: If a plate exists, plate positioning is performed; If it does not exist, an alarm signal is issued; The formula for setting the gripping force is based on the thickness of the plate: the gripping force is equal to the square of the smallest plate size divided by 35; The threshold value of the ROI is ensured by the plate edge detection to ensure that the plate completely covers the entire ROI.

8. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 7 is characterized in that: When the plate positioning device based on machine vision is used to set the condition of the plate in the region of interest ROI, it also includes: Calculate the coverage ratio P (%) of the plate in the set region of interest ROI; If P is less than 70%, reposition and acquire the image again; Set the plate position offset D, and issue an adjustment warning when | D | is greater than 3 mm; Judgment basis: If the value of D falls outside the 5 mm range, the adjustment mechanism needs to be recalibrated.

9. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 8 is characterized in that: After the process of calculating the coverage ratio of the plate in the set region of interest ROI by the plate positioning device based on machine vision includes: The detection coverage ratio P is higher than 85%; Perform precise measurement procedure, otherwise repeat positioning step, conditional formula: P >= 85%, perform further analysis; otherwise reposition; For each measurement result, check whether there are three consecutive cases where the coverage ratio is too low; If this exception occurs, the positioning mechanism is locked and the exception is reported. The condition is: the ratio P is lower than the threshold at least M times in N consecutive checks, and the device is locked, where M represents 3 and N represents 5, and the settings of N and M are determined by the control center.

10. The HPL fully automatic loading and unloading production line with an automatic control system according to claim 9, characterized in that: The operations involved in the locking and positioning mechanism process include: Record the number of abnormal coverage ratio events X and update the record; Determine whether the maximum number of abnormalities L is exceeded. The formula condition is X > L. If the condition is met, the system will automatically shut down for inspection and maintenance. In each cycle, the frequency F of abnormal occurrence is counted and sent to the control system before being forwarded to the maintenance team; The system determines whether the work intensity needs to be further reduced based on the abnormal frequency. If F is greater than 25 times / minute, the control system slows down the production rate.

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