Electrolytic cell gasket repair device

Abstract

The utility model discloses a kind of electrolytic cell gasket repair system and method, it is related to the field of electrolytic equipment assembly automation.System includes multimode visual perception system, adaptive mechanical arm control module and closed-loop control system.Multimode visual perception system is composed of infrared thermal imager and 3D structured light camera, and three-dimensional deformation model containing residual material geometric parameter and polar frame temperature distribution is generated by point cloud data fusion algorithm;Adaptive mechanical arm carries tungsten steel force control spade, adopts layered progressive finishing strategy, based on real-time force feedback and angle calibration, dynamically adjusts operating parameter to adapt complex working condition;Closed-loop control system realizes whole real-time feedback and correction, residual material thickness standard (<0.1mm) automatic shutdown, abnormal emergency stop alarm.The present application solves the problems of low efficiency of traditional manual, insufficient adaptability of existing equipment, etc., the time consumption of single electrolytic cell skin is shortened to 5-10 minutes, the working accuracy and safety are guaranteed, and it is suitable for large-scale electrolytic cell polar frame gasket intelligent finishing scene.

Description

Technical Field

[0001] This utility model relates to the field of automation technology for electrolysis equipment assembly, and specifically to an electrolysis cell gasket repair device. Background Technology

[0002] As the core equipment in the electrolysis process, the sealing performance of the electrolytic cell directly affects production efficiency and product quality. During the initial installation of the electrolytic cell, the sealing gasket between the electrode frame and the end plate is prone to extrusion deformation due to uneven installation pressure and differences in the flatness of the electrode frame surface, forming excess residue (referred to in the industry as "gasket extrusion"). If not cleaned in time, this can lead to poor sealing of the electrolytic cell, causing safety hazards such as electrolyte leakage.

[0003] Traditional methods rely on manual cleaning of extruded gasket residue using hand-held scrapers. This method has the following significant drawbacks: low efficiency of manual operation, with each electrolytic cell taking about 30-60 minutes to scrape, making it difficult to meet the installation requirements of large-scale production lines; the scraping force and range are entirely controlled by the operator's experience, which can easily scratch the electrode frame sealing surface and damage the electrolytic cell's sealing performance; and the manual labor intensity is high, with the scraper operation posing safety hazards such as cuts.

[0004] To address the aforementioned issues, some automated scraping devices have emerged in the existing technology. However, most of them rely on a single vision sensor for residual material detection, which can only identify the location of the residual material and cannot accurately obtain key parameters such as residual material thickness and electrode frame surface temperature. Furthermore, the robotic arms use fixed motion programs, which cannot adapt to the randomness of the extruded gasket shape. The lack of a real-time feedback mechanism makes them unable to adapt to complex scenarios such as residual material of varying thicknesses and electrode frame corners, resulting in incomplete cleaning, poor equipment adaptability, and an inability to meet the production demands for high precision and versatility. Therefore, there is an urgent need for an intelligent scraping device that integrates multi-dimensional sensing, adaptive control, and real-time feedback to overcome the bottlenecks of existing technologies. Utility Model Content

[0005] The purpose of this invention is to overcome the defects in the existing technology and provide an electrolytic cell gasket repair device.

[0006] To achieve the above objectives, the technical solution of this utility model is as follows: An electrolytic cell gasket repair device, comprising: Multimodal visual perception system: It includes an infrared thermal imager and a 3D structured light camera, which are used to collaboratively acquire multi-dimensional data and temperature distribution of the extrusion part of the electrode frame gasket in the monitoring area of ​​the electrolytic cell; The adaptive robotic arm control module is set at a preset work position next to the electrolytic cell via an adjustable base. The working part of the adaptive robotic arm control module moves to the defect area in a controlled manner and performs repair. The working part includes a fine-tuning joint and a blade structure. The fine-tuning joint controls the bucket tilt angle, cutting edge tilt angle, and wedge angle of the blade structure.

[0007] Furthermore, the multimodal visual perception system is mounted around the electrolytic cell via a mounting bracket. The mounting bracket is vertically positioned on the outer periphery of the electrolytic cell, and the multimodal visual perception system is mounted on the mounting bracket, ensuring that the sensing range of the infrared thermal imager and the 3D structured light camera completely covers the entire edge of the polar frame.

[0008] Furthermore, the height of the multimodal vision perception system on the mounting frame is adjustable. A ball screw is rotatably installed inside the mounting frame, and a servo motor is installed at the bottom of the mounting frame to drive the ball screw to rotate. A slider is slidably installed along the height direction of the mounting frame, and the multimodal vision perception system is mounted on the slider. The slider is screwed to the ball screw, so that the multimodal vision perception system moves up and down under the drive of the servo motor and the ball screw.

[0009] Furthermore, the adaptive robotic arm control module includes a six-axis collaborative robotic arm, the electrolytic cell is set on the assembly platform, the assembly platform is provided with an outer ring track, the ring track is provided with a trimming station, and the trimming station adjusts its position around the assembly platform via the ring track.

[0010] Furthermore, the shovel structure includes a C-shaped bucket, which moves along one side of the cutting edge to approach the gap between the pole frames to adjust the protruding pads.

[0011] Furthermore, the bottom of the C-shaped bucket is equipped with a support wheel, so that the lowest point of the support wheel is slightly lower than the cutting edge of the bucket.

[0012] Furthermore, the fine-tuning joint includes a rotating seat, one side of which is connected to the blade structure via a universal joint and a telescopic adjustment arm; the rotating seat is a rotary joint, and is rotatably connected to the end of the mounting rod to control the rotation of the blade structure at the end of the mounting rod.

[0013] Furthermore, the universal joint includes a universal joint, one end of which is connected to the C-shaped bucket and the other end is connected to the rotating seat. Two sets of telescopic adjustment arms are also provided between the rotating seat and the C-shaped bucket, and the two sets of telescopic adjustment arms are arranged in a figure-eight shape. Universal joints are provided at both ends of the telescopic adjustment arms, and the two ends are connected to the C-shaped bucket and the rotating seat respectively through the universal joints.

[0014] Furthermore, the blade structure has a built-in pressure sensor and an angle adjustment module. The pressure sensor detects the pressure generated when the blade makes contact during cutting, and the angle adjustment module is used to detect and control the angle formed between the blade surface and the base surface.

[0015] Furthermore, the adaptive robotic arm control module adjusts the downward pressure of the shovel in real time based on feedback from the pressure sensor. The threshold for the downward pressure is set to 5-20N to avoid damaging the electrode frame coating.

[0016] Furthermore, the adaptive robotic arm control module incorporates adaptive control logic to receive the three-dimensional deformation model transmitted by the vision perception system. Based on the data fed back by the vision system, it dynamically plans the scraper path and dynamically adjusts the robotic arm's motion trajectory, scraping force, and blade angle; adapting to different residual material thicknesses and the surface condition of the frame; when the residual material thickness is >2mm, the scraping force increases by 30%~50% compared to when the residual material thickness is ≤2mm; when cleaning the corner area of ​​the frame, the blade angle is adjusted to 30°~60°.

[0017] Furthermore, the force-controlled shovel at the end of the robotic arm is made of tungsten steel, and the cutting edge is designed with a rounded transition structure to avoid scratching the sealing surface of the electrolytic cell electrode frame during the cleaning process.

[0018] Furthermore, the working part of the adaptive robotic arm control module adopts a progressive trimming algorithm with a layered cutting depth of 0.2mm, and automatically raises the blade to avoid obstacles when it encounters a threshold exceeding the limit.

[0019] A method for intelligently repairing electrolytic cell electrode frame gaskets, using the aforementioned electrolytic cell gasket repair system, includes the following steps: Step S1: Data acquisition and modeling: After the electrolytic cell electrode frame is closed and pressurized, the gasket extrusion area is scanned by a multimodal visual perception system to generate a three-dimensional deformation model and mark the coordinates and risk level of the extrusion area. Step S2: Path planning and parameter adaptation: The control module in the robotic arm converts the three-dimensional deformation model data into the robotic arm's motion trajectory, uses a layered trimming strategy to plan the path, and sets the initial downward force. Step S3: Dynamic trimming execution: The robotic arm performs trimming, cutting layer by layer according to the layer strategy. During the process, it receives feedback data from the force sensor in real time, dynamically adjusts the downward pressure and movement speed of the blade, and triggers an adaptive obstacle avoidance mechanism when encountering abnormalities. Step S4: Quality Closed Loop: After the finishing is completed, a re-inspection is carried out through a visual perception system. If the residual height of the gasket meets the standard, it will proceed to the next process. If it does not meet the standard, it will be marked for rework.

[0020] Furthermore, in step S3, when the contact force fed back by the force sensor exceeds the set threshold, the robotic arm is controlled to automatically raise the blade by 0.5mm and decelerate by 30%, and resume trimming after completing local obstacle avoidance. In step S4, the preset threshold for the residual height of the gasket is 0.1 mm. If the re-inspection fails, a rework mark is generated in the system and the operator is notified.

[0021] The advantages and beneficial effects of this utility model are as follows: First, the multimodal visual perception system enables precise multi-dimensional detection of residual materials, integrating visible light, infrared, and 3D contour data to avoid blind spots and errors in single perception, providing data support for precise peeling.

[0022] Secondly, the adaptive robotic arm control logic can dynamically adapt to complex working conditions such as residual material thickness and corners of the frame, improving the equipment's versatility for different installation scenarios and solving the problem of incomplete cleaning by existing automated equipment.

[0023] Third, the closed-loop control mechanism enables real-time feedback and dynamic correction of the scraping process, ensuring the accuracy of residual material cleaning (residual material thickness < 0.1 mm), avoiding damage to the electrode frame sealing surface, and ensuring the sealing performance of the electrolytic cell.

[0024] Fourth, the fully automated operation significantly improves the efficiency of peeling (the peeling time for a single electrolytic cell is reduced to 5-10 minutes), reduces the risk of personnel working in the narrow space of the electrolytic cell, lowers labor costs and safety hazards, and is suitable for large-scale electrolytic cell installation scenarios. Attached Figure Description

[0025] Figure 1 This is one of the control logic diagrams of an electrolytic cell gasket repair method according to this utility model; Figure 2 This is the second control logic diagram of the electrolytic cell gasket repair method of this utility model; Figure 3 This is a schematic diagram of the structure of an electrolytic cell gasket repair device according to the present invention; Figure 4 This is a schematic diagram of the working part at the end of the robotic arm in this utility model; Figure 5 This is a schematic diagram of the bucket tilt angle formed by the blade and the pole frame in this utility model; Figure 6 This is a schematic diagram of the blade inclination angle formed by the shovel and the pole frame in this utility model; Figure 7 This is a schematic diagram of the wedge angle formed by the shovel and the pole frame in this utility model; In the diagram: 1. Multimodal vision perception system; 2. Adaptive robotic arm control module; 3. Electrolytic cell; 4. Polar frame; 5. Mounting frame; 6. Circular track; 7. Dressing station; 8. Six-axis collaborative robotic arm; 9. Working part; 10. Mounting rod; 11. Fine-tuning joint; 12. Shovel structure; 13. Bucket tilt angle; 14. Cutting edge tilt angle; 15. Wedge angle; 16. C-shaped bucket; 17. Support wheel; 18. Rotary seat; 19. Universal joint; 20. Telescopic adjustment arm; 21. Universal joint; 22. Blade face; 23. Base surface. Detailed Implementation

[0026] The specific embodiments of this utility model will be further described below with reference to examples. These examples are only used to more clearly illustrate the technical solution of this utility model and should not be construed as limiting the scope of protection of this utility model.

[0027] Example 1: An electrolytic cell gasket repair device, such as Figure 1 , 2 As shown, it includes: Multimodal visual perception system: This system constructs a 3D deformation model of the gasket in the target monitoring area in real time and accurately locates the defect area. It includes an infrared thermal imager and a 3D structured light camera, which work together to collect multi-dimensional data and temperature distribution of the extruded part of the gasket in the electrode frame 4 of the electrolytic cell 3 monitoring area, and generate a 3D deformation model. This visual perception system integrates the 3D structured light camera and the infrared thermal imager to construct a "morphology-temperature" dual-dimensional perception system. It is fixed to the four corners and the middle of the electrode frame 4 of the electrolytic cell through an integrated bracket to ensure that the sensing range covers the entire edge of the electrode frame 4. The 3D camera captures the gasket extrusion contour to measure the thickness of the residual material; the infrared thermal imager identifies the friction high-temperature area and collects the temperature distribution data of the electrode frame 4 to avoid damage to the electrode frame 4 under high-temperature conditions. The multi-dimensional data is then used to generate a 3D deformation model containing the extrusion height, length, and width through a point cloud data fusion algorithm, achieving millimeter-level positioning of the defect area. The adaptive robotic arm control module 2 is set at a preset work position next to the electrolytic cell 3 via an adjustable base. The working part 9 of the adaptive robotic arm control module 2 is controlled to move to the defect area and perform repair. Its control terminal is connected to the control module signal. A layered progressive trimming strategy is used to remove extruded material layer by layer at a cutting depth of 0.2mm / layer, avoiding stress concentration in a single cut. Built-in adaptive control logic dynamically plans the scraper path based on visual feedback, adjusting the robotic arm's motion trajectory, scraping force, and tool angle. Dynamic force feedback adjusts the downward pressure, and contact force is monitored in real time. An angle encoder monitors and calibrates the tool angle in real time. It adapts to different thicknesses of residual material and the surface condition of the pole frame 4; when the limit is exceeded, it automatically raises the tool by 0.5mm and reduces speed by 30%, ensuring zero damage to the pole frame 4 coating.

[0028] A closed-loop control system is connected to both the multimodal vision perception system 1 and the adaptive robotic arm control module 2. Through closed-loop visual re-inspection and intelligent anomaly handling, the residual height of the gasket is ensured to meet the standard. A built-in closed-loop feedback program is connected to both the multimodal vision perception system 1 and the adaptive robotic arm control module 2 via an industrial Ethernet network. It receives residual material cleaning data from the perception system in real time, compares it with a preset standard (residual material thickness < 0.1mm) and dynamically corrects the data. When the residual height is less than a preset threshold, a stop signal is sent to the robotic arm. Furthermore, an emergency stop is initiated and an alarm is issued when abnormalities such as gasket tearing or tool jamming are detected. A quality report is generated synchronously, enabling full-process traceability.

[0029] The closed-loop control process of the closed-loop control system includes: S1: The multimodal visual perception system 1 collects and transmits multidimensional data of the gasket extrusion area to the closed-loop control system in real time; S2: The closed-loop control system compares the multi-dimensional data with the preset standard. When the residual material thickness is detected to be ≥0.1mm, a start command is generated and sent to the adaptive robotic arm control module 2. S3: The adaptive robotic arm control module 2 performs the skin-scraping action, while the multimodal vision perception system 1 continuously feeds back multi-dimensional data after cleaning; S4: The closed-loop control system compares the feedback data with the preset standard in real time. When the residual material thickness is detected to be <0.1mm, a stop command is generated and the adaptive robotic arm control module 2 stops working.

[0030] In this embodiment, the point cloud data fusion algorithm built into the multimodal visual perception system 1 is based on weighted fusion and feature alignment technology of "shape-temperature" dual-dimensional data. Its core objective is to accurately fuse the three-dimensional contour point cloud data acquired by the 3D structured light camera with the temperature field data acquired by the infrared thermal imager, generating a three-dimensional deformation model that combines geometric morphological details and temperature attributes. This provides unified and high-precision data support for the adaptive operation of the robotic arm. Its specific implementation logic, steps, and key technologies are as follows: 1. Core Principles of the Algorithm By performing "spatial registration - feature extraction - weighted fusion - error correction" on two types of heterogeneous data (geometric point cloud data + temperature field data), the following problem of insufficient single data dimension is solved: The 3D structured light camera provides millimeter-level geometric accuracy (residual material thickness, length, width, and edge contour coordinates of pole frame 4), but it cannot reflect the effect of temperature on the material of pole frame 4. Infrared thermal imagers provide temperature distribution information (surface temperature of pole frame 4, high-temperature friction area), but lack spatial geometric details; This algorithm assigns temperature attributes to the geometric point cloud by aligning spatial coordinates, forming a four-dimensional data structure of "each spatial point = (X,Y,Z coordinates + temperature value T)", thus achieving synchronous perception of "shape + temperature".

[0031] 2. Specific Execution Steps of the Algorithm (1) Data preprocessing and spatial registration Data noise reduction: The point cloud data from the 3D structured light camera is processed using "statistical filtering + radius filtering": isolated noise points (such as dust interference in the air) that are more than 3 times the average distance from the adjacent points are removed, while the effective point cloud of the residual material and the surface of the pole frame 4 is retained. The temperature data from the infrared thermal imager is filtered using a "median filter": this eliminates environmental thermal noise (such as high-temperature radiation from the workshop background), smooths the temperature field distribution, and preserves the true temperature gradient on the surface of the pole frame 4.

[0032] Spatial registration: Using the physical corner of the electrolytic cell's pole frame 4 as a reference calibration object (preset coordinate origin), the 3D point cloud is aligned with the pixel coordinates of the infrared thermal image through the "ICP (Iterative Closest Point) algorithm": The 3D point cloud provides the three-dimensional coordinates of the edges of the polar frame 4 (e.g., corner point A: X=500mm, Y=100mm, Z=20mm). The pixel coordinates of the infrared thermal imager are converted into world coordinates through camera intrinsic parameters (focal length, principal point coordinates), which are consistent with the coordinate system of the 3D point cloud; Iterative optimization of alignment error (registration error ≤ 0.05mm) ensures accurate matching of geometric points and temperature values ​​at the same physical location.

[0033] (2) Feature extraction and weight allocation Geometric feature extraction: Extract key geometric parameters of the residual material from the 3D point cloud: use the “region growing algorithm” to segment the residual material and the body of the polar frame 4, and calculate the maximum thickness, average thickness, extrusion length, width and contour curvature of the residual material (such as the radius of curvature of the residual material in the corner area of ​​the polar frame 4). Mark different areas of the polar frame 4, such as the sealing surface, corners, and planes (determined by the angle between the point cloud normal vectors: the angle between the normal vectors of the corner area is >90°, and the angle between the normal vectors of the plane area is ≤30°).

[0034] Temperature feature extraction: Extract temperature parameters from the surface of pole frame 4 from the aligned temperature field data: calculate the average temperature and temperature difference between the residual material area and the body of pole frame 4, and mark the high temperature area (≥60℃ is the risk threshold). Establish the relationship between temperature and material condition: In the high temperature range (60-100℃), the material of the pole frame 4 is prone to softening, and the downward pressure of the scraper needs to be reduced; in the normal temperature range (<60℃), the operation can be carried out according to the normal parameters.

[0035] Dynamic weight allocation: An "adaptive weighting function" is used to assign fusion weights to the geometric and temperature data: Geometric weight Wg: The greater the thickness of the scrap material, the higher Wg (e.g., when the scrap material thickness is >2mm, Wg=0.7; when the scrap material thickness is ≤0.5mm, Wg=0.5), prioritizing the scraping accuracy; Temperature weight Wt: The higher the temperature, the higher Wt (e.g., Wt=0.6 when the temperature is ≥60℃; Wt=0.3 when the temperature is <40℃), to prioritize avoiding high temperature damage to the pole frame 4; Constraint: Wg + Wt = 1, to ensure the consistency of the fused data.

[0036] (3) Data fusion and model generation The preprocessed 3D point cloud and temperature data are fused according to weights to generate a 3D deformation model containing the following information: Spatial coordinate range of the residual material (X, Y, Z three-dimensional boundary); Thickness value of residual material at any location (accuracy ±0.01mm); Temperature distribution of residual material and the surface of pole frame 4 (accuracy ±1℃); The frame has 4 areas: area type (flat / corner) and material condition (normal temperature / high temperature softening).

[0037] Model output format: PCD (Point Cloud Data) format is adopted, which supports direct parsing by the robotic arm control system and provides data input for path planning.

[0038] (4) Error correction and dynamic update Real-time calculation of fusion error: By comparing the deviation between the standard model of pole frame 4 (preset three-dimensional data of pole frame 4 without residual material) and the current fusion model, the residual material thickness measurement value is corrected (correction error ≤ 0.03mm). Dynamic update mechanism: New sensor data is received every 0.1 seconds, and the above steps are repeated to update the three-dimensional deformation model to adapt to the morphological changes during the residual material cleaning process (such as the decrease in residual material thickness after layered peeling).

[0039] 3. Algorithm Advantages and Application Value Overcome the limitations of a single sensor: avoid misjudgment in high-temperature conditions caused by using only a 3D camera, or inaccurate geometric positioning caused by using only an infrared instrument; Millimeter-level fusion accuracy: Ensures that the matching error between the residual material position, thickness and temperature data is ≤0.05mm, meeting the requirements of high-precision scraping; Real-time adaptation: The algorithm takes ≤50ms per run, supporting the real-time data requirements of the robotic arm's dynamic operations.

[0040] A method for intelligent trimming of the 4 gaskets of an electrolytic cell electrode frame: (a) Equipment composition and installation, such as Figure 3 As shown 1. Installation of Multimodal Visual Perception System 1: Integrate the infrared thermal imager and 3D structured light camera into the same bracket, fix them at the four corners and the middle of the electrolytic cell frame 4, adjust the camera angle to ensure that the sensing range completely covers the entire edge of the frame 4; connect the data output terminal to the control system via industrial Ethernet.

[0041] 2. Adaptive robotic arm installation: Fix the robotic arm to the preset position next to the electrolytic cell 3 via the adjustable base, adjust the range of motion of the robotic arm to ensure that it can cover all edge areas of the pole frame 4; mount a peeling knife at the end of the robotic arm, and install a pressure sensor and an angle adjustment module, and connect its control end to the internal control module signal.

[0042] 3. Control system debugging: Pre-store parameters such as pole frame 4 size, gasket material, and residual material cleaning standard (residual material thickness < 0.1mm) in the control system, and debug the multimodal data fusion algorithm, adaptive control logic and closed-loop feedback program to ensure stable signal transmission and accurate command execution of each module.

[0043] (ii) Work process, such as Figure 1 , 2 As shown, Figure 2 The "Generate Startup Command + Initial Job Parameters" at the top is Figure 1 The continuation of the corresponding position in the text.

[0044] 1. Sensing Start-up: After the new gasket is installed in the electrolytic cell 3, the multimodal visual perception system 1 is started to simultaneously collect infrared temperature data and 3D contour data of the edge of the electrode frame 4, identify the location, thickness and temperature of the residual material and the electrode frame 4, fuse them to generate a three-dimensional deformation model and locate the coordinates of the trimming area.

[0045] 2. Adaptive decision-making: Analyze the data fed back by the visual perception system. If the temperature of the pole frame 4 is within the normal range (<60℃) and the residual material thickness is ≥0.1mm, the optimal motion trajectory, scraping force and blade angle of the robotic arm are calculated through adaptive control logic, and control commands are generated and sent to the robotic arm.

[0046] 3. Closed-loop execution: After receiving the instruction, the robotic arm drives the peeling blade to start peeling according to the preset trajectory and parameters; at the same time, the multimodal vision perception system 1 collects the images and data after cleaning in real time and feeds them back to the closed-loop control system.

[0047] 4. Stop Judgment: The closed-loop control system compares the feedback data with the preset standard. When the residual material thickness is detected to be <0.1mm (i.e., the cleaning standard is met), a stop command is generated, the robotic arm stops working, and the single peeling process is completed.

[0048] (III) Environmental Adaptation and Safety Design 1. Adaptable to high-temperature environments Both the multimodal vision perception module and the end effector of the robotic arm are equipped with water-cooled heat dissipation devices (cooling water flow rate 5L / min, inlet water temperature ≤25℃), and the outer shell is made of high-temperature resistant stainless steel (temperature resistance ≤300℃). The cable is made of high-temperature resistant silicone rubber (temperature resistance ≤180℃) and is protected by cable chain wiring to avoid high-temperature aging.

[0049] 2. Security Protection Mechanism Safety light curtains are installed around the equipment, which will immediately trigger an emergency stop of the robotic arm when personnel enter the work area; The peeling actuator is equipped with overload protection. When the torque sensor detects a resistance greater than 50 N·m, the robotic arm automatically retracts and triggers an alarm. It is equipped with an emergency button and a remote control terminal to support manual intervention for emergency shutdown.

[0050] (iv) Fault Handling Mechanism 1. Sensing module failure: If a single sensor fails (such as an industrial camera disconnection), the system will automatically switch to the remaining sensor data for reduced accuracy operation and send a fault alarm signal; if all sensors fail, the robotic arm will stop immediately.

[0051] 2. Robotic arm malfunction: When the servo motor is overloaded or the joint is stuck, the system records the fault code and controls the robotic arm to return to a safe position, waiting for manual inspection.

[0052] 3. Communication failure: If the communication between the sensing module and the controller is interrupted, the controller will save the current operation data. If communication is not restored within 30 seconds, the system will shut down and alarm.

[0053] Example 2: During assembly of the electrolytic cell 3, the electrode frame 4, gaskets, and other components are stacked and assembled sequentially, such as... Figure 3-7As shown, in actual use, the above assembly process is carried out on the assembly platform. The electrolytic cell 3 gasket repair system has multiple upright mounting brackets 5 around the assembly platform. The multimodal visual perception system 1 is set on the mounting brackets 5 so that the detection angle of the infrared thermal imager and the 3D structured light camera faces the electrolytic cell 3. In actual use, four mounting brackets 5 can be set around the assembly platform so that the sensing range of the infrared thermal imager and the 3D structured light camera completely covers the entire edge of the polar frame 4. Preferably, in this embodiment, the height of the multimodal vision sensing system 1 on the mounting frame 5 is adjustable. In actual use, it can be raised as the installation progress of the pole frame 4 increases. That is, as the number of pole frames 4 stacked increases, the height of the multimodal vision sensing system 1 is gradually increased, so that the pole frame 4 being installed is always within the sensing range. Specifically, a ball screw can be rotatably installed inside the mounting frame 5, and a servo motor is installed at the bottom of the mounting frame 5 to drive the ball screw to rotate. A slider is slidably installed along the height direction of the mounting frame 5, and the multimodal vision sensing system 1 is installed on the slider. The slider is screwed to the ball screw, so that the multimodal vision sensing system 1 moves up and down under the drive of the servo motor and the ball screw. This can reduce the number of multimodal vision sensing systems 1 and make the pole frame 4 as close as possible to the center of the sensing range for detection.

[0054] Furthermore, in this embodiment, a ring track 6 is provided on the outer ring of the assembly platform, a trimming station 7 is provided on the ring track 6, a six-axis collaborative robotic arm 8 is provided on the trimming station 7, and a working part 9 is provided at the end of the robotic arm. The working part 9 trims the protruding shims between the pole frames 4. The multi-degree-of-freedom of the robotic arm allows the working part 9 to be easily adjusted in height, tilt angle and other settings. Moreover, the ring track 6 can drive the trimming station to rotate around the center of the assembly platform, thereby allowing the robotic arm to work on various sides of the electrolytic cell 3.

[0055] Specifically, the working part 9 includes a mounting rod 10, a fine-tuning joint 11, and a shovel structure 12. One end of the mounting rod 10 is connected to the end of the robotic arm, and the other end is equipped with the fine-tuning joint 11. The shovel structure 12 is mounted on the fine-tuning joint 11. The fine-tuning joint 11 controls the bucket tilt angle 13, the cutting edge tilt angle 14, and the wedge angle 15 of the shovel structure 12, so that the shovel structure 12 can adapt to various side working conditions of the pole frame 4, effectively improving the adaptability of the device. The shovel structure has a built-in pressure sensor and an angle adjustment module. The pressure sensor detects the pressure generated when the blade makes contact during cutting, and the angle adjustment module is used to detect and control the angle formed between the blade surface and the base surface.

[0056] The shovel structure 12 includes a C-shaped bucket 16. The protruding pad is trimmed by moving one side of the cutting edge of the C-shaped bucket 16 closer to the gap between the pole frame 4. In actual use, the movement of the C-shaped bucket can be driven by a robotic arm or by fine-tuning the joint. Furthermore, in this embodiment, a support wheel 17 is provided at the bottom of the C-shaped bucket 16, so that the lowest point of the support wheel 17 is slightly lower than the cutting edge of the bucket. This can prevent the cutting edge from wearing the coating on the surface of the pole frame 4 when dealing with thin residual material trimming operations.

[0057] The fine-tuning joint 11 includes a rotating seat 18, one side of which is connected to the blade structure 12 via a universal joint 19 and a telescopic adjustment arm 20; the rotating seat 18 is a rotating joint, and the rotating seat 18 is rotatably connected to the end of the mounting rod 10 to control the blade structure 12 to rotate at the end of the mounting rod 10. The universal joint 19 includes a universal joint 21. One end of the universal joint 21 is connected to the C-shaped bucket 16 and the other end is connected to the rotating seat 18. Two sets of telescopic adjustment arms 20 are also provided between the rotating seat 18 and the C-shaped bucket 16. The two sets of telescopic adjustment arms 20 are arranged in a figure-eight shape. Universal joints 21 are provided at both ends of the telescopic adjustment arms 20. The two ends are connected to the C-shaped bucket 16 and the rotating seat 18 respectively through the universal joints 21. In this embodiment, the two sets of telescopic adjustment arms 20 are set close to the pole frame 4, while the universal joint 19 is set at a position relatively far away from the pole frame 4.

[0058] Specifically, the bucket tilt angle 13 of the C-shaped bucket 16 is the angle formed by the bucket's cutting edge 22 and the base surface 23 (the tangential surface at the contact point between the cutting edge and the pole frame 4), such as... Figure 5 As shown in the figure, this is a top view. The dotted line in the figure represents the tangential plane of the pole frame 4, and the center line represents the angle of the blade 22 of the shovel. The included angle formed by the two is the bucket tilt angle 13. The bucket tilt angle 13 can be controlled by synchronously controlling the extension or retraction of the two sets of telescopic adjustment arms 20. When the bucket needs to scrape at a larger tilt angle, the two sets of telescopic adjustment arms 20 can be retracted synchronously to increase the bucket tilt angle; the same applies to the opposite.

[0059] The blade inclination angle 14 is the angle formed by the straight line containing the bucket blade and the vertical direction, such as... Figure 6 As shown in the figure, the center line is a vertical virtual line, and the dashed line is the straight line where the blade is located. The angle between the two forms an opening in the longitudinal direction, which is the blade inclination angle 14. The blade inclination angle 14 can be adjusted by one of the two sets of telescopic adjustment arms 20 or by adjusting them in opposite directions. In actual use, for example, when correcting corners, this angle can be adjusted to form a certain blade inclination angle 14, thereby obtaining a better correction effect.

[0060] The wedge angle 15 is the longitudinal angle of inclination formed by the difference in distance between the two ends of the bucket and the outer wall of the electrolytic cell 3, such as... Figure 7 As shown, the dashed line is the straight line where the blade is located, and the angle formed by it and the vertical direction is the wedge angle 15. The adjustment of this angle is controlled by the rotation of the rotating seat 18. It is specifically applicable to some adjacent pole frames 4 with a slight misalignment. In this embodiment, the rotating seat 18 rotates to make the bucket blade and the outer wall of the electrolytic cell 3 form a certain wedge angle 15 to better fit the actual external state of the pole frame 4, thereby forming a more intelligent adaptive adjustment.

[0061] The above description is only a preferred embodiment of the present utility model. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present utility model, and these improvements and modifications should also be considered within the protection scope of the present utility model.

Claims

1. A device for repairing gaskets in an electrolytic cell, characterized in that, include: Multimodal visual perception system: It includes an infrared thermal imager and a 3D structured light camera, which are used to collaboratively acquire multi-dimensional data and temperature distribution of the extrusion part of the electrode frame gasket in the monitoring area of ​​the electrolytic cell; The adaptive robotic arm control module is set at a preset work position next to the electrolytic cell via an adjustable base. The working part of the adaptive robotic arm control module moves to the defect area in a controlled manner and performs repair. The working part includes a fine-tuning joint and a blade structure. The fine-tuning joint controls the bucket tilt angle, cutting edge tilt angle, and wedge angle of the blade structure.

2. The electrolytic cell gasket repair device according to claim 1, characterized in that, The multimodal visual perception system is mounted around the electrolytic cell via a mounting bracket. The mounting bracket is vertically positioned on the outer periphery of the electrolytic cell, and the multimodal visual perception system is mounted on the mounting bracket, ensuring that the sensing range of the infrared thermal imager and the 3D structured light camera completely covers the entire edge of the polar frame.

3. The electrolytic cell gasket repair device according to claim 2, characterized in that, The height of the multimodal vision perception system on the mounting frame is adjustable. A ball screw is rotatably installed inside the mounting frame, and a servo motor is installed at the bottom of the mounting frame to drive the ball screw to rotate. A slider is slidably installed along the height direction of the mounting frame. The multimodal vision perception system is installed on the slider, and the slider is screwed to the ball screw, so that the multimodal vision perception system moves up and down under the drive of the servo motor and the ball screw.

4. The electrolytic cell gasket repair device according to claim 1, characterized in that, The adaptive robotic arm control module includes a six-axis collaborative robotic arm. The electrolytic cell is set on the assembly platform. The assembly platform has an outer ring track, and a trimming station is provided on the ring track. The trimming station adjusts its position around the assembly platform via the ring track.

5. The electrolytic cell gasket repair device according to claim 1, characterized in that, The shovel structure includes a C-shaped bucket, which moves along one side of the blade to approach the gap between the pole frames to adjust the protruding pads.

6. The electrolytic cell gasket repair device according to claim 5, characterized in that, The bottom of the C-shaped bucket is also equipped with a support wheel, so that the lowest point of the support wheel is slightly lower than the cutting edge of the bucket.

7. The electrolytic cell gasket repair device according to claim 5, characterized in that, The fine-tuning joint includes a rotating seat, one side of which is connected to the blade structure via a universal joint and a telescopic adjustment arm; the rotating seat is a rotary joint, and is rotatably connected to the end of the mounting rod to control the rotation of the blade structure at the end of the mounting rod.

8. The electrolytic cell gasket repair device according to claim 7, characterized in that, The universal joint includes a universal joint, one end of which is connected to the C-shaped bucket and the other end is connected to the rotating seat. Two sets of telescopic adjustment arms are also provided between the rotating seat and the C-shaped bucket, and the two sets of telescopic adjustment arms are arranged in a figure-eight shape. Universal joints are provided at both ends of the telescopic adjustment arms, and the two ends are connected to the C-shaped bucket and the rotating seat respectively through the universal joints.

9. The electrolytic cell gasket repair device according to claim 1, characterized in that, The blade structure has a built-in pressure sensor and an angle adjustment module. The pressure sensor detects the pressure generated when the blade makes contact during cutting, and the angle adjustment module is used to detect and control the angle formed between the blade surface and the base surface.

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