Off-line programming-based grinding and polishing process optimization control method and device

Abstract

The embodiment of the invention provides a grinding and polishing process optimization control method and device based on offline programming, and the method comprises the steps: planning a photographing track of a robot in offline programming software, and transmitting a photographing track control program to a field robot controller; acquiring vehicle surface flaw spot data acquired by the field robot according to the photographing track; corresponding grinding and polishing process parameters are matched according to the flaw point data, a robot grinding and polishing track is planned, and a grinding and polishing track control program is issued to a field robot controller; and acquiring processing data after the field robot executes the first grinding and polishing task according to the grinding and polishing track, and adjusting the grinding and polishing track and the grinding and polishing process parameters according to the processing data until the surface of the workpiece meets the process requirements.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer software technology, and in particular, to a method and device for optimizing and controlling a grinding and polishing process based on offline programming, and a computer-readable storage medium storing a computer program. Background Art

[0002] Current multi-model automatic grinding and polishing technology still has significant deficiencies in adapting to different models. Traditional trajectory planning methods usually rely on CAD models or preset templates. Although this method is suitable for certain specific models, its adaptability and flexibility are very limited when faced with a variety of different models. In particular, when it comes to curved surfaces, sharp corners, free-form surfaces and asymmetric structures, traditional methods cannot effectively handle situations such as sudden changes in curvature. For example, to account for the differences in the smooth curves of sedans and the sharp corners and structures of SUVs, traditional trajectory planning needs to be individually debugged according to the characteristics of different models. This not only requires a lot of manual intervention, but also leads to low model change efficiency and long debugging cycles. In addition, this method has a low degree of flexibility and cannot adapt to the differences in geometric features between different models. Once the model is switched or new design changes are made, the trajectory often needs to be replanned and debugged, further affecting production efficiency and response speed. Summary of the Invention

[0003] The embodiments described herein provide a method and device for optimizing and controlling a grinding and polishing process based on offline programming, as well as a computer-readable storage medium storing a computer program, which aims to improve the grinding and polishing accuracy and efficiency of industrial robots on complex surfaces (such as automobile shells), and to achieve closed-loop control of defect recognition, adaptive process optimization, and trajectory adjustment.

[0004] According to a first aspect of the present disclosure, a grinding and polishing process optimization control method based on offline programming is provided, including: planning a robot photographing trajectory in offline programming software, and sending a photographing trajectory control program to an on-site robot controller; obtaining vehicle surface defect point data collected by the on-site robot according to the photographing trajectory; matching corresponding grinding and polishing process parameters according to the defect point data, and planning the robot grinding and polishing trajectory, and sending the grinding and polishing trajectory control program to the on-site robot controller; and obtaining processing data after the on-site robot performs the first grinding and polishing task according to the grinding and polishing trajectory, and adjusting the grinding and polishing trajectory and grinding and polishing process parameters according to the processing data until the workpiece surface meets the process requirements.

[0005] In some embodiments of the present disclosure, planning the robot's photographing trajectory in offline programming software and sending the photographing trajectory control program to the on-site robot controller includes: importing the three-dimensional models of the workpiece to be processed and the industrial robot into the offline programming software, and calibrating the three-dimensional models; performing spatial reachability analysis and collision detection on the robot during the photographing trajectory planning process, and transmitting the compiled photographing trajectory control program to the on-site robot controller.

[0006] In some embodiments of the present disclosure, obtaining vehicle surface defect point data collected by an on-site robot along a photographing trajectory includes: using a three-dimensional structured light camera equipped with the on-site robot to perform a full-coverage scan of the entire vehicle from multiple perspectives to obtain point cloud data of the vehicle body surface, the point cloud data including x, y, z spatial position coordinates and a, b, c rotation angles; identifying defect points and defect categories on the vehicle body surface based on the point cloud data, the defect categories including deep scratches, shallow scratches, dents and protrusions; transmitting the spatial position data of the defect points and their corresponding defect categories to the industrial robot offline programming software.

[0007] In some embodiments of the present disclosure, corresponding grinding and polishing process parameters are matched according to defect point data, and the robot grinding and polishing trajectory is planned, and the grinding and polishing trajectory control program is sent to the on-site robot controller, including: matching corresponding grinding and polishing process parameters from a pre-created process template library according to the defect category, and planning the grinding and polishing trajectory according to the grinding and polishing process parameters; performing spatial accessibility analysis and collision detection on the robot working range during the grinding and polishing trajectory planning process; and transmitting the compiled grinding and polishing trajectory program to the on-site robot controller.

[0008] In some embodiments of the present disclosure, spatial accessibility analysis and collision detection of the robot's working range are performed during the grinding and polishing trajectory planning process, including: based on xyzabc defect point data, checking whether each grinding and polishing target point is located within the robot's working space; checking whether there are singular points, axis overruns, and pipeline wrapping within the robot's motion range; checking whether the motion trajectory of each robot collides with the workpiece and the surrounding environment, and when a collision is detected, avoiding the collision by adjusting the grinding and polishing path.

[0009] In some embodiments of the present disclosure, corresponding grinding and polishing process parameters are matched from a pre-created process template library according to the defect category, and the grinding and polishing trajectory is planned according to the grinding and polishing process parameters, including: for shallow scratches, using fine-grained abrasives, reducing pressure, reducing single cutting depth, and smoothing through multiple light grinding; for deep scratches, using coarse-grained abrasives to roughen and increase cutting pressure to smooth the surface; for raised defects, using segmented pressure and spiral feeding methods, gradually moving from the periphery of the raised defect to the center; for recessed defects, using soft grinding discs, gradually raising the bottom height of the recess through fixed-point light grinding, increasing the grinding and polishing time, and monitoring the flatness in real time.

[0010] In some embodiments of the present disclosure, obtaining processing data after the on-site robot performs the first grinding and polishing task according to the grinding and polishing trajectory, and adjusting the grinding and polishing trajectory and grinding and polishing process parameters according to the processing data until the workpiece surface meets the process requirements includes: judging whether the grinding and polishing surface meets the process requirements according to the processing data after the first grinding and polishing task is performed; if there are still defects on the workpiece surface, performing the photo-taking task again, updating the spatial position and category of the defect point, and adjusting the grinding and polishing trajectory and grinding and polishing process parameters according to the updated spatial position and category of the defect point until the workpiece surface meets the preset process requirements.

[0011] In some embodiments of the present disclosure, judging whether the polished surface meets the process requirements based on the processing data after the first polishing task is performed includes: collecting the workpiece surface quality, grinding progress, force control status, and temperature change data in real time through a variety of sensors deployed on site, and transmitting the real-time collected data to the offline programming software; comparing the real-time collected data with the set process parameters to detect whether there is a deviation or whether there is an abnormality that exceeds the preset threshold.

[0012] According to the second aspect of the present disclosure, a grinding and polishing process optimization control device based on offline programming is provided. The device includes at least one processor; and at least one memory storing a computer program. When the computer program is executed by the at least one processor, the device: plans the robot photography trajectory in the offline programming software, and sends the photography trajectory control program to the on-site robot controller; obtains the vehicle surface defect point data collected by the on-site robot according to the photography trajectory; matches the corresponding grinding and polishing process parameters according to the defect point data, and plans the robot grinding and polishing trajectory, and sends the grinding and polishing trajectory control program to the on-site robot controller; and obtains the processing data after the on-site robot performs the first grinding and polishing task according to the grinding and polishing trajectory, and adjusts the grinding and polishing trajectory and grinding and polishing process parameters according to the processing data until the workpiece surface meets the process requirements.

[0013] According to a third aspect of the present disclosure, a computer-readable storage medium storing a computer program is provided, wherein the computer program implements the steps of the method according to the first aspect of the present disclosure when executed by a processor.

[0014] According to the offline programming-based grinding and polishing process optimization control method and device of the disclosed embodiments, on-site debugging time is reduced by pre-planning the photography and grinding and polishing trajectories in the offline programming software. The vehicle surface defect data collected by the on-site robot can accurately analyze the location and type of surface defects, ensuring that the grinding and polishing process parameters accurately match the actual workpiece defects. Directly utilizing the defect data can greatly improve the accuracy of the grinding and polishing process, avoid excessive or insufficient grinding, reduce material waste and workpiece damage, better cope with complex and changing process requirements, and ensure processing quality and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below. It should be noted that the drawings described below only relate to some embodiments of the present disclosure and are not intended to limit the present disclosure.

[0016] Figure 1 is an exemplary flow chart of a grinding and polishing process optimization control method based on offline programming according to an embodiment of the present disclosure;

[0017] Figure 2 It is a schematic block diagram of a grinding and polishing process optimization control device based on offline programming according to an embodiment of the present disclosure.

[0018] It should be noted that the elements in the drawings are schematic and not drawn to scale. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work also fall within the scope of protection of the present disclosure.

[0020] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the subject matter of the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant art, and will not be interpreted in an idealized or overly formal manner unless otherwise explicitly defined herein. In addition, terms such as "first" and "second" are only used to distinguish one component (or a portion of a component) from another component (or another portion of a component).

[0021] Figure 1 An exemplary flow chart of a grinding and polishing process optimization control method based on offline programming according to an embodiment of the present disclosure is shown. Figure 1 At block S102 , the robot's photographing trajectory is planned in the offline programming software, and the photographing trajectory control program is sent to the on-site robot controller.

[0022] According to one embodiment of the present disclosure, a 3D model of the workpiece to be machined and the industrial robot can be imported into offline programming software and calibrated. For example, by comparing the virtual workpiece position with the actual workpiece position, the positioning of the virtual workpiece can be calibrated so that it accurately docks with the robot's workspace during simulation. By setting the robot's initial and target positions and calibrating the relative positional relationship between the robot and the workpiece, the virtual robot model's joints and end effector can be ensured to have the same range of motion as the actual robot.

[0023] After the three-dimensional model is calibrated correctly, start planning the shooting trajectory. During the shooting trajectory planning process, the robot is subjected to spatial reachability analysis and collision detection, and the compiled shooting trajectory control program is transmitted to the on-site robot controller. First, the robot's shooting path is designed according to the surface shape of the workpiece, the distribution of defect points and the field of view of the camera. During the trajectory planning process, check whether the robot can reach all predetermined shooting points within its workspace. Spatial reachability analysis mainly includes: checking whether the robot joints can move to specific positions, and whether the end effector (such as a camera) can reach the designated shooting point. Check whether the robot trajectory is smooth to avoid excessive motion inertia or unnecessary pauses. Check the extreme positions to ensure that the robot does not exceed its range of motion or encounter other constraints (such as load, speed limits, etc.).

[0024] Collision detection includes: checking whether the various parts of the robot (such as robotic arms, joints, grippers, sensors, etc.) collide with each other during movement. When the robot performs a photo-taking task, it may need to shoot at multiple angles and positions, so it is necessary to ensure that the various parts of the robot do not interfere or collide in all motion states. Check whether the robot collides with the workpiece to be processed during movement. Especially when processing more complex workpieces, the robot's arm, camera and other end effectors need to flexibly avoid the various surfaces of the workpiece to avoid damaging the workpiece or equipment. Check whether the robot collides with obstacles in the surrounding environment. After trajectory planning and collision detection, the system will generate an executable control program, which is used to control the on-site robot to perform tasks according to the planned trajectory.

[0025] Then, in block S104 , the vehicle surface defect point data collected by the on-site robot along the photographing trajectory is obtained.

[0026] The on-site robot uses its equipped 3D structured light camera to perform a full-coverage scan of the vehicle from multiple perspectives, obtaining point cloud data of the vehicle surface. This point cloud data includes x, y, and z spatial position coordinates and a, b, and c rotation angles. A 3D structured light camera projects a known pattern of light (such as stripes, dots, or a grid) onto the surface of an object and uses the camera to capture the reflected light information to calculate the spatial position of each surface point. Here, x, y, and z represent the position of the defect point in 3D space. a, b, and c represent the surface normal vector of that point, indicating the surface orientation of that point.

[0027] Based on point cloud data, surface defects and their corresponding categories are identified. Defect categories include deep scratches, shallow scratches, dents, and bulges. For example, the characteristics of deep and shallow scratches are often associated with changes in the direction of the local surface normal or changes in point cloud density, while dents and bulges are often manifested as areas of varying surface curvature. The spatial location data of the defect points and their corresponding defect categories are converted into a format supported by the robot control system and then transmitted to the industrial robot's offline programming software.

[0028] Then in block S106 , the corresponding grinding and polishing process parameters are matched according to the defect point data, and the robot grinding and polishing trajectory is planned, and the grinding and polishing trajectory control program is sent to the on-site robot controller.

[0029] Based on the defect type, corresponding polishing parameters can be matched from a pre-created library of process templates, and polishing trajectories can be planned based on these parameters. According to one embodiment of the present disclosure, dedicated process templates are pre-designed for different defects (such as shallow scratches, deep scratches, and uneven surfaces). Each process template includes polishing parameters suitable for that type of defect: abrasive type, polishing speed, polishing pressure, polishing time, etc.

[0030] For example, for shallow scratches, use fine-grained abrasives (such as W10-W5 diamond abrasives or 1000-2000 mesh sandpaper) to reduce the abrasive cutting force and avoid expanding the scratches. Reduce the pressure to 0.1-0.3MPa, reduce the single cutting depth, and smooth the surface through multiple light grinding. Increase the spindle speed to 2000-2000rpm, refine the scratch edge through high-frequency friction, slow down the feed speed to 50-100mm / min, and increase the action time of the abrasive and the scratch area. For deep scratches, first use coarse-grained abrasives (such as W20-W40 abrasives or 400-800 mesh sandpaper) to roughen and remove the protruding material around the scratch. Increase the grinding and polishing pressure to 0.5-1MPa to increase the cutting capacity and quickly level the depth difference. Reduce the speed to 1000-1500rpm to avoid high speed causing heat accumulation and aggravated material damage. Increase the coolant flow to prevent the coarse abrasive from generating heat and causing surface annealing. For raised defects, use hard abrasives (such as silicon carbide and cubic boron nitride) to improve cutting efficiency. Use segmented pressure, with the pressure in the center area of ​​the protrusion at 0.6-0.8MPa, and gradually reduce it to 0.3MPa at the edge to avoid over-cutting. The feeding method adopts spiral feeding, moving from the periphery of the protrusion to the center, with an overlap rate of 50% each time to ensure uniform removal. Use rigid grinding discs to avoid incomplete leveling due to deformation of flexible grinding discs. For recessed defects, use medium-grained (W15-W20) with soft grinding discs, such as polyurethane discs, to reduce the amount of material removed around the recess. The grinding and polishing pressure is evenly applied at 0.2-0.4MPa, and the bottom height of the recess is slowly increased by fixed-point light grinding. The processing time of the recessed area is locally extended by 10%-20%, and a laser thickness gauge is used to monitor the flatness in real time.

[0031] Offline programming software generates the optimal grinding and polishing trajectory based on the spatial location and defect type of the defect, using matching template parameters. For example, spiral feed is suitable for situations where defects are relatively dispersed and require uniform grinding. The robot feeds the defect points along the spiral trajectory, ensuring uniform and efficient grinding depth in each area. Fixed-point light grinding is suitable for situations where defects are concentrated or require localized fine processing. The robot removes localized surface defects with high precision by lightly grinding at specific points. For some larger, irregularly shaped defects, an arc feed path can be used, allowing the robot to better cover the entire defect area and ensure grinding quality.

[0032] During trajectory planning, the robot's workspace is subjected to spatial accessibility analysis and collision detection. The main purpose of spatial accessibility analysis is to ensure that the robot can move freely within its workspace when performing a task, without being restricted by issues such as singularities, axis overruns, or entangled cable wraps. A singularity refers to a robot configuration in which its joints lose freedom of movement or the robot's motion efficiency decreases sharply near that point. By analyzing the robot's workspace and kinematic characteristics, each trajectory point is checked to see if it will lead to a singularity. Because the range of motion of each robot joint is limited, spatial accessibility analysis checks the range of motion of each robot to ensure that each joint remains within the specified range of motion when performing a task. Spatial accessibility analysis also needs to consider whether the robot's cable wraps are entangled. If the cable wrap is entangled, it will affect the robot's trajectory or cause the robot's motion to fail.

[0033] The primary goal of collision detection is to ensure that no physical collisions occur between the robot, tool, or workpiece during a robot's execution. The offline programming software simulates the robot's trajectory, checking each robot's motion for collisions with the workpiece and surrounding environment. If a collision is detected, the grinding and polishing path is adjusted to avoid it. For example, the robot's motion path can be adjusted to avoid interference with other objects. The relative position of the robot and workpiece can be adjusted to ensure no interference during the image capture process. The workspace layout can also be adjusted to ensure free space for the robot's movement. The compiled grinding and polishing trajectory program is then transferred to the on-site robot controller.

[0034] Finally, in block S108 , the processing data of the on-site robot after performing the first grinding and polishing task according to the grinding and polishing trajectory is obtained, and the grinding and polishing trajectory and grinding and polishing process parameters are adjusted according to the processing data until the workpiece surface meets the process requirements.

[0035] Each robot performs grinding and polishing tasks within its own task area. The processing data after the first grinding and polishing task can be used to determine whether the polished surface meets the process requirements. In one embodiment of the present disclosure, multiple sensors deployed on-site collect real-time data on workpiece surface quality, grinding progress, force control, and temperature changes. This data is then transmitted to offline programming software. This real-time data is compared with the set process parameters to detect any deviations or anomalies exceeding preset thresholds.

[0036] If there are still defects on the workpiece surface, the photo-taking task is performed again to update the spatial position and category of the defect point. The grinding and polishing trajectory and grinding and polishing process parameters are adjusted according to the updated spatial position and category of the defect point until the workpiece surface meets the preset process requirements.

[0037] Figure 21 is a schematic block diagram of a grinding and polishing process optimization control device based on offline programming according to an embodiment of the present disclosure. Figure 2 As shown, the apparatus 200 may include a processor 210 and a memory 220 storing a computer program. When the computer program is executed by the processor 210, the apparatus 200 may perform the following operations: Figure 1 The steps of method 100 are shown. In one example, apparatus 200 can be a computer device or a cloud computing node. Apparatus 200 can plan a robot photography trajectory in offline programming software and send a photography trajectory control program to an on-site robot controller; obtain vehicle surface defect point data collected by the on-site robot along the photography trajectory; match corresponding grinding and polishing process parameters based on the defect point data, plan a robot grinding and polishing trajectory, and send a grinding and polishing trajectory control program to the on-site robot controller; and obtain processing data after the on-site robot performs the first grinding and polishing task along the grinding and polishing trajectory, and adjust the grinding and polishing trajectory and grinding and polishing process parameters based on the processing data until the workpiece surface meets the process requirements.

[0038] In some embodiments of the present disclosure, the device 200 can import the three-dimensional model of the workpiece to be processed and the industrial robot into the offline programming software, and calibrate the three-dimensional model; perform spatial reachability analysis and collision detection on the robot during the shooting trajectory planning process, and transmit the compiled shooting trajectory control program to the on-site robot controller.

[0039] In some embodiments of the present disclosure, the device 200 can use the three-dimensional structured light camera equipped by the on-site robot to perform a full-coverage scan of the entire vehicle from multiple perspectives to obtain point cloud data of the vehicle body surface, and the point cloud data includes x, y, z spatial position coordinates and a, b, c rotation angles; identify defect points and defect categories on the vehicle body surface based on the point cloud data, and the defect categories include deep scratches, shallow scratches, depressions and protrusions; transmit the spatial position data of the defect points and their corresponding defect categories to the industrial robot offline programming software.

[0040] In some embodiments of the present disclosure, the device 200 can match corresponding grinding and polishing process parameters from a pre-created process template library according to the defect category, and plan the grinding and polishing trajectory according to the grinding and polishing process parameters; perform spatial accessibility analysis and collision detection on the robot's working range during the grinding and polishing trajectory planning process; and transmit the compiled grinding and polishing trajectory program to the on-site robot controller.

[0041] In some embodiments of the present disclosure, the device 200 can check whether each grinding and polishing target point is located within the robot's workspace based on the xyzabc defect point data; check whether there are singular points, axis overruns, and pipeline entanglements within the robot's motion range; check whether the motion trajectory of each robot collides with the workpiece and the surrounding environment, and when a collision is detected, avoid the collision by adjusting the grinding and polishing path.

[0042] In some embodiments of the present disclosure, the device 200 can determine whether the polished surface meets the process requirements based on the processing data after performing the first polishing task; if there are still defects on the workpiece surface, the photo-taking task is performed again, the spatial position and category of the defect point are updated, and the polishing trajectory and polishing process parameters are adjusted according to the updated spatial position and category of the defect point until the workpiece surface meets the preset process requirements.

[0043] In some embodiments of the present disclosure, the device 200 can collect data on workpiece surface quality, grinding progress, force control, and temperature change in real time through a variety of sensors deployed on site, and transmit the real-time collected data to offline programming software; compare the real-time collected data with the set process parameters to detect whether there is a deviation or whether there is an abnormality that exceeds the preset threshold.

[0044] In an embodiment of the present disclosure, the processor 210 may be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a processor based on a multi-core processor architecture, etc. The memory 220 may be any type of memory implemented using data storage technology, including but not limited to random access memory, read-only memory, semiconductor-based memory, flash memory, disk storage, etc.

[0045] In addition, in an embodiment of the present disclosure, the apparatus 200 may also include an input device 230, such as a keyboard, a mouse, etc. In addition, the apparatus 200 may also include an output device 240, such as a display, etc.

[0046] In other embodiments of the present disclosure, a computer-readable storage medium storing a computer program is further provided, wherein the computer program can achieve the following when executed by a processor: Figure 1 The steps of the method are shown.

[0047] In summary, according to the offline programming-based grinding and polishing process optimization control method and device of the embodiment of the present disclosure, the robot trajectory is planned through offline programming, which can effectively avoid the risks and errors in the on-site debugging process. By distributing the photography trajectory control program and the grinding and polishing trajectory control program to multiple robot controllers, multiple robots can work in parallel, greatly improving production efficiency. Based on the intelligent vision system to obtain the defect point data on the workpiece surface, the grinding and polishing trajectory can be dynamically adjusted according to the surface state and defect type of different workpieces, which greatly improves the system's adaptability to different workpieces and enhances the flexibility of the process.

[0048] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus and method according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0049] Unless the context clearly indicates otherwise, as used herein and in the appended claims, the singular includes the plural, and vice versa. Thus, when referring to the singular, the plural of the corresponding term is generally included. Similarly, the words "include" and "comprising" are to be interpreted as inclusive rather than exclusive. Likewise, the terms "include" and "or" should be interpreted as inclusive unless such interpretation is expressly prohibited herein. Where the term "example" is used herein, particularly when it follows a group of terms, the "example" is merely exemplary and illustrative and should not be considered exclusive or comprehensive.

[0050] Further aspects and scope of adaptability become apparent from the description provided herein. It should be understood that various aspects of the present application can be implemented individually or in combination with one or more other aspects. It should also be understood that the description and specific embodiments herein are intended to be illustrative only and are not intended to limit the scope of the present application.

[0051] Several embodiments of the present disclosure have been described in detail above, but it is obvious that those skilled in the art can make various modifications and variations to the embodiments of the present disclosure without departing from the spirit and scope of the present disclosure. The scope of protection of the present disclosure is defined by the appended claims.

Claims

1. A grinding and polishing process optimization control method based on offline programming, characterized in that: include: Plan the robot's photography trajectory in the offline programming software and send the photography trajectory control program to the on-site robot controller; Obtaining vehicle surface defect point data collected by the on-site robot along the photographing trajectory; Match the corresponding grinding and polishing process parameters according to the defect point data, plan the robot grinding and polishing trajectory, and send the grinding and polishing trajectory control program to the on-site robot controller; as well as The processing data of the on-site robot after performing the first grinding and polishing task according to the grinding and polishing trajectory is obtained, and the grinding and polishing trajectory and grinding and polishing process parameters are adjusted according to the processing data until the workpiece surface meets the process requirements.

2. The grinding and polishing process optimization control method based on offline programming according to claim 1 is characterized in that: The step of planning the robot's photographing trajectory in the offline programming software and sending the photographing trajectory control program to the on-site robot controller includes: Import the 3D models of the workpiece to be processed and the industrial robot into the offline programming software and calibrate the 3D models; During the photographing trajectory planning process, the robot is subjected to spatial reachability analysis and collision detection, and the compiled photographing trajectory control program is transmitted to the on-site robot controller.

3. The grinding and polishing process optimization control method based on offline programming according to claim 1 is characterized in that: The obtaining of vehicle surface defect point data collected by the on-site robot along the photographing trajectory includes: The on-site robot is equipped with a 3D structured light camera to perform a full-coverage scan of the vehicle from multiple perspectives to obtain point cloud data on the vehicle body surface. The point cloud data includes x, y, z spatial position coordinates and a, b, c rotation angles; Identifying defect points and defect categories on the vehicle body surface based on the point cloud data, wherein the defect categories include deep scratches, shallow scratches, dents, and bulges; and The spatial location data of the defect points and their corresponding defect categories are transmitted to the industrial robot offline programming software.

4. The grinding and polishing process optimization control method based on offline programming according to claim 1 is characterized in that: The matching of corresponding grinding and polishing process parameters according to the defect point data, planning the robot grinding and polishing trajectory, and sending the grinding and polishing trajectory control program to the on-site robot controller includes: According to the defect type, the corresponding grinding and polishing process parameters are matched from the pre-created process template library, and the grinding and polishing trajectory is planned according to the grinding and polishing process parameters; Perform spatial reachability analysis and collision detection on the robot's working range during grinding and polishing trajectory planning; and The compiled grinding and polishing trajectory program is transmitted to the on-site robot controller.

5. The grinding and polishing process optimization control method based on offline programming according to claim 4 is characterized in that: The spatial reachability analysis and collision detection of the robot's working range during the grinding and polishing trajectory planning process includes: Based on the xyzabc defect point data, check whether each grinding and polishing target point is located within the robot's workspace; Check for singularities, axis overruns, and cable wrapping within the robot's range of motion; and Check whether the motion trajectory of each robot collides with the workpiece and the surrounding environment. When a collision is detected, adjust the grinding and polishing path to avoid collision.

6. The grinding and polishing process optimization control method based on offline programming according to claim 4 is characterized in that: The matching of corresponding grinding and polishing process parameters from a pre-created process template library according to the defect category and planning of the grinding and polishing trajectory according to the grinding and polishing process parameters include: For shallow scratches, use fine-grain abrasive, reduce pressure, reduce the depth of single cut, and smooth through multiple light grinding; For deep scratches, use coarse-grit abrasive to roughen and increase cutting pressure to smooth the surface; For raised defects, use segmented pressure and spiral feeding, gradually moving from the periphery of the raised area to the center; For concave defects, use a soft grinding wheel and gradually raise the bottom height of the concave through fixed-point light grinding, increase the grinding and polishing time and monitor the flatness in real time.

7. The grinding and polishing process optimization control method based on offline programming according to claim 1 is characterized in that: The obtaining of processing data after the on-site robot performs the first grinding and polishing task according to the grinding and polishing trajectory, and adjusting the grinding and polishing trajectory and grinding and polishing process parameters according to the processing data until the workpiece surface meets the process requirements includes: Determine whether the polished surface meets the process requirements based on the processing data after the first polishing task; If there are still defects on the workpiece surface, the photo-taking task is performed again to update the spatial position and category of the defect point. The grinding and polishing trajectory and grinding and polishing process parameters are adjusted according to the updated spatial position and category of the defect point until the workpiece surface meets the preset process requirements.

8. The grinding and polishing process optimization control method based on offline programming according to claim 7 is characterized in that: The method of judging whether the polished surface meets the process requirements based on the processing data after the first polishing task is performed includes: Using a variety of sensors deployed on-site to collect real-time data on workpiece surface quality, grinding progress, force control, and temperature changes, the collected data is then transmitted to the offline programming software; and The data collected in real time is compared with the set process parameters to detect whether there are deviations or abnormal conditions that exceed the preset thresholds.

9. A grinding and polishing process optimization control device based on offline programming, characterized in that: The device comprises: at least one processor; and at least one memory storing a computer program; Wherein, when the computer program is executed by the at least one processor, the device is caused to perform the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: The computer program implements the steps of the method according to any one of claims 1 to 8 when executed by a processor.

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