A robotic arm control method for applying release agent.

Abstract

This invention relates to the field of automated spraying technology, and more particularly to a robotic arm control method for spraying release agents. The method includes: acquiring a three-dimensional point cloud of a mold and segmenting it into different feature regions; generating an initial spraying trajectory based on the feature regions and a process database; during the spraying process, real-time monitoring of surface state parameters in each region, calculating the real-time spraying quality deviation coefficient, and judging the spraying quality of the current point accordingly, and real-time compensation of robotic arm movement or spraying parameters for unqualified points; after completing the spraying of a single region, calculating the uniformity variation coefficient based on the film thickness distribution of that region, judging the stage quality, and implementing stage optimization strategies for unqualified regions in subsequent spraying of similar regions; after completing the overall mold spraying, calculating the global spraying quality evaluation value, judging the overall quality, and performing global parameter optimization for unqualified cases in the next spraying cycle. This invention achieves multi-level closed-loop optimization control from point and region to the global level.

Description

Technical Field

[0001] This invention relates to the field of automated spraying technology, and more particularly to a robotic arm control method for spraying release agents. Background Technology

[0002] In existing die-casting and molding processes, the quality of mold release agent spraying directly affects product quality and production efficiency. Currently, the commonly used robotic arm spraying method largely relies on pre-taught fixed trajectories. This method has significant drawbacks: First, for molds with complex geometries, especially those with irregular structures such as deep holes and narrow grooves, fixed trajectories cannot guarantee uniform and full coverage of the spray, easily leading to localized sticking or waste of mold release agent. Second, the spraying process is dynamic; fluctuations in factors such as mold temperature and spray gun distance can severely affect film quality, and current technologies lack effective real-time monitoring and closed-loop control mechanisms to compensate for these disturbances. Third, when changing mold models, tedious manual teaching or offline programming is required, resulting in long changeover times and insufficient flexible production capabilities. Finally, existing control systems lack self-learning and optimization capabilities; their process parameters cannot iterate with the accumulation of production data, always relying on manual experience for adjustments, hindering continuous improvement in production quality and intelligent development.

[0003] Therefore, there is an urgent need in this field for an intelligent spraying control method that can adapt to mold characteristics, respond to process changes in real time, and continuously optimize itself.

[0004] Chinese Patent Publication No. CN117943521A discloses a method and system for controlling the spraying of die-casting release agent. The method involves acquiring characteristic parameters of the release agent to be die-casted, determining whether the release agent meets preset spraying conditions based on these parameters, and injecting the release agent into a spraying device if it does. It also involves collecting parameter information of the casting to be sprayed, analyzing the parameter information to determine reference parameters, including the spraying area and spraying thickness of the casting; setting spraying instructions for the release agent based on the reference parameters; acquiring a preset spraying time; and controlling the spraying device according to the spraying time and spraying instructions. This invention controls the spraying device through spraying time and spraying instructions, enabling intelligent spraying of die-casting release agents, ensuring the accuracy of the release agent spraying, thereby improving the surface density and smoothness of the casting to be sprayed, and extending the service life of the casting.

[0005] Therefore, it can be seen that the aforementioned method and system for controlling the spraying of die casting release agent has the problem that the spraying pressure, spraying pressure correction coefficient, and spraying time adjustment coefficient are determined only based on the area, thickness, and density of the casting to be sprayed, without determining and adjusting the spraying parameters according to the geometry of the casting to be sprayed from point to surface during the spraying process, resulting in substandard spraying effects for different parts of the casting. Summary of the Invention

[0006] Therefore, the present invention provides a robotic arm control method for spraying release agent, which overcomes the problem in the prior art that the spraying parameters are not determined and adjusted from point to surface according to the geometry of the casting to be sprayed during the spraying process, resulting in substandard spraying effects for different parts of the casting.

[0007] To achieve the above objectives, the present invention provides a robotic arm control method for spraying release agents. It includes:

[0008] Acquire the three-dimensional point cloud data of the mold to be coated, and divide the surface of the mold to be coated into several different types of feature regions;

[0009] Based on the feature region and the spraying process database, the robot arm predicts and generates the initial spraying trajectory for the current mold to be sprayed and performs the spraying.

[0010] During the spraying process, the surface feature parameters of a single feature area of ​​the mold are monitored in real time, and the real-time spraying quality deviation coefficient is obtained to determine whether the spraying quality of the current spraying point of a single feature area of ​​the mold is qualified.

[0011] The control operation to be performed is determined based on the deviation value of the quality deviation coefficient. The control operation is one or more of the following: increasing the release agent spraying flow rate, reducing the moving speed of the robotic arm at the current nozzle position, and stabilizing the spraying posture of the spray gun at the current spraying position.

[0012] After completing the spraying of a single feature area, the surface area feature parameters of the single feature area after spraying are obtained to determine whether the stage spraying quality of the single feature area is qualified.

[0013] Based on the coefficient of variation deviation value, determine to simultaneously increase the release agent spraying flow rate and atomization pressure, and simultaneously reduce the robotic arm moving speed and path scanning interval for the next area with the same characteristics.

[0014] After the entire mold is coated, the overall coating quality evaluation value is obtained to determine whether the overall coating quality of the mold is qualified.

[0015] Based on the global quality evaluation deviation value, determine whether to increase the theoretical spraying fan width and increase or decrease the theoretical spraying distance to optimize the next spraying cycle globally.

[0016] Furthermore, the process of determining whether the coating quality of the current spraying point for a single feature area of ​​the mold is qualified includes,

[0017] The actual monitored temperature of the surface of a single feature area and the actual spraying distance between the spray gun and the single feature area are obtained;

[0018] The real-time spraying quality deviation coefficient is obtained by weighted summation.

[0019] The real-time spraying quality deviation coefficient is compared with the real-time spraying quality deviation coefficient threshold.

[0020] Based on the fact that the real-time spraying quality deviation coefficient is greater than the real-time spraying quality deviation coefficient threshold, it is determined that the spraying quality of the current spraying point in a single feature area is unqualified.

[0021] Furthermore, the process of determining, based on the quality deviation coefficient deviation value, to increase the release agent spraying flow rate and reduce the robotic arm's moving speed at the current nozzle position, and to stabilize the spraying posture of the spray gun at the current spraying position, includes,

[0022] The difference between the real-time spraying quality deviation coefficient and the real-time spraying quality deviation coefficient threshold is used to obtain the quality deviation coefficient deviation value;

[0023] The deviation value of the quality deviation coefficient is compared with the preset quality deviation coefficient value;

[0024] Based on the fact that the deviation value of the quality deviation coefficient is less than or equal to the preset quality deviation coefficient value, it is determined to increase the spraying flow rate of the release agent.

[0025] Furthermore, the process of determining the increase of the release agent spraying flow rate and the reduction of the robotic arm's moving speed at the current nozzle position and stabilizing the spraying posture of the spray gun at the current spraying position based on the quality deviation coefficient deviation value also includes,

[0026] Based on the fact that the deviation value of the mass deviation coefficient is greater than the preset mass deviation coefficient deviation value, it is determined to reduce the moving speed of the robotic arm at the current nozzle position and stabilize the spraying posture of the spray gun at the current spraying position.

[0027] Furthermore, the process of determining whether the staged spraying quality of a single feature area is qualified includes,

[0028] Obtain the film thickness at all film thickness measurement points on the surface of a single feature region;

[0029] Calculate the standard deviation and mean of all film thickness measurement points within a single feature area after spraying is completed;

[0030] The ratio of the standard deviation to the mean is used to obtain the coefficient of variation for uniform film thickness in the region.

[0031] The coefficient of variation of the uniform film thickness in the region is compared with the threshold of the coefficient of variation of the uniform film thickness in the region.

[0032] The coating quality of a single feature region is deemed unqualified based on the fact that the coefficient of variation of the film thickness in the region is greater than the threshold of the coefficient of variation of the film thickness in the region.

[0033] Furthermore, the process of determining, based on the coefficient of variation deviation value, to simultaneously increase the release agent spraying flow rate and atomization pressure, and simultaneously decrease the robotic arm moving speed and reduce the path scanning interval for the next region with the same characteristics includes,

[0034] The difference between the coefficient of variation of the film thickness uniformity in the region and the threshold value of the coefficient of variation of the film thickness uniformity in the region is used to obtain the coefficient of variation deviation value.

[0035] The deviation value of the coefficient of variation is compared with the preset deviation value of the coefficient of variation;

[0036] Based on the fact that the coefficient of variation deviation value is less than or equal to the preset coefficient of variation deviation value, the release agent spraying flow rate and atomization pressure are increased synchronously.

[0037] Furthermore, the process of determining, based on the coefficient of variation deviation value, to simultaneously increase the release agent spraying flow rate and atomization pressure, and simultaneously decrease the robotic arm moving speed and reduce the path scanning interval for the next region with the same characteristics also includes,

[0038] Based on the fact that the coefficient of variation deviation value is greater than the preset coefficient of variation deviation value, it is determined to synchronously reduce the robot arm's moving speed and decrease the path scanning interval.

[0039] Furthermore, the process of determining whether the overall coating quality of the mold is up to standard includes,

[0040] Obtain the film thickness at all measurement points on the entire mold surface and calculate the average film thickness;

[0041] The relative degree of film thickness fluctuation is obtained by calculating the ratio of the standard deviation of all film thickness values ​​to the average film thickness.

[0042] The film thickness uniformity is obtained by calculating the difference between the numerical value 1 and the relative fluctuation of the film thickness.

[0043] The global coating quality evaluation value is obtained by weighted summation;

[0044] The global spraying quality evaluation value is compared with the preset global spraying quality evaluation value;

[0045] The overall coating quality is determined to be substandard based on the fact that the global coating quality evaluation value is less than the preset global coating quality evaluation value.

[0046] Furthermore, the process of determining, based on the global quality evaluation deviation value, to increase the theoretical spraying fan width and increase or decrease the theoretical spraying distance for global optimization of the next spraying cycle includes,

[0047] The difference between the global coating quality and the preset global coating quality evaluation value is used to obtain the global quality evaluation deviation value;

[0048] The global quality evaluation deviation value is compared with the preset global quality evaluation deviation value;

[0049] Based on the fact that the global quality evaluation deviation value is less than or equal to the preset global quality evaluation deviation value, the theoretical spraying fan width is increased.

[0050] Furthermore, the process of determining, based on the global quality evaluation deviation value, to increase the theoretical spraying fan width and increase or decrease the theoretical spraying distance for global optimization of the next spraying cycle also includes,

[0051] Based on the fact that the global quality evaluation deviation value is greater than the preset global quality evaluation deviation value, the theoretical spraying distance is increased or decreased.

[0052] Compared with the prior art, the beneficial effects of the present invention are that it provides a fully intelligent solution integrating perception, decision-making, execution and learning. By constructing a three-level closed-loop control architecture of real-time point compensation, regional feedforward optimization and global iterative evolution, it organically combines three-dimensional vision, multi-sensor fusion, trajectory planning, real-time control and machine learning technology, systematically overcomes the problems of uniformity, adaptability and consistency in complex mold spraying, and realizes a fundamental leap from automation to intelligence in mold release agent spraying.

[0053] Furthermore, the automatic region segmentation method based on three-dimensional point cloud geometric features of this invention divides the mold to be sprayed into multiple different types of feature regions, including the core forming surface, deep hole and narrow groove area and non-spraying area, realizing the refinement and adaptability of the spraying strategy, laying a solid foundation for subsequent differentiated spraying based on individual feature regions, enabling the robotic arm to understand the mold structure like an experienced worker, fundamentally solving the technical pain point that a single trajectory cannot adapt to complex cavities.

[0054] Furthermore, this invention introduces a real-time spraying quality deviation coefficient calculated based on the actual monitored temperature and actual spraying distance. Based on this coefficient, it determines whether the spraying quality of the current spraying point is qualified and formulates a dual-modal real-time compensation strategy according to the deviation value of the quality deviation coefficient. This enables the system to sensitively perceive minute state changes at the spraying point and intelligently choose to adjust the spraying flow rate or the motion posture for compensation according to the severity of the deviation. This stabilizes the spraying process in a very short time and effectively ensures the instantaneous spraying quality of each point.

[0055] Furthermore, this invention establishes a phase evaluation and a feedforward compensation mechanism based on the coefficient of variation of regional film thickness uniformity calculated from the regional film thickness uniformity. This allows the system to immediately correct the robotic arm motion parameters and spraying parameters of the completed area from the results after completing the spraying of a region, and convert them into feedforward compensation instructions for the next similar area. This significantly improves the consistency within a single spraying task and enhances the spraying quality of the same area.

[0056] Furthermore, this invention obtains the average film thickness reflecting the actual amount of release agent sprayed on the entire mold surface by detecting the film thickness after the entire mold is sprayed, and calculates the film thickness uniformity reflecting the quality of the release agent spraying on the entire mold surface to obtain a global spraying quality evaluation value. Based on this, an overall spraying evaluation is performed and a global optimization strategy for the next spraying cycle is formulated. It intelligently selects whether to optimize the spraying fan width to improve coverage uniformity or to calibrate the theoretical spraying distance to return to the optimal working point. It can use the result of a single spraying task as the basis for executing the spraying task in the next spraying cycle, ensuring that the production quality can cross multiple production cycles and steadily and autonomously converge and evolve towards the preset goal, realizing a leap from single task control to long-term production quality management. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the steps of a robotic arm control method for applying a release agent according to an embodiment of the present invention.

[0058] Figure 2 This is a logic block diagram illustrating how the robotic arm determines whether the current spraying quality of a single feature area is qualified based on a real-time spraying quality deviation coefficient, according to an embodiment of the present invention.

[0059] Figure 3 This is a logic block diagram of an embodiment of the present invention for determining a real-time compensation strategy for the motion parameters or spraying parameters of a robotic arm based on the deviation value of the mass deviation coefficient.

[0060] Figure 4 This is a logic block diagram illustrating how the staged spraying quality of a single feature region is determined based on the coefficient of variation of the regional film thickness uniformity according to an embodiment of the present invention.

[0061] Figure 5 This is a logic block diagram of the stage compensation strategy for determining the motion parameters or spraying parameters of the robotic arm for spraying the next area with the same characteristics based on the deviation value of the coefficient of variation in an embodiment of the present invention.

[0062] Figure 6 This is a logic block diagram of an embodiment of the present invention, which determines whether the overall coating quality of the mold is qualified based on the global coating quality evaluation value.

[0063] Figure 7 This is a logic block diagram of an embodiment of the present invention for determining a global optimization strategy for the next spraying cycle of robotic arm motion parameters or spraying parameters based on the global quality evaluation deviation value. Detailed Implementation

[0064] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0065] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0066] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0067] Please see Figure 1 As shown, Figure 1 This is a flowchart illustrating the steps of a robotic arm control method for applying a release agent according to an embodiment of the present invention.

[0068] The present invention provides a robotic arm control method for applying release agent, comprising:

[0069] Step S1: Obtain the three-dimensional point cloud data of the mold to be coated. Based on the geometric features of the three-dimensional point cloud data, automatically divide the surface of the mold to be coated into several different types of feature regions.

[0070] Step S2: After dividing the mold to be sprayed into feature regions, the robot arm predicts and generates the initial spraying trajectory of the current mold to be sprayed based on the feature regions and the historical high-quality spraying process database, and then performs the spraying.

[0071] Step S3: During the spraying process, the actual temperature of the surface of a single feature area of ​​the mold and the actual spraying distance between the spray gun and the single feature area are monitored in real time, and the real-time spraying quality deviation coefficient is calculated. Based on the real-time spraying quality deviation coefficient, it is determined whether the spraying quality of the current spraying point of the single feature area of ​​the current mold by the robotic arm is qualified.

[0072] Step S4: Under the condition of being unqualified, based on the deviation value of the quality deviation coefficient between the real-time spraying quality deviation coefficient and the threshold of the real-time spraying quality deviation coefficient, determine to increase the spraying flow rate of the release agent and reduce the moving speed of the robotic arm at the current nozzle position and stabilize the spraying posture of the spray gun at the current spraying position.

[0073] Step S5: After completing the spraying of a single feature area, obtain the actual film thickness distribution data of the surface of the single feature area, calculate the uniform variation coefficient of the film thickness in the area, and determine whether the stage spraying quality of the single feature area is qualified based on the uniform variation coefficient of the film thickness in the area.

[0074] Step S6: Under the condition that the spraying quality of the determined stage is unqualified, based on the deviation value of the coefficient of variation of the uniform film thickness of the region from the threshold of the coefficient of variation of the uniform film thickness of the region, determine to perform synchronous increase of release agent spraying flow rate and atomization pressure, synchronous decrease of robotic arm moving speed and decrease of path scanning spacing for the next region with the same characteristics.

[0075] Step S7: After completing the spraying of the entire mold, obtain the film thickness distribution data of the entire mold surface and calculate the global spraying quality evaluation value. Determine whether the overall spraying quality of the mold is qualified based on the global spraying quality evaluation value.

[0076] Step S8: Under the condition that the overall spraying quality is unqualified, based on the global quality evaluation deviation value between the global spraying quality evaluation value and the preset global spraying quality evaluation value, determine to perform global optimization for the next spraying cycle by increasing the theoretical spraying fan width or increasing or decreasing the theoretical spraying distance.

[0077] Specifically, this invention provides a fully intelligent solution integrating perception, decision-making, execution, and learning. By constructing a three-level closed-loop control architecture of real-time point compensation, regional feedforward optimization, and global iterative evolution, it organically combines 3D vision, multi-sensor fusion, trajectory planning, real-time control, and machine learning technologies, systematically overcoming the challenges of uniformity, adaptability, and consistency in complex mold spraying, and achieving a fundamental leap from automation to intelligence in mold release agent spraying.

[0078] In this embodiment of the invention, step S1 involves acquiring the initial 3D point cloud of the mold and sequentially performing preprocessing, normal vector and curvature estimation, region segmentation, feature recognition, and result optimization. In practical applications, noise is first removed from the initial point cloud. Then, principal component analysis is used to calculate the normal vector and curvature of each point. Based on this, a region growing algorithm is used for preliminary segmentation to obtain multiple feature regions of different types. The algorithm starts with the point with the smallest curvature in the point cloud as the initial seed point, and points in its neighborhood with a normal vector angle less than 20° and a curvature difference less than 0.02 are grouped into the same region. The newly added points are used as new seed points for iterative growth until the region cannot be expanded further. Subsequently, a new point with the smallest curvature is selected from the remaining points, and the above process is repeated until the entire point cloud is traversed, resulting in multiple initial point cloud clusters. The segmented initial point cloud clusters are then further processed. The process involves feature recognition and classification. Clusters with an average curvature less than 0.075 and containing more than 8000 points are identified as core forming surfaces. Next, for each cluster, it is projected onto a plane along the main normal of its point cloud, and an outer cuboid is calculated. The minimum size of the outer cuboid is defined as the width, and the size along the main normal is defined as the depth. The ratio of depth to width is calculated to obtain the depth-width ratio. Clusters with a depth-width ratio greater than 3.0 are identified as deep holes or narrow groove areas. Finally, with the mold CAD design file pre-stored in the system, the segmented 3D point cloud is registered with the CAD model using an iterative nearest-point algorithm to achieve coordinate system unification. Subsequently, by querying the registered CAD model, the corresponding point cloud areas belonging to the geometric surfaces of the mold parting surface and the positioning pin hole are marked as non-painted areas.

[0079] In this embodiment of the invention, step S2 queries a historical high-quality spraying process database through the segmented feature regions. This database stores optimized combinations of robotic arm motion parameters and theoretical spraying parameters corresponding to different region types. For each different feature region, the system automatically matches and binds the robotic arm motion parameters and theoretical spraying parameters. After parameter matching, the centroid of each feature region is first abstracted as a path point, and the nearest neighbor search algorithm of the traveling salesman problem is applied. With the goal of minimizing the sum of Euclidean distances the robotic arm travels between region centroids, a region access sequence is calculated as the movement order between regions. Then, based on the movement order and the internal parameters of the regions, the theoretical spraying parameters are used to determine the optimal path point. The "bow"-shaped scanning pattern, determined by the theoretical spraying fan width, generates a continuous sequence of path points in Cartesian space. Then, a cubic B-spline curve is used to fit the path points to ensure smooth motion. Subsequently, using the DH parameter model of this robotic arm, the Cartesian space path points are converted into an angle sequence in joint space through inverse kinematics. The angles, velocities, and accelerations of each joint are checked to ensure they are within physical limits. Finally, the trajectory program is loaded into the provided RobotStudio offline programming simulation software for virtual operation. After confirming there is no collision risk using the software's built-in collision detection function, the trajectory file containing the spatial path, velocities, attitudes, and synchronous spraying instructions is downloaded to the robotic arm controller, and spraying is executed.

[0080] The theoretical motion parameters of the robotic arm include the theoretical moving speed, theoretical spraying distance, and theoretical spray gun end posture within the corresponding feature area. The theoretical spraying parameters include the theoretical release agent flow rate, theoretical atomization pressure, and theoretical spraying fan width.

[0081] Specifically, the automatic region segmentation method based on three-dimensional point cloud geometric features of this invention divides the mold to be sprayed into multiple different types of feature regions, including the core forming surface, deep hole and narrow groove area and non-spraying area, realizing the refinement and adaptability of the spraying strategy, laying a solid foundation for subsequent differentiated spraying based on individual feature regions, enabling the robotic arm to understand the mold structure like an experienced worker, fundamentally solving the technical pain point that a single trajectory cannot adapt to complex cavities.

[0082] Please see Figure 2 As shown, it is a logic block diagram of an embodiment of the present invention for determining whether the spraying quality of the current spraying point of a single feature area by the robotic arm is qualified based on the real-time spraying quality deviation coefficient.

[0083] Specifically, during the spraying process, the actual temperature of the surface of a single characteristic area of ​​the mold and the actual spraying distance between the spray gun and the single characteristic area are monitored in real time, and a real-time spraying quality deviation coefficient is calculated. Based on the comparison result of the real-time spraying quality deviation coefficient and the real-time spraying quality deviation coefficient threshold, it is determined whether the spraying quality of the current spraying point of the single characteristic area by the robotic arm is qualified.

[0084] If the real-time spraying quality deviation coefficient is less than or equal to the real-time spraying quality deviation coefficient threshold, then the spraying quality of the current spraying point of the robotic arm for a single feature area is determined to be qualified.

[0085] If the real-time spraying quality deviation coefficient is greater than the real-time spraying quality deviation coefficient threshold, then it is determined that the spraying quality of the current spraying point of the single feature area by the robotic arm is unqualified.

[0086] In this embodiment of the invention, the real-time spraying quality deviation coefficient is obtained by first calculating the absolute value of the difference between the actual monitored temperature and the theoretical temperature at the current spraying point on the mold surface and then dividing it by the theoretical temperature to obtain the relative temperature deviation value; then calculating the absolute value of the difference between the actual monitored distance and the theoretical spraying distance between the spray gun and the current spraying point on the mold surface and then dividing it by the theoretical spraying distance to obtain the relative distance deviation value; then calculating the product of the relative temperature deviation value and the temperature weighting coefficient to obtain the temperature factor; calculating the product of the relative distance deviation value and the distance weighting coefficient to obtain the distance factor; and finally calculating the sum of the temperature factor and the distance factor to obtain the real-time spraying quality deviation coefficient.

[0087] The temperature weighting coefficient ranges from 0.5 to 0.7, with a preferred value of 0.6 in this invention. The distance weighting coefficient ranges from 0.3 to 0.5, with a preferred value of 0.4 in this invention. The sum of the temperature weighting coefficient and the distance weighting coefficient is 1. The preferred range and preferred value can be determined according to the actual situation and are not specifically limited here.

[0088] In this embodiment of the invention, the real-time spraying quality deviation coefficient threshold is the statistical upper limit of the real-time spraying quality deviation coefficient of each point in several historical high-quality spraying processes. By extracting the real-time spraying quality deviation coefficient of all points in 50 historical high-quality spraying tasks, calculating its average value and standard deviation, and setting the average value plus twice the standard deviation as the real-time spraying quality deviation coefficient threshold, the value range is 0.15 to 0.25. The preferred value in this invention is 0.20. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0089] Please see Figure 3As shown, it is a logic block diagram of the real-time compensation strategy for the motion parameters or spraying parameters of the robotic arm based on the deviation value of the mass deviation coefficient in an embodiment of the present invention.

[0090] Specifically, when it is determined that the spraying quality of the current spraying point of a single feature area by the robotic arm is unqualified, a real-time compensation strategy for the robotic arm's motion parameters or spraying parameters is determined based on the comparison result of the quality deviation coefficient deviation value and the preset quality deviation coefficient deviation value.

[0091] If the deviation value of the quality deviation coefficient is less than or equal to the preset quality deviation coefficient value, then the first compensation strategy for real-time compensation of the spraying parameters is determined to be executed.

[0092] If the deviation value of the mass deviation coefficient is greater than the preset mass deviation coefficient deviation value, then a second compensation strategy for real-time compensation of the robot arm motion parameters is determined to be executed.

[0093] In this embodiment of the invention, the deviation value of the quality deviation coefficient is the difference between the real-time spraying quality deviation coefficient and the real-time spraying quality deviation coefficient threshold.

[0094] In this embodiment of the invention, the preset quality deviation coefficient deviation value is the average value of the quality deviation coefficient deviation values ​​of all unqualified points in a number of historical high-quality spraying processes. Specifically, all points with quality deviation coefficient deviation values ​​greater than 0 in 50 historical spraying processes are taken, the average value is calculated, and the average value is used as the preset quality deviation coefficient deviation value.

[0095] In this embodiment of the invention, the value of the preset quality deviation coefficient ranges from 0.05 to 0.10, and the preferred value is 0.075. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0096] In this embodiment of the invention, when the deviation value of the quality deviation coefficient is less than or equal to the preset quality deviation coefficient deviation value, it indicates that there is a slight deviation. At this time, the first compensation strategy is to compensate the spraying parameters in real time, that is, to increase the spraying flow rate of the release agent. Specifically, the adjustment method is to first calculate the product of the flow rate compensation coefficient and the deviation value of the quality deviation coefficient to obtain the flow rate compensation factor, and finally calculate the product of the theoretical release agent flow rate at the current point in the theoretical spraying parameters and the flow rate compensation factor to obtain the real-time flow rate after compensation.

[0097] Specifically, the value range of the flow compensation coefficient is 0.7 to 1.2, and the preferred value in this invention is 1.0. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0098] In this embodiment of the invention, when the deviation value of the mass deviation coefficient is greater than the preset mass deviation coefficient deviation value, it indicates a serious deviation. At this time, the second compensation strategy is to compensate the motion parameters of the robotic arm and the spraying posture of the spray gun in real time, that is, to reduce the moving speed of the robotic arm at the current nozzle position and stabilize the spraying posture of the spray gun at the current spraying position. Specifically, the adjustment method is as follows: First, calculate the product of the speed compensation coefficient and the mass deviation coefficient deviation value by subtracting the value 1 to obtain the speed compensation factor. Then, calculate the product of the theoretical moving speed at the current position in the theoretical motion parameters of the robotic arm and the speed compensation factor to obtain the real-time moving speed after compensation. Based on the deviation value between the actual spraying distance and the theoretical spraying distance monitored in real time, dynamically adjust the pitch angle and yaw angle of the spray gun end through robot inverse kinematics calculation, so that the angle between the spray gun axis and the theoretical normal direction of the mold surface is always kept within ±5°, so as to ensure that the atomized cone beam is facing the spraying surface.

[0099] Specifically, the speed compensation coefficient ranges from 0.4 to 0.8, and the preferred value in this invention is 0.5. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0100] Specifically, this invention introduces a real-time spraying quality deviation coefficient calculated based on the actual monitored temperature and actual spraying distance. Based on this coefficient, it determines whether the spraying quality of the current spraying point is qualified and formulates a dual-modal real-time compensation strategy according to the deviation value of the quality deviation coefficient. This enables the system to sensitively perceive minute state changes at the spraying point and intelligently choose to adjust the spraying flow rate or the motion posture for compensation according to the severity of the deviation. This stabilizes the spraying process in a very short time and effectively ensures the instantaneous spraying quality of each point.

[0101] Please see Figure 4 As shown, it is a logic block diagram of an embodiment of the present invention for determining whether the stage spraying quality of a single feature area is qualified based on the coefficient of variation of the uniformity of the regional film thickness.

[0102] Specifically, after real-time compensation of the robotic arm motion parameters or spraying parameters is completed at the current spraying point of a single feature area, the spraying of the single feature area continues. After the spraying of the single feature area is completed, the actual film thickness distribution data of the surface of the single feature area is obtained, and the regional film thickness uniformity variation coefficient is calculated. Based on the comparison result of the regional film thickness uniformity variation coefficient and the regional film thickness uniformity variation coefficient threshold, it is determined whether the stage spraying quality of the single feature area is qualified.

[0103] If the coefficient of variation of the uniform film thickness in the region is less than or equal to the threshold of the coefficient of variation of the uniform film thickness in the region, then the stage spraying quality of a single feature region is determined to be qualified.

[0104] If the coefficient of variation of the film thickness uniformity in the region is greater than the threshold of the coefficient of variation of the film thickness uniformity in the region, then the stage spraying quality of a single feature region is determined to be unqualified.

[0105] In this embodiment of the invention, the coefficient of variation of the film thickness uniformity in the region is the ratio of the standard deviation to the average value of all film thickness measurement points within a single characteristic region after spraying is completed.

[0106] In this embodiment of the invention, the threshold value of the uniform variation coefficient of the regional film thickness is the upper limit value of the uniform variation coefficient of the regional film thickness of the same type of characteristic region in several historical high-quality spraying processes, and the value range is 0.09 to 0.11. The preferred value in this invention is 0.1. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0107] Please see Figure 5 As shown, it is a logic block diagram of the stage compensation strategy for determining the robotic arm motion parameters or spraying parameters for spraying the next area with the same characteristics based on the deviation value of the coefficient of variation in an embodiment of the present invention.

[0108] Specifically, when the coating quality is determined to be substandard in a given stage, a stage compensation strategy for the robotic arm motion parameters or coating parameters for the next area with the same characteristics is determined based on the comparison between the coefficient of variation deviation value and the preset coefficient of variation deviation value.

[0109] If the coefficient of variation deviation value is less than or equal to the preset coefficient of variation deviation value, then the first stage compensation strategy for the spraying parameters is determined to be executed.

[0110] If the deviation value of the coefficient of variation is greater than the preset deviation value of the coefficient of variation, then it is determined that the second-stage compensation strategy for the motion parameters of the robotic arm will be executed.

[0111] In this embodiment of the invention, the coefficient of variation deviation value is the difference between the uniform coefficient of variation of the regional film thickness and the threshold value of the uniform coefficient of variation of the regional film thickness.

[0112] In this embodiment of the invention, the preset coefficient of variation deviation value ranges from 0.02 to 0.04, and the preferred value is 0.03. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0113] In this embodiment of the invention, when the coefficient of variation deviation value is less than or equal to the preset coefficient of variation deviation value, it indicates a slight deviation in the uniformity of the film thickness in the region. At this time, the first stage compensation strategy is to perform stage feedforward compensation on the spraying parameters. That is, based on the theoretical spraying parameters of the next region with the same characteristics, the release agent spraying flow rate and atomization pressure are increased simultaneously. Specifically, the adjustment method is as follows: First, the product of the value 1 and the spraying parameter compensation coefficient and the coefficient of variation deviation value is calculated to obtain the spraying parameter compensation factor. Then, the product of the theoretical release agent flow rate and the spraying parameter compensation factor is calculated to obtain the compensated release agent spraying flow rate, and the product of the theoretical atomization pressure and the spraying parameter compensation factor is calculated to obtain the compensated atomization pressure.

[0114] Specifically, the value range of the coating parameter compensation coefficient is 0.5 to 1.0, and the preferred value in this invention is 0.8. The preferred value range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0115] In this embodiment of the invention, when the coefficient of variation deviation value is greater than the preset coefficient of variation deviation value, it indicates that the film thickness uniformity of the region is seriously deviated. At this time, the second-stage compensation strategy is to perform stage feedforward compensation on the robot arm motion parameters, that is, in the spraying of the next area with the same feature, the robot arm moving speed is reduced and the path scanning spacing is reduced simultaneously. The specific adjustment method is as follows: first, the product of the speed compensation coefficient and the coefficient of variation deviation value is calculated to obtain the speed compensation factor. Then, the product of the theoretical moving speed and the speed compensation factor is calculated to obtain the compensated moving speed. At the same time, the path scanning spacing of the next area with the same feature is reduced, that is, the scanning spacing is reduced from 5mm~8mm, which is suitable for large flat areas, to 2mm~3mm, which is more suitable for fine spraying, to ensure that the spray gun trajectory covers the complex cavity more fully, thereby directly improving the film thickness uniformity.

[0116] Specifically, this invention establishes a phase evaluation and a feedforward compensation mechanism based on the coefficient of variation of regional film thickness uniformity calculated from the regional film thickness uniformity. This allows the system to immediately correct the robotic arm motion parameters and spraying parameters of the completed area from the results after completing the spraying of a region, and convert them into feedforward compensation instructions for the next similar area. This significantly improves the consistency within a single spraying task and enhances the spraying quality of the same area.

[0117] Please see Figure 6 As shown, it is a logic block diagram of an embodiment of the present invention for determining whether the overall coating quality of the mold is qualified based on the global coating quality evaluation value.

[0118] Specifically, after implementing the stage compensation strategy for the staged coating quality, the coating task continues to be performed on the mold. After the entire mold is coated, the film thickness distribution data of the entire mold surface is acquired and the global coating quality evaluation value is calculated. Based on the comparison result between the global coating quality evaluation value and the preset global coating quality evaluation value, it is determined whether the overall coating quality of the mold is qualified.

[0119] If the global coating quality evaluation value is greater than or equal to the preset global coating quality evaluation value, then the overall coating quality is determined to be qualified.

[0120] If the global coating quality evaluation value is less than the preset global coating quality evaluation value, then the overall coating quality is determined to be unqualified.

[0121] In this embodiment of the invention, the global coating quality evaluation value is obtained by first acquiring the film thickness at all measurement points on the surface of the mold after overall coating and calculating the average film thickness, then calculating the ratio of the standard deviation of all film thickness values ​​to their average value to obtain the relative fluctuation of film thickness, then calculating the film thickness uniformity by subtracting the relative fluctuation of film thickness from the value 1, then calculating the average film thickness factor by multiplying the average film thickness by the first weighting coefficient, calculating the film thickness uniformity factor by multiplying the film thickness uniformity by the second weighting coefficient, and finally calculating the sum of the average film thickness factor and the film thickness uniformity factor to obtain the global coating quality evaluation value.

[0122] Specifically, the first weighting coefficient ranges from 0.4 to 0.6, and is preferably 0.5 in this invention. The second weighting coefficient ranges from 0.4 to 0.6, and is preferably 0.5 in this invention. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0123] In this embodiment of the invention, the preset global spraying quality evaluation value ranges from 0.90 to 0.98, and the preferred value is 0.95. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0124] Please see Figure 7 As shown, it is a logic block diagram of an embodiment of the present invention for determining the global optimization strategy for the next spraying cycle of the robotic arm motion parameters or spraying parameters based on the global quality evaluation deviation value.

[0125] Specifically, given that the overall coating quality is determined to be substandard, a global optimization strategy for the next coating cycle is determined based on the comparison between the global quality evaluation deviation value and the preset global quality evaluation deviation value.

[0126] If the global quality evaluation deviation value is less than or equal to the preset global quality evaluation deviation value, then the first global optimization strategy for the spraying parameters is determined to be executed.

[0127] If the global quality evaluation deviation value is greater than the preset global quality evaluation deviation value, then the second global optimization strategy for the robot arm motion parameters is determined to be executed.

[0128] In this embodiment of the invention, the global quality evaluation deviation value is the difference between the preset global spraying quality evaluation value and the global spraying quality evaluation value.

[0129] In this embodiment of the invention, the preset global quality evaluation deviation value ranges from 0.02 to 0.05, and the preferred value is 0.03. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0130] In this embodiment of the invention, when the global quality evaluation deviation value is less than or equal to the preset global quality evaluation deviation value, it indicates that the overall spraying is slightly inadequate in quality. At this time, the first global optimization strategy is to optimize the spraying parameters, that is, to increase the theoretical spraying fan width. The specific adjustment method is as follows: first, calculate the product of the value 1 and the fan width optimization coefficient and the global quality evaluation deviation value to obtain the fan width optimization factor; finally, calculate the product of the theoretical spraying fan width and the fan width optimization factor to obtain the optimized theoretical spraying fan width, and update it to the database for the next spraying cycle.

[0131] Specifically, the value range of the fan width optimization coefficient is 1.0 to 3.0, and the preferred value in this invention is 2.0. The preferred value range and the preferred value can be determined according to the actual situation, and no specific limitation is made here.

[0132] In this embodiment of the invention, when the global quality evaluation deviation value is greater than the preset global quality evaluation deviation value, it indicates that the quality is seriously insufficient. At this time, the second global optimization strategy is to optimize the motion parameters of the robotic arm, that is, to optimize the theoretical spraying distance. Specifically, the adjustment method is as follows: first, calculate 1 plus the product of the spraying distance optimization coefficient and the global quality evaluation deviation value to obtain the spraying distance optimization factor; then calculate the product of the theoretical spraying distance and the spraying distance optimization factor to obtain the optimized theoretical spraying distance, and update it to the database for the next spraying cycle.

[0133] It is worth noting that the value of the spraying distance optimization coefficient can be positive or negative, and is determined by the system based on historical data trends: if historical data shows that the current distance setting generally leads to spraying too close, the coefficient is positive to increase the distance; if it generally leads to spraying too far, the coefficient is negative to decrease the distance.

[0134] Specifically, the absolute value of the spraying distance optimization coefficient ranges from 0.5 to 2.0, and the preferred value in this invention is 1.0. The preferred range and preferred value can be determined according to the actual situation, and are not specifically limited here.

[0135] Specifically, this invention obtains the average film thickness reflecting the actual amount of release agent sprayed on the entire mold surface by detecting the film thickness after the entire mold is sprayed, and calculates the film thickness uniformity reflecting the quality of the release agent spraying on the entire mold surface to obtain a global spraying quality evaluation value. Based on this, an overall spraying evaluation is performed and a global optimization strategy for the next spraying cycle is formulated. It intelligently selects whether to optimize the spraying fan width to improve coverage uniformity or to calibrate the theoretical spraying distance to return to the optimal working point. It can use the result of a single spraying task as the basis for executing the spraying task in the next spraying cycle, ensuring that the production quality can cross multiple production cycles and steadily and autonomously converge and evolve towards the preset goal, realizing a leap from single task control to long-term production quality management.

[0136] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A robotic arm control method for spraying release agent, characterized in that, include, Acquire the three-dimensional point cloud data of the mold to be coated, and divide the surface of the mold to be coated into several different types of feature regions; Based on the feature region and the spraying process database, the robot arm predicts and generates the initial spraying trajectory for the current mold to be sprayed and performs the spraying. During the spraying process, the surface feature parameters of a single feature area of ​​the mold are monitored in real time, and the real-time spraying quality deviation coefficient is obtained to determine whether the spraying quality of the current spraying point of a single feature area of ​​the mold is qualified. The process of determining whether the spraying quality of the current spraying point of a single feature area of ​​the mold is qualified includes, The actual monitored temperature of the surface of a single feature area and the actual spraying distance between the spray gun and the single feature area are obtained; The real-time spraying quality deviation coefficient is obtained by weighted summation. The real-time spraying quality deviation coefficient is compared with the real-time spraying quality deviation coefficient threshold. Based on the fact that the real-time spraying quality deviation coefficient is greater than the real-time spraying quality deviation coefficient threshold, it is determined that the spraying quality of the current spraying point in a single feature area is unqualified; The control operation to be performed is determined based on the deviation value of the quality deviation coefficient. The control operation is one or more of the following: increasing the release agent spraying flow rate, reducing the moving speed of the robotic arm at the current nozzle position, and stabilizing the spraying posture of the spray gun at the current spraying position. After completing the spraying of a single feature area, the surface area feature parameters of the single feature area after spraying are obtained to determine whether the stage spraying quality of the single feature area is qualified. The process of determining whether the staged spraying quality of a single feature area is qualified includes, Obtain the film thickness at all film thickness measurement points on the surface of a single feature region; Calculate the standard deviation and mean of all film thickness measurement points within a single feature area after spraying is completed; The ratio of the standard deviation to the mean is used to obtain the coefficient of variation for uniform film thickness in the region. The coefficient of variation of the uniform film thickness in the region is compared with the threshold of the coefficient of variation of the uniform film thickness in the region. Based on the fact that the coefficient of variation of the film thickness uniformity in the region is greater than the threshold of the coefficient of variation of the film thickness uniformity in the region, it is determined that the stage spraying quality of a single feature region is unqualified. Based on the coefficient of variation deviation value, determine to simultaneously increase the release agent spraying flow rate and atomization pressure, simultaneously reduce the robotic arm moving speed and reduce the path scanning interval for the next area with the same characteristics; After the entire mold is coated, the overall coating quality evaluation value is obtained to determine whether the overall coating quality of the mold is qualified. The process of determining whether the overall coating quality of the mold is up to standard includes, Obtain the film thickness at all measurement points on the entire mold surface and calculate the average film thickness; The relative degree of film thickness fluctuation is obtained by calculating the ratio of the standard deviation of all film thickness values ​​to the average film thickness. The film thickness uniformity is obtained by subtracting the difference between the numerical value 1 and the relative fluctuation of the film thickness. The global coating quality evaluation value is obtained by weighted summation; The global spraying quality evaluation value is compared with the preset global spraying quality evaluation value; The overall spraying quality is determined to be unqualified based on the fact that the global spraying quality evaluation value is less than the preset global spraying quality evaluation value; Based on the global quality evaluation deviation value, determine whether to increase the theoretical spraying fan width or increase or decrease the theoretical spraying distance to optimize the next spraying cycle globally.

2. The robotic arm control method for spraying release agent according to claim 1, characterized in that, The process of determining, based on the deviation value of the quality deviation coefficient, to increase the release agent spraying flow rate and reduce the moving speed of the robotic arm at the current nozzle position, and to stabilize the spraying posture of the spray gun at the current spraying position, includes... The difference between the real-time spraying quality deviation coefficient and the real-time spraying quality deviation coefficient threshold is used to obtain the quality deviation coefficient deviation value; The deviation value of the quality deviation coefficient is compared with the preset quality deviation coefficient value; Based on the fact that the deviation value of the quality deviation coefficient is less than or equal to the preset quality deviation coefficient value, it is determined to increase the spraying flow rate of the release agent.

3. The robotic arm control method for spraying release agent according to claim 2, characterized in that, The process of determining the increase of release agent spraying flow rate and the reduction of the robotic arm's moving speed at the current nozzle position and stabilizing the spraying posture of the spray gun at the current spraying position based on the quality deviation coefficient deviation value also includes... Based on the fact that the deviation value of the mass deviation coefficient is greater than the preset mass deviation coefficient deviation value, it is determined to reduce the moving speed of the robotic arm at the current nozzle position and stabilize the spraying posture of the spray gun at the current spraying position.

4. The robotic arm control method for spraying release agent according to claim 3, characterized in that, The process of determining, based on the coefficient of variation deviation value, to simultaneously increase the release agent spraying flow rate and atomization pressure, and simultaneously decrease the robotic arm movement speed and path scanning interval for the next region with the same characteristics includes: The difference between the coefficient of variation of the film thickness uniformity in the region and the threshold value of the coefficient of variation of the film thickness uniformity in the region is used to obtain the coefficient of variation deviation value. The deviation value of the coefficient of variation is compared with the preset deviation value of the coefficient of variation; Based on the fact that the coefficient of variation deviation value is less than or equal to the preset coefficient of variation deviation value, the release agent spraying flow rate and atomization pressure are increased synchronously.

5. The robotic arm control method for spraying release agent according to claim 4, characterized in that, The process of determining, based on the coefficient of variation deviation value, to simultaneously increase the release agent spraying flow rate and atomization pressure, and simultaneously decrease the robotic arm movement speed and path scanning interval for the next region with the same characteristics also includes... Based on the fact that the coefficient of variation deviation value is greater than the preset coefficient of variation deviation value, it is determined to synchronously reduce the robot arm's moving speed and decrease the path scanning interval.

6. The robotic arm control method for spraying release agent according to claim 5, characterized in that, The process of determining whether to increase the theoretical spraying sector width or increase or decrease the theoretical spraying distance to globally optimize the next spraying cycle based on the global quality evaluation deviation value includes the following steps: The difference between the global coating quality and the preset global coating quality evaluation value is used to obtain the global quality evaluation deviation value; The global quality evaluation deviation value is compared with the preset global quality evaluation deviation value; Based on the fact that the global quality evaluation deviation value is less than or equal to the preset global quality evaluation deviation value, the theoretical spraying fan width is increased.

7. The robotic arm control method for spraying release agent according to claim 6, characterized in that, The process of determining whether to increase the theoretical spraying fan width or increase or decrease the theoretical spraying distance to globally optimize the next spraying cycle based on the global quality evaluation deviation value also includes... Based on the fact that the global quality evaluation deviation value is greater than the preset global quality evaluation deviation value, the theoretical spraying distance is increased or decreased.

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