Robot control data processing method

Abstract

The invention relates to the technical field of robot control, and discloses a robot control data processing method, which comprises the following steps of: 1, acquiring operation parameters of all manipulators, design data of all molds and detection data of all workpieces, and classifying the operation parameters, the design data and the detection data to form a data set; 2, analyzing the area and the overall volume of each plane of the mold, and generating a corresponding production data set; 3, according to the production data set, a three-dimensional coordinate system and a three-dimensional model are constructed, the operation frequency, the operation force and the rotation angle needed by a mechanical arm for completing demolding are analyzed in combination with a mechanical data set and a mold data set, a corresponding demolding data set is generated, the mechanical arm is controlled to conduct demolding operation, and three-dimensional modeling is high in flexibility and adaptability; 4, analyzing the fluctuation trend of the number of the workpiece flaws, and generating a corresponding quality inspection index; and fifthly, an index threshold value of a fixed range is set, the demolding stability of the mechanical arm is judged, a corresponding management signal is generated, efficient demolding is achieved, and the production quality is good.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and in particular to a robot control data processing method. Background Art

[0002] With the advancement of science and technology and the development of automation technology, more and more processes have begun to use automated demoulding equipment to improve production efficiency and product quality. However, in certain specific cases, in the fields of rubber products, powder metallurgy and medical gloves, manual demoulding is still an important demoulding method due to the limitations of product characteristics and production conditions. In the production process of rubber products, especially in the manufacture of some rubber parts with complex shapes or special requirements, rubber products fit closely with the mold during the vulcanization process, especially in structures such as deep cavities and thin walls. Mechanical demoulding may cause product deformation or damage. In powder metallurgy manufacturing technology, it is difficult to demould smoothly for powder molded parts with certain special shapes or high viscosity materials by mechanical force, vibration or air pressure. In the production of medical gloves, especially in the production process of latex or PVC gloves, the operation of removing gloves from the hand mold requires special attention to strength. Although there are automated demoulding equipment such as air blowing on the market, manual demoulding has greater advantages in certain cases.

[0003] At present, traditional robot control data processing methods have poor adaptability and require a lot of time for manual design and parameter adjustment to adapt to different demoulding tasks. In addition, in actual use, it is difficult to detect the failure of some robots in time, which may lead to inaccurate product processing, inconsistent dimensions or appearance defects, thus affecting product quality. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a robot control data processing method, which has the advantages of flexible adaptability of three-dimensional modeling, efficient demoulding and good production quality, and solves the problems of poor adaptability and poor production demoulding quality of traditional robot control data processing methods.

[0005] The present invention provides the following technical solution: a robot control data processing method, comprising the following steps: Step 1: Connect the central console and high-definition camera through the network to obtain the operating parameters of all robots, the design data of all molds, and the inspection data of all workpieces, and classify them into mechanical data sets, mold data sets, and workpiece data sets; Step 2: Based on the mold data set, analyze the area and overall volume of each plane of the mold and generate the corresponding production data set ; Step 3: According to the production data group , construct a three-dimensional coordinate system and 3D models , and then combined with the mechanical data set and the mold data set, the number of operations, operation force and rotation angle required for the robot to complete demoulding are analyzed to generate the corresponding demoulding data set , and control the robot to perform demoulding operation; Step 4: Analyze the fluctuation trend of the number of workpiece defects based on the workpiece data set and generate the corresponding quality inspection index ; Step 5: Set the index threshold with a fixed range , and combined with the quality inspection index , judge the demoulding stability of the robot and generate corresponding management signals.

[0006] Preferably, in step 1, the expression of the mechanical data set is , to The first to the Only the operating parameters of the robot, including operating length, operating force and rotation angle, Indicates the workbench number corresponding to a single robot; Preferably, in step 1, the expression of the mold data set is , to The first to the The design data of each mold includes structural properties, mold size and hardness. Indicates the workbench number corresponding to a single mold; Preferably, in step 1, the expression of the workpiece data set is , to The first to the The inspection data of each workpiece is the number of defects. Indicates the workbench number corresponding to a single workpiece.

[0007] Preferably, in step 2, the production data set The calculation process is as follows: Extract the mold data set The design data of the mold is marked as ; like The structural property of the mold is a solid mold. The surface length of each mold is marked as , will The surface width of each mold is marked as , will The surface height of each mold is marked as ; In the formula, represents a production data group, Indicates Multiply the surface length of the first mold by the surface width to get the The top and bottom areas of the mold's outer surface, Indicates Multiply the surface width of the first mold by the surface height to get the The left and right areas of the outer surface of the mold, Indicates Multiply the surface length of the first mold by the surface height to get the The front and back areas of the outer surface of the mold, Indicates The solid volume of the mold; like The structural property of the cavity mold is The cavity length of each mold is marked as , will The cavity width of each mold is marked as , will The cavity height of each mold is marked as ; In the formula, represents a production data group, Indicates The cavity length of the first mold is multiplied by the cavity width to get the The top and bottom areas of the mold cavity, Indicates The cavity width of the first mold is multiplied by the cavity height to get the The left and right areas of the mold cavity, Indicates The cavity length of the first mold is multiplied by the cavity height to get the The front and back areas of the mold cavity, Indicates The cavity volume of the mold.

[0008] Preferably, in step 3, the center point of the top surface of the workbench is taken as the origin Constructing a 3D coordinate system , and then the production data group Substitute into the three-dimensional coordinate system , build 3D models in proportion , 3D model The center point of the bottom surface and the origin coincide.

[0009] Preferably, in step 3, the demoulding data set The calculation process is as follows: Extract the first Only the operating parameters of the robot are marked as ; according to , will The operating length of the robot is marked as , will The operating force of the robot is marked as , will The rotation angle of the robot is marked as ; Extracting 3D Models The two endpoints that are farthest apart in the horizontal direction are marked as and ; In the formula, Indicates the demoulding data group, Indicates that according to the Euclidean distance formula, a three-dimensional model is obtained middle and The straight-line distance between two endpoints, The straight-line distance between the two endpoints divided by Only the operation length of the manipulator is used to obtain the number of operations of the manipulator. Indicates The hardness of the mold, Indicates the conversion factor for mold hardness, Indicates that the The hardness of each mold is converted into the corresponding operating force, which is the standard force. Indicates The ratio of the manipulator's operating force to the standard force, Representing 3D models The angle of the sharp corner, and Three-dimensional models The minimum and maximum angles of the sharp corners, Indicates that the The rotation angle of the robot arm is compared with the 3D model The sharp angle of the corner.

[0010] Preferably, in step 3, if the structural property of the mold is a solid mold, the manipulator is controlled to move along the three-dimensional model The bottom edge is lifted to achieve demoulding. If the structural property of the mold is a cavity mold, the robot is controlled to move along the 3D model. The bottom edge is bent inward to facilitate demoulding.

[0011] Preferably, in step 4, the quality inspection index The calculation process is as follows: According to the workbench number, filter out the workpieces produced in the same workbench, and mark the number of defects of the workpieces produced in the same workbench as , to The first to the second pieces on the same workbench are The number of defects in a production workpiece; In the formula, Represents the quality inspection index, Indicates the average number of defects obtained by dividing the total number of defects by the number of workpieces , Indicates the same workbench The number of defects in a production workpiece, It means that according to the variance formula, the variance value obtained is the quality inspection index of robot demoulding.

[0012] Preferably, in step 5, if the quality inspection index Below index threshold , indicating that the demoulding stability of the robot is poor, and a corresponding mechanical abnormality signal is generated to remind the management personnel to replace the spare parts of the robot.

[0013] Compared with the prior art, the present invention provides a robot control data processing method, which has the following beneficial effects: 1. The present invention connects the central control console and the high-definition camera through the network to obtain the operating parameters of all manipulators, the design data of all molds and the inspection data of all workpieces, and classifies them into mechanical data sets, mold data sets and workpiece data sets. According to the mold data sets, the area and overall volume of each plane of the mold are analyzed to generate the corresponding production data sets. , and then take the center point of the top surface of the workbench as the origin Constructing a 3D coordinate system , and then the production data group Substitute into the three-dimensional coordinate system , build 3D models in proportion , 3D model The center point of the bottom surface and the origin Overlap, according to the 3D model , mechanical data set and mold data set, analyze the number of operations, operation force and rotation angle required for the robot to complete demoulding, and generate the corresponding demoulding data set Once the robot's operating force exceeds the standard force, the system will intelligently adjust the force parameters. If the mold's structural attributes are solid molds, the robot will be controlled to move along the 3D model. The bottom edge is lifted to achieve demoulding. If the structural property of the mold is a cavity mold, the robot is controlled to move along the 3D model. The bottom edge is inward-curled to achieve demoulding, and the 3D modeling is flexible and adaptable.

[0014] 2. The present invention analyzes the fluctuation trend of the number of workpiece defects through the workpiece data set and generates the corresponding quality inspection index , and then set the index threshold with a fixed range , judge the demoulding stability of the robot, if the quality inspection index Below index threshold , indicating that the demoulding stability of the robot is poor, and the corresponding mechanical abnormality signal is generated to remind the management personnel to replace the spare parts of the robot, discover the cause of the fault in time, avoid wasting more production resources, and achieve efficient demoulding and good production quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Since the traditional robot control data processing method has poor adaptability, it takes a lot of time to manually design and adjust parameters to adapt to different demoulding tasks. In addition, in the actual use process, it is difficult to find the failure of some robots in time, which may lead to inaccurate product processing, inconsistent dimensions or appearance defects, thus affecting product quality. Therefore, a robot control data processing method is provided. Please refer to Figure 1 , a robot control data processing method, comprising the following steps: Step 1: Connect the central console and high-definition camera through the network to obtain the operating parameters of all robots, the design data of all molds, and the inspection data of all workpieces, and classify them into mechanical data sets, mold data sets, and workpiece data sets; The expression for the mechanical data set is , to The first to the Only the operating parameters of the robot, including operating length, operating force and rotation angle, Indicates the workbench number corresponding to a single robot. Each workbench corresponds to a robot for demoulding operation; The expression of the mold data set is , to The first to the The design data of each mold includes structural properties, mold size and hardness. Indicates the workbench number corresponding to a single mold and collects mold design parameters, which is conducive to subsequent rapid modeling and analysis of demoulding parameters; The expression of the artifact dataset is , to The first to the The inspection data of each workpiece is the number of defects. Indicates the workbench number corresponding to a single workpiece. Through the quality inspection data, the demoulding level of the robot can be quickly determined; Step 2: Based on the mold data set, analyze the area and overall volume of each plane of the mold and generate the corresponding production data set , the calculation process is as follows: Extract the mold data set The design data of the mold is marked as ; like The structural property is a solid mold, and the workpiece is covered on the outer surface of the mold. The surface length of each mold is marked as , will The surface width of each mold is marked as , will The surface height of each mold is marked as ; In the formula, represents a production data group, Indicates Multiply the surface length of the first mold by the surface width to get the The top and bottom areas of the mold's outer surface, Indicates Multiply the surface width of the first mold by the surface height to get the The left and right areas of the outer surface of the mold, Indicates Multiply the surface length of the first mold by the surface height to get the The front and back areas of the outer surface of the mold, Indicates The solid volume of the mold; like The structural property is a cavity mold, and the workpieces are embedded in the inner wall of the mold cavity. The cavity length of each mold is marked as , will The cavity width of each mold is marked as , will The cavity height of each mold is marked as ; In the formula, represents a production data group, Indicates The cavity length of the first mold is multiplied by the cavity width to get the The top and bottom areas of the mold cavity, Indicates The cavity width of the first mold is multiplied by the cavity height to get the The left and right areas of the mold cavity, Indicates The cavity length of the first mold is multiplied by the cavity height to get the The front and back areas of the mold cavity, Indicates The cavity volume of the mold; Step 3: According to the production data group , with the center point of the top surface of the workbench as the origin Constructing a 3D coordinate system , and then the production data group Substitute into the three-dimensional coordinate system , build 3D models in proportion , 3D model The center point of the bottom surface and the origin Overlap, according to the 3D model , mechanical data set and mold data set, analyze the number of operations, operation force and rotation angle required for the robot to complete demoulding, and generate the corresponding demoulding data set , the calculation process is as follows: Extract the first Only the operating parameters of the robot are marked as ; according to , will The operating length of the robot is marked as , will The operating force of the robot is marked as , will The rotation angle of the robot is marked as ; Extracting 3D Models The two endpoints that are farthest apart in the horizontal direction are marked as and ; In the formula, Indicates the demoulding data group, Indicates that according to the Euclidean distance formula, a three-dimensional model is obtained middle and The straight-line distance between two endpoints, The straight-line distance between the two endpoints divided by Only the operation length of the manipulator is used to obtain the number of operations of the manipulator. Indicates The hardness of the mold, Indicates the conversion factor for mold hardness, Indicates that the The hardness of each mold is converted into the corresponding operating force, which is the standard force. Indicates The ratio of the manipulator's operating force to the standard force, Representing 3D models The angle of the sharp corner, and Three-dimensional models The minimum and maximum angles of the sharp corners, Indicates that the The rotation angle of the robot arm is compared with the 3D model The sharp angle of the robot arm, once the operating force exceeds the standard force, the system will intelligently adjust the force parameters; If the structural attribute of the mold is a solid mold, control the robot to move along the 3D model The bottom edge is lifted to achieve demoulding. If the structural property of the mold is a cavity mold, the robot is controlled to move along the 3D model. The bottom edge is buckled inward to achieve demoulding, and the 3D modeling is flexible and adaptable; Step 4: Analyze the fluctuation trend of the number of workpiece defects based on the workpiece data set and generate the corresponding quality inspection index , the calculation process is as follows: According to the workbench number, filter out the workpieces produced in the same workbench, and mark the number of defects of the workpieces produced in the same workbench as , to The first to the second pieces on the same workbench The number of defects in a production workpiece; In the formula, Represents the quality inspection index, Indicates the average number of defects obtained by dividing the total number of defects by the number of workpieces. , Indicates the same workbench The number of defects in a production workpiece, It means that according to the variance formula, the variance value obtained is the quality inspection index of the robot demoulding; Step 5: Set the index threshold with a fixed range , and combined with the quality inspection index , judge the demoulding stability of the robot, if the quality inspection index Below index threshold , indicating that the demoulding stability of the robot is poor, and the corresponding mechanical abnormality signal is generated to remind the management personnel to replace the spare parts of the robot, discover the cause of the fault in time, avoid wasting more production resources, and achieve efficient demoulding and good production quality.

[0018] Example 1: In this experiment, a solid mold with a surface length of 5 cm, a surface width of 3 cm, and a surface height of 2 cm was selected as the experimental object. The production data set of the solid mold The calculation formula is as follows: In the formula, represents a production data group, represents the top area and bottom area of ​​the outer surface of the solid mold, represents the left and right areas of the outer surface of the solid mold, represents the front area and back area of ​​the outer surface of the solid mold, Represents the solid volume of the solid mold.

[0019] Example 2: In this experiment, a spherical mold was selected as the experimental object. After testing, in the three-dimensional model of the mold, the straight-line distance between the two farthest endpoints in the horizontal direction was 2.5 cm, the mold hardness was 20 degrees, the angle range of the mold sharp angle was 30°-90°, the single operation length of the manipulator was 0.2 cm, the operation force was 10 N, the rotation angle was 45°, the conversion coefficient for the mold hardness was 0.5, and the demoulding data set of the manipulator was The calculation formula is as follows: In the formula, Indicates the demoulding data group, The straight-line distance between the two endpoints is divided by the single operation length of the manipulator, and the number of operations of the manipulator is 12.5 times. It means converting the hardness of the mold into the corresponding operating force, which is the standard force. Indicates that the actual operating force is the same as the standard force. It means that the actual rotation angle of the robot arm matches the sharp angle of the 3D model.

[0020] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A robot control data processing method, characterized in that: The following steps are involved: Step 1: Connect the central console and high-definition camera through the network to obtain the operating parameters of all robots, the design data of all molds, and the inspection data of all workpieces, and classify them into mechanical data sets, mold data sets, and workpiece data sets; Step 2: Based on the mold data set, analyze the area and overall volume of each plane of the mold and generate the corresponding production data set ; Step 3: According to the production data group , construct a three-dimensional coordinate system and 3D models , and then combined with the mechanical data set and the mold data set, the number of operations, operation force and rotation angle required for the robot to complete demoulding are analyzed to generate the corresponding demoulding data set , and control the robot to perform demoulding operation; Step 4: Analyze the fluctuation trend of the number of workpiece defects based on the workpiece data set and generate the corresponding quality inspection index ; Step 5: Set the index threshold with a fixed range , and combined with the quality inspection index , judge the demoulding stability of the robot and generate corresponding management signals.

2. A robot control data processing method according to claim 1, characterized in that: In step 1, the expression of the mechanical data set is: , to The first to the Only the operating parameters of the robot, including operating length, operating force and rotation angle, Indicates the workbench number corresponding to a single robot.

3. A robot control data processing method according to claim 2, characterized in that: In step 1, the expression of the mold data set is: , to The first to the The design data of each mold includes structural properties, mold size and hardness. Indicates the workbench number corresponding to a single mold.

4. A robot control data processing method according to claim 3, characterized in that: In step 1, the expression of the workpiece data set is: , to The first to the The inspection data of each workpiece is the number of defects. Indicates the workbench number corresponding to a single workpiece.

5. A robot control data processing method according to claim 4, characterized in that: In the step 2, the production data set The calculation process is as follows: Extract the mold data set The design data of the mold is marked as ; like The structural property of the mold is a solid mold. The surface length of each mold is marked as , will The surface width of each mold is marked as , will The surface height of each mold is marked as ; In the formula, represents a production data group, Indicates Multiply the surface length of the first mold by the surface width to get the The top and bottom areas of the mold's outer surface, Indicates Multiply the surface width of the first mold by the surface height to get the The left and right areas of the outer surface of the mold, Indicates Multiply the surface length of the first mold by the surface height to get the The front and back areas of the outer surface of the mold, Indicates The solid volume of the mold; like The structural property of the cavity mold is The cavity length of each mold is marked as , will The cavity width of each mold is marked as , will The cavity height of each mold is marked as ; In the formula, represents a production data group, Indicates The cavity length of the first mold is multiplied by the cavity width to get the The top and bottom areas of the mold cavity, Indicates The cavity width of the first mold is multiplied by the cavity height to get the The left and right areas of the mold cavity, Indicates The cavity length of the first mold is multiplied by the cavity height to get the The front and back areas of the mold cavity, Indicates The cavity volume of the mold.

6. A robot control data processing method according to claim 5, characterized in that: In step 3, the center point of the top surface of the workbench is taken as the origin. Constructing a 3D coordinate system , and then the production data group Substitute into the three-dimensional coordinate system , build 3D models in proportion , 3D model The center point of the bottom surface and the origin coincide.

7. A robot control data processing method according to claim 6, characterized in that: In the step 3, the demoulding data set The calculation process is as follows: Extract the first Only the operating parameters of the robot are marked as ; according to , will The operating length of the robot is marked as , will The operating force of the robot is marked as , will The rotation angle of the robot is marked as ; Extracting 3D Models The two endpoints that are farthest apart in the horizontal direction are marked as and ; In the formula, Indicates the demoulding data group, Indicates that according to the Euclidean distance formula, a three-dimensional model is obtained middle and The straight-line distance between two endpoints, The straight-line distance between the two endpoints divided by Only the operation length of the manipulator is used to obtain the number of operations of the manipulator. Indicates The hardness of the mold, Indicates the conversion factor for mold hardness, Indicates that the The hardness of each mold is converted into the corresponding operating force, which is the standard force. Indicates The ratio of the manipulator's operating force to the standard force, Representing 3D models The angle of the sharp corner, and Three-dimensional models The minimum and maximum angles of the sharp corners, Indicates that the The rotation angle of the robot arm is compared with the 3D model The sharp angle of the corner.

8. A robot control data processing method according to claim 7, characterized in that: In the step 3, if the structural property of the mold is a solid mold, the manipulator is controlled to move along the three-dimensional model. The bottom edge is lifted to achieve demoulding. If the structural property of the mold is a cavity mold, the robot is controlled to move along the 3D model. The bottom edge is bent inward to facilitate demoulding.

9. A robot control data processing method according to claim 8, characterized in that: In step 4, the quality inspection index The calculation process is as follows: According to the workbench number, filter out the workpieces produced in the same workbench, and mark the number of defects of the workpieces produced in the same workbench as , to The first to the second pieces on the same workbench The number of defects in a production workpiece; In the formula, Represents the quality inspection index, Indicates the average number of defects obtained by dividing the total number of defects by the number of workpieces. , Indicates the same workbench The number of defects in a production workpiece, It means that according to the variance formula, the variance value obtained is the quality inspection index of robot demoulding.

10. A robot control data processing method according to claim 9, characterized in that: In step 5, if the quality inspection index Below index threshold , indicating that the demoulding stability of the robot is poor, and a corresponding mechanical abnormality signal is generated to remind the management personnel to replace the spare parts of the robot.

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