A welding robot and injection molding part positioning simulation linkage method

Abstract

This invention relates to the field of welding technology and discloses a method for linkage between a welding robot and the positioning simulation of injection molded parts, comprising the following steps: Step 1: Constructing a dual-camera vision positioning system with a resolution of 4K and a positioning accuracy of ±0.05mm. After pre-processing and cleaning the injection molded part, it is placed on a positioning worktable. Image data of three preset feature holes on the injection molded part are simultaneously acquired by the dual cameras. The diameter of the holes is 5mm and the positional tolerance is ±0.1mm. Step 2: Performing grayscale enhancement and edge extraction processing on the acquired image data to accurately identify the center coordinates of the three feature holes. Combining the dual-camera calibration parameters, the translational deviation and rotational angle deviation of the injection molded part in the X and Y axes are calculated to generate a complete positioning deviation dataset. Step 3: Selecting RobotStudio simulation software. This invention achieves precise positioning: the 4K dual-camera vision system combined with a weighted iterative algorithm achieves a positioning accuracy of ±0.05mm, effectively avoiding glare interference and accurately capturing the deviation data of the injection molded part.

Description

Technical Field

[0001] This invention relates to the field of welding technology, specifically to a method for the linkage of welding robot and injection molded part positioning simulation. Background Technology

[0002] In the field of injection molded part welding, traditional welding operations often face multiple technical bottlenecks: On the one hand, the positioning of injection molded parts relies on manual calibration or single visual inspection, which is affected by factors such as surface reflection and clamping errors, resulting in insufficient positioning accuracy and difficulty in accurately capturing X-axis and Y-axis translational deviations and rotational angle deviations, leading to deviations between the welding path and actual requirements; on the other hand, welding path planning lacks precise simulation verification, fails to fully integrate the structural tolerances of injection molded parts, the constraints of the worktable and fixtures, and does not consider the dynamic constraints such as robot movement speed and acceleration, which easily leads to problems such as uneven paths and sudden stops and starts, thus affecting weld quality; at the same time, weld quality inspection is mostly limited to a single penetration depth index, lacking multi-dimensional monitoring of width and reinforcement height, and the positioning and welding process data are fragmented, with no full-process traceability mechanism, making it difficult to quickly trace and troubleshoot once quality problems occur; in addition, traditional methods have not established a dynamic linkage optimization mechanism between positioning and welding, and cannot promptly warn and pause when deviations exceed the threshold, resulting in poor adaptability between actual welding and preset paths, ultimately leading to unstable welding accuracy and difficulty in meeting the requirements of high-precision production.

[0003] To address this issue, we propose a method that links welding robots with injection molded part positioning simulation. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method for the positioning simulation linkage between a welding robot and an injection molded part, thus solving the problems mentioned in the background technology.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for positioning and simulating linkage between a welding robot and an injection molded part, comprising the following steps: Step 1: Build a dual-camera vision positioning system with a resolution of 4K and a positioning accuracy of ±0.05mm. After pre-processing and cleaning the injection molded part, place it on the positioning worktable and use dual cameras to synchronously acquire image data of three preset feature holes on the injection molded part. The diameter of the holes is 5mm and the position tolerance is ±0.1mm. Step 2: Perform grayscale enhancement and edge extraction on the acquired image data, accurately identify the center coordinates of the three feature circular holes, and calculate the translational and rotational deviations of the injection molded part in the X and Y axes by combining the dual-camera calibration parameters, and generate a complete positioning deviation dataset. Step 3: Select RobotStudio simulation software, import the 3D model of the FANUCARCMate100iD welding robot with a working range of 1.4m, and at the same time build a 1:1 digital model according to the actual size and structural tolerance of the injection molded part to complete the simulation scene construction of the robot and the injection molded part. Step 4: Import the positioning deviation dataset obtained in Step 2 into the simulation software, and adjust the preset welding path point by point based on the deviation data, including correcting the path point coordinates by translating according to the X-axis and Y-axis deviations, and adjusting the path rotation angle by reverse compensation according to the rotation angle deviation. Step 5: Configure welding process parameters in the simulation software, set the welding current to 120A±5A and the voltage to 20V±1V, start the welding process simulation, and monitor the weld penetration depth in real time through the built-in detection module of the simulation to ensure that the penetration depth is controlled within the range of 2mm±0.2mm. Step 6: If the simulated weld quality meets the standard, the control module receives the real-time positioning deviation data and simulation verification results, generates a path adjustment command, and sends it to the welding robot; if the simulation does not meet the standard, return to step 4 to re-optimize the path and realize the dynamic linkage between positioning and welding.

[0006] Preferably, in step 1, the dual cameras adopt a cross-shooting layout, with a ring-shaped supplementary light source. The brightness of the light source is automatically adjusted according to the surface material of the injection molded part to avoid reflection interference with feature point recognition.

[0007] Preferably, the positioning deviation calculation in step 2 adopts a weighted iterative algorithm, which assigns different weights to the three feature holes according to their stress importance in the welding process of the injection molded part, iteratively optimizes the deviation results, and improves the accuracy of deviation calculation.

[0008] Preferably, in step 4, the path adjustment adopts a coordinate mapping strategy, which maps the positioning deviation of the injection molded part to the robot joint motion parameter correction value according to the robot kinematic model, so as to ensure the continuity and accuracy of the path adjustment.

[0009] Preferably, in step 5, the simulated welding process also simultaneously monitors the weld width and reinforcement height parameters, and sets multi-dimensional quality judgment standards. Only when the weld depth, width, and reinforcement height all meet the preset requirements is the simulation judged to be up to standard.

[0010] Preferably, the method further includes step 7: during the actual welding process, the actual welding path data is collected by the displacement sensor mounted on the end of the robot, compared and analyzed with the simulated path data, and the path adjustment algorithm is dynamically corrected to stabilize the welding accuracy at ±0.1mm.

[0011] Preferably, when building the simulation scene in step 3, the three-dimensional models of the workbench and fixtures are also imported to simulate the actual clamping state of the injection molded parts and restore the constraints of the real welding environment.

[0012] Preferably, the control module adopts an architecture that combines PLC and industrial IoT module, and the positioning deviation data, simulation data and robot operation data are uploaded to the monitoring platform in real time to realize full-process data traceability.

[0013] Preferably, in step 6, the control module is set with a deviation threshold judgment mechanism. When the positioning deviation exceeds ±0.3mm, the linkage process is automatically paused and an early warning is issued. The process is resumed only after manual verification and adjustment.

[0014] Preferably, the path optimization in step 4 also considers the constraints of robot movement speed and acceleration, and optimizes the smoothness of robot movement trajectory while adjusting path points, so as to reduce the impact of sudden stops and starts on welding quality.

[0015] (III) Beneficial Effects Compared with the prior art, the present invention provides a method for the linkage of welding robot and injection molded part positioning simulation, which has the following beneficial effects: 1. The invention provides precise positioning: the 4K dual-camera vision system combined with a weighted iterative algorithm achieves a positioning accuracy of ±0.05mm, effectively avoiding glare interference and accurately capturing deviation data of injection molded parts.

[0016] 2. Path optimization of this invention: By adapting the coordinate mapping strategy to the robot motion constraints, the welding path is corrected point by point, improving the smoothness of the trajectory and reducing the impact of sudden stops and starts on the welding quality.

[0017] 3. The quality of this invention is controllable: multi-dimensional monitoring of weld penetration, width, and reinforcement height, combined with simulation pre-verification and deviation threshold early warning, ensures that the welding accuracy is stable at ±0.1mm.

[0018] 4. This invention is highly efficient: it achieves dynamic linkage between positioning, simulation, and welding, and can quickly backtrack and optimize non-compliant paths, thereby improving the continuity of the welding process.

[0019] 5. This invention offers convenient traceability: The PLC and industrial IoT module work together to upload data to the monitoring platform in real time throughout the entire process, supporting data traceability and dynamic algorithm correction. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] A method for linkage between a welding robot and the positioning simulation of an injection molded part, characterized by the following steps: Step 1: Build a dual-camera vision positioning system with a resolution of 4K and a positioning accuracy of ±0.05mm. After pre-processing and cleaning the injection molded part, place it on the positioning worktable and use dual cameras to synchronously acquire image data of three preset feature holes on the injection molded part. The diameter of the holes is 5mm and the position tolerance is ±0.1mm. The dual cameras adopt a cross-shooting layout and are equipped with a ring-shaped supplementary light source. The brightness of the light source is automatically adjusted according to the surface material of the injection molded part to avoid reflection interference with feature point recognition. Step 2: Perform grayscale enhancement and edge extraction on the acquired image data, accurately identify the center coordinates of the three feature circular holes, and calculate the translational and rotational deviations of the injection molded part in the X and Y axes by combining the dual-camera calibration parameters, and generate a complete positioning deviation dataset. Among them, the positioning deviation calculation adopts a weighted iterative algorithm, which assigns different weights to the three feature holes according to their stress importance in the welding process of the injection molded part, and iteratively optimizes the deviation results to improve the accuracy of deviation calculation. Step 3: Select RobotStudio simulation software, import the 3D model of the FANUCARCMate100iD welding robot with a working range of 1.4m, and at the same time build a 1:1 digital model according to the actual size and structural tolerance of the injection molded part to complete the simulation scene construction of the robot and the injection molded part. When building the simulation scene, the three-dimensional models of the workbench and fixtures are also imported to simulate the actual clamping state of the injection molded parts and restore the constraints of the real welding environment. Step 4: Import the positioning deviation dataset obtained in Step 2 into the simulation software, and adjust the preset welding path point by point based on the deviation data, including correcting the path point coordinates by translating according to the X-axis and Y-axis deviations, and adjusting the path rotation angle by reverse compensation according to the rotation angle deviation. Among them, the path adjustment adopts a coordinate mapping strategy, which maps the positioning deviation of the injection molded part to the robot joint motion parameter correction value according to the robot kinematic model, so as to ensure the continuity and accuracy of the path adjustment. Furthermore, path optimization also considers robot speed and acceleration constraints, and optimizes the smoothness of robot trajectory while adjusting path points to reduce the impact of sudden stops and starts on welding quality. Step 5: Configure welding process parameters in the simulation software, set the welding current to 120A±5A and the voltage to 20V±1V, start the welding process simulation, and monitor the weld penetration depth in real time through the built-in detection module of the simulation to ensure that the penetration depth is controlled within the range of 2mm±0.2mm. The simulated welding process also monitors the weld width and reinforcement height parameters simultaneously, and sets multi-dimensional quality judgment standards. Only when the penetration depth, width, and reinforcement height all meet the preset requirements is the simulation judged to be up to standard. Step 6: If the simulated weld quality meets the standard, the control module receives the real-time positioning deviation data and simulation verification results, generates a path adjustment command, and sends it to the welding robot; if the simulation does not meet the standard, return to step 4 to re-optimize the path and realize the dynamic linkage between positioning and welding. The control module adopts an architecture that combines PLC and industrial IoT module. Positioning deviation data, simulation data, and robot operation data are uploaded to the monitoring platform in real time to achieve full-process data traceability. Furthermore, the control module is equipped with a deviation threshold judgment mechanism. When the positioning deviation exceeds ±0.3mm, the linkage process is automatically paused and an early warning is issued. Operation will resume after manual verification and adjustment. Step 7: During the actual welding process, the actual welding path data is collected by the displacement sensor mounted on the end of the robot, compared and analyzed with the simulated path data, and the path adjustment algorithm is dynamically corrected to stabilize the welding accuracy at ±0.1mm.

[0022] In summary, this welding robot and injection molded part positioning simulation linkage method achieves full-process dynamic linkage between positioning, simulation, and welding by building a dual-camera vision positioning system with 4K resolution and ±0.05mm positioning accuracy, combining a weighted iterative algorithm to accurately obtain the positioning deviation of the injection molded part, constructing a 1:1 simulation scene including the robot, injection molded part, worktable, and fixture using RobotStudio simulation software, optimizing the welding path point by point based on the deviation data while taking into account the robot's motion constraints through a coordinate mapping strategy, and combining it with a multi-dimensional weld quality monitoring and deviation threshold early warning mechanism, and then using a dynamic correction algorithm based on actual welding data feedback. This not only effectively avoids problems such as reflection interference and path deviation, but also keeps the welding accuracy stable at ±0.1mm. At the same time, relying on PLC and industrial IoT modules to achieve full-process data traceability, it significantly improves the accuracy, stability, and traceability of injection molded part welding.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for linkage between a welding robot and the positioning simulation of an injection molded part, characterized in that, Includes the following steps: Step 1: Build a dual-camera vision positioning system with a resolution of 4K and a positioning accuracy of ±0.05mm. After pre-processing and cleaning the injection molded part, place it on the positioning worktable and use dual cameras to synchronously acquire image data of three preset feature holes on the injection molded part. The diameter of the holes is 5mm and the position tolerance is ±0.1mm. Step 2: Perform grayscale enhancement and edge extraction on the acquired image data, accurately identify the center coordinates of the three feature circular holes, and calculate the translational and rotational deviations of the injection molded part in the X and Y axes by combining the dual-camera calibration parameters, and generate a complete positioning deviation dataset. Step 3: Select RobotStudio simulation software, import the 3D model of the FANUCARCMate100iD welding robot with a working range of 1.4m, and at the same time build a 1:1 digital model according to the actual size and structural tolerance of the injection molded part to complete the simulation scene construction of the robot and the injection molded part. Step 4: Import the positioning deviation dataset obtained in Step 2 into the simulation software, and adjust the preset welding path point by point based on the deviation data, including correcting the path point coordinates by translating according to the X-axis and Y-axis deviations, and adjusting the path rotation angle by reverse compensation according to the rotation angle deviation. Step 5: Configure welding process parameters in the simulation software, set the welding current to 120A±5A and the voltage to 20V±1V, start the welding process simulation, and monitor the weld penetration depth in real time through the built-in detection module of the simulation to ensure that the penetration depth is controlled within the range of 2mm±0.2mm. Step 6: If the simulated weld quality meets the standard, the control module receives the real-time positioning deviation data and simulation verification results, generates a path adjustment command, and sends it to the welding robot; if the simulation does not meet the standard, return to step 4 to re-optimize the path and realize the dynamic linkage between positioning and welding.

2. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, In step 1, the dual cameras adopt a cross-shooting layout, paired with a ring-shaped fill light source. The brightness of the light source is automatically adjusted according to the surface material of the injection molded part to avoid reflections interfering with feature point recognition.

3. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, In step 2, the positioning deviation calculation adopts a weighted iterative algorithm, which assigns different weights to the three feature holes according to their stress importance in the injection molding process, and iteratively optimizes the deviation results to improve the accuracy of deviation calculation.

4. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, In step 4, the path adjustment adopts a coordinate mapping strategy, which maps the positioning deviation of the injection molded part to the robot joint motion parameter correction value according to the robot kinematic model, so as to ensure the continuity and accuracy of the path adjustment.

5. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, In step 5, the simulated welding process also monitors the weld width and reinforcement height parameters simultaneously, and sets multi-dimensional quality judgment standards. Only when the weld penetration, width, and reinforcement height all meet the preset requirements is the simulation judged to be up to standard.

6. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, It also includes step 7: In the actual welding process, the actual welding path data is collected by the displacement sensor mounted on the end of the robot, compared and analyzed with the simulated path data, and the path adjustment algorithm is dynamically corrected to stabilize the welding accuracy at ±0.1mm.

7. The method for linkage between a welding robot and a positioning simulation of an injection molded part according to claim 1, characterized in that, In step 3, when building the simulation scene, the three-dimensional models of the workbench and fixtures are also imported to simulate the actual clamping state of the injection molded parts and restore the constraints of the real welding environment.

8. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, The control module adopts an architecture that combines PLC and industrial IoT module. Positioning deviation data, simulation data, and robot operation data are uploaded to the monitoring platform in real time to achieve full-process data traceability.

9. The method for linkage between welding robot and injection molded part positioning simulation according to claim 1, characterized in that, In step 6, the control module sets a deviation threshold judgment mechanism. When the positioning deviation exceeds ±0.3mm, the linkage process is automatically paused and an early warning is issued. Operation will resume after manual verification and adjustment.

10. The method for linkage between a welding robot and a positioning simulation of an injection molded part according to claim 1, characterized in that, Step 4 path optimization also considers robot motion speed and acceleration constraints, and optimizes the smoothness of robot motion trajectory while adjusting path points to reduce the impact of sudden stops and starts on welding quality.