CN118357649B - Automatic intelligent welding system of correction - Google Patents
Automatic intelligent welding system of correction Download PDFInfo
- Publication number
- CN118357649B CN118357649B CN202410553393.2A CN202410553393A CN118357649B CN 118357649 B CN118357649 B CN 118357649B CN 202410553393 A CN202410553393 A CN 202410553393A CN 118357649 B CN118357649 B CN 118357649B
- Authority
- CN
- China
- Prior art keywords
- welding
- robot
- subunit
- real
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003466 welding Methods 0.000 title claims abstract description 575
- 238000012937 correction Methods 0.000 title abstract description 9
- 238000000034 method Methods 0.000 claims abstract description 58
- 230000008569 process Effects 0.000 claims abstract description 53
- 238000004458 analytical method Methods 0.000 claims abstract description 51
- 238000005457 optimization Methods 0.000 claims description 29
- 238000012544 monitoring process Methods 0.000 claims description 28
- 238000004364 calculation method Methods 0.000 claims description 24
- 238000001514 detection method Methods 0.000 claims description 21
- 238000013507 mapping Methods 0.000 claims description 16
- 238000006243 chemical reaction Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 9
- 238000007405 data analysis Methods 0.000 claims description 8
- 230000001133 acceleration Effects 0.000 claims description 6
- 239000003086 colorant Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 11
- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000007547 defect Effects 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 206010063385 Intellectualisation Diseases 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 239000012636 effector Substances 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000004043 responsiveness Effects 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 229910000746 Structural steel Inorganic materials 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 238000005493 welding type Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K37/00—Auxiliary devices or processes, not specially adapted for a procedure covered by only one of the other main groups of this subclass
- B23K37/02—Carriages for supporting the welding or cutting element
- B23K37/0252—Steering means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Robotics (AREA)
- Physics & Mathematics (AREA)
- Optics & Photonics (AREA)
- Manipulator (AREA)
Abstract
本发明涉及工业焊接技术领域，尤其涉及一种自动校正的智能焊接系统，本发明通过识别模块获取焊接件和焊接机器人的实时图像信息，并确认焊接件的空间位置信息；采集模块实时采集焊接过程中的电压、电流等数据，分析模块根据这些数据计算焊接功率、热输入和焊接间隙，以判断焊接质量；参数规划模块根据焊接间隙和目标间隙计算偏差，结合焊接件的位置信息和机器人的实时状态，规划运动路径和参数，生成焊接指令；最后，机器人执行模块接收并解析焊接指令，调整焊接机器人的状态，完成焊接过程。该系统能自动校正焊接参数，提升焊接质量，提高生产效率。
The present invention relates to the field of industrial welding technology, and in particular to an automatic correction intelligent welding system. The present invention obtains real-time image information of welded parts and welding robots through an identification module, and confirms the spatial position information of welded parts; the acquisition module collects data such as voltage and current in the welding process in real time, and the analysis module calculates welding power, heat input and welding gap according to these data to judge welding quality; the parameter planning module calculates deviation according to welding gap and target gap, combines the position information of welded parts and the real-time state of the robot, plans motion path and parameters, and generates welding instructions; finally, the robot execution module receives and analyzes welding instructions, adjusts the state of the welding robot, and completes the welding process. The system can automatically correct welding parameters, improve welding quality, and improve production efficiency.
Description
Technical Field
The invention relates to the technical field of industrial welding, in particular to an automatic correction intelligent welding system.
Background
Welding is an important connection technology in the manufacturing industry and is widely applied to the fields of construction, ships, automobiles, aerospace and the like. The traditional manual welding mode often has the problems of unstable welding quality, low production efficiency, high labor intensity and the like due to the technical level of operators, physical limitation, environmental factors and the like. With the progress of technology, automatic welding equipment is generated, and the welding efficiency and quality are greatly improved.
The patent document with the publication number of CN111644800A discloses a clamping device for welding a heating pipe with an automatic correction axis position, the device comprises a workbench, an electric push rod and a screw rod, a movable groove is formed in the edge of the top of the workbench, the electric push rod is fixed in the movable groove through bolts, an installing seat is connected to the output end of the electric push rod, the top of the installing seat is located above the workbench, a telescopic groove is formed in the outer side of the installing seat, a telescopic block is placed in the telescopic groove, a clamping block is integrally arranged in the outer side of the telescopic block, a gear is connected to the inner shaft of the installing seat, a bearing seat is fixed on the top bolt of the workbench, a top cover is hinged to the top of the bearing seat, and through grooves are formed in the top of the bearing seat and the bottom of the top cover.
Therefore, the problems that in the prior art, because strong arc light, smoke dust and other interference factors possibly exist in the welding process, the accuracy of a sensor and an image recognition technology can be influenced, so that misjudgment of welding parameters is caused, and the problems of poor welding quality and low efficiency are further influenced.
Disclosure of Invention
To this end, the present invention provides an automatically calibrated intelligent welding system. The welding parameter error judgment method is used for solving the problems that in the prior art, as strong arc light, smoke dust and other interference factors possibly exist in the welding process, the accuracy of a sensor and an image recognition technology is possibly affected, the misjudgment of the welding parameter is caused, and the welding quality is poor and the efficiency is low. Is a problem of (a).
To achieve the above object, the present invention provides an automatic correction intelligent welding system, comprising:
The identification module is used for acquiring real-time image information containing a welding piece, a welding robot and the surrounding environment thereof, identifying the welding piece and the welding robot according to the real-time image information, and confirming the spatial position information of the welding piece by taking the real-time position of the welding robot as a reference point;
The acquisition module is connected with the identification module and used for acquiring welding voltage, welding current, welding area, welding time and welding speed in the welding process in real time;
the analysis module is connected with the acquisition module, calculates welding power according to the welding voltage and the welding current, calculates welding heat input according to the welding power, the welding area and the welding time, calculates welding gaps according to the welding speed and the welding heat input, and judges whether the welding piece accords with welding quality according to the welding gaps and a preset target gap;
The parameter planning module is connected with the identification module and the analysis module and used for calculating welding deviation according to comparison of the welding gap which is judged to be inconsistent with the quality standard and the target gap, and planning a motion path and welding parameters of the welding robot according to the spatial position information of the welding piece, the welding deviation and the real-time state of the welding robot to generate a welding instruction;
and the robot execution module is connected with the parameter planning module and used for receiving and analyzing the welding instruction, adjusting the real-time state of the welding robot according to the analyzed instruction to obtain a target state, and welding the welding piece according to the target state.
Further, the identification module includes:
the target monitoring unit is used for identifying the preliminary positions of the welding piece and the welding robot according to the real-time image information;
The feature extraction unit is used for extracting the geometric shapes, textures and colors of the welding piece and the welding robot according to the real-time image information so as to obtain image features;
and the coordinate analysis unit is connected with the target monitoring unit and the feature extraction unit and is used for analyzing the spatial position of the welding piece on the welding robot according to the preliminary position and the image features.
Further, the coordinate analysis unit includes:
A coordinate conversion subunit for converting the position and the posture of the welding piece from a camera coordinate system to a robot coordinate system to obtain a robot coordinate mapping;
The depth calculation subunit is connected with the coordinate conversion unit and used for calculating the depth between the welding piece and the camera according to the robot target mapping and the real-time image information so as to obtain a depth vector;
And the position calculating subunit is connected with the coordinate conversion subunit and the depth calculating subunit and is used for calculating the target position of the welding piece on the robot coordinate mapping according to the robot coordinate mapping and the depth vector.
Further, the analysis module includes:
The heat input calculation unit is used for obtaining welding density according to the welding power and the welding time and obtaining heat input according to the quotient of the welding density and the welding area;
and the gap calculating unit is connected with the heat input calculating unit and is used for calculating the welding gap according to the heat input quantity and the welding speed.
Further, the analysis module further comprises:
the data analysis unit is used for comparing the welding gap with a preset target gap to obtain a comparison result;
A comparison unit connected with the data processing unit for judging whether the welding piece accords with the quality standard according to the comparison result, wherein,
If the welding gap is larger than the target gap, judging that the welding piece does not accord with a welding quality standard;
and if the welding gap is smaller than or equal to the target gap, judging that the welding piece meets the welding quality standard.
Further, the parameter planning module includes:
the path planning unit is used for planning the path of the welding robot according to the spatial position information of the welding piece and the welding deviation;
And the parameter optimization unit is used for optimizing welding parameters according to the welding deviation and the real-time state of the welding robot.
Further, the path planning unit includes:
the point position determining subunit is used for determining a starting point and an ending point of the welding process;
the path generation subunit is connected with the point position determination subunit and is used for generating a motion path of the welding robot according to the shape and the size of the welding piece;
The obstacle avoidance detection subunit is connected with the path generation subunit and used for detecting obstacles encountered in the motion path to obtain a detection result;
And the dynamic adjustment subunit is connected with the obstacle avoidance detection subunit and used for adjusting a motion path according to the detection result and the real-time state of the welding robot.
Further, the parameter optimization unit includes:
The deviation analysis subunit is used for analyzing the welding deviation to obtain a target deviation value;
The real-time state monitoring subunit is used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time state of the welding robot;
and the optimization parameter calculation subunit is connected with the deviation analysis subunit and the real-time state monitoring subunit and used for optimizing the welding parameters according to the target deviation value and the real-time state to obtain target welding parameters.
Further, the robot execution module includes:
the instruction analysis unit is used for receiving and analyzing the welding instruction to obtain an analysis result;
The target determining unit is connected with the instruction analyzing unit and used for determining the target state of the welding robot according to the instruction result;
And the welding execution unit is connected with the target determination unit and used for welding the welding piece according to the target state.
Further, the welding execution unit includes:
a path execution subunit for controlling the movement of the welding robot according to the planned path;
a parameter optimization subunit for adjusting the parameters of the welding robot according to the target welding parameters
And the gesture control subunit is used for controlling the gesture of the welding robot.
Compared with the prior art, the invention has the beneficial effects that the real-time monitoring of the welding process is realized by the cooperation of the acquisition module and the identification module, and the accuracy and timeliness of welding parameters are ensured. And according to the real-time acquisition and analysis of the welding parameters, the welding process is optimized, the rejection rate is reduced, and the production efficiency is improved. The parameter planning module intelligently plans the motion path and welding parameters of the welding robot according to the welding gap deviation and the spatial position information of the welding piece, and improves the flexibility and adaptability of the welding process. The robot execution module can accurately receive and analyze the welding instruction, adjust the real-time state of the robot, and ensure the accuracy and consistency of welding operation.
In particular, by monitoring and tracking the position of the weldment and welding robot in real time, the target monitoring unit ensures that the system is able to timely sense changes in the welding site. The application of the object detection algorithm improves the accuracy and speed of identification, thereby improving the responsiveness and stability of the whole welding system. The feature extraction unit provides rich information by extracting geometric shapes, textures and color features of the welding piece and the welding robot, and is helpful for more accurately identifying and distinguishing different welding objects. Through the deep analysis of the primary position and the image characteristics, the coordinate analysis unit can accurately calculate the position of the welding piece in the welding robot coordinate system, and an accurate basis is provided for subsequent path planning and parameter adjustment.
In particular, the coordinate conversion subunit ensures the accuracy of the position and posture of the welding member in the robot coordinate system through accurate coordinate conversion, which is important for the accurate control of the robot. The depth calculation subunit is able to provide accurate depth information between the weldment and the camera, which is critical for understanding the spatial structure of the welding scene. The accuracy of the depth information directly affects the planning of the welding path and the evaluation of the welding quality. The position calculation subunit improves the operation efficiency and stability of the welding robot by calculating the target position of the welding piece in the robot coordinate system, and reduces repeated operation and adjustment time caused by improper operation.
In particular, by the heat input amount calculating unit and the gap calculating unit, the welding process can be controlled more precisely, and the welding quality can be improved. The use of the calculation unit is beneficial to realizing automation and intellectualization of the welding process and improving production efficiency.
In particular, the data analysis unit is used for improving the monitoring precision of the welding process and ensuring the controllability of the welding quality by comparing the actually measured welding gap with the target gap. The comparison unit judges whether the welding piece meets the quality standard according to the comparison result provided by the data analysis unit, ensures the consistency and reliability of the welding piece, and meets strict quality requirements. The production efficiency is improved, and reworking and rejection rate caused by the problem of welding quality are reduced.
In particular, the path planning unit can optimize the welding path, reduce the moving distance in the welding process and improve the welding efficiency. When planning the path, the working space of the welding robot and surrounding obstacles are taken into account to ensure that the welding process is safe and reliable. The parameter optimization unit dynamically adjusts welding parameters according to real-time data in the welding process to compensate welding deviation, ensure welding quality and timely adjust the parameters to prevent welding defects.
In particular, the point determination subunit ensures accurate setting of the starting point and the end point of the welding process, provides clear guidance for the whole welding process, and avoids welding errors caused by uncertain starting and end points. The path generation subunit ensures accurate setting of the starting point and the end point of the welding process, provides clear guidance for the whole welding process, and avoids welding errors caused by uncertain starting and end points. The obstacle avoidance detection subunit detects obstacles in the motion path in real time, so that the welding robot is prevented from colliding with other objects when performing a welding task, and equipment damage and welding defects caused by collision are avoided. The dynamic adjustment subunit dynamically adjusts the motion path according to the detection result of the obstacle avoidance detection subunit and the real-time state of the welding robot, so that the continuity and stability of the welding process are ensured, and the welding quality can be ensured even in a complex environment.
In particular, the deviation analysis subunit can accurately identify problems in the welding process by analyzing the welding deviation, and provides a direct basis for subsequent parameter adjustment. The real-time state subunit monitors the real-time position, speed and acceleration of the welding robot, can comprehensively know the working state of the welding robot, and provides real-time feedback for parameter optimization. The optimizing parameter calculating subunit can dynamically calculate the optimal welding parameter according to the target deviation value and the real-time state so as to eliminate or reduce the welding deviation.
In particular, the command analysis unit can accurately and rapidly analyze complex welding commands, and ensure that the welding robot correctly understands the commands of the control system. The target determining unit ensures that the welding robot executes the welding task according to a preset target state, and improves the welding accuracy and consistency. The welding execution unit can accurately execute welding operation according to the target state provided by the target determination unit, and welding quality is ensured.
In particular, the path execution subunit ensures that the welding robot can accurately move according to a predetermined path, improving the repeatability and consistency of the welding process. The parameter optimization subunit dynamically adjusts parameters of the welding robot according to the target welding parameters, and ensures optimization of the welding process and stability of quality. The gesture control subunit is beneficial to realizing higher welding precision, reducing defects and improving joint quality by controlling the gesture of the robot.
Drawings
FIG. 1 is a schematic diagram of an automatic calibration intelligent welding system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an identification module in an automatic calibration intelligent welding system according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an analysis module in an automatic calibration intelligent welding system according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a parameter planning module in an automatic correction intelligent welding system according to an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to examples for the purpose of making the objects and advantages of the invention more apparent, it being understood that the specific examples described herein are given by way of illustration only and are not intended to be limiting.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In addition, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediate medium, or in communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, the present invention provides an automatic correction intelligent welding system, comprising:
The identification module is used for acquiring real-time image information containing a welding piece, a welding robot and the surrounding environment thereof, identifying the welding piece and the welding robot according to the real-time image information, and confirming the spatial position information of the welding piece by taking the real-time position of the welding robot as a reference point;
The acquisition module is connected with the identification module and used for acquiring welding voltage, welding current, welding area, welding time and welding speed in the welding process in real time;
the analysis module is connected with the acquisition module, calculates welding power according to the welding voltage and the welding current, calculates welding heat input according to the welding power, the welding area and the welding time, calculates welding gaps according to the welding speed and the welding heat input, and judges whether the welding piece accords with welding quality according to the welding gaps and a preset target gap;
The parameter planning module is connected with the identification module and the analysis module and used for calculating welding deviation according to comparison of the welding gap which is judged to be inconsistent with the quality standard and the target gap, and planning a motion path and welding parameters of the welding robot according to the spatial position information of the welding piece, the welding deviation and the real-time state of the welding robot to generate a welding instruction;
and the robot execution module is connected with the parameter planning module and used for receiving and analyzing the welding instruction, adjusting the real-time state of the welding robot according to the analyzed instruction to obtain a target state, and welding the welding piece according to the target state.
Specifically, the identification module obtains a high definition image of the welding site using a high resolution camera or other image sensor. Image processing algorithms, such as Convolutional Neural Networks (CNNs) in deep learning, are applied to identify and locate weldments and welding robots. And comparing the identification result with a pre-stored three-dimensional model, and correcting the spatial position information caused by visual deviation or robot position error. The acquisition module is matched with the identification module to monitor and record parameters such as voltage, current, area, time and speed in the welding process in real time. The analysis module calculates welding power, heat input and welding gap through the data of the acquisition module. And comparing the welding gap with a preset target gap, and evaluating whether the welding quality meets the standard. If the welding piece is judged to be not in accordance with the quality standard, calculating welding deviation, and planning a movement path and welding parameters of the welding robot according to the spatial position information of the welding piece, the welding deviation and the real-time state of the welding robot. Welding instructions are generated, including motion paths, adjustment values of welding parameters (e.g., current, voltage, speed, etc.). And the robot execution module receives the welding instruction generated by the parameter planning module. And analyzing the instruction, and adjusting the real-time state of the welding robot to reach the target state. And executing welding operation according to the target state to finish the welding task.
Specifically, through the cooperation of the acquisition module and the identification module, the real-time monitoring of the welding process is realized, and the accuracy and timeliness of welding parameters are ensured. And according to the real-time acquisition and analysis of the welding parameters, the welding process is optimized, the rejection rate is reduced, and the production efficiency is improved. The parameter planning module intelligently plans the motion path and welding parameters of the welding robot according to the welding gap deviation and the spatial position information of the welding piece, and improves the flexibility and adaptability of the welding process. The robot execution module can accurately receive and analyze the welding instruction, adjust the real-time state of the robot, and ensure the accuracy and consistency of welding operation.
Specifically, as shown in fig. 2, the identification module includes:
the target monitoring unit is used for identifying the preliminary positions of the welding piece and the welding robot according to the real-time image information;
The feature extraction unit is used for extracting the geometric shapes, textures and colors of the welding piece and the welding robot according to the real-time image information so as to obtain image features;
and the coordinate analysis unit is connected with the target monitoring unit and the feature extraction unit and is used for analyzing the spatial position of the welding piece on the welding robot according to the preliminary position and the image features.
Specifically, the target monitoring unit uses computer vision algorithms (e.g., edge detection, contour recognition, etc.) to monitor the weld and welding robot in the image. An object detection algorithm (e.g., YOLO, fasterR-CNN, etc.) is applied to locate the preliminary positions of the weldment and welding robot. The motion trajectories of the weldment and welding robot in successive image frames are tracked by real-time tracking algorithms (e.g., ka lman filters, particle filters, etc.). The feature extraction unit applies image processing techniques (such as edge enhancement, filtering, thresholding, etc.) to extract geometric features of the weldment and welding robot. Color segmentation and texture analysis are used to extract color and texture features of the weldment and welding robot. And a deep learning model (such as a convolutional neural network) is utilized to learn characteristic representations of the welding piece and the welding robot, so that the recognition accuracy is improved. The coordinate analysis unit matches the extracted image features with a pre-trained model or dictionary to determine the precise location of the weldment and welding robot. And calculating the position of the welding piece in the three-dimensional space by combining a three-dimensional modeling and reconstruction technology with a camera calibration and stereoscopic vision method. And converting the image coordinates of the welding piece into coordinates in a machine coordinate system according to the real-time position and direction of the welding robot so as to facilitate subsequent path planning and welding parameter adjustment.
Specifically, by monitoring and tracking the positions of the weldment and the welding robot in real time, the target monitoring unit ensures that the system is able to timely sense changes in the welding site. The application of the object detection algorithm improves the accuracy and speed of identification, thereby improving the responsiveness and stability of the whole welding system. The feature extraction unit provides rich information by extracting geometric shapes, textures and color features of the welding piece and the welding robot, and is helpful for more accurately identifying and distinguishing different welding objects. Through the deep analysis of the primary position and the image characteristics, the coordinate analysis unit can accurately calculate the position of the welding piece in the welding robot coordinate system, and an accurate basis is provided for subsequent path planning and parameter adjustment.
Specifically, the coordinate analysis unit includes:
A coordinate conversion subunit for converting the position and the posture of the welding piece from a camera coordinate system to a robot coordinate system to obtain a robot coordinate mapping;
The depth calculation subunit is connected with the coordinate conversion unit and used for calculating the depth between the welding piece and the camera according to the robot target mapping and the real-time image information so as to obtain a depth vector;
And the position calculating subunit is connected with the coordinate conversion subunit and the depth calculating subunit and is used for calculating the target position of the welding piece on the robot coordinate mapping according to the robot coordinate mapping and the depth vector.
Specifically, the coordinate rotor unit acquires internal and external parameters of the camera using a camera calibration technique so as to convert image coordinates in a camera coordinate system into actual coordinates in a world coordinate system. And (3) applying a reverse kinematics algorithm, and calculating a transformation matrix of the robot coordinate system relative to the world coordinate system according to the current joint angle and the gesture of the welding robot. And converting the position and the posture of the welding piece from the camera coordinate system to the robot coordinate system to obtain the mapping of the welding piece in the robot coordinate system. The depth calculation subunit acquires depth information of the welding scene using a stereoscopic vision or depth camera. Depth image processing algorithms, such as correction, denoising and interpolation of depth maps, are applied to obtain accurate depth values. And calculating the depth between the welding piece and the camera according to the robot coordinate mapping and the real-time image information to obtain a depth vector. The position calculation subunit calculates the target position of the welding piece in the robot coordinate mapping by using a triangulation principle and combining a depth vector and an internal parameter and an external parameter of a camera. Spatial geometric algorithms, such as vector operations and matrix calculations, are applied to determine the three-dimensional coordinates of the weldment. Calculating the target position of the welding piece in the robot coordinate system according to the robot coordinate mapping and the depth vector,
Specifically, the coordinate conversion subunit ensures the accuracy of the position and posture of the welding piece in the robot coordinate system through accurate coordinate conversion, which is important for the accurate control of the robot. The depth calculation subunit is able to provide accurate depth information between the weldment and the camera, which is critical for understanding the spatial structure of the welding scene. The accuracy of the depth information directly affects the planning of the welding path and the evaluation of the welding quality. The position calculation subunit improves the operation efficiency and stability of the welding robot by calculating the target position of the welding piece in the robot coordinate system, and reduces repeated operation and adjustment time caused by improper operation.
Specifically, as shown in fig. 3, the analysis module includes:
the heat input calculation unit is used for obtaining welding energy according to the welding power and the welding time and obtaining heat input according to the quotient of the welding energy and the welding area;
and the gap calculating unit is connected with the heat input calculating unit and is used for calculating the welding gap according to the heat input quantity and the welding speed.
Specifically, the heat input refers to the amount of heat input to a unit area of the work piece per unit time during welding. The calculation formula for the heat input can be expressed as:
where p represents the welding power, which refers to the total power applied to the workpiece during welding, typically in kilowatts (kW). Welding power is the product of welding current and welding voltage. t represents the welding time, which means the total time required to complete welding, in seconds(s). A represents the welding area, and refers to the total area of the welded area, and the unit is square centimeter.
The welding gap refers to the distance between the welding material (electrode, wire, etc.) and the workpiece during welding. It affects the weld quality, weld shape and weld defects. The size of the welding gap can be calculated from the amount of heat input and the welding speed. The calculation formula is as follows:
G=G0+k×Hi
Wherein G 0 represents the base value (mm) of the weld gap, k is a constant (no unit) related to the material and weld parameters, hi is the heat input (W/mm) that has been calculated
Specifically, by the heat input amount calculating unit and the gap calculating unit, the welding process can be controlled more accurately, and the welding quality can be improved. The use of the calculation unit is beneficial to realizing automation and intellectualization of the welding process and improving production efficiency.
Specifically, the analysis module further includes:
the data analysis unit is used for comparing the welding gap with a preset target gap to obtain a comparison result;
A comparison unit connected with the data processing unit for judging whether the welding piece accords with the quality standard according to the comparison result, wherein,
If the welding gap is larger than the target gap, judging that the welding piece does not accord with a welding quality standard;
and if the welding gap is smaller than or equal to the target gap, judging that the welding piece meets the welding quality standard.
In particular, the target gap provides accurate guidance for the welding operation, ensuring that the welding robot is able to maintain a proper welding gap while performing the welding task. Proper weld clearances help ensure the mechanical and sealing properties of the weld joint, thereby improving weld quality. For common structural steel welds, the target gap may be between 0.5mm and 4mm, while for thin wall structures or welds requiring high accuracy, the target gap may be smaller. The range of the target gap is 1mm to 3mm, and the range can adapt to various welding conditions and ensure welding quality.
Specifically, the data analysis unit compares the actually measured welding gap with the target gap, so that the monitoring precision of the welding process is improved, and the controllability of the welding quality is ensured. The comparison unit judges whether the welding piece meets the quality standard according to the comparison result provided by the data analysis unit, ensures the consistency and reliability of the welding piece, and meets strict quality requirements. The production efficiency is improved, and reworking and rejection rate caused by the problem of welding quality are reduced.
Specifically, as shown in fig. 4, the parameter planning module includes:
the path planning unit is used for planning the path of the welding robot according to the spatial position information of the welding piece and the welding deviation;
And the parameter optimization unit is used for optimizing welding parameters according to the welding deviation and the real-time state of the welding robot.
Specifically, first, it is necessary to acquire actual position data including the size, shape, and position of the weld site of the weldment. Deviations that may occur during welding include positioning errors of the welding robot, welding parameter control errors, and the like. Based on the information, the path planning unit calculates the optimal path of the welding robot, ensures that the robot can accurately reach the welding position and avoid the obstacle. The parameter optimization unit monitors the actual welding deviation parameter optimization unit in the welding process, and adjusts welding parameters according to the welding deviation and the real-time state of the robot so as to eliminate or reduce the welding deviation and improve the welding quality. The optimized welding parameters are used for controlling the welding robot, and the parameter settings of the welding process are updated in real time.
Specifically, the path planning unit can optimize the welding path, reduce the moving distance in the welding process and improve the welding efficiency. When planning the path, the working space of the welding robot and surrounding obstacles are taken into account to ensure that the welding process is safe and reliable. The parameter optimization unit dynamically adjusts welding parameters according to real-time data in the welding process to compensate welding deviation, ensure welding quality and timely adjust the parameters to prevent welding defects.
Specifically, the path planning unit includes:
the point position determining subunit is used for determining a starting point and an ending point of the welding process;
the path generation subunit is connected with the point position determination subunit and is used for generating a motion path of the welding robot according to the shape and the size of the welding piece;
The obstacle avoidance detection subunit is connected with the path generation subunit and used for detecting obstacles encountered in the motion path to obtain a detection result;
And the dynamic adjustment subunit is connected with the obstacle avoidance detection subunit and used for adjusting a motion path according to the detection result and the real-time state of the welding robot.
Specifically, first, a three-dimensional model or actual size of the weldment, including the size, shape, and location of the weld site, needs to be obtained.
In this embodiment, the welding robot is required to weld body parts of an automobile on an automobile production line.
First, the starting point of the robot is determined to be at the left front corner of the vehicle body, and the ending point is determined to be at the right rear corner of the vehicle body. According to the shape and the size of the vehicle body part, a movement path of the robot along the vehicle body edge is generated, and the robot is ensured to weld smoothly along the vehicle body edge. In the process of generating the path, whether other robots, equipment or fixed structures on the production line collide with the path of the welding robot is detected, so that the safety of the welding process is ensured. If the obstacle is encountered in the welding process, the movement path of the robot is adjusted according to the position of the obstacle and the real-time state of the robot, so that the smooth proceeding of the welding process is ensured.
Specifically, the point determination subunit ensures accurate setting of the starting point and the end point of the welding process, provides clear guidance for the whole welding process, and avoids welding errors caused by uncertain starting and end points. The path generation subunit ensures accurate setting of the starting point and the end point of the welding process, provides clear guidance for the whole welding process, and avoids welding errors caused by uncertain starting and end points. The obstacle avoidance detection subunit detects obstacles in the motion path in real time, so that the welding robot is prevented from colliding with other objects when performing a welding task, and equipment damage and welding defects caused by collision are avoided. The dynamic adjustment subunit dynamically adjusts the motion path according to the detection result of the obstacle avoidance detection subunit and the real-time state of the welding robot, so that the continuity and stability of the welding process are ensured, and the welding quality can be ensured even in a complex environment.
Specifically, the parameter optimization unit includes:
The deviation analysis subunit is used for analyzing the welding deviation to obtain a target deviation value;
The real-time state monitoring subunit is used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time state of the welding robot;
and the optimization parameter calculation subunit is connected with the deviation analysis subunit and the real-time state monitoring subunit and used for optimizing the welding parameters according to the target deviation value and the real-time state to obtain target welding parameters.
Specifically, the deviation analysis subunit collects data during the welding process, including welding current, voltage, speed, and the like. The collected data is analyzed, and the magnitude of the welding deviation is calculated to obtain a target deviation value. And setting a target deviation value by the deviation analysis subunit according to the welding quality standard, and taking the target deviation value as the input of the optimization parameter calculation subunit. The real-time state monitoring subunit monitors the real-time position of the welding robot through equipment such as a sensor or a camera. The real-time state monitoring subunit also monitors the real-time speed and acceleration of the welding robot to obtain the real-time state of the welding robot. The real-time state monitoring subunit transmits the collected real-time state data to the optimization parameter calculating subunit. The optimization parameter calculation subunit receives the target deviation value from the deviation analysis subunit and the real-time state data from the real-time state monitoring subunit. And the optimization parameter calculation subunit calculates optimal welding parameters such as current, voltage, speed and the like according to the target deviation value and the real-time state. The optimization parameter calculation subunit outputs the calculated optimization parameters to the welding control system so as to adjust the welding behavior of the welding robot.
Specifically, the deviation analysis subunit can accurately identify the problems in the welding process by analyzing the welding deviation, and provides a direct basis for subsequent parameter adjustment. The real-time state subunit monitors the real-time position, speed and acceleration of the welding robot, can comprehensively know the working state of the welding robot, and provides real-time feedback for parameter optimization. The optimizing parameter calculating subunit can dynamically calculate the optimal welding parameter according to the target deviation value and the real-time state so as to eliminate or reduce the welding deviation.
Specifically, the robot execution module includes:
the instruction analysis unit is used for receiving and analyzing the welding instruction to obtain an analysis result;
The target determining unit is connected with the instruction analyzing unit and used for determining the target state of the welding robot according to the instruction result;
And the welding execution unit is connected with the target determination unit and used for welding the welding piece according to the target state.
Specifically, the command parsing unit receives welding commands from a control system or other module, which typically include information of welding type, welding parameters, welding path, etc. And analyzing the received welding instruction, and extracting key information such as a welding method, a welding position, a welding speed and the like. The target determination unit receives the analysis result from the instruction analysis unit. Based on the analysis result, the target determination unit determines a target state of the welding robot, including a welding position, a posture, a welding parameter, and the like. The target determination unit transmits the determined target state to the welding execution unit so as to execute the welding action. The welding execution unit receives the target state information from the target determination unit. And carrying out actual welding operation on the welding piece according to the target state, such as moving a welding robot, adjusting welding parameters and the like. The welding execution unit feeds back the welding result to the control system after executing the welding operation so as to monitor and adjust.
Specifically, the command analysis unit can accurately and rapidly analyze complex welding commands, and ensure that the welding robot correctly understands the commands of the control system. The target determining unit ensures that the welding robot executes the welding task according to a preset target state, and improves the welding accuracy and consistency. The welding execution unit can accurately execute welding operation according to the target state provided by the target determination unit, and welding quality is ensured.
Specifically, the welding execution unit includes:
a path execution subunit for controlling the movement of the welding robot according to the planned path;
a parameter optimization subunit for adjusting the parameters of the welding robot according to the target welding parameters
And the gesture control subunit is used for controlling the gesture of the welding robot.
Specifically, the path execution subunit controls the welding robot to move in the welding process according to the received path instruction, so as to ensure that the robot welds according to the planned path. The parameter optimization subunit receives the target welding parameters from the target determination unit, including welding current, voltage, speed, welding mode, and the like. And adjusting the current parameters of the welding robot according to the target parameters so as to achieve the optimal welding effect. The attitude control subunit determines a target attitude of the welding robot, which may include an angle, a position, etc. of the robot end effector, as required by the welding task. By controlling the joint movement of the welding robot, the precise control of the gesture of the end effector of the robot is realized. The gesture of welding robot is monitored in real time, if deviation is found, adjustment is immediately performed to ensure welding quality.
Specifically, the path execution subunit ensures that the welding robot can accurately move according to a predetermined path, thereby improving the repeatability and consistency of the welding process. The parameter optimization subunit dynamically adjusts parameters of the welding robot according to the target welding parameters, and ensures optimization of the welding process and stability of quality. The gesture control subunit is beneficial to realizing higher welding precision, reducing defects and improving joint quality by controlling the gesture of the robot.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An automatically calibrated intelligent welding system, comprising:
The identification module is used for acquiring real-time image information containing a welding piece, a welding robot and the surrounding environment thereof, identifying the welding piece and the welding robot according to the real-time image information, and confirming the spatial position information of the welding piece by taking the real-time position of the welding robot as a reference point;
The acquisition module is connected with the identification module and used for acquiring welding voltage, welding current, welding area, welding time and welding speed in the welding process in real time;
the analysis module is connected with the acquisition module, calculates welding power according to the welding voltage and the welding current, calculates welding heat input according to the welding power, the welding area and the welding time, calculates welding gaps according to the welding speed and the welding heat input, and judges whether the welding piece accords with welding quality according to the welding gaps and a preset target gap;
The parameter planning module is connected with the identification module and the analysis module and used for calculating welding deviation according to comparison of the welding gap which is judged to be inconsistent with the quality standard and the target gap, and planning a motion path and welding parameters of the welding robot according to the spatial position information of the welding piece, the welding deviation and the real-time state of the welding robot to generate a welding instruction;
The robot execution module is connected with the parameter planning module and used for receiving and analyzing the welding instruction, adjusting the real-time state of the welding robot according to the analyzed instruction to obtain a target state, and welding the welding piece according to the target state;
The identification module comprises:
the target monitoring unit is used for identifying the preliminary positions of the welding piece and the welding robot according to the real-time image information;
The feature extraction unit is used for extracting the geometric shapes, textures and colors of the welding piece and the welding robot according to the real-time image information so as to obtain image features;
The coordinate analysis unit is connected with the target monitoring unit and the feature extraction unit and used for analyzing the spatial position of the welding piece on the welding robot according to the preliminary position and the image features;
The coordinate analysis unit includes:
A coordinate conversion subunit for converting the position and the posture of the welding piece from a camera coordinate system to a robot coordinate system to obtain a robot coordinate mapping;
The depth calculation subunit is connected with the coordinate conversion subunit and used for calculating the depth between the welding piece and the camera according to the robot target mapping and the real-time image information so as to obtain a depth vector;
The position calculating subunit is connected with the coordinate conversion subunit and the depth calculating subunit and used for calculating the target position of the welding piece on the robot coordinate mapping according to the robot coordinate mapping and the depth vector;
the analysis module comprises:
The heat input calculation unit is used for obtaining welding density according to the welding power and the welding time and obtaining heat input according to the quotient of the welding density and the welding area;
a gap calculating unit connected to the heat input calculating unit, the gap calculating unit calculating the welding gap according to the heat input amount and the welding speed;
The analysis module further includes:
the data analysis unit is used for comparing the welding gap with a preset target gap to obtain a comparison result;
a comparison unit connected with the data analysis unit for judging whether the welding piece accords with the quality standard according to the comparison result, wherein,
If the welding gap is larger than the target gap, judging that the welding piece does not accord with a welding quality standard;
and if the welding gap is smaller than or equal to the target gap, judging that the welding piece meets the welding quality standard.
2. The automatically calibrated intelligent welding system of claim 1, wherein the parameter planning module comprises:
the path planning unit is used for planning the path of the welding robot according to the spatial position information of the welding piece and the welding deviation;
And the parameter optimization unit is used for optimizing welding parameters according to the welding deviation and the real-time state of the welding robot.
3. The automatically calibrated intelligent welding system of claim 2, wherein the path planning unit comprises:
the point position determining subunit is used for determining a starting point and an ending point of the welding process;
the path generation subunit is connected with the point position determination subunit and is used for generating a motion path of the welding robot according to the shape and the size of the welding piece;
The obstacle avoidance detection subunit is connected with the path generation subunit and used for detecting obstacles encountered in the motion path to obtain a detection result;
And the dynamic adjustment subunit is connected with the obstacle avoidance detection subunit and used for adjusting a motion path according to the detection result and the real-time state of the welding robot.
4. The automatically calibrated intelligent welding system according to claim 3, wherein said parameter optimization unit comprises:
The deviation analysis subunit is used for analyzing the welding deviation to obtain a target deviation value;
The real-time state monitoring subunit is used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time state of the welding robot;
and the optimization parameter calculation subunit is connected with the deviation analysis subunit and the real-time state monitoring subunit and used for optimizing the welding parameters according to the target deviation value and the real-time state to obtain target welding parameters.
5. The automatically calibrated smart welding system of claim 4, wherein the robotic execution module comprises:
the instruction analysis unit is used for receiving and analyzing the welding instruction to obtain an analysis result;
The target determining unit is connected with the instruction analyzing unit and used for determining the target state of the welding robot according to the instruction result;
And the welding execution unit is connected with the target determination unit and used for welding the welding piece according to the target state.
6. The self-calibrating intelligent welding system of claim 5, wherein the welding execution unit comprises:
a path execution subunit for controlling the movement of the welding robot according to the planned path;
a parameter optimization subunit for adjusting the parameters of the welding robot according to the target welding parameters
And the gesture control subunit is used for controlling the gesture of the welding robot.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410553393.2A CN118357649B (en) | 2024-05-07 | 2024-05-07 | Automatic intelligent welding system of correction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410553393.2A CN118357649B (en) | 2024-05-07 | 2024-05-07 | Automatic intelligent welding system of correction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118357649A CN118357649A (en) | 2024-07-19 |
| CN118357649B true CN118357649B (en) | 2025-01-07 |
Family
ID=91883773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410553393.2A Active CN118357649B (en) | 2024-05-07 | 2024-05-07 | Automatic intelligent welding system of correction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118357649B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118559718B (en) * | 2024-07-23 | 2024-12-20 | 江西龙恩智能装备有限公司 | Industrial robot control system and robot control method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104907746A (en) * | 2015-06-11 | 2015-09-16 | 唐山松下产业机器有限公司 | Robot welding system and welding quality detecting method thereof |
| CN116275742A (en) * | 2023-04-11 | 2023-06-23 | 扬州工业职业技术学院 | A Welding Method Based on Digital Modeling and Flexible Weld Seam Recognition |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100794714B1 (en) * | 2004-08-03 | 2008-01-15 | 현대중공업 주식회사 | Continuous welding line following control system of robot and its control method |
| CN113172307A (en) * | 2021-03-24 | 2021-07-27 | 苏州奥天智能科技有限公司 | Industrial robot system of visual module based on laser and visible light fusion |
| CN114851195B (en) * | 2022-04-24 | 2024-09-24 | 重庆创御智能装备有限公司 | Control method of visual welding process system |
| CN115121905A (en) * | 2022-07-01 | 2022-09-30 | 西南石油大学 | Prediction method of welding residual stress of bimetal clad pipe based on finite element method |
| CN115723133B (en) * | 2022-11-22 | 2024-08-06 | 湘潭大学 | A robot space weld automatic positioning and correction system based on virtual and real combination |
| CN116833645A (en) * | 2023-07-10 | 2023-10-03 | 上海赛威德机器人有限公司 | Weld autonomous identification and welding method and system based on mobile robot |
| CN117697080A (en) * | 2024-01-30 | 2024-03-15 | 滕州市中等职业教育中心学校 | Large-scale curved plate fillet weld corner compensating system |
| CN117961382B (en) * | 2024-03-28 | 2024-05-28 | 江苏恒久钢构股份有限公司 | Intelligent processing control system for net rack rod pieces |
-
2024
- 2024-05-07 CN CN202410553393.2A patent/CN118357649B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104907746A (en) * | 2015-06-11 | 2015-09-16 | 唐山松下产业机器有限公司 | Robot welding system and welding quality detecting method thereof |
| CN116275742A (en) * | 2023-04-11 | 2023-06-23 | 扬州工业职业技术学院 | A Welding Method Based on Digital Modeling and Flexible Weld Seam Recognition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN118357649A (en) | 2024-07-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111745267A (en) | A real-time tracking system and method for groove weld based on laser displacement sensor | |
| CN106994684B (en) | Method for controlling a robot tool | |
| CN111014879B (en) | Automatic welding method for corrugated plate of robot based on laser weld seam tracking | |
| CN113427168A (en) | Real-time welding seam tracking device and method for welding robot | |
| CN114769988B (en) | Welding control method, system, welding equipment and storage medium | |
| Chen et al. | A robust visual servo control system for narrow seam double head welding robot | |
| Hou et al. | A teaching-free welding method based on laser visual sensing system in robotic GMAW | |
| CN105643062B (en) | Complex-curved shape recognition and welding gun posture control method based on rotating the arc | |
| CN112958959A (en) | Automatic welding and detection method based on three-dimensional vision | |
| CN118357649B (en) | Automatic intelligent welding system of correction | |
| Zhou et al. | Intelligent guidance programming of welding robot for 3D curved welding seam | |
| CN113798634A (en) | Method, system and equipment for teaching spatial circular weld and tracking weld | |
| JP3317101B2 (en) | Welding robot | |
| CN113119122A (en) | Hybrid off-line programming method of robot welding system | |
| Geng et al. | A novel 3D vision-based robotic welding path extraction method for complex intersection curves | |
| CN114536346A (en) | Mechanical arm accurate path planning method based on man-machine cooperation and visual detection | |
| CN111687515A (en) | Intelligent welding guide system for large steel structure | |
| CN115723133B (en) | A robot space weld automatic positioning and correction system based on virtual and real combination | |
| Wu et al. | Research on welding guidance system of intelligent perception for steel weldment | |
| CN116160160A (en) | A welding method and system based on robot traversing space curve parameters | |
| Lei et al. | The development of tube-to-tubesheet welding from automation to digitization | |
| CN115041887A (en) | Contour measuring and recognizing system for robot welding workpiece | |
| Hanh et al. | Simultaneously extract 3D seam curve and weld head angle for robot arm using passive vision | |
| CN117885096B (en) | Method and device for controlling welding operation of robot tail end welding gun | |
| Penttilä et al. | Virtual reality enabled manufacturing of challenging workpieces |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |