CN116852371A - Be used for welded orbital robot of steel construction - Google Patents
Be used for welded orbital robot of steel construction Download PDFInfo
- Publication number
- CN116852371A CN116852371A CN202310962321.9A CN202310962321A CN116852371A CN 116852371 A CN116852371 A CN 116852371A CN 202310962321 A CN202310962321 A CN 202310962321A CN 116852371 A CN116852371 A CN 116852371A
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- welding
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- trolley
- camera
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 18
- 239000010959 steel Substances 0.000 title claims abstract description 18
- 238000010276 construction Methods 0.000 title description 5
- 238000003466 welding Methods 0.000 claims abstract description 140
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000005259 measurement Methods 0.000 claims abstract description 11
- 238000013528 artificial neural network Methods 0.000 claims abstract description 9
- 238000013135 deep learning Methods 0.000 claims abstract description 9
- 238000012545 processing Methods 0.000 claims description 8
- 238000004148 unit process Methods 0.000 abstract 1
- 230000033001 locomotion Effects 0.000 description 4
- 230000007547 defect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- 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
-
- 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 to a procedure covered by only one of the preceding main groups
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J5/00—Manipulators mounted on wheels or on carriages
- B25J5/02—Manipulators mounted on wheels or on carriages travelling along a guideway
-
- 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
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Robotics (AREA)
- Physics & Mathematics (AREA)
- Optics & Photonics (AREA)
- Manipulator (AREA)
Abstract
The invention belongs to the technical field of welding robots, and discloses a track robot for welding steel structures, which comprises a welding trolley and a welding gun; the camera acquires an image of the welding workpiece, and the image recognition unit processes the acquired image of the welding workpiece through the artificial neural network and the deep learning algorithm to obtain measurement data of a welding area, a welding result and quality data; the upper computer receives and processes the measurement data and calculates welding parameters; the control module receives welding parameters sent by the upper computer, and adjusts and controls the moving path of the welding trolley and the welding operation of the welding gun in real time. The robot can adapt to welding of steel structures with different shapes, and has high adaptability and autonomy; the welding operation of the steel structure welding seam can be performed rapidly and effectively, the manpower consumption is reduced and the operation efficiency and accuracy are improved under the condition of no supervision.
Description
Technical Field
The invention belongs to the technical field of welding robots, and particularly relates to a track robot for welding a steel structure.
Background
The welding of the steel structure weld joint has stronger technical performance, has higher requirements on construction precision and quality, has uneven professional level of traditional manual welding technicians, and is difficult to ensure the quality of welding construction under the condition that the quality of the whole workers in China is not high. With the development of automation technology equipment, the application of various automatic welding robots improves the overall efficiency and accuracy of steel structure welding to a certain extent. Due to the complex environment and high precision requirements of steel structure welding, the welding robot has the defects of application condition limitation and problem under different scenes and working conditions. For example, the position and angle adjustment of the robot needs to be performed manually, and the adjustment process is time-consuming and inaccurate; the welding quality is difficult to guarantee, additional manual inspection, adjustment and the like are needed, only semi-automatic construction operation can be realized, and the manpower resource consumption cannot be effectively reduced.
Disclosure of Invention
The present invention aims to solve the above technical problems at least to some extent. To this end, the invention aims to provide a rail robot for welding steel structures.
The technical scheme adopted by the invention is as follows:
a rail robot for welding steel structures, comprising:
the welding device comprises a base, wherein a straight rail is arranged on the base, a cross beam is arranged on the straight rail in a sliding manner, a welding trolley and a servo motor for driving the cross beam to move on the straight rail are arranged on the cross beam, and a welding gun is arranged on one side surface of the welding trolley;
the machine vision module comprises a camera and an image recognition unit, the camera is arranged on the welding trolley, the camera and the welding gun are positioned on the same side face of the welding trolley, and the camera and the welding gun are arranged in parallel; the camera is used for acquiring an image of the welding workpiece, and the image recognition unit is used for processing the acquired image of the welding workpiece through the artificial neural network and the deep learning algorithm to obtain measurement data of a welding area, a welding result and quality data;
the upper computer is used for receiving and processing the measurement data sent by the machine vision module and calculating welding parameters through a control algorithm; the method comprises the steps of carrying out real-time feedback on a welding result and quality data, and optimizing and updating an artificial neural network and a deep learning algorithm according to the welding result and the quality data fed back in real time;
and the control module is used for receiving the welding parameters sent by the upper computer, and adjusting and controlling the moving path of the welding trolley and the welding operation of the welding gun in real time.
Preferably, the control module comprises a controller, an encoder and a sensor, wherein the encoder and the sensor are used for collecting and processing working data in the welding process, and the controller is used for feeding back the working data in the welding process, welding results and quality data to the upper computer so that the upper computer can adjust and optimize a control algorithm.
The beneficial effects of the invention are as follows:
1. the machine vision module is used for measuring and collecting shape information such as bulges and depressions on the surface of a welded steel structure, estimating the position relationship and the attitude angle, processing and analyzing input data by an upper computer, automatically adjusting output signals of a control module, and automatically controlling the welding work of the robot by the control module. The robot can adapt to welding of steel structures with different shapes, and has high adaptability and autonomy; the welding operation of the steel structure welding seam can be performed rapidly and effectively, the manpower consumption is reduced and the operation efficiency and accuracy are improved under the condition of no supervision.
2. The invention integrates an artificial neural network and a deep learning algorithm, and has the functions of self-adaptive adjustment, high-precision measurement and automatic control; the optimal welding parameters and paths adapting to different working conditions can be automatically selected and matched according to the increase of the number of test and operation samples, and the method has high adaptability and autonomy; the automation degree of the robot is high, manual intervention and operation errors can be reduced, welding efficiency and stability are improved, and human resource waste is reduced.
Drawings
Fig. 1 is a schematic block diagram of the orbital robot of the invention.
Fig. 2 is a schematic view of a robot structure of the orbital robot of the invention.
Fig. 3 is a schematic view of another angular robot structure of the orbital robot of the invention.
In the figure: 1-a base; 2-straight rails; 3-a cross beam; 4-a welding trolley; 5-a servo motor; 6-welding gun; 7-camera.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements.
As shown in fig. 1 to 3, the track robot for welding steel structures of the present embodiment includes a robot structure, a machine vision module, an upper computer and a control module; the robot structure comprises a base 1, a straight rail 2, a cross beam 3, a welding trolley 4, a servo motor 5 and a welding gun 6, wherein the base 1 is a supporting platform of the whole robot structure, the straight rail 2 is fixedly arranged on the base 1, and the straight rail 2 is a moving track of the welding trolley 4. The cross beam 3 is slidably mounted on the straight rail 2 and can move along the straight rail 2; the welding trolley 4 and the servo motor 5 are both arranged on the cross beam 3, and the servo motor 5 and the transmission mechanism form a linear servo execution unit, so that the cross beam 3 is driven to move on the straight rail 2 by receiving a control signal, and the high-speed and high-precision linear motion of the welding trolley is realized. The welding gun 6 is provided on one side surface of the welding carriage 4. In the welding process, the movement and operation of the robot structure are realized through a control module, and the control module is used for controlling the movement, speed, position and welding operation of the welding trolley 4, so that the stability and precision of the welding process are ensured.
The machine vision module comprises a camera 7, a light source and an image recognition unit, wherein the camera 7 is arranged on the welding carriage 4, the camera 7 and the welding gun 6 are positioned on the same side surface of the welding carriage 4, and the camera 7 and the welding gun 6 are arranged in parallel; the camera 7 is used for acquiring an image of a welding workpiece, and the image recognition unit is used for analyzing and classifying the acquired image of the welding workpiece through an artificial neural network and a deep learning algorithm so as to recognize the position and state of the welding workpiece and obtain measurement data, welding result and quality data of a welding area.
The machine vision module sends the obtained measurement data of the welding area, the welding result and the quality data to the upper computer, wherein the measurement data of the welding area comprises information such as the position, the size and the shape of a welding seam; the upper computer comprises a CPU unit, a GPU unit, a memory and a control circuit. The upper computer receives and processes the measurement data sent by the machine vision module, calculates welding parameters through a control algorithm, wherein the welding parameters comprise a motion track, a welding speed, welding power, welding time and the like, and automatically adjusts output signals of the control module, so that automatic adjustment and control of the welding parameters are realized. And the upper computer optimizes and updates the artificial neural network and the deep learning algorithm according to the welding result and the quality data fed back in real time, improves the accuracy and the robustness of the algorithm, realizes the real-time monitoring and control of the robot structure, and ensures the stability and the consistency of the welding quality.
The control module is used for receiving welding parameters sent by the upper computer, so that the robot structure can execute automatic welding tasks according to the welding parameters, and the moving path of the welding trolley 4 and the welding operation of the welding gun 6 are adjusted and controlled in real time to realize automatic welding. In particular, the adjustment and control of the welding operation of the welding gun 6 are realized by setting the position, angle, speed, and the like of the welding gun. The control module comprises a controller, an encoder and a sensor, wherein the encoder and the sensor are used for collecting and processing working data such as angles, displacements, temperatures, pressures and the like in the welding process, monitoring the environment and equipment states around the robot structure, and feeding back the working data, welding results and quality data in the welding process to the upper computer, so that the upper computer can adjust and optimize a control algorithm, and can automatically select and adapt to optimal welding parameters and paths of different working conditions.
The invention is used for the track robot for welding the steel structure, and the machine vision module acquires and extracts the surface characteristic information and the relative position information of the welding workpiece on line, so as to realize the real-time detection of the problems of surface defects, foreign matters and the like of the workpiece; combining an artificial neural network and a deep learning algorithm, performing online optimization on welding process parameters through training and autonomous learning of a large number of samples, realizing full-automatic operation of robot welding work, and greatly improving production efficiency and quality level; the upper computer receives and processes data in real time, and simultaneously carries a GPU unit and a CPU unit, and accurately calculates parameters such as welding distance, pressure, angle, temperature and the like, so that the welding efficiency and accuracy of workpieces are ensured; in practical application, the invention can be flexibly configured and customized according to different welding requirements and workpiece shapes, and is widely applied to the field of steel structure welding.
The invention is not limited to the above-described alternative embodiments, and any person who may derive other various forms of products in the light of the present invention, however, any changes in shape or structure thereof, all falling within the technical solutions defined in the scope of the claims of the present invention, fall within the scope of protection of the present invention.
Claims (5)
1. A rail robot for welding a steel structure, comprising:
the welding device comprises a base (1), wherein a straight rail (2) is arranged on the base (1), a cross beam (3) is arranged on the straight rail (2) in a sliding manner, a welding trolley (4) and a servo motor (5) for driving the cross beam (3) to move on the straight rail (2) are arranged on the cross beam (3), and a welding gun (6) is arranged on one side surface of the welding trolley (4);
the machine vision module comprises a camera (7) and an image recognition unit, wherein the camera (7) is arranged on the welding trolley (4), the camera (7) and the welding gun (6) are positioned on the same side face of the welding trolley (4), and the camera (7) and the welding gun (6) are arranged in parallel; the camera (7) is used for acquiring an image of the welding workpiece, and the image recognition unit is used for processing the acquired image of the welding workpiece through the artificial neural network and the deep learning algorithm to obtain measurement data of a welding area, a welding result and quality data;
the upper computer is used for receiving and processing the measurement data sent by the machine vision module and calculating welding parameters through a control algorithm; the method comprises the steps of carrying out real-time feedback on a welding result and quality data, and optimizing and updating an artificial neural network and a deep learning algorithm according to the welding result and the quality data fed back in real time;
and the control module is used for receiving the welding parameters sent by the upper computer, and adjusting and controlling the moving path of the welding trolley (4) and the welding operation of the welding gun (6) in real time.
2. The orbital robot of claim 1 wherein: the upper computer comprises a CPU unit, a GPU unit, a memory and a control circuit.
3. The orbital robot of claim 1 wherein: the control module comprises a controller, an encoder and a sensor, wherein the encoder and the sensor are used for collecting and processing working data in the welding process, and the controller is used for feeding back the working data in the welding process, welding results and quality data to the upper computer so that the upper computer can adjust and optimize a control algorithm.
4. The orbital robot of claim 1 wherein: the machine vision module also includes a light source.
5. The orbital robot of claim 1 wherein: the welding parameters comprise welding speed, welding power and welding time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310962321.9A CN116852371A (en) | 2023-08-01 | 2023-08-01 | Be used for welded orbital robot of steel construction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310962321.9A CN116852371A (en) | 2023-08-01 | 2023-08-01 | Be used for welded orbital robot of steel construction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116852371A true CN116852371A (en) | 2023-10-10 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310962321.9A Pending CN116852371A (en) | 2023-08-01 | 2023-08-01 | Be used for welded orbital robot of steel construction |
Country Status (1)
| Country | Link |
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| CN (1) | CN116852371A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117943256A (en) * | 2024-03-27 | 2024-04-30 | 海斯坦普汽车组件(北京)有限公司 | Self-adaptive gluing process control method and system |
-
2023
- 2023-08-01 CN CN202310962321.9A patent/CN116852371A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117943256A (en) * | 2024-03-27 | 2024-04-30 | 海斯坦普汽车组件(北京)有限公司 | Self-adaptive gluing process control method and system |
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