An automatic correction intelligent welding system

Abstract

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

[0001] The invention relates to the technical field of industrial welding, and in particular to an automatic correction intelligent welding system. Background Art

[0002] Welding is an important connection technology in the manufacturing industry, widely used in the fields of construction, shipbuilding, automobiles, aerospace, etc. Traditional manual welding methods often have problems such as unstable welding quality, low production efficiency and high labor intensity due to the operator's technical level, physical limitations and environmental factors. With the advancement of science and technology, automated welding equipment has emerged, greatly improving the efficiency and quality of welding.

[0003] Patent document with publication number CN111644800A discloses a clamping device for HVAC pipe welding with automatic correction of axial position, the device comprises a workbench, an electric push rod and a screw, a movable groove is provided at the top edge of the workbench, and an electric push rod is bolted in the movable groove, and the output end of the electric push rod is connected to a mounting seat, the top of the mounting seat is located above the workbench, and a telescopic groove is provided on the outside of the mounting seat, a telescopic block is placed in the telescopic groove, and a clamping block is integrally provided on the outside of the telescopic block, the internal shaft of the mounting seat is connected to a gear, a receiving seat is bolted to the top of the workbench, and a top cover is hinged on the top of the receiving seat, and through grooves are provided at the top of the receiving seat and the bottom of the top cover.

[0004] It can be seen from this that the following problems exist: in the prior art, due to the strong interference factors such as arc light and smoke that may exist during the welding process, the accuracy of sensors and image recognition technology may be affected, resulting in misjudgment of welding parameters, and further affecting the problems of poor welding quality and low efficiency. Summary of the invention

[0005] To this end, the present invention provides an automatic correction intelligent welding system to overcome the problem in the prior art that strong arc light, smoke and other interference factors may exist in the welding process, which may affect the accuracy of sensors and image recognition technology, thereby leading to misjudgment of welding parameters, and further affecting poor welding quality and low efficiency.

[0006] To achieve the above object, the present invention provides an automatic correction intelligent welding system, comprising:

[0007] an identification module, used to obtain real-time image information including a welded part, a welding robot and its surrounding environment, identify the welded part and the welding robot according to the real-time image information, and confirm the spatial position information of the welded part with the real-time position of the welding robot as a reference point;

[0008] A collection module, connected to the identification module, for collecting welding voltage, welding current, welding area, welding time and welding speed in real time during the welding process;

[0009] an analysis module connected to the acquisition module, calculating the welding power according to the welding voltage and the welding current, calculating the welding heat input according to the welding power, the welding area and the welding time, calculating the welding gap according to the welding speed and the welding heat input, and judging whether the weldment meets the welding quality according to the welding gap and a preset target gap;

[0010] a parameter planning module connected to the identification module and the analysis module, for calculating the welding deviation according to the comparison between the welding gap determined to be not meeting the quality standard and the target gap, planning the motion path and welding parameters of the welding robot according to the spatial position information of the welded part, the welding deviation and the real-time state of the welding robot, and generating a welding instruction;

[0011] The robot execution module is connected to the parameter planning module, and is used to receive and parse the welding instruction, adjust the real-time state of the welding robot according to the analyzed instruction to obtain the target state, and weld the weldment according to the target state.

[0012] Furthermore, the identification module includes:

[0013] A target monitoring unit, used to identify the preliminary positions of the weldment and the welding robot according to the real-time image information;

[0014] A feature extraction unit, used to extract the geometric shape, texture and color of the weldment and the welding robot according to the real-time image information to obtain image features;

[0015] A coordinate analysis unit is connected to the target monitoring unit and the feature extraction unit, and is used to analyze the spatial position of the weldment on the welding robot according to the preliminary position and the image feature.

[0016] Furthermore, the coordinate analysis unit comprises:

[0017] A coordinate conversion subunit, used to convert the position and posture of the weldment from the camera coordinate system to the robot coordinate system to obtain a robot coordinate mapping;

[0018] A depth calculation subunit, connected to the coordinate conversion unit, for calculating the depth between the weldment and the camera according to the robot target mapping and the real-time image information to obtain a depth vector;

[0019] The position calculation subunit is connected to the coordinate conversion subunit and the depth calculation subunit, and is used to calculate the target position of the weldment in the robot coordinate mapping according to the robot coordinate mapping and the depth vector.

[0020] Furthermore, the analysis module includes:

[0021] A heat input calculation unit, used to obtain a welding density according to the welding power and the welding time, and to obtain a heat input amount according to a quotient of the welding density and the welding area;

[0022] The gap calculation unit is connected to the heat input calculation unit and calculates the welding gap according to the heat input amount and the welding speed.

[0023] Furthermore, the analysis module also includes:

[0024] A data analysis unit, used for comparing the welding gap with a preset target gap to obtain a comparison result;

[0025] A comparison unit is connected to the data processing unit and determines whether the weldment meets the quality standard according to the comparison result, wherein:

[0026] If the welding gap is larger than the target gap, it is determined that the welded part does not meet the welding quality standard;

[0027] If the welding gap is less than or equal to the target gap, it is determined that the welded part meets the welding quality standard.

[0028] Furthermore, the parameter planning module includes:

[0029] A path planning unit, used to plan the path of the welding robot according to the spatial position information of the welded part and the welding deviation;

[0030] A parameter optimization unit is used to optimize welding parameters according to the welding deviation and the real-time status of the welding robot.

[0031] Furthermore, the path planning unit includes:

[0032] Point determination subunit, used to determine the starting and ending points of the welding process;

[0033] A path generation subunit, connected to the point determination subunit, for generating a motion path of the welding robot according to the shape and size of the weldment;

[0034] an obstacle avoidance detection subunit, connected to the path generation subunit, for detecting obstacles encountered in the motion path to obtain a detection result;

[0035] The dynamic adjustment subunit is connected to the obstacle avoidance detection subunit and is used to adjust the motion path according to the detection result and the real-time status of the welding robot.

[0036] Furthermore, the parameter optimization unit includes:

[0037] A deviation analysis subunit, used for analyzing the size of the welding deviation to obtain a target deviation value;

[0038] A real-time status monitoring subunit, used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time status of the welding robot;

[0039] The optimization parameter calculation subunit is connected to the deviation analysis subunit and the real-time status monitoring subunit, and is used to optimize the welding parameters according to the target deviation value and the real-time status to obtain target welding parameters.

[0040] Furthermore, the robot execution module includes:

[0041] An instruction parsing unit, used for receiving and parsing the welding instruction to obtain a parsing result;

[0042] A target determination unit, connected to the instruction parsing unit, for determining a target state of the welding robot according to the instruction result;

[0043] A welding execution unit is connected to the target determination unit and is used to weld the weldment according to the target state.

[0044] Furthermore, the welding execution unit comprises:

[0045] A path execution subunit, used to control the movement of the welding robot according to the planned path;

[0046] A parameter optimization subunit is used to adjust the parameters of the welding robot according to the target welding parameters.

[0047] The posture control subunit is used to control the posture of the welding robot.

[0048] Compared with the prior art, the beneficial effect of the present invention is that, through the cooperation of the acquisition module and the recognition module, real-time monitoring of the welding process is achieved, ensuring the accuracy and timeliness of the welding parameters. According to the real-time acquisition and analysis of welding parameters, it is helpful to optimize the welding process, reduce the scrap rate, and thus improve production efficiency. 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 welded parts, thereby improving the flexibility and adaptability of the welding process. The robot execution module can accurately receive and parse welding instructions, adjust the real-time status of the robot, and ensure the accuracy and consistency of the welding operation.

[0049] In particular, by real-time monitoring and tracking the positions of welded parts and welding robots, the target monitoring unit ensures that the system can perceive changes in the welding site in a timely manner. The application of object detection algorithms improves the accuracy and speed of recognition, thereby improving the responsiveness and stability of the entire welding system. The feature extraction unit extracts the geometric shape, texture and color features of welded parts and welding robots, and the feature extraction unit provides rich information, which helps to more accurately identify and distinguish different welding objects. Through in-depth analysis of preliminary position and image features, the coordinate analysis unit can accurately calculate the position of the welded part in the welding robot coordinate system, providing an accurate basis for subsequent path planning and parameter adjustment.

[0050] In particular, the coordinate conversion subunit ensures the accuracy of the position and posture of the weldment in the robot coordinate system through precise coordinate conversion, which is essential for precise control of the robot. The depth calculation subunit can provide accurate depth information between the weldment and the camera, which is essential 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 operating efficiency and stability of the welding robot by calculating the target position of the weldment in the robot coordinate system, and reduces repeated operations and adjustment time caused by improper operation.

[0051] In particular, the heat input calculation unit and the gap calculation unit can more accurately control the welding process and improve the welding quality. The use of the calculation unit helps to realize the automation and intelligence of the welding process and improve production efficiency.

[0052] In particular, the data analysis unit compares the actual measured welding gap with the target gap to improve the monitoring accuracy of the welding process and ensure the controllability of the welding quality. The comparison unit determines whether the welded part meets the quality standard based on the comparison result provided by the data analysis unit, ensures the consistency and reliability of the welded part, and meets strict quality requirements. It improves production efficiency and reduces rework and scrap rates caused by welding quality problems.

[0053] In particular, the path planning unit can optimize the welding path, reduce the moving distance during the welding process, and improve the welding efficiency. When planning the path, the working space and surrounding obstacles of the welding robot are considered to ensure the safety and reliability of the welding process. The parameter optimization unit dynamically adjusts the welding parameters according to the real-time data during the welding process to compensate for the welding deviation, ensure the welding quality, and adjust the parameters in time to prevent the occurrence of welding defects.

[0054] In particular, the point determination subunit ensures that the starting point and end point of the welding process are accurately set, provides clear guidance for the entire welding process, and avoids welding errors caused by uncertain starting and ending points. The path generation subunit ensures that the starting point and end point of the welding process are accurately set, provides clear guidance for the entire welding process, and avoids welding errors caused by uncertain starting and ending points. The obstacle avoidance detection subunit detects obstacles in the motion path in real time to ensure that the welding robot does not collide with other objects when performing welding tasks, avoiding equipment damage and welding defects caused by collisions. The dynamic adjustment subunit dynamically adjusts the motion path according to the detection results of the obstacle avoidance detection subunit and the real-time status of the welding robot, ensuring the continuity and stability of the welding process, and ensuring welding quality even in complex environments.

[0055] In particular, the deviation analysis subunit can accurately identify problems in the welding process by analyzing the size of the welding deviation, providing a direct basis for subsequent parameter adjustment. The real-time status subunit monitors the real-time position, speed and acceleration of the welding robot, can fully understand the working status of the welding robot, and provide real-time feedback for parameter optimization. The optimized parameter calculation subunit can dynamically calculate the optimal welding parameters according to the target deviation value and the real-time status to eliminate or reduce the welding deviation.

[0056] In particular, the instruction parsing unit can accurately and quickly parse complex welding instructions, ensuring that the welding robot correctly understands the instructions of the control system. The target determination unit ensures that the welding robot performs the welding task according to the predetermined target state, improving the accuracy and consistency of welding. The welding execution unit can accurately perform the welding operation according to the target state provided by the target determination unit, ensuring the welding quality.

[0057] In particular, the path execution subunit ensures that the welding robot can move accurately along the predetermined path, improving the repeatability and consistency of the welding process. The parameter optimization subunit dynamically adjusts the parameters of the welding robot according to the target welding parameters, ensuring the optimization of the welding process and the stability of the quality. The posture control subunit controls the posture of the robot, which helps to achieve higher welding accuracy, reduce defects and improve joint quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the structure of an automatic correction intelligent welding system provided by an embodiment of the present invention;

[0059] Figure 2 A structural schematic diagram of an identification module in an automatic correction intelligent welding system provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the structure of an analysis module in an automatic correction intelligent welding system provided by an embodiment of the present invention;

[0061] Figure 4 A schematic diagram of the structure of a parameter planning module in an automatic correction intelligent welding system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] The 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 only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0064] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0065] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] See also Figure 1 As shown, the present invention provides an automatic correction intelligent welding system, comprising:

[0067] an identification module, used to obtain real-time image information including a welded part, a welding robot and its surrounding environment, identify the welded part and the welding robot according to the real-time image information, and confirm the spatial position information of the welded part with the real-time position of the welding robot as a reference point;

[0068] A collection module, connected to the identification module, for collecting welding voltage, welding current, welding area, welding time and welding speed in real time during the welding process;

[0069] an analysis module connected to the acquisition module, calculating the welding power according to the welding voltage and the welding current, calculating the welding heat input according to the welding power, the welding area and the welding time, calculating the welding gap according to the welding speed and the welding heat input, and judging whether the weldment meets the welding quality according to the welding gap and a preset target gap;

[0070] a parameter planning module connected to the identification module and the analysis module, for calculating the welding deviation according to the comparison between the welding gap determined to be not meeting the quality standard and the target gap, planning the motion path and welding parameters of the welding robot according to the spatial position information of the welded part, the welding deviation and the real-time state of the welding robot, and generating a welding instruction;

[0071] The robot execution module is connected to the parameter planning module, and is used to receive and parse the welding instruction, adjust the real-time state of the welding robot according to the analyzed instruction to obtain the target state, and weld the weldment according to the target state.

[0072] Specifically, the recognition module uses a high-resolution camera or other image sensor to obtain a high-definition image of the welding site. Image processing algorithms, such as convolutional neural networks (CNNs) in deep learning, are applied to identify and locate welded parts and welding robots. The recognition results are compared with the pre-stored three-dimensional model to correct the spatial position information caused by visual deviation or robot position error. The acquisition module cooperates with the recognition module to monitor and record parameters such as voltage, current, area, time and speed during the welding process in real time. The analysis module calculates the welding power, heat input and welding gap through the data of the acquisition module. The welding gap is compared with the preset target gap to evaluate whether the welding quality meets the standard. If the welded part is judged to be not meeting the quality standard, the welding deviation is calculated. According to the spatial position information of the welded part, the welding deviation and the real-time state of the welding robot, the motion path and welding parameters of the welding robot are planned. A welding instruction is generated, including the adjustment values ​​of the motion path and welding parameters (such as current, voltage, speed, etc.). The robot execution module receives the welding instruction generated by the parameter planning module. The instruction is parsed and the real-time state of the welding robot is adjusted to achieve the target state. According to the target state, the welding operation is performed to complete the welding task.

[0073] Specifically, through the cooperation of the acquisition module and the recognition module, real-time monitoring of the welding process is achieved, ensuring the accuracy and timeliness of the welding parameters. Real-time acquisition and analysis of welding parameters can help optimize the welding process, reduce the scrap rate, and thus improve production efficiency. The parameter planning module intelligently plans the motion path and welding parameters of the welding robot based on the welding gap deviation and the spatial position information of the welded parts, thereby improving the flexibility and adaptability of the welding process. The robot execution module can accurately receive and parse welding instructions, adjust the real-time status of the robot, and ensure the accuracy and consistency of the welding operation.

[0074] Specifically, if Figure 2 As shown, the identification module includes:

[0075] A target monitoring unit, used to identify the preliminary positions of the weldment and the welding robot according to the real-time image information;

[0076] A feature extraction unit, used to extract the geometric shape, texture and color of the weldment and the welding robot according to the real-time image information to obtain image features;

[0077] A coordinate analysis unit is connected to the target monitoring unit and the feature extraction unit, and is used to analyze the spatial position of the weldment on the welding robot according to the preliminary position and the image feature.

[0078] Specifically, the target monitoring unit uses computer vision algorithms (such as edge detection, contour recognition, etc.) to monitor welds and welding robots in the image. Object detection algorithms (such as YOLO, FasterR-CNN, etc.) are used to locate the preliminary positions of welds and welding robots. Real-time tracking algorithms (such as Ka lman filtering, particle filtering, etc.) are used to track the motion trajectories of welds and welding robots in continuous image frames. The feature extraction unit uses image processing techniques (such as edge enhancement, filtering, threshold segmentation, etc.) to extract geometric shape features of welds and welding robots. Color segmentation and texture analysis are used to extract color and texture features of welds and welding robots. Deep learning models (such as convolutional neural networks) are used to learn the feature representation of welds and welding robots to improve the accuracy of recognition. The coordinate analysis unit matches the extracted image features with a pre-trained model or dictionary to determine the precise position of the welds and welding robots. Three-dimensional modeling and reconstruction technology is applied, combined with camera calibration and stereo vision methods, to calculate the position of welds in three-dimensional space. According to the real-time position and orientation of the welding robot, the image coordinates of the welded part are converted into coordinates in the machine coordinate system for subsequent path planning and welding parameter adjustment.

[0079] Specifically, by real-time monitoring and tracking the positions of welded parts and welding robots, the target monitoring unit ensures that the system can perceive changes in the welding site in a timely manner. The application of object detection algorithms improves the accuracy and speed of recognition, thereby improving the responsiveness and stability of the entire welding system. The feature extraction unit extracts the geometric shape, texture and color features of welded parts and welding robots, and the feature extraction unit provides rich information, which helps to more accurately identify and distinguish different welding objects. Through in-depth analysis of preliminary position and image features, the coordinate analysis unit can accurately calculate the position of the welded part in the welding robot coordinate system, providing an accurate basis for subsequent path planning and parameter adjustment.

[0080] Specifically, the coordinate analysis unit includes:

[0081] A coordinate conversion subunit, used to convert the position and posture of the weldment from the camera coordinate system to the robot coordinate system to obtain a robot coordinate mapping;

[0082] A depth calculation subunit, connected to the coordinate conversion unit, for calculating the depth between the weldment and the camera according to the robot target mapping and the real-time image information to obtain a depth vector;

[0083] The position calculation subunit is connected to the coordinate conversion subunit and the depth calculation subunit, and is used to calculate the target position of the weldment in the robot coordinate mapping according to the robot coordinate mapping and the depth vector.

[0084] Specifically, the coordinate rotor unit uses camera calibration technology to obtain the intrinsic and extrinsic parameters of the camera so as to convert the image coordinates in the camera coordinate system into actual coordinates in the world coordinate system. The inverse kinematics algorithm is applied to calculate the transformation matrix of the robot coordinate system relative to the world coordinate system according to the current joint angle and posture of the welding robot. The position and posture of the weldment are converted from the camera coordinate system to the robot coordinate system to obtain the mapping of the weldment in the robot coordinate system. The depth calculation subunit uses stereo vision or a depth camera to obtain the depth information of the welding scene. A depth image processing algorithm is applied, such as correction, denoising and interpolation of the depth map, to obtain an accurate depth value. According to the robot coordinate mapping and real-time image information, the depth between the weldment and the camera is calculated to obtain a depth vector. The position calculation subunit uses the triangulation principle, combined with the depth vector and the internal and external parameters of the camera, to calculate the target position of the weldment in the robot coordinate mapping. Spatial geometry algorithms, such as vector operations and matrix calculations, are applied to determine the three-dimensional coordinates of the weldment. According to the robot coordinate mapping and the depth vector, the target position of the weldment in the robot coordinate system is calculated.

[0085] Specifically, the coordinate conversion subunit ensures the accuracy of the position and posture of the weldment in the robot coordinate system through precise coordinate conversion, which is essential for precise control of the robot. The depth calculation subunit can provide accurate depth information between the weldment and the camera, which is essential 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 operating efficiency and stability of the welding robot by calculating the target position of the weldment in the robot coordinate system, and reduces repeated operations and adjustment time caused by improper operation.

[0086] Specifically, if Figure 3 As shown, the analysis module includes:

[0087] A heat input calculation unit, used to obtain welding energy according to the welding power and the welding time, and to obtain heat input according to a quotient of the welding energy and the welding area;

[0088] The gap calculation unit is connected to the heat input calculation unit and calculates the welding gap according to the heat input amount and the welding speed.

[0089] Specifically, heat input refers to the amount of heat input per unit area of ​​the workpiece per unit time during the welding process. The heat input calculation formula can be expressed as:

[0090]

[0091] Among them, p represents welding power, which refers to the total power applied to the workpiece during welding, usually in kilowatts (kW). Welding power is the product of welding current and welding voltage. t represents welding time, which refers to the total time required to complete welding, in seconds (s). A represents welding area, which refers to the total area of ​​the welded area, in square centimeters.

[0092] The welding gap refers to the distance between the welding material (welding rod, welding wire, etc.) and the workpiece during welding. It affects the welding quality, weld shape and welding defects. The size of the welding gap can be calculated by the heat input and welding speed. The calculation formula is as follows:

[0093] G=G0+k×Hi

[0094] Where G0 is the base value of the welding gap (mm), k is a constant (unitless) related to the material and welding parameters, and Hi is the calculated heat input (W / mm).

[0095] Specifically, the heat input calculation unit and the gap calculation unit can more accurately control the welding process and improve the welding quality. The use of the calculation unit helps to realize the automation and intelligence of the welding process and improve production efficiency.

[0096] Specifically, the analysis module also includes:

[0097] A data analysis unit, used for comparing the welding gap with a preset target gap to obtain a comparison result;

[0098] A comparison unit is connected to the data processing unit and determines whether the weldment meets the quality standard according to the comparison result, wherein:

[0099] If the welding gap is larger than the target gap, it is determined that the welded part does not meet the welding quality standard;

[0100] If the welding gap is less than or equal to the target gap, it is determined that the welded part meets the welding quality standard.

[0101] Specifically, the target gap provides precise guidance for the welding operation, ensuring that the welding robot can maintain a suitable welding gap when performing welding tasks. A suitable welding gap helps to ensure the mechanical properties and sealing properties of the welded joint, thereby improving the welding quality. For common structural steel welding, the target gap may be between 0.5mm and 4mm; for thin-walled structures or welding requiring high precision, the target gap may be smaller. The target gap in this embodiment ranges from 1mm to 3mm, which can adapt to a variety of welding situations while ensuring welding quality.

[0102] Specifically, the data analysis unit compares the actual measured welding gap with the target gap to improve the monitoring accuracy of the welding process and ensure the controllability of the welding quality. The comparison unit determines whether the welded part meets the quality standard based on the comparison result provided by the data analysis unit, ensures the consistency and reliability of the welded part, and meets strict quality requirements. This improves production efficiency and reduces rework and scrap rates caused by welding quality problems.

[0103] Specifically, if Figure 4 As shown, the parameter planning module includes:

[0104] A path planning unit, used to plan the path of the welding robot according to the spatial position information of the welded part and the welding deviation;

[0105] A parameter optimization unit is used to optimize welding parameters according to the welding deviation and the real-time status of the welding robot.

[0106] Specifically, first, it is necessary to obtain the actual position data, including the size and shape of the welded part and the position of the welding part. Deviations that may occur during the welding process include positioning errors of the welding robot and welding parameter control errors. Based on the above information, the path planning unit will calculate the optimal path of the welding robot to ensure that the robot can accurately reach the welding position and avoid obstacles. The parameter optimization unit monitors the actual welding deviation during the welding process. The parameter optimization unit will adjust the welding parameters according to the welding deviation and the real-time status of the robot to eliminate or reduce the welding deviation and improve the welding quality. The optimized welding parameters will be used to control the welding robot and update the parameter settings of the welding process in real time.

[0107] Specifically, the path planning unit can optimize the welding path, reduce the moving distance during the welding process, and improve the welding efficiency. When planning the path, the working space and surrounding obstacles of the welding robot are considered to ensure the safety and reliability of the welding process. The parameter optimization unit dynamically adjusts the welding parameters according to the real-time data during the welding process to compensate for the welding deviation, ensure the welding quality, and adjust the parameters in time to prevent the occurrence of welding defects.

[0108] Specifically, the path planning unit includes:

[0109] Point determination subunit, used to determine the starting and ending points of the welding process;

[0110] A path generation subunit, connected to the point determination subunit, for generating a motion path of the welding robot according to the shape and size of the weldment;

[0111] an obstacle avoidance detection subunit, connected to the path generation subunit, for detecting obstacles encountered in the motion path to obtain a detection result;

[0112] The dynamic adjustment subunit is connected to the obstacle avoidance detection subunit and is used to adjust the motion path according to the detection result and the real-time status of the welding robot.

[0113] Specifically, first, it is necessary to obtain a three-dimensional model or actual size of the weldment, including the size and shape of the weldment and the position of the welding part.

[0114] In this embodiment, on an automobile production line, a welding robot needs to weld body parts of an automobile.

[0115] First, determine that the robot's starting point is at the left front corner of the car body and the end point is at the right rear corner of the car body. Generate the robot's motion path along the edge of the car body based on the shape and size of the car body parts to ensure that the robot can weld smoothly along the edge of the car body. In the process of generating the path, check whether there are other robots, equipment or fixed structures on the production line that conflict with the path of the welding robot to ensure the safety of the welding process. If an obstacle is encountered during the welding process, adjust the robot's motion path based on the location of the obstacle and the real-time status of the robot to ensure the smooth progress of the welding process.

[0116] Specifically, the point determination subunit ensures that the starting point and end point of the welding process are accurately set, provides clear guidance for the entire welding process, and avoids welding errors caused by uncertain starting and ending points. The path generation subunit ensures that the starting point and end point of the welding process are accurately set, provides clear guidance for the entire welding process, and avoids welding errors caused by uncertain starting and ending points. The obstacle avoidance detection subunit detects obstacles in the motion path in real time to ensure that the welding robot does not collide with other objects when performing welding tasks, avoiding equipment damage and welding defects caused by collisions. The dynamic adjustment subunit dynamically adjusts the motion path according to the detection results of the obstacle avoidance detection subunit and the real-time status of the welding robot, ensuring the continuity and stability of the welding process, and ensuring welding quality even in complex environments.

[0117] Specifically, the parameter optimization unit includes:

[0118] A deviation analysis subunit, used for analyzing the size of the welding deviation to obtain a target deviation value;

[0119] A real-time status monitoring subunit, used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time status of the welding robot;

[0120] The optimization parameter calculation subunit is connected to the deviation analysis subunit and the real-time status monitoring subunit, and is used to optimize the welding parameters according to the target deviation value and the real-time status to obtain target welding parameters.

[0121] Specifically, the deviation analysis subunit collects data during the welding process, including welding current, voltage, speed, etc. The collected data is analyzed to calculate the size of the welding deviation to obtain a target deviation value. According to the welding quality standard, the deviation analysis subunit sets the target deviation value as the input of the optimization parameter calculation subunit. The real-time status monitoring subunit monitors the real-time position of the welding robot through devices such as sensors or cameras. The real-time status monitoring subunit also monitors the real-time speed and acceleration of the welding robot to obtain the real-time status of the welding robot. The real-time status monitoring subunit transmits the collected real-time status data to the optimization parameter calculation subunit. The optimization parameter calculation subunit receives the target deviation value from the deviation analysis subunit and the real-time status data from the real-time status monitoring subunit. The optimization parameter calculation subunit calculates the optimal welding parameters, such as current, voltage, speed, etc., according to the target deviation value and the real-time status. The optimization parameter calculation subunit outputs the calculated optimization parameters to the welding control system to adjust the welding behavior of the welding robot.

[0122] Specifically, the deviation analysis subunit can accurately identify problems in the welding process by analyzing the size of the welding deviation, providing a direct basis for subsequent parameter adjustment. The real-time status subunit monitors the real-time position, speed and acceleration of the welding robot, can fully understand the working status of the welding robot, and provide real-time feedback for parameter optimization. The optimized parameter calculation subunit can dynamically calculate the optimal welding parameters according to the target deviation value and the real-time status to eliminate or reduce the welding deviation.

[0123] Specifically, the robot execution module includes:

[0124] An instruction parsing unit, used for receiving and parsing the welding instruction to obtain a parsing result;

[0125] A target determination unit, connected to the instruction parsing unit, for determining a target state of the welding robot according to the instruction result;

[0126] A welding execution unit is connected to the target determination unit and is used to weld the weldment according to the target state.

[0127] Specifically, the instruction parsing unit receives welding instructions from a control system or other modules, and these instructions generally include information such as welding type, welding parameters, and welding path. The received welding instructions are parsed to extract key information, such as welding method, welding position, welding speed, etc. The target determination unit receives the parsing result from the instruction parsing unit. Based on the parsing result, the target determination unit determines the target state of the welding robot, including welding position, posture, welding parameters, etc. The target determination unit transmits the determined target state to the welding execution unit to execute the welding action. The welding execution unit receives the target state information from the target determination unit. According to the target state, actual welding operations are performed on the welded parts, such as moving the welding robot, adjusting welding parameters, etc. After performing the welding operation, the welding execution unit feeds back the welding results to the control system for monitoring and adjustment.

[0128] Specifically, the instruction parsing unit can accurately and quickly parse complex welding instructions, ensuring that the welding robot correctly understands the instructions of the control system. The target determination unit ensures that the welding robot performs the welding task according to the predetermined target state, improving the accuracy and consistency of welding. The welding execution unit can accurately perform the welding operation according to the target state provided by the target determination unit, ensuring the welding quality.

[0129] Specifically, the welding execution unit includes:

[0130] A path execution subunit, used to control the movement of the welding robot according to the planned path;

[0131] A parameter optimization subunit is used to adjust the parameters of the welding robot according to the target welding parameters.

[0132] The posture control subunit is used to control the posture of the welding robot.

[0133] Specifically, the path execution subunit controls the movement of the welding robot during the welding process according to the received path instructions 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 method, etc. The current parameters of the welding robot are adjusted according to the target parameters to achieve the best welding effect. The posture control subunit determines the target posture of the welding robot according to the requirements of the welding task, which may include the angle and position of the robot's end effector. By controlling the joint movement of the welding robot, the precise control of the robot's end effector posture is achieved. The posture of the welding robot is monitored in real time, and if deviations are found, adjustments are made immediately to ensure welding quality.

[0134] Specifically, the path execution subunit ensures that the welding robot can move accurately according to the predetermined path, improving the repeatability and consistency of the welding process. The parameter optimization subunit dynamically adjusts the parameters of the welding robot according to the target welding parameters to ensure the optimization of the welding process and the stability of the quality. The posture control subunit controls the posture of the robot, which helps to achieve higher welding accuracy, reduce defects and improve joint quality.

[0135] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic correction intelligent welding system, characterized in that: include: an identification module, used to obtain real-time image information including a welded part, a welding robot and its surrounding environment, identify the welded part and the welding robot according to the real-time image information, and confirm the spatial position information of the welded part with the real-time position of the welding robot as a reference point; A collection module, connected to the identification module, for collecting welding voltage, welding current, welding area, welding time and welding speed in real time during the welding process; an analysis module connected to the acquisition module, calculating the welding power according to the welding voltage and the welding current, calculating the welding heat input according to the welding power, the welding area and the welding time, calculating the welding gap according to the welding speed and the welding heat input, and judging whether the weldment meets the welding quality according to the welding gap and a preset target gap; a parameter planning module connected to the identification module and the analysis module, for calculating the welding deviation according to the comparison between the welding gap determined to be not meeting the quality standard and the target gap, planning the motion path and welding parameters of the welding robot according to the spatial position information of the welded part, the welding deviation and the real-time state of the welding robot, and generating a welding instruction; A robot execution module, connected to the parameter planning module, for receiving and parsing the welding instruction, adjusting the real-time state of the welding robot according to the parsed instruction to obtain a target state, and welding the weldment according to the target state; The identification module comprises: A target monitoring unit, used to identify the preliminary positions of the weldment and the welding robot according to the real-time image information; A feature extraction unit, used to extract the geometric shape, texture and color of the weldment and the welding robot according to the real-time image information to obtain image features; A coordinate analysis unit connected to the target monitoring unit and the feature extraction unit, for analyzing the spatial position of the weldment on the welding robot according to the preliminary position and the image feature; The coordinate analysis unit comprises: A coordinate conversion subunit, used to convert the position and posture of the weldment from the camera coordinate system to the robot coordinate system to obtain a robot coordinate mapping; A depth calculation subunit, connected to the coordinate conversion subunit, for calculating the depth between the weldment and the camera according to the robot target mapping and the real-time image information to obtain a depth vector; A position calculation subunit, connected to the coordinate conversion subunit and the depth calculation subunit, for calculating a target position of the weldment in the robot coordinate mapping according to the robot coordinate mapping and the depth vector; The analysis module comprises: A heat input calculation unit, used to obtain a welding density according to the welding power and the welding time, and to obtain a heat input amount according to a quotient of the welding density and the welding area; a gap calculation unit connected to the heat input calculation unit, and calculating the welding gap according to the heat input and the welding speed; The analysis module also includes: A data analysis unit, used for comparing the welding gap with a preset target gap to obtain a comparison result; A comparison unit is connected to the data analysis unit and determines whether the weldment meets the quality standard according to the comparison result, wherein: If the welding gap is larger than the target gap, it is determined that the welded part does not meet the welding quality standard; If the welding gap is less than or equal to the target gap, it is determined that the welded part meets the welding quality standard.

2. The automatic correction intelligent welding system according to claim 1 is characterized in that: The parameter planning module includes: A path planning unit, used to plan the path of the welding robot according to the spatial position information of the welded part and the welding deviation; A parameter optimization unit is used to optimize welding parameters according to the welding deviation and the real-time status of the welding robot.

3. The automatic correction intelligent welding system according to claim 2 is characterized in that: The path planning unit comprises: Point determination subunit, used to determine the starting and ending points of the welding process; A path generation subunit, connected to the point determination subunit, for generating a motion path of the welding robot according to the shape and size of the weldment; an obstacle avoidance detection subunit, connected to the path generation subunit, for detecting obstacles encountered in the motion path to obtain a detection result; The dynamic adjustment subunit is connected to the obstacle avoidance detection subunit and is used to adjust the motion path according to the detection result and the real-time status of the welding robot.

4. The automatic correction intelligent welding system according to claim 3 is characterized in that: The parameter optimization unit comprises: A deviation analysis subunit, used for analyzing the size of the welding deviation to obtain a target deviation value; A real-time status monitoring subunit, used for monitoring the real-time position, speed and acceleration of the welding robot to obtain the real-time status of the welding robot; The optimization parameter calculation subunit is connected to the deviation analysis subunit and the real-time status monitoring subunit, and is used to optimize the welding parameters according to the target deviation value and the real-time status to obtain target welding parameters.

5. The automatic correction intelligent welding system according to claim 4 is characterized in that: The robot execution module includes: An instruction parsing unit, used for receiving and parsing the welding instruction to obtain a parsing result; A target determination unit, connected to the instruction parsing unit, for determining a target state of the welding robot according to an instruction result; A welding execution unit is connected to the target determination unit and is used to weld the weldment according to the target state.

6. The automatic correction intelligent welding system according to claim 5 is characterized in that: The welding execution unit comprises: A path execution subunit, used to control the movement of the welding robot according to the planned path; A parameter optimization subunit is used to adjust the parameters of the welding robot according to the target welding parameters. The posture control subunit is used to control the posture of the welding robot.

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