Robot collaborative welding control method and system for water purifier cylinder and end cover

Abstract

This invention discloses a robotic collaborative welding control method and system for water purifier cylinders and end caps, relating to the field of welding technology. The method includes: acquiring N welding robots, allocating welding areas to obtain N robot welding work areas; constructing a historical welding control database for the water purifier, and performing optimization within the database according to the attribute parameter set of the N robot welding work areas to generate N initial robot welding control parameters; identifying the posture and position information of the target water purifier cylinder and end cap, and collaboratively optimizing the N initial robot welding control parameters to determine the robot welding collaborative control parameters; and performing collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters. This invention solves the technical problem of existing welding operations being difficult to achieve efficient collaborative operation of multiple robots, achieving the technical effect of efficient collaborative welding of multiple robots.

Description

Technical Field

[0001] This invention relates to the field of welding technology, specifically to a robotic collaborative welding control method and system for water purifier cylinders and end caps. Background Technology

[0002] In the production process of water purifier cylinders and end caps, welding quality directly affects the product's sealing performance and service life. Traditional single-robot or manual welding methods are insufficient to meet the demands of high-efficiency and high-precision production. Multi-robot collaborative welding, due to the lack of effective area allocation and coordination control, is prone to operational conflicts and insufficient precision, resulting in low collaborative efficiency, poor welding consistency, and difficulty in supporting large-scale, continuous automated production. Summary of the Invention

[0003] This application provides a robotic collaborative welding control method and system for water purifier cylinder and end cap, which is used to address the technical problem that it is difficult to achieve efficient collaborative operation of multiple robots in welding operations in the prior art.

[0004] In view of the above problems, this application provides a robotic collaborative welding control method and system for water purifier cylinder and end cap.

[0005] The first aspect of this application provides a robotic collaborative welding control method for a water purifier cylinder and end cap, the method comprising:

[0006] N welding robots are acquired, and welding areas are allocated to the target water purifier cylinder and end cap based on the N welding robots, resulting in N robot welding operation areas. A historical welding control database of the water purifier is constructed, and the attribute parameter set of the N robot welding operation areas is traversed and optimized within the historical welding control database to generate N initial robot welding control parameters. The posture and position information of the target water purifier cylinder and end cap are identified using a visual sensor, and the N initial robot welding control parameters are collaboratively optimized based on the posture and position information to determine the robot welding collaborative control parameters. The N welding robots are then started to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters.

[0007] A second aspect of this application provides a robotic collaborative welding control system for the water purifier cylinder and end cap, the system comprising:

[0008] The module consists of several sub-modules: a welding area allocation module, a welding area allocation module, and a welding control module. The former acquires N welding robots and allocates welding areas to the target water purifier cylinder and end cap based on these robots, resulting in N robot welding operation areas. The latter constructs a historical welding control database for the water purifier and performs optimization within this database based on the attribute parameter sets of the N robot welding operation areas, generating N initial robot welding control parameters. The former uses a visual sensor to identify the posture and position information of the target water purifier cylinder and end cap, and performs collaborative optimization on the N initial robot welding control parameters based on this information, determining the robot welding collaborative control parameters. Finally, the former initiates the welding operation of the N welding robots based on the robot welding collaborative control parameters, performing collaborative welding work and feedback compensation control on the target water purifier cylinder and end cap.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application acquires N welding robots, allocates welding areas to the target water purifier cylinder and end cap based on the N welding robots, and obtains N robot welding operation areas; constructs a historical welding control database for the water purifier, and iterates through the historical welding control database according to the attribute parameter set of the N robot welding operation areas to generate N initial robot welding control parameters; uses a vision sensor to identify the posture and position information of the target water purifier cylinder and end cap, and performs collaborative optimization on the N initial robot welding control parameters based on the posture and position information to determine the robot welding collaborative control parameters; and starts the N welding robots to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters. This invention solves the technical problem of the difficulty in achieving efficient collaborative operation of multiple robots in welding operations in the prior art. By rationally allocating welding areas, generating initial control parameters based on the historical welding database, using vision sensors to achieve posture recognition and collaborative optimization of control parameters, and introducing feedback compensation control during the welding process, the technical effect of achieving efficient collaborative welding of multiple robots is achieved. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of the robot collaborative welding control method for the water purifier cylinder and end cap provided in the embodiments of this application;

[0013] Figure 2 This is a schematic diagram of the robot collaborative welding control system for the water purifier cylinder and end cap provided in an embodiment of this application.

[0014] Figure labeling: Welding area allocation module 11, Traversal optimization module 12, Collaborative optimization module 13, Welding control module 14. Detailed Implementation

[0015] This application provides a robotic collaborative welding control method and system for water purifier cylinders and end caps. It addresses the technical problem that existing welding operations struggle to achieve efficient collaborative welding by multiple robots. By rationally allocating the welding area, generating initial control parameters based on a historical welding database, using visual sensors to achieve posture recognition and collaborative optimization of control parameters, and introducing feedback compensation control during the welding process, the technical effect of achieving efficient collaborative welding by multiple robots is achieved.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a robotic collaborative welding control method for the water purifier cylinder and end cap, the method including:

[0019] Step S100: Obtain N welding robots, and allocate welding areas to the target water purifier cylinder and end cap based on the N welding robots to obtain N robot welding operation areas.

[0020] In this embodiment, N welding robots for welding are first acquired. Then, welding areas are allocated to the target water purifier cylinder and end cap based on the N welding robots. In this process, a 3D model of the target water purifier cylinder and end cap is first created, generating a 3D model of the water purifier cylinder and end cap. Then, welding area markings are made on the 3D model of the water purifier cylinder and end cap to determine the areas to be welded. Next, the capabilities of the N welding robots are evaluated to obtain N welding capability constraint parameters. Finally, based on the welding capability constraint parameters, the work areas for welding the water purifier are allocated, resulting in N robot welding work areas.

[0021] Furthermore, the method provided in the application embodiment, in obtaining N robotic welding work areas, also includes:

[0022] Based on the structural design data and material property data of the target water purifier cylinder and end cap, a 3D model is generated to produce a 3D model of the water purifier cylinder and end cap. According to the welding operation target, the welding area is marked on the 3D model of the water purifier cylinder and end cap to obtain the area to be welded of the water purifier. N welding capability constraint parameters of N welding robots are evaluated and obtained. Based on the N welding capability constraint parameters, the operation area of ​​the area to be welded of the water purifier is allocated to obtain N robot welding operation areas.

[0023] In this embodiment, a 3D model is first created based on pre-stored structural design data and material property data of the target water purifier cylinder and end cap. The structural design data includes geometric dimensions, assembly datum, and weld design symbols; the material property data includes the coefficient of thermal expansion, thermal conductivity, and yield strength. During the 3D modeling process, a parametric modeling method is used. The geometric dimensions, assembly datum, and weld design symbols from the structural design data are input, and combined with the coefficient of thermal expansion, thermal conductivity, and yield strength from the material property data. The process sequentially completes steps such as establishing the part coordinate system, generating the geometric contour, defining assembly relationships, and adding weld features, resulting in a 3D model of the water purifier cylinder and end cap containing geometric shape, welding interface, and spatial pose information.

[0024] Next, welding areas are marked on the 3D models of the water purifier cylinder and end cap according to the welding operation objectives. The welding operation objectives are the specific areas to be welded. Geometric feature recognition methods are used to extract weld feature lines and bevel boundaries from the 3D models of the water purifier cylinder and end cap, identifying the welding interfaces. Path tracing methods are used to mark the arc initiation point, arc termination point, welding direction, and weld length. The areas are then classified according to weld type and bevel form, transforming the structural connection locations into spatial operation objects that can be performed by the robot, thus obtaining the areas of the water purifier to be welded.

[0025] Subsequently, the welding capabilities of the N welding robots involved in the welding were evaluated. In this process, kinematic modeling was used to calculate the reachable workspace and attitude feasible region of the end effector using forward and inverse kinematics. The range of process parameters was determined in combination with the power supply and wire feeding device. The reachability and trajectory interference in the area to be welded in the water purifier were verified through offline simulation, resulting in N welding capability constraint parameters, including spatial constraint parameters, process constraint parameters, and attitude constraint parameters.

[0026] Finally, the welding area of ​​the water purifier is allocated based on N welding capability constraint parameters. In this process, firstly, weld seam, material, and location features are extracted from the welding area of ​​the water purifier, forming a multi-dimensional feature set of the welding area. Then, cluster analysis and region division are performed based on the multi-dimensional feature set of the welding area, resulting in a clustered welding area set of the water purifier. Next, preliminary matching is performed on the clustered welding area set of the water purifier according to the N welding capability constraint parameters to determine N preliminary robot welding areas. Finally, conflict detection and load balancing optimization are performed on the N preliminary robot welding areas to obtain N robot welding operation areas.

[0027] Furthermore, in the method provided in the application embodiment, the work area is allocated to the water purifier to be welded area based on N welding capability constraint parameters to obtain N robot welding work areas, and it also includes:

[0028] The weld, material, and location features of the water purifier's welding area are extracted to obtain a multi-dimensional feature set of the welding area. Based on the multi-dimensional feature set of the welding area, cluster analysis and region division are performed to obtain a clustered welding area set of the water purifier. The clustered welding area set of the water purifier is initially matched according to N welding capability constraint parameters to determine N preliminary robot welding areas. Conflict detection and load balancing optimization are performed on the N preliminary robot welding areas to obtain N robot welding operation areas.

[0029] In this embodiment, the weld, material, and location features of the area to be welded in the water purifier are first extracted. Weld features include weld type, weld length, weld direction, and spatial orientation; material features include the type and thermophysical properties of the base material; and location features include the spatial coordinates and orientation information of the weld within the three-dimensional models of the cylinder and end cap. Through feature extraction, a multi-dimensional feature set of the water purifier's welding area is formed.

[0030] Next, cluster analysis and region partitioning are performed based on the multidimensional feature set of the welding area of ​​the water purifier. In this process, the K-means clustering method is used to standardize the multidimensional feature set of the welding area of ​​the water purifier, and the similarity between features is calculated using Euclidean distance. Based on the similarity, the welding area is divided into several classes. By setting a similarity threshold and a minimum cluster size, the automatic grouping and spatial partitioning of the weld area are completed, resulting in a clustered welding area set of the water purifier.

[0031] Subsequently, a preliminary matching of the clustered welding regions of the water purifiers was performed according to N welding capability constraint parameters. An accessibility-based matching method was adopted, using the forward and inverse kinematics models of N welding robots to calculate the reachable workspace and feasible orientation region of each robot in the workpiece coordinate system. The spatial location characteristics of the clustered welding regions were compared with the reachable regions of the robots, and the matching results were recorded using an accessibility matrix. A linear allocation method was used to complete task allocation under the premise of satisfying spatial constraints, resulting in N preliminary robot welding regions.

[0032] Finally, conflict detection and load balancing optimization were performed on the N preliminary robot welding areas. A trajectory conflict detection method based on time and space constraints was adopted, and a robot trajectory planning algorithm was used to simulate the motion trajectory of each robot within its corresponding welding area, detecting path intersections, trajectory interference, and workstation conflicts. For conflicts, the work order was adjusted, the start time was modified, and local trajectories were replanned to resolve them. Subsequently, a load balancing optimization method was used to adjust and redistribute tasks based on the number of welds, total weld length, and operation time for each robot, making the task distribution of each robot more balanced. Through these steps, N robot welding areas were obtained.

[0033] Step S200: Construct a historical welding control database for water purifiers. Optimize the historical welding control database by traversing the attribute parameter sets of N robot welding operation areas to generate N initial robot welding control parameters.

[0034] In this embodiment, a data collection and classification modeling method is first employed to categorize and organize historical welding control data generated during previous water purifier welding processes according to dimensions such as weld type, bevel form, material properties, welding position, welding posture, and welding parameters. This data is then stored in a structured manner to obtain a historical welding control database for water purifiers. The historical welding control database includes control parameters such as current, voltage, wire feed speed, welding speed, and welding heat input, along with their corresponding welding effect records. The welding effect records include penetration depth deviation, weld width deviation, welding heat input, welding speed stability, and spatter control.

[0035] Next, the historical welding control database for water purifiers is traversed and optimized according to the attribute parameter sets of N robot welding operation areas. This process begins by classifying the attribute parameter sets of the N robot welding operation areas into a dimensional set of welding operation area attribute parameters. Then, the historical welding control database for water purifiers is categorized and integrated according to these dimensional sets, resulting in a dimensional database of water purifier welding control parameters. Next, similarity matching is performed within the dimensional database based on the attribute parameter sets, yielding N matching datasets for water purifier welding control. Finally, global parameter optimization is performed on the matching datasets to generate N initial robot welding control parameters.

[0036] Furthermore, in the method provided in the application embodiments, generating N initial robot welding control parameters further includes:

[0037] The attribute parameter sets of N robot welding operation areas are classified by dimension to obtain the welding operation area attribute parameter dimension set; the historical welding control database of water purifiers is classified and integrated according to the welding operation area attribute parameter dimension set to obtain the water purifier welding control dimension database; based on the attribute parameter sets of N robot welding operation areas, similarity matching is performed within the water purifier welding control dimension database to obtain N water purifier welding control matching datasets; global parameter optimization is performed on the N water purifier welding control matching datasets to generate N initial robot welding control parameters.

[0038] In this embodiment, the attribute parameter set of N robot welding operation areas is first classified by dimension. The attribute parameter set includes weld length, bevel angle, weld type, spatial location, posture information, and material properties. Using a parameter dimension division method, these parameters are divided into geometric, process, and material dimensions based on their physical properties and process characteristics, thus forming a welding operation area attribute parameter dimension set with a clear structure and corresponding relationships.

[0039] Next, the historical welding control database for water purifiers is categorized and integrated according to the dimension set of attribute parameters for welding operation areas. By adopting a hierarchical archiving method, the original historical welding control data in the historical welding control database for water purifiers is reorganized and indexed according to geometric, process, and material dimensions. Historical control parameters such as current, voltage, wire feed speed, welding speed, and heat input are mapped one-to-one with their corresponding attribute dimensions to construct a structured data indexing system, resulting in a dimension database for welding control of water purifiers.

[0040] Subsequently, similarity matching is performed based on the attribute parameter sets of N robot welding operation areas. This process uses a similarity calculation method based on Euclidean distance to calculate the similarity between the attribute parameter vector of each welding area and each historical record in the database, and filters them according to a preset similarity threshold to obtain the historical data set that is closest to the current welding area in terms of weld features, material properties, and spatial location, forming N water purifier welding control matching datasets.

[0041] Finally, global parameter optimization is performed on each of the N water purifier welding control matching datasets. This process begins by constructing a multi-objective function for welding effect based on the welding effect requirements of the water purifiers. Then, the welding effect is evaluated on the N water purifier welding control matching datasets according to this multi-objective function, resulting in N sets of water purifier welding control effects. Finally, based on these N sets of welding control effects, global parameter optimization is performed on the N water purifier welding control matching datasets to determine N initial robot welding control parameters.

[0042] Furthermore, the method provided in the application embodiment, which performs global parameter optimization on N water purifier welding control matching datasets to generate N initial robot welding control parameters, also includes:

[0043] Based on the welding effect requirements of water purifiers, a multi-objective function for welding effect is constructed. The welding effect is evaluated on N water purifier welding control matching datasets according to the multi-objective function for welding effect, resulting in N sets of water purifier welding control effects. Based on the N sets of water purifier welding control effects, global parameter optimization is performed on the N sets of water purifier welding control matching datasets to determine N initial robot welding control parameters.

[0044] In this embodiment, a multi-objective function for welding effect is first constructed based on the welding effect requirements of the water purifier. Using index standardization and weighted summation methods, penetration depth deviation, weld width deviation, welding heat input, welding speed stability, and spatter control are selected as the main evaluation indicators. Range standardization converts the indicators of different dimensions into a range of 0 to 1, with a unified direction of smaller values ​​being better. The multi-objective function for welding effect can be expressed as follows: .in, Indicates the depth deviation. Indicates weld width deviation. Indicates heat input, This indicates fluctuations in welding speed. Indicates the amount of splash. , , , , The weights are pre-set.

[0045] Next, the welding effect of N water purifier welding control matching datasets is evaluated according to the multi-objective function of welding effect. For each set of welding control parameters, including current, voltage, wire feed speed, welding speed, and shielding gas flow rate, a process boundary check is first performed to remove records that do not meet the heat input or arc stability constraints. Then, based on the corresponding index values, standardization is performed and substituted into the multi-objective function to obtain the function value F corresponding to each set of parameters, forming N sets of water purifier welding control effects.

[0046] Finally, global parameter optimization is performed on N water purifier welding control matching datasets based on N sets of water purifier welding control effects. This process begins by optimizing the control parameters of the N sets of water purifier welding control effects to obtain N parent robot welding control parameters. Then, cross-mutation expansion is performed based on these parent robot welding control parameters to construct N robot welding control parameter spaces. Finally, a multi-objective function of welding effects is used to perform global optimization within these N robot welding control parameter spaces to determine N initial robot welding control parameters.

[0047] Furthermore, in the method provided in the application embodiments, determining N initial robot welding control parameters further includes:

[0048] Based on N sets of welding control effects for water purifiers, the control parameters of N sets of welding control matching datasets for water purifiers are optimized to obtain N parent robot welding control parameters. Based on the N parent robot welding control parameters, cross-mutation expansion is performed to construct N robot welding control parameter spaces. Using the welding effect multi-objective function, global optimization is performed in the N robot welding control parameter spaces to determine N initial robot welding control parameters.

[0049] In this embodiment, the control parameters of N water purifier welding control matching datasets are first optimized based on N water purifier welding control effect sets. By performing multi-objective function calculations and sorting on the welding control parameter combinations in each water purifier welding control matching dataset, parameter combinations that meet the requirements of heat input range, upper limit of penetration depth deviation, upper limit of weld width deviation, speed stability threshold, and spatter control are selected. The optimal parameter combinations for each welding operation area are selected by comparing the multi-objective function values ​​in ascending order, resulting in N parent robot welding control parameters.

[0050] Next, cross-mutation expansion is performed based on N parent robot welding control parameters. Cross-mutation operations expand the parameter range. The cross-mutation operation randomly exchanges some parameter values ​​among different parent parameters, generating new parameter combinations. The mutation operation introduces small perturbations within the allowable range of key parameters such as current, voltage, wire feed speed, welding speed, and shielding gas flow rate to enhance the coverage and diversity of the search. Through cross-mutation and mutation, a large-scale candidate parameter set is obtained, constructing N robot welding control parameter spaces.

[0051] Subsequently, for each group of candidate control parameters in the robot welding control parameter space, the evaluation indicators in the multi-objective function of welding effect were calculated using simulation methods. The penetration depth deviation was obtained by calculating the theoretical penetration depth using a heat input and empirical penetration depth model and comparing it with the target value; the weld width deviation was obtained by estimating the width using a geometric forming model and comparing it with the designed width; the heat input was directly calculated using voltage, current, and welding speed; the welding speed stability was obtained by analyzing the speed time series fluctuations; and the spatter amount was obtained by simulating voltage and current fluctuations. These indicators were standardized and then input into the multi-objective function of welding effect for calculation. After calculating the objective function values ​​of all candidate parameters, global optimization was performed. Parameter combinations that did not meet the constraints were eliminated by sorting them from smallest to largest function values, such as heat input exceeding the upper limit, welding speed fluctuation exceeding the limit, or spatter amount being unacceptable. For the retained parameter combinations, penetration depth deviation, weld width deviation, and speed stability were compared sequentially when the function values ​​were the same, and a unique optimal solution was selected. Finally, the optimal control parameters were determined for each welding operation area, resulting in N initial robot welding control parameters.

[0052] Step S300: Use a vision sensor to identify the posture and position information of the target water purifier cylinder and end cap, and perform collaborative optimization of N initial robot welding control parameters based on the posture and position information to determine the robot welding collaborative control parameters.

[0053] In this embodiment, a vision sensor is first used to identify the posture and position information of the target water purifier cylinder and end cap. Image and contour information of the target water purifier cylinder and end cap are acquired using a multi-view industrial camera and a laser contour sensor, and a calibration method is used to establish the correspondence between the sensor coordinates and the robot's base coordinates. Then, through feature extraction and 3D reconstruction, the translational and rotational deviations of the target water purifier cylinder and end cap are calculated to obtain the posture and position information.

[0054] Next, the N initial robot welding control parameters are collaboratively optimized based on the attitude and position information. In this process, firstly, the N initial robot welding control parameters are mapped and adjusted based on the attitude and position information to obtain N corrected robot welding control parameters; then, according to the N spatial operation constraint parameters of the N welding robots, the N corrected robot welding control parameters are collaboratively optimized based on a multi-objective function of welding effect, ultimately determining the collaborative robot welding control parameters.

[0055] Furthermore, in the method provided in the application embodiments, determining the robot welding collaborative control parameters further includes:

[0056] Based on the attitude and position information, N initial robot welding control parameters are mapped, adjusted, and corrected to determine N robot welding correction control parameters; according to the N spatial operation constraint parameters of the N welding robots, the N robot welding correction control parameters are collaboratively optimized based on the welding effect multi-objective function to determine robot welding collaborative control parameters.

[0057] In this embodiment, when mapping and adjusting N initial robot welding control parameters based on attitude position information, the first step is to extract the pose data of the water purifier cylinder and end cap, along with the corresponding robot welding control data, from the historical welding control database of the water purifier. Then, based on the extracted data, an association mapping fit is performed to establish a piecewise adjustment model between pose deviation and welding parameters. Finally, this model, combined with the attitude position information, is used to adjust and correct the N initial robot welding control parameters, determining the N corrected robot welding control parameters.

[0058] Next, based on the N spatial operation constraints of the N welding robots and a multi-objective function of welding effect, the N robot welding correction control parameters are collaboratively optimized. The spatial operation constraints include the reachable space of each robot, joint range of motion, path safety distance, operation time sequence, and welding trajectory interference conditions. By calculating the objective function values ​​of each combination of correction control parameters within the constraints, combinations with excessive heat input, excessive welding speed fluctuations, and excessive weld deviations are eliminated. The remaining combinations are then sorted in ascending order of welding effect multi-objective function values, and the combination with the best welding effect is selected first. The corresponding robot welding correction control parameters are then determined as the robot welding collaborative control parameters.

[0059] Furthermore, in the method provided in the application embodiments, determining N robot welding correction control parameters further includes:

[0060] Based on the historical welding control database of water purifiers, the pose data of the water purifier cylinder and end cap, and the corresponding robot welding control data are extracted. Based on the pose data of the water purifier cylinder and end cap, and the corresponding robot welding control data, an association mapping and fitting is performed to establish a pose deviation-welding parameter segmented adjustment model. The pose deviation-welding parameter segmented adjustment model is used to adjust and correct N initial robot welding control parameters based on the posture position information to determine N robot welding correction control parameters.

[0061] In this embodiment, the posture data of the water purifier cylinder and end cap, along with corresponding robot welding control data, are first extracted from the historical welding control database of water purifiers. The historical welding control database contains posture information of the water purifier cylinder and end cap under different clamping states, as well as control parameters such as current, voltage, wire feed speed, welding speed, and welding torch angle corresponding to these postures. During this process, a structured query is performed on the historical welding control database of water purifiers, and records are matched according to batch identifiers, timestamps, and weld numbers to extract the posture data and welding control data for different posture conditions. The posture data includes translation and rotation information, while the welding control data includes current, voltage, wire feed speed, welding speed, welding torch angle, oscillation amplitude, and oscillation frequency.

[0062] Next, based on the extracted pose data and welding control data, a correlation mapping fitting was performed to establish a piecewise adjustment model for pose deviation and welding parameters. By fitting and analyzing data from different deviation ranges, the pose deviation was divided into small, medium, and large deviation regions, and mapping relationships were established between the deviation and trajectory coordinate compensation, welding speed correction, welding torch angle correction, and oscillation amplitude correction, respectively. During the fitting process, piecewise linear regression was used to calculate the correction coefficients for different ranges, and the range boundaries and coefficient matrix were determined through breakpoint search and least squares method. After fitting and verification, a piecewise adjustment model for pose deviation and welding parameters that can be used for parameter correction was formed.

[0063] Finally, the established pose deviation-welding parameter segmented adjustment model is used to adjust and correct N initial robot welding control parameters based on the attitude position information. During this process, based on the translational and rotational deviations in the attitude position information, corresponding parameter ranges are selected, and trajectory coordinate compensation, welding torch angle correction, current and welding speed adjustment, wire feed speed and oscillation parameter adjustment are calculated. These corrections are then added to the initial control parameters. After parameter correction, the correction results are checked for reachability, joint limits, path safety distance, and heat input upper limit. Corrections exceeding the allowable range are trimmed. This process yields N corrected robot welding control parameters.

[0064] Step S400: Start N welding robots to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters.

[0065] Furthermore, in the method provided in the application embodiments, starting N welding robots to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on robot welding collaborative control parameters further includes:

[0066] N welding robots are activated to perform collaborative welding operations and status feedback monitoring on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters, and the current welding status parameters of the water purifier are obtained. A PID controller is used to compensate and analyze the robot welding collaborative control parameters based on the deviation between the expected welding effect parameters and the current welding status parameters of the water purifier, and the closed-loop welding control of the water purifier is performed through the compensated robot welding collaborative control parameters.

[0067] In this embodiment, when N welding robots are activated to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on robot welding collaborative control parameters, the N welding robots are first activated to perform collaborative welding operations on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters. Each welding robot synchronously executes the welding task according to predetermined trajectory position, welding current, welding voltage, welding speed, and wire feeding speed control parameters, realizing the parallel processing of multiple weld seams. During the welding process, by coordinating the movement rhythm and parameter execution sequence of each welding robot, the synchronous and stable welding process of multiple robots on the same workpiece is ensured.

[0068] During the welding process, status feedback monitoring is performed simultaneously to collect real-time data on the welding status of the target water purifier cylinder and end cap. Real-time parameters such as welding current, welding voltage, welding speed, penetration depth, weld width, heat input, and spatter amount are acquired using devices such as arc sensors, voltage and current sensors, forming the current welding status parameters of the water purifier.

[0069] Subsequently, a PID controller was used to compensate for the deviation between the desired welding effect parameters and the current welding state parameters of the water purifier. In this process, the preset desired welding effect parameters were compared with the current welding state parameters, the deviation was calculated, and the compensation amount was obtained through proportional, integral, and derivative operations. The compensation amount was applied to the original robot welding collaborative control parameters to correct parameters such as welding current, welding voltage, welding speed, and welding torch angle in real time, reducing the deviation. After the compensation analysis was completed, the compensated robot welding collaborative control parameters were used for closed-loop welding control of the water purifier cylinder and end cap. As the welding process progressed, the feedback parameters were continuously updated, and the control parameters were continuously adjusted according to the deviation changes, achieving dynamic correction and stable control of the welding state. Ultimately, the welding deviation was effectively suppressed, completing the closed-loop welding control of the water purifier.

[0070] In summary, the embodiments of this application have at least the following technical effects:

[0071] This application acquires N welding robots, allocates welding areas to the target water purifier cylinder and end cap based on the N welding robots, and obtains N robot welding operation areas; constructs a historical welding control database for the water purifier, and iterates through the historical welding control database according to the attribute parameter set of the N robot welding operation areas to generate N initial robot welding control parameters; uses a vision sensor to identify the posture and position information of the target water purifier cylinder and end cap, and performs collaborative optimization on the N initial robot welding control parameters based on the posture and position information to determine the robot welding collaborative control parameters; and starts the N welding robots to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters. This invention solves the technical problem of the difficulty in achieving efficient collaborative operation of multiple robots in welding operations in the prior art. By rationally allocating welding areas, generating initial control parameters based on the historical welding database, using vision sensors to achieve posture recognition and collaborative optimization of control parameters, and introducing feedback compensation control during the welding process, the technical effect of achieving efficient collaborative welding of multiple robots is achieved.

[0072] Example 2, based on the same inventive concept as the robotic collaborative welding control method for the water purifier cylinder and end cap in the aforementioned examples, such as... Figure 2 As shown, this application provides a robotic collaborative welding control system for the water purifier cylinder and end cap. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0073] The welding area allocation module 11 is used to acquire N welding robots and allocate welding areas to the target water purifier cylinder and end cap based on the N welding robots, resulting in N robot welding operation areas; the traversal optimization module 12 is used to construct a historical welding control database for the water purifier, and traverse and optimize within the historical welding control database according to the attribute parameter set of the N robot welding operation areas to generate N initial robot welding control parameters; the collaborative optimization module 13 is used to identify the posture and position information of the target water purifier cylinder and end cap using a visual sensor, and collaboratively optimize the N initial robot welding control parameters based on the posture and position information to determine the robot welding collaborative control parameters; the welding control module 14 is used to start the N welding robots to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters.

[0074] Furthermore, the system is also used to achieve the following functions:

[0075] Based on the structural design data and material property data of the target water purifier cylinder and end cap, a 3D model is generated to produce a 3D model of the water purifier cylinder and end cap. According to the welding operation target, the welding area is marked on the 3D model of the water purifier cylinder and end cap to obtain the area to be welded of the water purifier. N welding capability constraint parameters of N welding robots are evaluated and obtained. Based on the N welding capability constraint parameters, the operation area of ​​the area to be welded of the water purifier is allocated to obtain N robot welding operation areas.

[0076] Furthermore, the system is also used to achieve the following functions:

[0077] The weld, material, and location features of the water purifier's welding area are extracted to obtain a multi-dimensional feature set of the welding area. Based on the multi-dimensional feature set of the welding area, cluster analysis and region division are performed to obtain a clustered welding area set of the water purifier. The clustered welding area set of the water purifier is initially matched according to N welding capability constraint parameters to determine N preliminary robot welding areas. Conflict detection and load balancing optimization are performed on the N preliminary robot welding areas to obtain N robot welding operation areas.

[0078] Furthermore, the system is also used to achieve the following functions:

[0079] The attribute parameter sets of N robot welding operation areas are classified by dimension to obtain the welding operation area attribute parameter dimension set; the historical welding control database of water purifiers is classified and integrated according to the welding operation area attribute parameter dimension set to obtain the water purifier welding control dimension database; based on the attribute parameter sets of N robot welding operation areas, similarity matching is performed within the water purifier welding control dimension database to obtain N water purifier welding control matching datasets; global parameter optimization is performed on the N water purifier welding control matching datasets to generate N initial robot welding control parameters.

[0080] Furthermore, the system is also used to achieve the following functions:

[0081] Based on the welding effect requirements of water purifiers, a multi-objective function for welding effect is constructed. The welding effect is evaluated on N water purifier welding control matching datasets according to the multi-objective function for welding effect, resulting in N sets of water purifier welding control effects. Based on the N sets of water purifier welding control effects, global parameter optimization is performed on the N sets of water purifier welding control matching datasets to determine N initial robot welding control parameters.

[0082] Furthermore, the system is also used to achieve the following functions:

[0083] Based on N sets of welding control effects for water purifiers, the control parameters of N sets of welding control matching datasets for water purifiers are optimized to obtain N parent robot welding control parameters. Based on the N parent robot welding control parameters, cross-mutation expansion is performed to construct N robot welding control parameter spaces. Using the welding effect multi-objective function, global optimization is performed in the N robot welding control parameter spaces to determine N initial robot welding control parameters.

[0084] Furthermore, the system is also used to achieve the following functions:

[0085] Based on the attitude and position information, N initial robot welding control parameters are mapped, adjusted, and corrected to determine N robot welding correction control parameters; according to the N spatial operation constraint parameters of the N welding robots, the N robot welding correction control parameters are collaboratively optimized based on the welding effect multi-objective function to determine robot welding collaborative control parameters.

[0086] Furthermore, the system is also used to achieve the following functions:

[0087] Based on the historical welding control database of water purifiers, the pose data of the water purifier cylinder and end cap, and the corresponding robot welding control data are extracted. Based on the pose data of the water purifier cylinder and end cap, and the corresponding robot welding control data, an association mapping and fitting is performed to establish a pose deviation-welding parameter segmented adjustment model. The pose deviation-welding parameter segmented adjustment model is used to adjust and correct N initial robot welding control parameters based on the posture position information to determine N robot welding correction control parameters.

[0088] Furthermore, the system is also used to achieve the following functions:

[0089] N welding robots are activated to perform collaborative welding operations and status feedback monitoring on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters, and the current welding status parameters of the water purifier are obtained. A PID controller is used to compensate and analyze the robot welding collaborative control parameters based on the deviation between the expected welding effect parameters and the current welding status parameters of the water purifier, and the closed-loop welding control of the water purifier is performed through the compensated robot welding collaborative control parameters.

[0090] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0091] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A robot collaborative welding control method for a water purifier cylinder and an end cover, characterized in that the method include: Obtain N welding robots, and allocate welding areas to the target water purifier cylinder and end cap based on the N welding robots to obtain N robot welding operation areas; Construct a historical welding control database for water purifiers, and perform optimization by traversing the historical welding control database for N robot welding operation areas according to the attribute parameter set of N robot welding operation areas to generate N initial robot welding control parameters. The posture and position information of the target water purifier cylinder and end cap are identified by a vision sensor. Based on the posture and position information, N initial robot welding control parameters are collaboratively optimized to determine the robot welding collaborative control parameters. N welding robots are activated to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on robot welding collaborative control parameters. Among them, determining the robot welding collaborative control parameters includes: Based on the attitude and position information, N initial robot welding control parameters are mapped, adjusted, and corrected to determine N corrected robot welding control parameters; Based on the N spatial operation constraint parameters of N welding robots and the multi-objective function of welding effect, the N robot welding correction control parameters are collaboratively optimized to determine the robot welding collaborative control parameters. Among them, N robot welding correction control parameters are determined, including: Based on the historical welding control database of water purifiers, extract the posture data of the water purifier cylinder and end cap and the corresponding robot welding control data. Based on the posture data of the water purifier cylinder and end cap and the corresponding robot welding control data, a correlation mapping and fitting was performed to establish a piecewise adjustment model of posture deviation and welding parameters. A pose deviation-welding parameter segmented adjustment model is adopted to adjust and correct N initial robot welding control parameters based on posture and position information, thereby determining N robot welding correction control parameters.

2. The robot collaborative welding control method of a water purifier cylinder and an end cover according to claim 1, characterized in that, We obtain N areas for robot welding, including: Based on the structural design data and material property data of the target water purifier cylinder and end cap, a three-dimensional model is generated to produce a three-dimensional model of the water purifier cylinder and end cap. According to the welding operation target, the welding area is marked on the three-dimensional model of the water purifier cylinder and end cap to obtain the area of ​​the water purifier to be welded. Evaluate and obtain N welding capability constraint parameters for N welding robots; Based on N welding capability constraint parameters, the work area is allocated to the area to be welded in the water purifier, resulting in N robot welding work areas.

3. The robot collaborative welding control method of a water purifier cylinder and an end cover according to claim 2, characterized in that, Based on N welding capability constraint parameters, the work area is allocated to the area to be welded in the water purifier, resulting in N robot welding work areas, including: We extract the weld, material, and location features of the area to be welded in the water purifier to obtain a multi-dimensional feature set of the welding area of ​​the water purifier. Cluster analysis and region division were performed based on the multidimensional feature set of the welding area of ​​the water purifier to obtain the clustered welding area set of the water purifier. Based on N welding capability constraint parameters, a preliminary matching of the clustered welding area set of water purifiers is performed to determine N preliminary robot welding areas; Conflict detection and load balancing optimization are performed on N preliminary robot welding areas to obtain N robot welding operation areas.

4. The robot collaborative welding control method of a water purifier cylinder and an end cover according to claim 1, characterized in that, Generate N initial robot welding control parameters, including: The attribute parameter sets of N robot welding operation areas are classified by dimension to obtain the attribute parameter dimension set of the welding operation area. The historical welding control database of water purifiers is classified and integrated according to the dimension set of attribute parameters of welding operation area to obtain the welding control dimension database of water purifiers. Based on the attribute parameter set of N robot welding operation areas, similarity matching is performed within the water purifier welding control dimension database to obtain N water purifier welding control matching datasets. Global parameter optimization is performed on N water purifier welding control matching datasets to generate N initial robot welding control parameters.

5. The robot collaborative welding control method of a water purifier cylinder and an end cap according to claim 4, characterized in that, Global parameter optimization is performed on N water purifier welding control matching datasets to generate N initial robot welding control parameters, including: Based on the welding effect requirements of water purifiers, a multi-objective function for welding effect is constructed. Welding effect evaluation is performed on N water purifier welding control matching datasets according to the multi-objective function of welding effect, resulting in N water purifier welding control effect sets; Based on N sets of welding control effects for water purifiers, global parameter optimization is performed on N sets of matching datasets for welding control of water purifiers to determine N initial robot welding control parameters.

6. The robot collaborative welding control method of a water purifier cylinder and an end cover according to claim 5, characterized in that, Determine N initial robot welding control parameters, including: Based on N water purifier welding control effect sets, the control parameters of N water purifier welding control matching datasets are optimized to obtain N parent robot welding control parameters. Based on the cross-mutation expansion of N parent robot welding control parameters, an N robot welding control parameter space is constructed. By utilizing a multi-objective function of welding effect, global optimization is performed within the space of N robot welding control parameters to determine N initial robot welding control parameters.

7. The robotic collaborative welding control method for the water purifier cylinder and end cap as described in claim 1, characterized in that, N welding robots are activated to perform collaborative welding operations and feedback compensation control on the target water purifier cylinder and end cap based on robot welding collaborative control parameters, including: N welding robots are activated to perform collaborative welding operations and status feedback monitoring on the target water purifier cylinder and end cap based on the robot welding collaborative control parameters, so as to obtain the current welding status parameters of the water purifier. A PID controller is used to compensate for the robot welding collaborative control parameters based on the deviation between the expected welding effect parameters and the current welding status parameters of the water purifier. The compensated robot welding collaborative control parameters are then used for closed-loop welding control of the water purifier.

8. A robot collaborative welding control system for a water purifier cylinder and end cap, characterized by, The system is used to execute the robotic collaborative welding control method for the water purifier cylinder and end cap as described in any one of claims 1-7, and the system includes: The welding area allocation module is used to acquire N welding robots, and based on the N welding robots, allocate the welding area to the target water purifier cylinder and end cap to obtain N robot welding operation areas. The traversal optimization module is used to build a historical welding control database for water purifiers. It traverses and optimizes the historical welding control database for water purifiers according to the attribute parameter set of N robot welding operation areas to generate N initial robot welding control parameters. A cooperative optimization module is configured to identify pose position information of the target water purifier cylinder and end cover by using a visual sensor, cooperatively optimize N initial robot welding control parameters based on the pose position information, and determine robot welding cooperative control parameters; A welding control module is configured to start N welding robots to cooperatively weld the target water purifier cylinder and end cover based on the robot welding cooperative control parameters and perform feedback compensation control.

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