Workpiece identity control method for iron tower component steel plate tailor-welding robot

Abstract

The invention relates to a workpiece identity control method for an iron tower component steel plate tailor-welding robot, and belongs to the technical field of welding automation and intelligent manufacturing. S2, workpiece clamping and positioning control; s3, performing three-dimensional identification and identity confirmation; s4, the workpiece state and the program are subjected to self-adaption; s5, dynamically correcting the welding path; s6, process real-time monitoring and alarming are carried out; and S7, process tracing and database archiving are conducted, accurate recognition and identity confirmation of workpieces are achieved by constructing a steel plate component three-dimensional model database and combining image recognition and 3D scanning, a welding program and a path template are automatically matched based on a recognition result, weld joint deviation is dynamically corrected, and the welding quality is improved. According to the method, the recognition accuracy, the path tracking precision and the process stability of the welding robot can be remarkably improved, manual intervention is reduced, and the automatic high-precision welding production requirements of multi-model and multi-batch iron tower components are met.

Description

Technical Field

[0001] The present invention belongs to the field of welding automation technology, and specifically relates to a control method suitable for ensuring the model consistency, dimensional controllability and program adaptability of steel plate welded workpieces during the automated manufacturing process of iron tower components. The method is particularly suitable for controlling the workpiece identity in achieving efficient batch welding operations through welding robots. Background Art

[0002] As the structural complexity and manufacturing batches of power and telecommunications towers increase, industrial robots are increasingly being used to replace manual labor in tower component welding to improve quality and efficiency. However, due to the wide variety and similar shapes of steel plate workpieces used in tower components, and the large errors in manual loading, welding robots often encounter quality issues such as weld misalignment and weld failures during automated production due to workpiece recognition errors, program mismatches, and clamping errors.

[0003] Traditional welding automation relies on manual instruction and selection procedures, making it difficult to adapt to the needs of flexible mixed-line production. Furthermore, inconsistent clamping methods also affect welding consistency. Therefore, a comprehensive, systematic, and applicable method for controlling the workpiece identity of welding robots for multiple types of components is urgently needed to address these bottlenecks. To this end, a method for controlling the workpiece identity of a robot for tailor-welding steel plates for tower components is proposed. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides a workpiece identity control method for a tower component steel plate welding robot, which solves the following problems: traditional welding automation relies on manual teaching and manual selection programs, which is difficult to adapt to the needs of flexible mixed-line production; at the same time, the inconsistent clamping method also affects the welding consistency.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A workpiece identity control method for a tower component steel plate tailor welding robot includes the following method steps:

[0008] S1, classification modeling of steel plate welded parts;

[0009] S2, workpiece clamping and positioning control;

[0010] S3, three-dimensional recognition and identity confirmation;

[0011] S4, workpiece status and program self-adaptation;

[0012] S5, dynamic correction of welding path;

[0013] S6, real-time process monitoring and alarm;

[0014] S7, process traceability and database archiving.

[0015] As a further preferred embodiment of the present invention, in step S1, workpiece classification: various types of steel plates involved in welding in the tower components are classified, including tower leg plates, connecting plates, flange plates, support plates, and tower foot plates; dimension statistics and process analysis: based on actual production drawings and processing experience, the main geometric dimensions, edge contours, hole distribution, and joint seam style of each type of component are counted; feature extraction modeling: CAD modeling software is used to establish three-dimensional models of various standard components, and their workpiece identification feature value sets are extracted; model database construction: the above-mentioned model data and their identification feature values ​​are entered into the model recognition database, and the process number, welding path template, and allowable error threshold metadata are associated.

[0016] As a further preferred embodiment of the present invention, in step S2, the universal design of the fixture: a multifunctional combination fixture is designed according to the type of workpiece, including a sliding limit block, an adjustable clamp, and a quick locking structure; a reference plane control strategy: the positioning surface, the clamping surface and the reference edge in the fixture are set, and the longest side of the component is used as the main reference datum; clamping error detection: after the fixture is clamped, a micro-displacement sensor or a contact switch is used to measure whether the workpiece is in place, and the clamping data is fed back to the main control system; a tooling module switching mechanism: for different components, replaceable limit components are set to achieve rapid changeover compatibility.

[0017] As a further preferred embodiment of the present invention, in step S3, image recognition initial screening: after the workpiece is clamped, the image is captured by the top or side camera, and the AI ​​model performs image classification and recognition on the workpiece; structured light / laser scanning: the workpiece contour is scanned using a 3D scanning system to generate a point cloud model; feature comparison analysis: the extracted workpiece feature value set is compared with the model database; error judgment and identification locking: the recognition threshold is set, and the matching degree is ≥98%. If it passes, it is considered that the recognition is successful and enters the next stage, otherwise an alarm prompts the operator to manually confirm; identity recording: after recognition confirmation, a unique identification code is assigned to the workpiece, and the corresponding model is locked.

[0018] As a further preferred embodiment of the present invention, in step S4, the model is associated with the welding procedure: the standard welding process procedure bound to the workpiece model is searched according to the recognition result; path template selection: the system automatically calls the corresponding weld path template, arc starting parameters, and welding layer settings; welding parameter loading: the current, voltage, and welding speed are automatically loaded according to the steel plate thickness, welding method, and material parameters; special parameter calibration: for the variable-section components of the tower foot, the size parameters are called, the welding path is dynamically adjusted, and parameterized path generation is realized; process version check: if there are multiple versions of the process, the system identifies the corresponding version according to the workpiece batch / task order.

[0019] As a further preferred embodiment of the present invention, in step S5, actual coordinate extraction: using the workpiece scanning data, extract the starting point, end point, and angle coordinates of the key weld; path deviation analysis: comparing the scanning results with the theoretical path, analyzing the offset and angle deviation; affine transformation correction: correcting the path offset by constructing an affine transformation matrix, and performing offset compensation in the robot control system; posture optimization algorithm: compensating for the posture angle of the welding gun end to ensure the stability of the wire arc entry angle and the normal angle; compensation effect simulation verification: after dynamic compensation, the path is virtually simulated to confirm whether it meets the welding tolerance requirements.

[0020] As a further preferred embodiment of the present invention, in step S6, voltage and current monitoring: real-time collection of welding voltage and current data to determine whether it is in the process window; welding posture tracking: using the welding gun position feedback system to track the trajectory and analyze whether jitter or tailing occurs; abnormality detection and early warning: when the recognition model is inconsistent with the clamping state, or the electrical parameter fluctuation exceeds the limit, an alarm is triggered; linkage safety mechanism: linked with the PLC control system, if an alarm occurs, it can be interlocked and shut down, return to zero or prompt manual intervention; dynamic correction secondary confirmation: after the path is automatically compensated, the weld position is scanned again for fine-tuning.

[0021] As a further preferred embodiment of the present invention, in step S7, the data binding mechanism: the workpiece identification ID is bound and stored with the identification image, the scanned point cloud, and the welding program; the welding log record: the welding start time, operator, program number used, and path correction amount of each workpiece are recorded; the quality assessment upload: the welding qualification is automatically determined and written into the system in combination with the post-weld image or external inspection data; the MES system docking: an interface with the manufacturing execution system or ERP system is realized to realize synchronous data upload; and the reporting and tracking are supported: exportable reports are generated for rework rate statistics, process comparison, worker operation records, and batch problem traceability.

[0022] (3) Beneficial effects

[0023] The present invention provides a workpiece identity control method for a tower component steel plate tailor-welding robot. It has the following beneficial effects: improving the accuracy of workpiece recognition, avoiding incorrect welding and missed welding, and accurately identifying the models and characteristics of various tower steel plate components through an identity confirmation mechanism that combines image recognition with three-dimensional scanning, thereby avoiding problems such as welding program call errors and weld offsets caused by manual recognition errors, and greatly improving welding accuracy and consistency. Automatic adaptation of welding programs and self-correction of paths are achieved. The system can automatically retrieve matching welding process parameters and path templates based on the recognition results, and perform path deviation analysis and dynamic correction based on the actual workpiece coordinate data of the three-dimensional scan, thereby achieving flexible welding without "teaching and manual path adjustment", effectively adapting to dimensional deviations caused by changes in workpiece batches, and improving the robot's adaptive ability. It realizes automated universal fixture clamping of multiple components and models. Through a modular and switchable combined fixture system, it can realize rapid positioning and clamping of multiple components such as tower legs, tower bases, connecting plates, flange plates, etc., effectively improving clamping efficiency, avoiding recognition failure or welding offset due to clamping errors, and improving the flexibility and adaptability of the overall production line. It also implements full process monitoring and closed-loop quality control, and introduces real-time feedback mechanisms such as welding voltage and current monitoring, trajectory tracking analysis, and abnormal identification alarms to ensure that every weld is operating within the process window. Once a welding anomaly is detected, the system can immediately trigger an alarm or shut down, implement timely fault intervention, ensure welding quality, support production traceability and process quality analysis, and realize the collection and association of information from workpiece identification, welding process, parameter call to result detection through data binding and database archiving mechanism. Combined with MES system upload, it can support report export, batch quality analysis, operator performance evaluation and subsequent problem tracking, laying the foundation for digital workshops and intelligent manufacturing. Through intelligent identification, adaptive compensation and robotic automatic welding, it greatly reduces the number of manual intervention steps, shortens the average cycle time by 20%-35%, effectively reduces the dependence on experienced welding workers, and improves standardized and batch production capabilities. It is suitable for various structural components such as tower legs, cross connecting plates, tower foot plates, etc., and builds a set of scalable and reusable control systems, which not only improves the comprehensive utilization rate of robotic equipment, but also facilitates the subsequent expansion to more complex workpiece automatic welding scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process flow of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1 The embodiment of the present invention provides a technical solution: a workpiece identity control method for a tower component steel plate tailor welding robot, comprising the following method steps:

[0027] S1, classification modeling of steel plate welded parts;

[0028] S2, workpiece clamping and positioning control;

[0029] S3, three-dimensional recognition and identity confirmation;

[0030] S4, workpiece status and program self-adaptation;

[0031] S5, dynamic correction of welding path;

[0032] S6, real-time process monitoring and alarm;

[0033] S7, process traceability and database archiving.

[0034] In step S1, workpiece classification: classify all types of steel plates involved in welding in the tower components, including tower leg plates, connecting plates, flange plates, support plates, and tower foot plates; dimension statistics and process analysis: based on actual production drawings and processing experience, count the main geometric dimensions, edge contours, hole distribution, and joint seam patterns of each type of component; feature extraction modeling: use CAD modeling software to establish three-dimensional models of various standard components, and extract their workpiece identification feature value sets; model database construction: enter the above model data and its identification feature values ​​into the model recognition database, and associate the process number, welding path template, and allowable error threshold metadata.

[0035] In step S2, the universal design of the fixture: a multifunctional combination fixture is designed according to the type of workpiece, including a sliding limit block, adjustable clamping jaws, and a quick locking structure; the reference plane control strategy: the positioning surface, clamping surface, and reference edge in the fixture are set, with the longest side of the component as the main reference datum; clamping error detection: after the fixture is clamped, a micro-displacement sensor or contact switch is used to measure whether the workpiece is in place, and the clamping data is fed back to the main control system; the tooling module switching mechanism: for different components, replaceable limit components are set to achieve rapid changeover compatibility.

[0036] In step S3, image recognition screening: after the workpiece is clamped, the image is captured by the top or side camera, and the AI ​​model performs image classification and recognition on the workpiece; structured light / laser scanning: the workpiece contour is scanned using a 3D scanning system to generate a point cloud model; feature comparison analysis: the extracted workpiece feature value set is compared with the model database; error judgment and recognition locking: the recognition threshold is set, and the matching degree is ≥98%. If it passes, it is considered a successful recognition and enters the next stage. Otherwise, an alarm prompts the operator to manually confirm; identity recording: after recognition confirmation, a unique identification code is assigned to the workpiece, and the corresponding model is locked.

[0037] In step S4, the model is associated with the welding procedure: the standard welding process procedure bound to the workpiece model is searched according to the recognition result; path template selection: the system automatically calls the corresponding weld path template, arc starting parameters, and welding layer settings; welding parameter loading: the current, voltage, and welding speed are automatically loaded according to the steel plate thickness, welding method, and material parameters; special parameter calibration: for the variable-section components of the tower foot, the size parameters are called and the welding path is dynamically adjusted to realize parameterized path generation; process version check: if there are multiple versions of the process, the system identifies the corresponding version according to the workpiece batch / task order.

[0038] In step S5, actual coordinate extraction: using the workpiece scanning data, extract the starting point, end point, and angle coordinates of the key weld; path deviation analysis: comparing the scanning results with the theoretical path, analyzing the offset and angle deviation; affine transformation correction: correcting the path offset by constructing an affine transformation matrix, and performing offset compensation in the robot control system; posture optimization algorithm: compensating the posture angle of the welding gun end to ensure the stability of the wire arc entry angle and the normal angle; compensation effect simulation verification: after dynamic compensation, the path is virtually simulated to confirm whether it meets the welding tolerance requirements.

[0039] In step S6, voltage and current monitoring: real-time collection of welding voltage and current data to determine whether it is in the process window; welding posture tracking: using the welding gun position feedback system to track the trajectory and analyze whether jitter or tailing occurs; abnormality detection and early warning: triggering an alarm when the recognition model is inconsistent with the clamping state, or the electrical parameter fluctuation exceeds the limit; linkage safety mechanism: linked with the PLC control system, if an alarm occurs, it can be interlocked to stop, return to zero or prompt manual intervention; dynamic correction secondary confirmation: after the path is automatically compensated, the weld position is scanned again for fine-tuning.

[0040] In step S7, the data binding mechanism: binds and stores the workpiece identification ID with the identification image, scanned point cloud, and welding program; welding log records: records the welding start time, operator, program number used, and path correction amount for each workpiece; quality assessment upload: automatically determines the welding qualification and writes it to the system in combination with post-weld images or external inspection data; MES system docking: implements an interface with the manufacturing execution system or ERP system to achieve synchronous data upload; supports reporting and tracking: generates exportable reports for rework rate statistics, process comparison, worker operation records, and batch problem traceability.

[0041] Example 1

[0042] The tower leg connecting plate is the main load-bearing component of the iron tower. The welds are mostly straight-line splicing welds. The structural forms are similar but the models are diverse, and the size differences are small, which makes it very easy to have the problem of wrong materials.

[0043] Implementation steps:

[0044] Place the tower leg connection plate to be welded in a standard fixture. The fixture is equipped with a limit guide device to automatically locate the reference edge.

[0045] Start the workpiece recognition system, capture the overall contour image of the workpiece through the top vision system, and obtain the edge contour and hole layout in combination with laser line scanning;

[0046] The system automatically compares the image with the feature value set in the model database, confirms that the component is a "TT-L350 connecting plate", and automatically generates a model code;

[0047] The welding robot system retrieves the welding path template and process parameters associated with the model;

[0048] The laser scanning point cloud is compared with the theoretical path, and the system automatically performs 1.2mm path compensation and posture angle correction;

[0049] Start the welding process and monitor the current and voltage stability in real time;

[0050] After welding is completed, the welding results, identification process, fixture number, and deviation compensation records are uploaded to the MES system.

[0051] Effect: The tower leg connecting plate welding is free of teaching and recognition errors, with weld deviation ≤ 0.6mm, a welding yield rate of 98.7%, and 85% reduction in manual intervention time.

[0052] Example 2

[0053] Parametric modeling and teaching-free welding of tower foot welded parts

[0054] Application Background:

[0055] The tower foot welded workpiece is an assembly, often composed of angle steels and connecting plates of different sizes, which are varied and require robots to flexibly adapt to the sizes of different batches.

[0056] Implementation steps:

[0057] At the robot loading station, 3D structured light is used to scan the relative positions of the base plate and angle steel of the tower foot welded workpiece;

[0058] The system analyzes key parameters such as splicing angle, board width, long side / short side ratio, and constructs input vectors;

[0059] Using a parametric modeling program, the system automatically calculates and generates the welding path, including the starting point position, weld direction, and welding sequence;

[0060] The robot completes path loading and corrects posture in real time according to the generated program;

[0061] Start the welding operation, and combine the welding gun force feedback and displacement correction to achieve edge welding;

[0062] The parameterized task model is automatically recorded in the case library for subsequent optimization.

[0063] Effect: Achieve "zero-teaching automatic welding of tower foot workpieces", with a one-time welding success rate of over 95% and a parametric modeling adaptation success rate of 100%, providing an efficient solution for batch deformation tower foot welding.

[0064] Example 3

[0065] Cross plate fast switching welding recognition and path calling

[0066] Application background: Cross connectors are usually symmetrical structures with multiple similar models (e.g., the difference is only in the center hole diameter or ear plate length). The recognition accuracy determines whether the welding path matches.

[0067] Implementation steps:

[0068] On a multi-machine mixed line, the operator places the cross-connected plate into a universal fixture;

[0069] The laser dot matrix and camera jointly identify the hole diameter (such as Φ80mm) and the length of the ear plates on both sides;

[0070] The system determines the model as "CB-XΦ80-L150" and automatically calls the corresponding program "P-CB080150";

[0071] Perform pre-welding path simulation detection and correct the starting position by 0.8mm through edge scanning;

[0072] Start the welding process and perform four-section seam welding using synchronized swing welding technology;

[0073] After welding, the visual system is used to review the weld quality, record the weld image and upload it for archiving.

[0074] Effect: Achieve cross-connected plate model recognition in seconds, fast path loading, weld consistency error <0.4mm, reduce the wrong welding rate to 0, and realize multi-model mixed-line automated welding.

[0075] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all perspectives, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims, not the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be included within the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0076] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for controlling the workpiece identity of a tower component steel plate tailor welding robot, characterized by: The method comprises the following steps: S1, classification modeling of steel plate welded parts; S2, workpiece clamping and positioning control; S3, three-dimensional recognition and identity confirmation; S4, workpiece status and program self-adaptation; S5, dynamic correction of welding path; S6, real-time process monitoring and alarm; S7, process traceability and database archiving.

2. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S1, workpiece classification: classify all types of steel plates involved in welding in the tower components, including tower leg plates, connecting plates, flange plates, support plates, and tower foot plates; dimension statistics and process analysis: based on actual production drawings and processing experience, count the main geometric dimensions, edge contours, hole distribution, and joint seam patterns of each type of component; feature extraction modeling: use CAD modeling software to establish three-dimensional models of various standard components, and extract their workpiece identification feature value sets; model database construction: enter the above model data and its identification feature values ​​into the model recognition database, and associate the process number, welding path template, and allowable error threshold metadata.

3. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S2, the universal design of the fixture: a multifunctional combination fixture is designed according to the type of workpiece, including a sliding limit block, adjustable clamping jaws, and a quick locking structure; the reference plane control strategy: the positioning surface, clamping surface, and reference edge in the fixture are set, with the longest side of the component as the main reference datum; clamping error detection: after the fixture is clamped, a micro-displacement sensor or contact switch is used to measure whether the workpiece is in place, and the clamping data is fed back to the main control system; the tooling module switching mechanism: for different components, replaceable limit components are set to achieve rapid changeover compatibility.

4. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S3, image recognition screening: after the workpiece is clamped, the top or side camera is used to capture an image, and the AI ​​model classifies and recognizes the workpiece; structured light / laser scanning: a 3D scanning system is used to scan the workpiece contour and generate a point cloud model; feature comparison analysis: the extracted workpiece feature value set is compared with the model database; Error judgment and identification lock: set the identification threshold, the matching degree is ≥98%. If it passes, it is considered as a successful identification and enters the next stage. Otherwise, an alarm will be issued to prompt the operator to manually confirm; Identity record: After identification confirmation, a unique identification code is assigned to the workpiece and the corresponding model is locked.

5. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S4, the model is associated with the welding procedure: the standard welding process procedure bound to the workpiece model is searched according to the recognition result; path template selection: the system automatically calls the corresponding weld path template, arc starting parameters, and welding layer settings; welding parameter loading: the current, voltage, and welding speed are automatically loaded according to the steel plate thickness, welding method, and material parameters; special parameter calibration: for the variable-section components of the tower foot, the size parameters are called and the welding path is dynamically adjusted to realize parameterized path generation; process version check: if there are multiple versions of the process, the system identifies the corresponding version according to the workpiece batch / task order.

6. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S5, actual coordinate extraction: using the workpiece scanning data, extract the starting point, end point, and angle coordinates of the key weld; path deviation analysis: comparing the scanning results with the theoretical path, analyzing the offset and angle deviation; affine transformation correction: correcting the path offset by constructing an affine transformation matrix, and performing offset compensation in the robot control system; posture optimization algorithm: compensating the posture angle of the welding gun end to ensure the stability of the wire arc entry angle and the normal angle; compensation effect simulation verification: after dynamic compensation, the path is virtually simulated to confirm whether it meets the welding tolerance requirements.

7. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S6, voltage and current monitoring: real-time collection of welding voltage and current data to determine whether it is within the process window; welding posture tracking: using the welding gun position feedback system to track the trajectory and analyze whether jitter or tailing occurs; Abnormal detection and early warning: When the recognition model is inconsistent with the clamping status, or the electrical parameter fluctuation exceeds the limit, an alarm is triggered; Linkage safety mechanism: Linked with the PLC control system, if an alarm occurs, it can interlock and shut down, return to zero, or prompt manual intervention; Dynamic correction secondary confirmation: After the path is automatically compensated, the weld position is scanned again for fine-tuning.

8. The workpiece identity control method for a tower component steel plate tailor welding robot according to claim 1, characterized in that: In step S7, the data binding mechanism: binds and stores the workpiece identification ID with the identification image, scan point cloud, and welding program; welding log records: records the welding start time, operator, program number used, and path correction amount for each workpiece; quality assessment upload: automatically determines the welding qualification and writes it to the system in combination with post-weld images or external inspection data; MES system docking: implements an interface with the manufacturing execution system or ERP system to achieve synchronous data upload; supports reporting and tracking: generates exportable reports for rework rate statistics, process comparison, worker operation records, and batch problem traceability.

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