A gear welding device and welding method for a press

Abstract

This invention relates to a gear welding device and method for a press, belonging to the field of press manufacturing. The technical solution is as follows: A gear welding device for a press includes a work platform with a ground rail. A transfer trolley is movably mounted on the ground rail to carry the gear to be welded. Positioners are mounted on both sides of the ground rail, each equipped with a rotatable gripper. A gantry frame is also mounted on the work platform, spanning the ground rail. A welding robot is movably mounted on the gantry frame, its movement direction being horizontal and perpendicular to the ground rail. The welding robot is equipped with a machine vision module. This solution, through the positioner grippers, can precisely achieve 180° rotation of the gear, providing a stable posture adjustment basis for double-sided welding. The welding robot, combined with the machine vision module, enables automated and high-precision acquisition of weld information, providing data support for intelligent welding path planning.

Description

Technical Field

[0001] This invention relates to the field of press manufacturing, and in particular to a press gear welding device and welding method. Background Technology

[0002] Press gears are crucial transmission components in the overall press system, widely used in press equipment across various industrial production fields such as machining, automobile manufacturing, and metal stamping. Their function is to convert the rotational power output from the power source into the precise reciprocating linear motion of the press slide, thereby enabling stamping, bending, and forming operations on various workpieces. The structural integrity and operational stability of the gears directly affect the overall operating state of the machine; their transmission accuracy directly determines the stamping accuracy of the press; and the overall performance of the gears also influences the service life of the press.

[0003] Welding is a crucial process for ensuring the stability of gear structures. The weld seams of press gears primarily consist of single V-shaped welds and fillet welds. These two types of welds are mostly distributed in core stress areas such as the connection between the gear ring and the hub, and the joints between the rib plate and the gear ring, and the hub. Furthermore, press gears require welding on both sides, and to ensure the quality of the gears in use, the welding on both sides must maintain high consistency.

[0004] Current automated welding equipment generally adopts the form of production lines, such as CN220217201U, which cannot achieve double-sided welding of gears. Moreover, current automated welding still relies on manual setting of welding parameters, which is highly experience-based. Although it can achieve automatic welding, its level of intelligence is low. Summary of the Invention

[0005] This invention addresses the need for double-sided welding of press gears by providing a press gear welding device.

[0006] To solve the above problems, the technical solution adopted by the present invention is a gear welding device for a press, including a working platform, a ground rail on the working platform, a transfer trolley movably mounted on the ground rail, the transfer trolley being used to carry the gear to be welded, positioners on both sides of the ground rail, each positioner having a rotatable gripper, the rotation axis of the gripper being horizontally set and perpendicular to the ground rail, the grippers of the two positioners being arranged opposite each other; the working platform also includes a gantry frame spanning the ground rail, along the extension direction of the ground rail, the gantry frame and the positioners being arranged front and rear, a welding robot movably mounted on the gantry frame, the movement direction of the welding robot being horizontally set and perpendicular to the ground rail, the welding robot having a machine vision module. This solution achieves fully automated gear transfer through the cooperation of a ground rail and a transfer trolley. The positioner's grippers feature a horizontal rotation design with their axis perpendicular to the ground rail, enabling precise 180° gear rotation and providing a stable posture adjustment basis for double-sided welding. The welding robot, equipped with a machine vision module, can replace manual visual identification of weld seams, achieving automated and high-precision acquisition of weld seam information and providing data support for subsequent intelligent welding path planning. The front-to-back layout of the gantry and positioner ensures seamless connection between welding and rotation processes, improving overall process continuity and meeting the requirements of continuous flow in intelligent production lines.

[0007] As a preferred embodiment of a press gear welding device, the working platform is equipped with two slides, which are located on both sides of the ground rail. The movement direction of the slides is perpendicular to the ground rail, and two positioners are respectively mounted on the two slides. The slides can drive the positioners to flexibly adjust the spacing along the direction perpendicular to the ground rail, which can adapt to press gears of different specifications, solving the limitation of traditional welding devices that can only adapt to a single specification of gear. It can meet the flipping requirements of multiple specifications of gears without replacing the positioners or adjusting the overall equipment layout, improving the flexible production capacity of the device, reducing the equipment investment cost when producing multiple types of gears, and the electric drive of the slides can realize digital control of the spacing adjustment, ensuring the clamping and positioning accuracy of the grippers on the gears.

[0008] As a preferred embodiment of a gear welding device for a press, the positioner includes a lifting column, on which a lifting plate is movably mounted, and grippers are rotatably mounted on the lifting plate. The lifting plate's height is adjustable via the lifting column. On one hand, this allows for adjustment of the gripper's clamping height according to the gear diameter, preventing interference between the grippers and the transfer trolley or ground rail, ensuring a safe and stable flipping process. On the other hand, the lifting function, in conjunction with the gripper's rotation, ensures that the gear accurately returns to the preset bearing position of the transfer trolley after flipping, eliminating the need for manual secondary calibration, reducing the interval between double-sided welding processes, significantly shortening the flipping cycle, and improving production efficiency.

[0009] As a preferred embodiment of a gear welding device for a press, the transfer trolley is equipped with two centering push plates that can move in opposite directions. This opposing movement of the push plates achieves fully automatic gear centering, improving centering accuracy and ensuring that the central axis of the gear is aligned with the welding robot's working baseline during each welding operation. This avoids weld misalignment caused by gear deviation, and is particularly effective in resolving the issue of misalignment between the second weld and the first weld in double-sided welding. Furthermore, the centering process requires no manual intervention and can be automatically linked with the movement of the transfer trolley and the scanning process of the welding robot, reducing manual operation and labor intensity. The flexible clamping design of the centering push plates also prevents damage to the gear surface, protecting the workpiece's appearance quality.

[0010] On the other hand, the present invention also provides a method for welding press gears, using the above-mentioned press gear welding apparatus, the method comprising the following steps:

[0011] S1. Before welding begins, the transfer trolley is located in the buffer area at one end of the ground rail. The gear is placed on the transfer trolley by the traveling car. Then the transfer trolley runs along the ground rail, carrying the gear forward to the area below the welding robot.

[0012] S2. The welding robot moves on the gantry to directly above the gear. The welding robot scans the weld seam of the gear through the machine vision module to obtain the weld seam information. The control station automatically plans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the welding path and welding parameters.

[0013] S3. After single-sided welding is completed, the welding robot moves to a safe position. The transfer trolley carrying the gear continues to move forward between the two positioners. The slide table drives the positioners to move closer to each other, the gripper clamps the gear, and the lifting column drives the gripper to rise to a certain height. After that, the gripper drives the gear to flip.

[0014] S4. After the flip is completed, the lifting column is lowered to the appropriate position, the gripper places the gear on the transfer trolley, the centering push plate re-aligns the gear, and after alignment, the transfer trolley carries the gear back to below the welding robot.

[0015] S5. The welding robot moves on the gantry to directly above the gear. The welding robot scans the weld seam of the gear through the machine vision module to obtain the weld seam information. The control station replans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the replanned welding path and welding parameters.

[0016] S6. After double-sided welding is completed, the welding robot control station moves the welding robot to a safe position, and the transfer trolley carrying the gear moves back to the buffer area. The gear is then lifted off the transfer trolley by a crane.

[0017] The entire method automates the entire process of buffer area loading, double-sided welding, and buffer area unloading. Manual intervention is only required for initial loading and final unloading, improving production efficiency. Through the precise coordination of the positioner and the transfer trolley, the double-sided welding of gears is achieved by flipping the welding, avoiding the precision loss caused by multiple lifting operations of traditional overhead cranes, and ensuring the connection accuracy of the double-sided welds. The linkage between the welding robot and the machine vision module ensures that weld information can be accurately obtained before welding on both sides, avoiding welding defects caused by changes in weld position after flipping, reducing the double-sided welding defect rate, and ensuring the welding pass rate. At the same time, the automatic connection of each process eliminates the gap of manual waiting, shortening the total welding time of a single gear.

[0018] As a preferred implementation of a gear welding method for presses, after planning the welding path and welding parameters in steps S2 and S5, the welding robot performs a no-load teaching exercise. If interference occurs, the welding path is further optimized. The no-load teaching exercise can simulate the welding path in advance, accurately identify potential interference risks between the welding robot and the gear, transfer trolley, and positioner, and avoid accidents such as robot damage and gear scrapping caused by equipment collisions during welding, thus reducing equipment maintenance costs and production losses. Through path optimization, the welding torch posture and movement trajectory can be adjusted to ensure that the welding torch always maintains the optimal working position during welding. Especially for complex fillet welds and multi-layer, multi-pass welds, it can effectively avoid problems such as missed welds and poor weld overlap, further improving weld formation quality and welding stability.

[0019] As a preferred implementation of a press gear welding method, in steps S2 and S5, the machine vision module is a laser scanning sensor or a structured light camera; acquiring the gear weld information includes the following steps:

[0020] The vision module collects point cloud data of the weld area, filters and extracts features from the point cloud data to generate a 3D model of the weld that includes bevel size, 3D morphology and fillet weld location information; the automatic planning of the welding path includes the following steps: based on the 3D model of the weld, the start and end points of the weld are identified, the center line of the welding path is calculated, and layered and channeled planning is automatically performed according to the bevel shape and size to generate a sequence of instructions to drive the movement of each axis of the welding robot. Laser scanning sensors or structured light cameras offer significantly higher acquisition accuracy than human visual recognition, capturing minute geometric features such as bevel root gaps and misalignment, thus solving the problem of traditional manual methods failing to accurately determine bevel dimensions. Point cloud data is filtered to remove interference from dust, reflections, and other factors, and after feature extraction, the generated 3D weld model can completely restore the true morphology of the weld, providing precise data support for path planning. Layered and segmented planning can automatically calculate the number of weld passes and the cross-sectional area of ​​each weld pass based on the bevel depth, ensuring uniform bevel filling and adequate penetration, thereby avoiding problems such as incomplete fusion at the root of single-sided welds and insufficient connection of double-sided welds. At the same time, the generated robot axis motion command sequence enables digital control of the welding torch movement, ensuring consistency in welding paths for different batches of gears and controlling weld dimensional tolerances.

[0021] As a preferred implementation of a gear welding method for presses, the control station has a built-in welding parameter database, which is a structured process knowledge base. The welding parameter database uses material type, plate thickness range, bevel form, and welding position as key indexes and stores parameter groups such as welding current, voltage, welding speed, weld sequence, and number of layers.

[0022] In steps S2 and S5, the control station performs parameter matching based on the groove depth, groove width, and weld type information extracted from the 3D weld model, combined with pre-stored gear material and plate thickness data. First, the basic process range is determined based on the gear material and plate thickness. Then, the required number of weld passes and the cross-sectional area of ​​each weld pass are calculated based on the groove depth and width. Finally, the corresponding welding current, voltage, and welding speed parameters are obtained from the database using interpolation or nearest neighbor matching. The parameter database can correct and update parameters based on historical welding results. The structured parameter database replaces traditional welder experience-based judgment, avoiding parameter fluctuations caused by differences in welder skill levels and ensuring the accuracy and consistency of welding parameters. Multi-dimensional indexing allows for rapid parameter matching, significantly shortening parameter debugging time and improving production efficiency. Interpolation / nearest neighbor matching can adapt to non-standard groove sizes, avoiding weld defects caused by parameter mismatch. The database's self-correction function can optimize parameters based on historical welding quality data, enabling continuous iterative upgrades of process parameters and sustained improvement in welding quality stability over long-term use.

[0023] As a preferred implementation of a press gear welding method, the machine vision module is mounted on the end effector of a welding robot. During the scanning process in steps S2 and S5, the vision module is driven by the axial motion system of the welding robot to perform multi-viewpoint and multi-angle scanning around the weld seam, and the point cloud data acquired from multiple perspectives is processed as follows to construct a complete three-dimensional morphology of the weld seam:

[0024] The original point clouds acquired from each viewpoint are denoised and filtered. Based on feature point matching and iterative nearest point algorithm, the multi-view point clouds are accurately registered. The registered point clouds are then fused and reconstructed to generate a 3D weld morphology model with a continuous topological structure. Based on the constructed 3D weld morphology model, fine geometric features, including bevel angle, root gap, misalignment, and weld cross-sectional layer height distribution, are extracted. When planning the welding path, the following adaptive adjustments are performed based on the fine geometric features: the working angle and travel angle of the welding torch are dynamically calculated and adjusted according to the bevel angle; the welding speed and wire feed speed are corrected in real time according to the root gap and misalignment; and the weld bead arrangement sequence and overlap amount are planned according to the cross-sectional layer height distribution for multi-layer and multi-pass welding. The vision module moves with the robot's end effector, enabling multi-view scanning to cover all areas of the weld, such as the inner corner weld of gears and the bevel blind zone, avoiding information loss from a single perspective and ensuring the integrity of the weld's 3D model. Point cloud denoising, registration, and surface reconstruction technologies eliminate dust and light interference, improve model accuracy, and ensure minimal errors in extracted features such as bevel angles and root gaps. Adaptive adjustment based on fine geometric features can specifically address weld geometric deviations, ensuring the welding torch is always in the optimal welding posture. When the root gap is too large, the wire feed speed can be increased simultaneously to prevent incomplete fusion. Optimized weld bead layout for multi-layer, multi-pass welding reduces poor weld bead overlap, lowers weld stress concentration, and improves weld fatigue strength, especially suitable for the precise connection requirements of the second weld in double-sided welding.

[0025] As a preferred implementation of a gear welding method for presses, the control station is equipped with a rule engine and a data-driven optimization module. The parameter matching of the welding parameter database includes: the rule engine matching basic process parameters based on the weld type, bevel geometry, and gear material thickness extracted from the three-dimensional weld model; the data-driven optimization module dynamically fine-tuning the basic process parameters based on real-time collected historical welding process data and welding quality inspection results to achieve adaptive optimization of welding parameters; the control station also monitors the arc voltage and current in real time during the welding process, compares them with the currently executed optimized parameters, and implements closed-loop control. The rules engine ensures that basic process parameters conform to the process safety range, preventing parameters from exceeding reasonable limits. The data-driven optimization module combines historical data to fine-tune parameters, achieving personalized optimization. For example, for a batch of gears made of softer material, deformation can be reduced by lowering the welding current, improving parameter adaptability. Real-time monitoring and closed-loop control of arc voltage and current during welding can quickly respond to parameter drift and promptly correct parameters to target values, avoiding defects such as weld burn-through and incomplete fusion caused by parameter fluctuations. The entire parameter matching-optimization-control process forms a closed loop, conforming to the core logic of intelligent production data-driven and real-time correction. At the same time, all parameter adjustment data can be synchronously uploaded to the system, providing a complete data chain for welding quality traceability and process improvement.

[0026] As can be seen from the above technical solutions, the beneficial effects of this invention are as follows: This device, through the cooperation of the ground rail and the transfer trolley, can achieve fully automatic transfer of gears between the buffer area, welding area, and flipping area, replacing traditional manual crane lifting. This avoids positioning deviations and eliminates the safety hazards of frequent lifting. The positioner's gripper adopts a horizontal rotation design with its axis perpendicular to the ground rail, enabling precise 180° gear flipping. This provides stable posture support for double-sided welding, solving the gear misalignment problem easily caused by traditional manual flipping. Simultaneously, the front-to-back arrangement of the gantry and positioner ensures no spatial interference during the welding and flipping processes, improving the overall process continuity and better adapting to the continuous flow production requirements of intelligent production lines. The slide table can flexibly adjust the spacing of the positioner along a direction perpendicular to the ground rail, adapting to different specifications of press gears without changing equipment or adjusting the overall layout. This breaks through the limitation of traditional welding devices that can only adapt to a single gear specification, improving the device's flexible production capacity, reducing equipment investment costs for producing multiple types of gears, and the slide table is electrically driven, enabling multiple spacing adjustments. Digital control ensures the clamping and positioning accuracy of the grippers on the gears. The lifting column of the positioner can drive the lifting plate to adjust its height. On the one hand, it can adjust the clamping position of the grippers according to the gear diameter, avoiding interference between the grippers and the transfer trolley and the ground rail, ensuring a safe and stable flipping process. On the other hand, the lifting function can cooperate with the rotation of the grippers to ensure that the gear can accurately fall back to the preset bearing position of the transfer trolley after flipping, eliminating the need for manual secondary calibration, reducing the process interval of double-sided welding, and significantly improving production efficiency. The centering push plate on the transfer trolley can achieve fully automatic gear centering through opposite movement, improving centering accuracy and ensuring that the central axis of the gear is aligned with the working baseline of the welding robot during each welding, avoiding weld misalignment caused by gear offset. In particular, it can solve the problem of the connection deviation between the second weld and the first weld in double-sided welding. At the same time, the centering process does not require manual intervention and can be automatically linked with the movement of the transfer trolley and the scanning process of the welding robot, reducing manual operation links and reducing the labor intensity of workers. Moreover, the centering push plate adopts a flexible clamping design, which can avoid crushing the gear surface and protect the appearance quality of the workpiece.The corresponding welding method automates the entire process from loading into the buffer zone, double-sided welding, to unloading from the buffer zone. Only initial loading and final unloading require manual intervention, solving the problem of needing multiple workers in traditional processes and significantly improving per capita output efficiency. Through the precise coordination of the positioner and the transfer trolley, double-sided gear welding is achieved with a single clamping and two welding operations, avoiding the precision loss caused by multiple lifting operations in traditional overhead cranes and ensuring the connection accuracy of the double-sided welds. The welding robot, linked with a machine vision module, ensures accurate acquisition of weld information before welding on both sides, avoiding welding defects caused by changes in weld position after flipping, reducing the defect rate of double-sided welding, and ensuring a high welding pass rate. Simultaneously, each process is automatically connected, eliminating... The waiting time between manual operations effectively shortens the total welding time for a single gear. In the process, after the welding robot completes the welding path and welding parameter planning, it performs a no-load teaching demonstration, which can simulate the welding path in advance and accurately identify potential interference risks between the welding robot and the gear, transfer trolley, and positioner. This avoids accidents such as robot damage and gear scrapping caused by equipment collisions during the welding process, reducing equipment maintenance costs and production losses. Through path optimization, the welding torch posture and movement trajectory can also be adjusted to ensure that the welding torch is always in the optimal working position during the welding process. Especially for the path planning of complex fillet welds and multi-layer multi-pass welds, it can effectively avoid problems such as missed welds and poor weld overlap, further improving the weld formation quality and welding stability.The machine vision module boasts significantly higher acquisition accuracy than human visual recognition, capturing minute geometric features such as bevel root gaps and misalignment, thus solving the problem of traditional manual methods failing to accurately determine bevel dimensions. After filtering and feature extraction, the point cloud data generates a 3D weld model that completely recreates the weld's true morphology, providing precise data support for path planning. Based on this 3D model, layered and segmented planning automatically calculates the number of weld passes and the cross-sectional area of ​​each weld pass according to the bevel depth, ensuring uniform bevel filling and adequate penetration. This avoids issues such as incomplete fusion at the root of single-sided welds and insufficient connection between double-sided welds. Simultaneously, the generated instruction sequence driving the welding robot's axes enables digital control of the welding torch's movement, ensuring consistency in welding paths across different batches of gears and effectively controlling weld dimensional tolerances. Furthermore, the machine vision module, mounted on the welding robot's end effector and driven by the robot's axial motion system, can perform multi-viewpoint and multi-angle scanning around the weld, covering all areas including the gear's inner corner weld and bevel blind spots, avoiding the limitations of single-viewpoint scanning. To address missing information and ensure the integrity of the 3D weld model, the point cloud data acquired from multiple perspectives is denoised, filtered, and precisely registered using feature point matching and iterative nearest-point algorithms, and then reconstructed using fused surfaces. The resulting 3D weld topology model has a continuous topological structure, and the extracted fine geometric features, such as bevel angle, root gap, misalignment, and weld cross-sectional layer height distribution, have smaller errors. When planning the welding path, adaptive adjustments are made based on these fine geometric features. The working angle and travel angle of the welding torch can be dynamically calculated and adjusted according to the bevel angle. The welding speed and wire feed speed can be corrected in real time according to the root gap and misalignment. The weld bead arrangement sequence and overlap amount for multi-layer and multi-pass welding can be planned according to the cross-sectional layer height distribution. This not only addresses weld geometric deviation issues and ensures that the welding torch is always in the optimal welding posture, but also increases the wire feed speed simultaneously when the root gap is too large to avoid incomplete fusion. Optimized weld bead arrangement can also reduce poor weld bead overlap, reduce weld stress concentration, and improve weld fatigue strength. It is especially suitable for the precise connection requirements of the second weld in double-sided welding.The control station's built-in structured welding parameter database uses material type, plate thickness range, bevel type, and welding position as key indexes. This replaces the traditional method of welders relying on experience to judge parameters, avoiding parameter fluctuations caused by differences in welder skill levels and ensuring the accuracy and consistency of welding parameters. Multi-dimensional indexing enables rapid parameter matching, significantly shortening parameter debugging time and improving production efficiency. Using interpolation or nearest neighbor matching, it can adapt to non-standard bevel sizes, avoiding weld defects caused by parameter mismatch. The database can also correct and update parameters based on historical welding results, achieving continuous iterative upgrades of process parameters and continuously improving welding quality stability over long-term use. Simultaneously, the control station's rule engine can match weld types, bevel geometry features, and gear material thickness extracted from the 3D weld model to... Basic process parameters must conform to the safe range of the process to avoid exceeding reasonable limits. The data-driven optimization module dynamically fine-tunes the basic process parameters based on real-time collected historical welding process data and welding quality inspection results, achieving personalized optimization of welding parameters. For example, for a batch of gears made of softer material, deformation can be reduced by lowering the welding current, thus improving parameter adaptability. Furthermore, the control station monitors the arc voltage and current in real time during the welding process, compares them with the currently executed optimized parameters, and implements closed-loop control. This allows for rapid response to parameter drift, timely correction of parameters to the target value, and avoids defects such as weld burn-through and lack of fusion caused by parameter fluctuations. The entire parameter matching-optimization-control process forms a closed loop, and all parameter adjustment data can be synchronously uploaded to the system, providing complete data support for welding quality traceability and process improvement. Attached Figure Description

[0027] To more clearly illustrate the technical solution of this patent, the drawings used in the description will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this patent. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram of the structure of Embodiment 1 of the present invention.

[0029] Figure 2 This is a schematic diagram of the positioner in Embodiment 1 of the present invention.

[0030] Explanation of main figure symbols

[0031] 00. Work platform, 1. Transfer trolley, 2. Centering push plate, 3. Control station, 4. Welding robot, 5. Positioner, 6. Slide table, 7. Gripper, 8. Lifting column, 9. Ground rail, 10. Gantry, 11. Lifting plate, 12. Machine vision module. Detailed Implementation

[0032] To make the objectives, features, and advantages of this patent more apparent and understandable, the technical solutions of this patent will be clearly and completely described below with reference to the accompanying drawings of the specific embodiments. Obviously, the embodiments described below are only some embodiments of this patent, and not all embodiments. Based on the embodiments of this patent, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this patent.

[0033] Example 1

[0034] like Figure 1 , 2 As shown, a gear welding device for a press includes a work platform 00, on which a ground rail 9 is provided. A transfer trolley 1 for carrying the gear to be welded is movably mounted on the ground rail 9. The transfer trolley 1 is equipped with two centering push plates 2 that can move in opposite directions. The opposing movement of the centering push plates can achieve fully automatic gear centering, avoiding errors from manual calibration, ensuring that the central axis of the gear is aligned with the working reference line of the welding robot during welding, while reducing manual operation and labor intensity. The flexible clamping design can also prevent damage to the gear surface. The ground rail 9 has two sliding tables 6 on each side. The sliding tables 6 move perpendicular to the ground rail 9. Each sliding table 6 is equipped with a positioner 5. The sliding tables can drive the positioners to flexibly adjust the spacing. Different gear specifications can be adapted without changing the equipment, which improves the flexible production capacity of the device and reduces the equipment investment cost for multi-variety production. Moreover, the electrically driven sliding tables can realize digital control of the spacing to ensure the positioning accuracy of the grippers. The positioner 5 includes a lifting column 8, on which a lifting plate 11 is flexibly installed. Grippers are rotatably mounted on the lifting plate 11. 7. The rotation axis of the gripper 7 is horizontally set and perpendicular to the ground rail 9. The grippers 7 of the two positioners 5 are set opposite each other. The lifting column drives the lifting plate to adjust the height. The position of the gripper can be adjusted according to the gear diameter to avoid interference and ensure safe flipping. The horizontal rotation design of the gripper can accurately achieve 180° rotation of the gear, providing stable posture support for double-sided welding and solving the offset problem of traditional manual flipping. The work platform 00 is also equipped with a gantry frame 10 spanning the ground rail 9. Along the extension direction of the ground rail 9, the gantry frame 10 and the positioner 5 are set in front and behind each other. A welding robot 4 is movably mounted on the frame 10. The welding robot 4 moves horizontally and is perpendicular to the ground rail 9. The welding robot 4 has a machine vision module 12. The front and rear layout of the gantry and the positioner can avoid spatial interference between welding and flipping processes, improving process continuity. The machine vision module 12 can replace manual visual identification of weld seams, realize automated and high-precision acquisition of weld seam information, and provide data support for subsequent welding path planning. With the movable welding robot, it can accurately cover the gear weld seam area and ensure welding quality.

[0035] Example 2

[0036] A method for welding press gears, using the press gear welding apparatus described in Embodiment 1, includes the following steps:

[0037] S1. Before welding begins, the transfer trolley 1 is located in the buffer area at one end of the ground rail 9. The gear is placed on the transfer trolley 1 by the traveling car. Then the transfer trolley 1 runs along the ground rail 9, carrying the gear forward to the area below the welding robot 4.

[0038] S2. Welding robot 4 moves to directly above the gear on the gantry 10. Welding robot 4 scans the gear's weld seam using machine vision module 12 to obtain weld seam information. Control station 3 automatically plans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the welding path and welding parameters. Specifically:

[0039] The machine vision module 12 is a laser scanning sensor or a structured light camera; acquiring the weld information of the gear includes the following steps:

[0040] The visual module collects point cloud data of the weld area, filters and extracts features from the point cloud data, and generates a 3D model of the weld that includes bevel size, 3D morphology and fillet weld location information.

[0041] Automatic welding path planning includes the following steps:

[0042] Based on the three-dimensional model of the weld, the start and end points of the weld are identified, the center line of the welding path is calculated, and layered and channeled planning is automatically performed according to the shape and size of the bevel, generating a sequence of instructions to drive the four axes of the welding robot to move.

[0043] The control station 3 has a built-in welding parameter database, which is a structured process knowledge base. The database uses material type, plate thickness range, bevel type, and welding position as key indexes, and stores parameter groups for welding current, voltage, welding speed, weld sequence, and number of layers. The control station 3 performs parameter matching based on the bevel depth, bevel width, and weld type information extracted from the weld 3D model, combined with pre-stored gear material and plate thickness data. First, it determines the basic process range based on the gear material and plate thickness. Then, it calculates the required number of weld passes and the cross-sectional area of ​​each weld pass based on the bevel depth and width. Finally, it retrieves the corresponding welding current, voltage, and welding speed parameters from the database through interpolation or nearest neighbor matching. The parameter database can correct and update the parameters based on historical welding results.

[0044] The machine vision module 12 is mounted on the end effector of the welding robot 4. During the scanning process in steps S2 and S5, the axial motion system of the welding robot 4 drives the vision module to perform multi-viewpoint and multi-angle scanning around the weld. The point cloud data collected from multiple perspectives is processed as follows to construct a complete three-dimensional weld morphology: the original point cloud collected from each perspective is denoised and filtered; the multi-view point cloud is accurately registered based on feature point matching and iterative nearest point algorithm; the registered point cloud is fused and surface reconstructed to generate a three-dimensional weld morphology model with a continuous topological structure; based on the constructed three-dimensional weld morphology model, fine geometric features including bevel angle, root gap, misalignment, and weld cross-sectional layer height distribution are extracted; when planning the welding path, the following adaptive adjustments are performed based on the fine geometric features: the working angle and travel angle of the welding torch are dynamically calculated and adjusted according to the bevel angle; the welding speed and wire feed speed are corrected in real time according to the root gap and misalignment; the weld bead arrangement sequence and overlap amount are planned according to the cross-sectional layer height distribution for multi-layer and multi-pass welding.

[0045] The control station 3 is equipped with a rule engine and a data-driven optimization module. The parameter matching of the welding parameter database includes: the rule engine matching basic process parameters based on the weld type, groove geometry features, and gear material thickness extracted from the weld 3D model; the data-driven optimization module dynamically fine-tuning the basic process parameters based on real-time collected historical welding process data and welding quality inspection results to achieve adaptive optimization of welding parameters.

[0046] Control station 3 also monitors the arc voltage and current in real time during the welding process, compares them with the currently executed optimized parameters, and implements closed-loop control.

[0047] S3. After the single-sided welding is completed, the welding robot 4 moves to a safe position, and the transfer trolley 1 carries the gear and continues to move forward between the two positioners 5. The slide table 6 drives the positioners to move closer to each other, the gripper 7 clamps the gear, and the lifting column 8 drives the gripper 7 to rise to a certain height. After the gripper 7 drives the gear to flip.

[0048] S4. After the flipping is completed, the lifting column 8 is lowered to the appropriate position, the gripper 7 places the gear on the transfer trolley 1, the centering push plate 2 re-centers the gear, and after centering is completed, the transfer trolley 1 carries the gear back to below the welding robot.

[0049] S5. Welding robot 4 moves to directly above the gear on the gantry 10. Welding robot 4 scans the weld seam of the gear through machine vision module 12 to obtain the weld seam information. Control station 3 replans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the replanned welding path and welding parameters. The specific steps are the same as in S2. When replanning the welding path, adjustments are made based on the micro-deformation generated by welding in S2 (the morphology data after micro-deformation can be obtained during rescanning and recognition).

[0050] S6. After double-sided welding is completed, the welding robot control station 3 moves the welding robot 4 to a safe position, and the transfer trolley 1 carrying the gear moves back to the buffer area. The gear is then lifted off the transfer trolley 1 by a crane.

[0051] Furthermore, in steps S2 and S5, after completing the planning of the welding path and welding parameters, the welding robot 4 performs a no-load teaching exercise. If there is interference, the welding path is further optimized.

[0052] The processing example of this method is as follows:

[0053] Taking a large gear of a certain type of press as an example, the gear is made of Q345B low-alloy structural steel, with an outer diameter of 1200 mm and a plate thickness of 40 mm. It has a single V-groove and multiple stiffening fillet welds. Welding is performed using the device described in Example 1, and the pre-set process database in the control station already contains the optimized welding parameter set for this material and plate thickness.

[0054] Before welding begins, the operator uses a crane to hoist the gear to be welded onto a transfer trolley in the buffer area. After selecting the corresponding program at the control station, the transfer trolley automatically moves along the ground rail, precisely delivering the gear to the area below the welding robot's workstation. The welding robot moves directly above the gear, and its end-mounted laser scanning sensor activates, performing multi-angle scanning around the weld seam to collect high-density point cloud data. The control system processes the point cloud data in real time, filtering, multi-view registration, and fusion to construct a precise 3D digital model of the gear weld seam. The system automatically extracts key geometric features from the model, including a single V-groove depth of 35 mm and a groove angle of 60 degrees, and identifies the exact location and size of all stiffener fillet welds. Based on the extracted 3D model features, the control system executes adaptive planning. The path planning module automatically calculates the required 8 weld passes to completely fill the weld based on the V-groove dimensions, and plans the centerline of the welding trajectory for each weld pass and the welding sequence of all fillet welds. Simultaneously, the parameter matching module begins operation. The control station queries the built-in welding parameter database based on the workpiece material (Q345B), plate thickness (40 mm), and bevel geometry. According to preset process rules, the database matches the basic parameters for the first layer of welding as follows: welding current 280 amps, arc voltage 29 volts, welding speed 35 cm / min, and wire feed speed 7.5 m / min. For subsequent weld passes, the system automatically adjusts the parameters layer by layer based on the calculated changes in weld cross-sectional area. For example, the parameters for the fourth layer are adjusted to a current of 260 amps, a voltage of 28 volts, and a speed of 38 cm / min. Furthermore, for localized changes in root gap detected during scanning, such as an increase from the standard 1 mm to 1.5 mm, the system dynamically fine-tunes the parameters in that area through a data-driven optimization module, appropriately reducing the welding speed and slightly increasing the current to ensure root fusion quality.

[0055] After planning and parameter matching are completed, the welding robot first performs a no-load teaching demonstration to verify that the planned path does not interfere with the complex stiffener structure. Once confirmed, the automatic welding program is started. During welding, the system monitors the arc voltage and current in real time, compares them with the currently executed optimized parameters, and implements closed-loop control to ensure process stability. After all welds on the front side are completed, the transport trolley delivers the gear to the positioner station. The positioner's grippers clamp the gear, and the lifting column raises it by approximately 500 mm, driving the gear to automatically rotate 180 degrees. After resetting and re-aligning, the gear is returned to the welding robot. The system performs a secondary scan and modeling of the back weld, and based on a new 3D model incorporating the slight deformations caused by the front welding, replans the path and matches the parameters, completing the back welding using the same adaptive process.

[0056] Through the aforementioned fully automated process, the welding of this large gear is completed in a single clamping and automatic flipping operation, achieving 100% automated weld coverage. Regarding specific processing parameters, the system automatically matches and dynamically optimizes the parameters of each weld pass based on real-time scanning of the weld's three-dimensional morphology. For example, the current is between 260 and 280 amperes, the voltage between 28 and 29 volts, and the speed between 35 and 40 centimeters per minute. Compared to the approximately 8-hour operation time of traditional manual welding, this method controls the net welding time to approximately 4.5 hours, significantly improving efficiency. Throughout the process, the welding parameters precisely correspond to the weld geometry, and continuous optimization through closed-loop control and historical data learning ultimately ensures uniform weld formation and adequate penetration, effectively guaranteeing the gear's load-bearing capacity and product consistency, making it suitable for continuous, mass production.

[0057] As can be seen from the above embodiments, the beneficial effects of this invention are that the processing accuracy and quality of the device and method are greatly improved. Through multi-view scanning with laser scanning sensors and precise processing of point cloud data, the constructed three-dimensional weld model can capture subtle features such as bevel depth and misalignment. The control station rule engine and data-driven optimization module work together to complete parameter matching. In addition, the closed-loop control of arc voltage and current during welding ensures full-section fusion of the single V-groove, uniform formation of fillet welds, and no defects such as incomplete fusion or porosity. The overall deformation of the gear meets the accuracy requirements of the press transmission gear, effectively solving the problems of traditional manual welding relying on experience and large quality fluctuations. The degree of automation is significantly improved, and the labor intensity is greatly reduced. From gear loading, transfer, scanning and welding to flipping, secondary alignment, and unloading, only a small amount of manual operation is required throughout the process, replacing the traditional mode of multiple workers coordinating hoisting, calibration, and welding, greatly reducing manual intervention and avoiding the error risks caused by manual operation. Production efficiency is significantly improved. The transfer trolley, positioner, and welding robot achieve automatic process connection without manual waiting time. The no-load teaching function proactively avoids interference risks, eliminating rework due to equipment collisions and reducing wasted time, making it more suitable for batch production needs. The equipment is highly flexible and adaptable. The slide table drives the positioner to flexibly adjust the spacing, adapting to the flipping requirements of different gear specifications without replacing the positioner or adjusting the equipment layout. The control station's welding parameter database supports rapid matching of parameters for multiple materials, bevel types, and plate thicknesses. Combined with the dynamic fine-tuning of the data-driven optimization module, it can quickly adapt to gear welding with different process requirements, reducing equipment investment and debugging costs for multi-variety production. Operational safety and stability are effectively guaranteed. The welding robot's no-load teaching function proactively identifies and avoids interference risks, preventing damage caused by equipment collisions during welding. The positioner's grippers use flexible clamping, combined with precise lifting of the lifting column, ensuring smooth gear flipping without damaging the gear surface. After completing single-sided welding, the welding robot automatically moves to a safe position, eliminating the safety hazards of human-machine collaborative operation and improving the overall safety of the processing process and the stability of equipment operation.

[0058] The above description of the disclosed embodiments enables those skilled in the art to implement or use this patent. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this patent. Therefore, this patent is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for welding gears in a press, characterized in that, A gear welding device for a press is adopted. The gear welding device for a press includes a working platform (00), on which a ground rail (9) is provided. A transfer trolley (1) is movably arranged on the ground rail (9). The transfer trolley (1) is used to carry the gear to be welded. A positioner (5) is provided on both sides of the ground rail (9). The positioner (5) is provided with a rotatable gripper (7). The rotation axis of the gripper (7) is horizontally arranged and perpendicular to the ground rail (9). The grippers (7) of the two positioners (5) are arranged opposite to each other. A gantry frame (10) is also provided on the working platform (00). The gantry frame (10) spans across the ground rail (9). Along the extension direction of the ground rail (9), the gantry frame (10) and the positioner (5) are arranged one in front of the other. 10) A welding robot (4) is movably mounted on the platform. The direction of movement of the welding robot (4) is horizontal and perpendicular to the ground rail (9). The welding robot (4) has a machine vision module (12). The work platform (00) is provided with two slides (6). The two slides (6) are located on both sides of the ground rail (9). The direction of movement of the slides (6) is perpendicular to the ground rail (9). Two positioners (5) are installed on the two slides (6). The positioner (5) includes a lifting column (8). A lifting plate (11) is installed on the lifting column (8). The gripper (7) is rotatably mounted on the lifting plate (11). The transfer trolley (1) is provided with two centering push plates (2). The centering push plates (2) can move towards each other. The welding method includes the following steps: S1. Before welding begins, the transfer trolley (1) is located in the buffer area at one end of the ground rail (9). The gear is placed on the transfer trolley (1) by the traveling car. Then the transfer trolley (1) runs along the ground rail (9) and carries the gear to the bottom of the welding robot (4). S2. The welding robot (4) moves on the gantry (10) directly above the gear. The welding robot (4) scans the weld seam of the gear through the machine vision module (12) to obtain the weld seam information of the gear. The control station (3) automatically plans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the welding path and welding parameters. S3. After the single-sided welding is completed, the welding robot (4) moves to a safe position, and the transfer trolley (1) carries the gear to continue moving between the two positioners (5). The slide table (6) drives the positioners to move closer to each other, the gripper (7) clamps the gear, and the lifting column (8) drives the gripper (7) to rise to a certain height. Then, the gripper (7) drives the gear to flip. S4. After the flip is completed, the lifting column (8) is lowered to the appropriate position, the gripper (7) puts the gear on the transfer trolley (1), the centering push plate (2) re-centers the gear, and after centering is completed, the transfer trolley (1) carries the gear back to the bottom of the welding robot. S5. The welding robot (4) moves on the gantry (10) directly above the gear. The welding robot (4) scans the weld seam of the gear through the machine vision module (12) to obtain the weld seam information of the gear. The control station (3) replans the welding path and welding parameters based on the collected weld seam information, and then starts the welding program to perform automatic welding according to the replanned welding path and welding parameters. S6. After the double-sided welding is completed, the welding robot control station (3) is operated to move the welding robot (4) to a safe position. The transfer trolley (1) carries the gear back to the buffer area and the gear is lifted off the transfer trolley (1) by the crane.

2. The press gear welding method according to claim 1, characterized in that, In steps S2 and S5, after the welding path and welding parameters are planned, the welding robot (4) performs a no-load teaching exercise. If there is interference, the welding path is further optimized.

3. The press gear welding method according to claim 2, characterized in that, In steps S2 and S5, the machine vision module (12) is a laser scanning sensor or a structured light camera; acquiring the weld information of the gear includes the following steps: The visual module collects point cloud data of the weld area, filters and extracts features from the point cloud data, and generates a 3D model of the weld that includes bevel size, 3D morphology and fillet weld location information. Automatic welding path planning includes the following steps: Based on the three-dimensional model of the weld, the start and end points of the weld are identified, the center line of the welding path is calculated, and the layered and channeled planning is automatically performed according to the shape and size of the bevel, generating the instruction sequence to drive the welding robot (4) to move along each axis.

4. The press gear welding method according to claim 3, characterized in that, The control station (3) has a built-in welding parameter database, which is a structured process knowledge base. The welding parameter database uses material type, plate thickness range, groove form and welding position as key indexes, and stores parameter groups for welding current, voltage, welding speed, weld sequence and number of layers. In steps S2 and S5, the control station (3) performs parameter matching based on the groove depth, groove width, and weld type information extracted from the three-dimensional model of the weld, and in combination with the pre-stored gear material and plate thickness data: First, the basic process range is determined based on the gear material and plate thickness, and then the required number of weld passes and the cross-sectional area of ​​each weld pass are calculated based on the groove depth and width. Then, the corresponding welding current, voltage, and welding speed parameters are obtained from the database through interpolation or nearest neighbor matching. The parameter database can correct and update the parameters based on historical welding results.

5. The press gear welding method according to claim 4, characterized in that, The machine vision module is installed on the end effector of the welding robot (4); during the scanning process in steps S2 and S5, the vision module is driven by the axial motion system of the welding robot (4) to perform multi-viewpoint and multi-angle scanning around the weld, and the point cloud data collected from multiple perspectives is processed as follows to construct a complete three-dimensional morphology of the weld: The original point cloud acquired from each viewpoint is denoised and filtered. Based on feature point matching and iterative nearest point algorithm, the point cloud from multiple views is accurately registered. The registered point cloud is then fused and surface reconstructed to generate a three-dimensional weld morphology model with a continuous topological structure. Based on the constructed 3D weld morphology model, fine geometric features, including bevel angle, root gap, misalignment, and weld cross-sectional layer height distribution, are extracted. When planning the welding path, the following adaptive adjustments are performed based on these fine geometric features: The working angle and travel angle of the welding torch are dynamically calculated and adjusted based on the bevel angle. The welding speed and wire feed speed are adjusted in real time based on the root gap and misalignment. The layout sequence and overlap amount of welds for multi-layer, multi-pass welding are planned according to the cross-sectional height distribution.

6. The press gear welding method according to claim 5, characterized in that, The control station (3) is equipped with a rule engine and a data-driven optimization module. The parameter matching of the welding parameter database includes: the rule engine matching the basic process parameters based on the weld type, groove geometry features and gear material thickness extracted from the weld three-dimensional model; the data-driven optimization module dynamically fine-tunes the basic process parameters based on real-time collected historical welding process data and welding quality detection results to achieve adaptive optimization of welding parameters. The control station (3) also monitors the arc voltage and current in real time during the welding process, compares them with the currently executed optimized parameters, and implements closed-loop control.

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