Laser welding robot for wind power blade production

Abstract

The invention provides a laser welding robot for wind power blade production, which relates to the technical field of wind power generation and comprises a mechanical main body and a software control system. The mechanical body comprises a bottom plate, a supporting assembly with a gradienter, a mechanical arm, a quick dismounting and mounting structure with an identity recognition part, a horizontal fine adjustment structure and a mounting plate integrated with a welding state detection part. The software control system comprises a self-adaptive leveling control module, a modular quick-change control module, a multi-dimensional fine-adjustment compensation module and a welding quality closed-loop control module, the self-adaptive leveling module calculates the stable level of the adjusting quantity through PID, the quick-change module reads welding head information matching parameters, the fine-adjustment module calculates motor angle compensation errors, and the closed-loop module analyzes data to adjust the process. Through three core designs of dynamic leveling, intelligent quick change and real-time quality closed loop, high precision, automation and high reliability of wind power blade laser welding are realized, and blade manufacturing quality and production efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and in particular to a laser welding robot for wind turbine blade production. Background Technology

[0002] As wind power equipment develops towards larger sizes and higher power, the size of wind turbine blades continues to increase, and their welding quality directly determines the operational safety and lifespan of the unit. Traditional blade welding relies on manual labor or general-purpose welding robots, which faces problems such as insufficient welding precision for curved workpieces and lagging thermal deformation compensation. Welding large blades requires millimeter-level trajectory control, but existing equipment is unable to cope with the dynamic changes of complex curved surfaces. Laser welding has become the mainstream choice due to its concentrated energy and small heat-affected zone, but existing systems have technical bottlenecks in welding reference stability and dynamic parameter adaptation.

[0003] Existing technologies have several limitations: leveling relies on manual labor or simple sensing, making it difficult to compensate for horizontal deviations caused by vibration and deformation in real time; cumulative errors in the robotic arm joints, combined with blade thermal deformation, cause trajectory deviations; welding head replacement requires manual parameter readjustment, which is inefficient and prone to errors; welding quality inspection is mostly done offline by sampling, lacking real-time closed-loop control, resulting in a high defect rate and failing to meet the high-precision manufacturing requirements of large wind turbine blades. Therefore, it is necessary to provide a laser welding robot for wind turbine blade production to solve the above-mentioned technical problems. Summary of the Invention

[0004] This invention provides a laser welding robot for wind turbine blade production, which solves the problems of unstable welding reference, low welding head switching efficiency and easy parameter adaptation errors in existing wind turbine blade laser welding, and lagging welding quality detection and difficulty in real-time closed-loop control. It aims to improve welding accuracy, automation and reliability, and ensure blade manufacturing quality and production efficiency.

[0005] To solve the above-mentioned technical problems, the present invention provides a laser welding robot for wind turbine blade production, comprising a mechanical body and a software control system; The main mechanical body includes a base plate, a support assembly, a robotic arm, a quick-assembly and disassembly structure, a horizontal fine-tuning structure, and a mounting plate. The support assembly is located around the bottom surface of the base plate and includes a level for collecting horizontal status data, an adjusting component for adjusting the height of the base plate, and a driving component for driving the adjusting component. The robotic arm is fixedly installed in the middle of the top surface of the base plate, and its end can move with multiple degrees of freedom. The quick-assembly and disassembly structure includes a rotating disk fixed to the end of the robotic arm, an abutment disk located at the rear end of the horizontal fine-tuning structure, and an identification component on the abutment disk. The front end of the rotating disk has a card interface, and the outer side of the abutment disk has a card-fitting piece adapted to the card interface. The horizontal fine-tuning structure includes a shell, a drive motor, a transmission mechanism, and a guide rod. The drive motor drives the guide rod to move through the transmission mechanism. The mounting plate is used to install the laser welding head and integrates a welding status detection component for collecting welding status data. The software control system is electrically connected to the drive components, level, identification components, welding status detection components, and robotic arm of the main mechanical body, and includes: The adaptive leveling control module is used to receive data from the level, calculate the adjustment amount, and control the movement of the support component drive to adjust the adjustment component. The modular quick-change control module is used to read the laser welding head information of the identification device, match the welding process parameters, and monitor the connection status between the card connector and the card interface. The multi-dimensional fine-tuning compensation module is used to receive welding status data and robotic arm end position data, calculate the horizontal fine-tuning amount, and control the drive motor action; The welding quality closed-loop control module is used to analyze welding status data, compare with preset quality standards, and adjust welding process parameters.

[0006] Preferably, the adjusting component of the support assembly includes a rotating sleeve rotatably connected to the bottom surface of the base plate, and an adjusting screw engaged with the internal thread of the rotating sleeve, with a washer fixed to the bottom end of the adjusting screw; the driving component of the support assembly is a servo motor or a stepper motor, and is connected to the rotating sleeve in a transmission manner to drive its rotation.

[0007] Preferably, the identification device is an RFID chip or a QR code tag; the modular quick-change control module has a pre-stored welding process database, which can retrieve the appropriate laser power, welding speed and defocusing parameters from the database based on the laser welding head information read by the identification device.

[0008] Preferably, the welding status detection component includes a visual sensor for acquiring weld image data and a temperature sensor for acquiring molten pool temperature data; the welding quality closed-loop control module analyzes the weld image using an image recognition algorithm to determine weld defects and analyzes the molten pool temperature using a threshold comparison to determine whether the temperature is abnormal.

[0009] Preferably, the adaptive leveling control module specifically uses a PID control algorithm to calculate the adjustment amount of the support component, and can monitor the level status data fed back by the level in real time. When the level deviation of the base plate exceeds the preset threshold, the drive component is dynamically triggered to compensate for the deviation.

[0010] Preferably, the transmission mechanism of the horizontal fine-tuning structure includes a driving gear fixedly connected to the drive motor shaft, a driven gear meshing with the driving gear, and a lead screw coaxially fixedly connected to the driven gear and rotatably connected inside the housing. A threaded seat is meshed and sleeved on the outer side of the lead screw. One end of the guide rod is fixedly connected to the threaded seat, and the other end passes through the limiting plate at the front end of the housing and is connected to the mounting plate. The multi-dimensional fine-tuning compensation module can calculate the fine-tuning amount of the horizontal X-axis and Y-axis. By controlling the forward and reverse rotation and speed of the drive motor, it drives the mounting plate to move along the X-axis or Y-axis to compensate for the joint force of the robotic arm.

[0011] Preferably, the software control system further includes a human-computer interaction and data management module. This module provides a visual operation interface for setting welding parameters and viewing equipment status, while storing adaptive leveling records, quick change records, and welding quality data, and supports data export and historical traceability.

[0012] Preferably, weld defects are determined by analyzing weld images using image recognition algorithms, specifically as follows: The image recognition algorithm includes image preprocessing, edge enhancement, defect feature extraction, and defect determination. Image preprocessing: To address the overexposure interference in the red channel caused by welding arc light, a dynamic grayscale algorithm is used to optimize the image. The pixel values ​​of the red, green, and blue channels of the image acquired by the vision sensor are recorded as follows: Then, the arc compensation coefficient is dynamically set based on the real-time ambient light intensity. The grayscale value is calculated by combining the pixel values ​​of the red, green, and blue channels with their corresponding channel pixel weights and the arc compensation coefficient. The formula is as follows: ,in , , These represent the basic grayscale weight coefficients for the red, green, and blue channels, respectively. Edge enhancement: An improved Sobel operator is used to enhance the continuity of weld edges, with a horizontal gradient. with vertical gradient Calculate using the following formulas respectively: ; ; in, , To preprocess the grayscale image in coordinates Pixel value at; Real-time calculation of adaptive binarization threshold based on Otsu algorithm After dividing the weld seam and defect area, the core features are extracted, including defect area, defect roundness, and grayscale difference, which are calculated as follows; Through formula Calculate the defect area S, where D is the set of pixels in the defect region; Through formula Calculate the defect roundness C, where L represents the defect boundary perimeter, and , The defect boundary pixel set is defined. The defect circularity C is used to distinguish between pores and cracks. When C≈1, it is determined to be a pore, and when C≈0, it is determined to be a crack. Through formula Calculate grayscale difference , This represents the average gray level of the defective area. The average grayscale value and grayscale difference value represent the average grayscale value of the normal weld area. Used to identify incomplete fusion defects; Set a standard threshold for any feature among the core features, including an area threshold that is dynamically adjusted with the weld width. Circularity threshold Length threshold and grayscale difference threshold ; like This indicates that a defect exists; like This indicates the presence of a crack; like This indicates the presence of a non-fusion defect.

[0013] Preferably, the molten pool temperature is analyzed by threshold comparison to determine whether the temperature is abnormal. The specific analysis includes temperature preprocessing, dynamic threshold determination, and real-time power compensation, and is implemented as follows: Temperature preprocessing: A sliding window filter is used to eliminate instantaneous fluctuations in temperature data. The filtered temperature... Calculate according to this formula Where n is the width of the sliding window, This refers to the temperature acquisition interval. Historical temperature data; Dynamic threshold determination: The temperature threshold is adjusted based on the weld thickness w, including the static temperature threshold and the temperature change rate threshold, while simultaneously monitoring the temperature change rate. Static temperature threshold: if To determine temperature anomalies; the relationship between the dynamic threshold and weld thickness is as follows: , ,in , Based on the minimum and maximum temperature thresholds, , This is the corresponding thickness correction factor; Rate of change threshold: Calculate the rate of temperature change The formula is Preset maximum allowable deformation rate ,like If so, the temperature stability is determined to be abnormal; Real-time power compensation: When the temperature is abnormal, the laser power is adjusted using a PID algorithm to obtain the adjusted laser power. Calculate according to the following formula ,in The initial laser power, To achieve the optimal molten pool temperature, , , These are the proportional, integral, and derivative coefficients of the PID controller.

[0014] Compared with related technologies, the laser welding robot for wind turbine blade production provided by this invention has the following advantages: 1. This solution uses a level to collect the status of the base plate in real time. The adaptive leveling control module uses a PID algorithm to calculate the adjustment amount and drive the adjustment screw of the support component to dynamically correct the level, always maintaining the stability of the welding reference, avoiding weld trajectory deviation caused by the tilt of the base plate, and ensuring the welding accuracy of complex curved surfaces.

[0015] 2. This solution automatically reads the welding head information through the identification component of the contact plate, and the modular quick-change control module calls the adaptation parameters from the process database. At the same time, it monitors the connection status of the card contact piece and the card interface. The replacement and parameter configuration can be completed without manual intervention, which improves efficiency and avoids parameter mismatch.

[0016] 3. This solution uses the vision / temperature sensor on the mounting plate to collect weld images and molten pool temperature in real time. The welding quality closed-loop control module uses image recognition to identify defects and PID algorithm to adjust laser power, dynamically correcting temperature anomalies and trajectory deviations, forming a detection and adjustment closed loop to reduce defects such as incomplete fusion and porosity, and reduce rework rate.

[0017] In summary, this solution addresses the problems of unstable welding reference, low welding head replacement efficiency, and lagging quality inspection in the background technology through three core designs: dynamic leveling, intelligent quick replacement, and real-time quality closed loop. It achieves high precision, automation, and high reliability in laser welding of wind turbine blades, significantly improving blade manufacturing quality and production efficiency. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a three-dimensional schematic diagram of the mechanical body proposed in this invention; Figure 2 The present invention proposes Figure 1 3D schematic diagram of part A in the middle; Figure 3 This is a three-dimensional schematic diagram of the quick assembly / disassembly structure proposed in this invention; Figure 4 This is a three-dimensional schematic diagram of the horizontal fine-tuning structure proposed in this invention; Figure 5 This is a schematic diagram of the software control system proposed in this invention.

[0019] The following are the components listed in the diagram: 1. Base plate; 2. Level; 3. Robotic arm; 4. Mounting plate; 5. Rotating sleeve; 6. Adjusting screw; 7. Shim; 8. Rotating disk; 9. Snap-fit ​​interface; 10. Snap-fit ​​piece; 11. Abutment plate; 12. Housing; 13. Motor; 14. Drive gear; 15. Driven gear; 16. Lead screw; 17. Threaded seat; 18. Guide rod; 19. Limiting plate. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The singular forms “group,” “class,” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are used only to distinguish information of the same type from one another. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0023] Please refer to the following: Figures 1-5 A laser welding robot for wind turbine blade production includes a mechanical body and a software control system; The main body of the machine includes a base plate 1, a support assembly, a robotic arm 3, a quick-assembly and disassembly structure, a horizontal fine-tuning structure, and a mounting plate 4. The support assembly is located around the bottom surface of the base plate 1 and includes a level 2 for collecting horizontal status data, an adjusting component for adjusting the height of the base plate 1, and a driving component for driving the adjusting component. The robotic arm 3 is fixedly installed in the middle of the top surface of the base plate 1, and its end can move with multiple degrees of freedom. The quick-assembly and disassembly structure includes a rotating disk 8 fixed to the end of the robotic arm 3, an abutment disk 11 located at the rear end of the horizontal fine-tuning structure, and an identification component on the abutment disk 11. The front end of the rotating disk 8 is provided with a card interface 9, and the outer side of the abutment disk 11 is provided with a card contact piece 10 adapted to the card interface 9. The horizontal fine-tuning structure includes a housing 12, a drive motor 13, a transmission mechanism, and a guide rod 18. The drive motor 13 drives the guide rod 18 to move through the transmission mechanism. The mounting plate 4 is used to install the laser welding head and integrates a welding status detection component for collecting welding status data. The software control system is electrically connected to the drive components, level 2, identification component, welding status detection component, and robotic arm 3 of the main mechanical body, including: The adaptive leveling control module is used to receive data from the level 2, calculate the adjustment amount, and control the movement of the support component drive to adjust the adjustment component; The modular quick-change control module is used to read the laser welding head information of the identification component, match the welding process parameters, and monitor the connection status between the card connector 10 and the card interface 9. The multi-dimensional fine-tuning compensation module is used to receive welding status data and end position data of robotic arm 3, calculate the horizontal fine-tuning amount, and control the action of drive motor 13. The welding quality closed-loop control module is used to analyze welding status data, compare with preset quality standards, and adjust welding process parameters.

[0024] Specifically, the adjusting component of the support assembly includes a rotating sleeve 5 rotatably connected to the bottom surface of the base plate 1, and an adjusting screw 6 that meshes with the internal thread of the rotating sleeve 5. A washer 7 is fixedly connected to the bottom end of the adjusting screw 6. The driving component of the support assembly is a servo motor or a stepper motor, and is connected to the rotating sleeve 5 in a transmission connection to drive its rotation.

[0025] Specifically, the identification device is an RFID chip or a QR code tag; the modular quick-change control module has a pre-stored welding process database, which can retrieve the appropriate laser power, welding speed and defocusing parameters from the database based on the laser welding head information read by the identification device.

[0026] Specifically, the welding status detection components include a vision sensor for acquiring weld image data and a temperature sensor for acquiring molten pool temperature data; the welding quality closed-loop control module analyzes the weld image through image recognition algorithms to determine weld defects, and analyzes the molten pool temperature through threshold comparison to determine whether the temperature is abnormal.

[0027] Specifically, the adaptive leveling control module uses a PID control algorithm to calculate the adjustment amount of the support components. It can monitor the level status data fed back by the level instrument 2 in real time. When the level deviation of the base plate 1 exceeds the preset threshold, the drive component is dynamically triggered to compensate for the deviation, as follows: The real-time horizontal status data of the base plate 1 collected by the level 2 is the tilt angle in two orthogonal directions: the tilt angle in the X-axis direction. Y-axis tilt angle The preset horizontal threshold is The real-time horizontal deviation is: ;when When this occurs, the horizontal state PID control is triggered; The total PID regulation is calculated separately for the deviations in the X and Y axes. and The calculation formula is:

[0028]

[0029] in, , This is the proportionality coefficient. , The integral coefficient is... , The coefficients are differential coefficients; the integral term is treated with anti-saturation measures, and the integral limit is . , Indicates the maximum integral output; The adjustment range of the support components is distributed by identifying four sets of support components located around the bottom surface of the base plate 1, which are respectively denoted as... These correspond to the top left, top right, bottom left, and bottom right corners, respectively. Based on total adjustment Calculate the adjustment amount of each support component group. , This represents the amount of extension or retraction of the adjusting screw, specifically: Where k is the conversion coefficient, calculated based on the base plate size and screw pitch, to convert angular deviation into linear expansion and contraction. Drive component motion control, by adjusting the amount Converted to the rotation angle of the drive component in the support assembly The formula is Where P is the pitch of the adjusting screw 6; the driving component is adjusted according to the rotation angle. Rotate to extend or retract the adjusting screw 6 until... Stop adjusting at that time; During the adjustment process, a preset sampling interval period is used, and the deviation is updated at each sampling interval period. , Repeat the PID total adjustment calculation, support component adjustment allocation, and rotating sleeve 5 action control operation at least three times. If the sampling period deviation is less than the deviation threshold for a consecutive set number of times, the leveling is determined to be complete and the system enters sleep mode.

[0030] Specifically, the transmission mechanism of the horizontal fine-tuning structure includes a drive gear 14 fixedly connected to the shaft of the drive motor 13, a driven gear 15 meshing with the drive gear 14, and a lead screw 16 coaxially fixedly connected to the driven gear 15 and rotatably connected inside the housing 12. A threaded seat 17 is meshed and sleeved on the outer side of the lead screw 16. One end of the guide rod 18 is fixedly connected to the threaded seat 17, and the other end passes through the limiting plate 19 at the front end of the housing 12 and is connected to the mounting plate 4. The multi-dimensional fine-tuning compensation module can calculate the fine-tuning amount of the horizontal X-axis and Y-axis. By controlling the forward and reverse rotation and speed of the drive motor 13, it drives the mounting plate 4 to move along the X-axis or Y-axis to compensate for the cumulative joint error of the robotic arm 3 and the thermal deformation error of the blades. Specifically, it includes the following steps: Transmission displacement conversion, identification of the number of teeth on the driving gear. The number of teeth of the driven gear 15 The lead screw 16 has a pitch of P1, and the rotation angle of the drive motor 13 is... Then the gear transmission ratio The linear displacement L generated by the rotation of the lead screw 16 driving the threaded seat 17 and guide rod 18 is ; The fine-tuning requirement is calculated by identifying the cumulative error of the robotic arm's three joints in the X-axis (denoted as ) and the Y-axis (denoted as ). The total fine-tuning requirement in the X-axis is then calculated. Total fine-tuning requirements for the Y-axis The formula is: ; PID control of motor angle: The PID control algorithm is used to calculate the required rotation angle of the drive motor 13. For the X-axis, the current X-axis position of the mounting plate 4 is identified. Positional deviation The target rotation angle of drive motor 13 in the X-axis direction. for: For the Y-axis, let the current Y-axis position of mounting plate 4 be... Positional deviation The target rotation angle of drive motor 13 in the Y-axis direction for: ;in, The proportional, integral, and derivative coefficients of the X-axis PID controller. These are the proportional, integral, and derivative coefficients of the Y-axis PID controller. The integral term uses anti-saturation limiting, and its limiting value is... , This represents the maximum allowable rotation angle of the motor.

[0031] Specifically, the software control system also includes a human-machine interaction and data management module. This module provides a visual operation interface to set welding parameters and view equipment status, while storing adaptive leveling records, quick change records and welding quality data, and supporting data export and historical traceability.

[0032] Specifically, image recognition algorithms are used to analyze weld images to determine weld defects. Image recognition algorithms include image preprocessing, edge enhancement, defect feature extraction, and defect determination. Image preprocessing: To address the overexposure interference in the red channel caused by welding arc light, a dynamic grayscale algorithm is used to optimize the image. The pixel values ​​of the red, green, and blue channels of the image acquired by the vision sensor are recorded as follows: Then, the arc compensation coefficient is dynamically set based on the real-time ambient light intensity. The grayscale value is calculated by combining the pixel values ​​of the red, green, and blue channels with their corresponding channel pixel weights and the arc compensation coefficient. The formula is as follows: ,in , , These represent the basic grayscale weight coefficients for the red, green, and blue channels, respectively. Edge enhancement: An improved Sobel operator is used to enhance the continuity of weld edges, with a horizontal gradient. with vertical gradient Calculate using the following formulas respectively: ; ; in, , To preprocess the grayscale image in coordinates The pixel value at the location; by using a 3:2 gradient weight ratio, the problem of discontinuity in capturing the edges of narrow weld seams and curved weld seams by the traditional Sobel operator is solved; Real-time calculation of adaptive binarization threshold based on Otsu algorithm After dividing the weld seam and defect area, the core features are extracted, including defect area, defect roundness, and grayscale difference, which are calculated as follows; Through formula Calculate the defect area S, where D is the set of pixels in the defect region; Through formula Calculate the defect roundness C, where L represents the defect boundary perimeter, and , The defect boundary pixel set is defined. The defect circularity C is used to distinguish between pores and cracks. When C≈1, it is determined to be a pore, and when C≈0, it is determined to be a crack. Through formula Calculate grayscale difference , This represents the average gray level of the defective area. The average grayscale value and grayscale difference value represent the average grayscale value of the normal weld area. Used to identify incomplete fusion defects; Set a standard threshold for any feature among the core features, including an area threshold that is dynamically adjusted with the weld width. Circularity threshold Length threshold and grayscale difference threshold ; like This indicates that a defect exists; like This indicates the presence of a crack; like This indicates the presence of a non-fusion defect.

[0033] Specifically, threshold comparison analysis of the molten pool temperature is used to determine whether the temperature is abnormal. The specific analysis includes temperature preprocessing, dynamic threshold determination, and real-time power compensation, and is implemented as follows: Temperature preprocessing: A sliding window filter is used to eliminate instantaneous fluctuations in temperature data. The filtered temperature... Calculate according to this formula Where n is the width of the sliding window, This refers to the temperature acquisition interval. Historical temperature data; Dynamic threshold determination: The temperature threshold is adjusted based on the weld thickness w, including the static temperature threshold and the temperature change rate threshold, while simultaneously monitoring the temperature change rate. Static temperature threshold: if To determine temperature anomalies; the relationship between the dynamic threshold and weld thickness is as follows: , ,in , Based on the minimum and maximum temperature thresholds, , This is the corresponding thickness correction factor; Rate of change threshold: Calculate the rate of temperature change The formula is Preset maximum allowable deformation rate ,like If so, the temperature stability is determined to be abnormal; Real-time power compensation: When the temperature is abnormal, the laser power is adjusted using a PID algorithm to obtain the adjusted laser power. Calculate according to the following formula ,in The initial laser power, To achieve the optimal molten pool temperature, , , The proportional, integral, and derivative coefficients of the PID controller are used to achieve real-time correction of temperature anomalies.

[0034] The working principle of the laser welding robot for wind turbine blade production provided by this invention is as follows: I. Working Principle of the Main Body of the Machinery The mechanical system of this laser welding robot achieves high-precision welding operations through multi-stage transmission and sensor integration.

[0035] The support components around the bottom surface of the base plate 1 monitor the tilt status of the base plate 1 in real time through the level 2. When a horizontal deviation is detected, the drive component in the support components drives the adjusting screw 6 to rotate, and adjusts the height of the base plate 1 through thread transmission to achieve horizontal correction of the welding reference surface.

[0036] The robotic arm 3 is fixed to the top surface of the base plate 1, and its end is connected to the laser welding head through a quick-release structure. This structure achieves mechanical positioning through the card interface 9 of the rotating disk 8 and the card contact piece 10 of the abutment disk 11. At the same time, the welding head type is identified through the identification component on the abutment disk 11.

[0037] The horizontal fine-tuning structure is set between the end of the robotic arm 3 and the welding head. The drive motor 13 drives the lead screw 16 to rotate through the active gear 14 and the driven gear 15, which drives the threaded seat 17 and the guide rod 18 to move linearly, so as to realize the micron-level position compensation of the welding head in the X and Y axis directions, which is used to eliminate the cumulative error of the joint of the robotic arm 3 and the influence of blade thermal deformation.

[0038] The integrated welding status detection component on mounting plate 4 includes a vision sensor and a temperature sensor to collect weld images and molten pool temperature data in real time, providing a sensing basis for welding quality control.

[0039] II. Working Principle of Software Control System The software control system achieves full-process automated control based on closed-loop feedback logic.

[0040] The adaptive leveling control module receives data from the level gauge 2, calculates the horizontal deviation adjustment amount through a PID control algorithm, and converts it into action commands for the support component drive, dynamically maintaining the level accuracy of the base plate 1. The modular quick-change control module reads the information from the identification component on the abutment plate 11, retrieves the appropriate initial parameters such as laser power and welding speed from the process database, and verifies the connection reliability between the rotating disk 8 and the abutment plate 11 through a snap-fit ​​status sensor. After successful parameter verification, the data is transmitted to the welding quality closed-loop control module.

[0041] During the welding process, the welding quality closed-loop control module analyzes the weld images and molten pool temperature data collected by the welding status detection component. When a weld deviation or temperature abnormality is detected, it outputs a position fine-tuning command to the multi-dimensional fine-tuning compensation module and directly adjusts the laser power parameters.

[0042] The multi-dimensional fine-tuning compensation module calculates the rotation angle of the drive motor 13 based on the deviation data, and corrects the welding head position through the transmission of the drive gear 14, driven gear 15, lead screw 16, threaded seat 17, and guide rod 18.

[0043] The human-computer interaction and data management module records leveling data, quick change records, and quality parameters throughout the entire process, providing a visual operation interface and data traceability function, forming a controllable closed loop from parameter setting to welding completion.

[0044] It should be noted that: Formula Calculation Instructions The dynamic grayscale formula, PID control formula, and sliding window filtering formula involved in this solution are all calculated after dimensionless removal. Dimensionless removal can be achieved through standardization and other methods, the specific process of which will not be elaborated here. The formulas are obtained by collecting a large amount of wind turbine blade welding data and fitting it through software simulation, which can closely approximate the real welding scenario. The preset parameters and PID coefficients in the formulas are set by those skilled in the art based on the actual welded workpiece (such as blade thickness and material) and process requirements. Implementation

[0045] The above embodiments can be implemented through software, hardware, firmware, or any combination thereof. If implemented in software, it can be embodied in the form of a computer program product, containing one or more computer instructions; when loaded and executed on a computer (such as a robot controller or industrial PC), it can realize module functions such as adaptive leveling, welding quality closed-loop, and multi-dimensional fine-tuning. The computer instructions can be stored in a computer-readable storage medium (such as a USB flash drive, solid-state ATA hard drive, ROM, or RAM), or transmitted between devices via wired / wireless methods (such as industrial Ethernet or WiFi).

[0046] Execution logic and function implementation The sequence numbers of each module process in this solution (such as "leveling → quick change → welding → fine-tuning") do not represent the execution order and must be determined according to functional logic (for example, adaptive leveling takes precedence over quick change of welding head, and the quality closed-loop module dynamically triggers multi-dimensional fine-tuning during welding). The implementation method (hardware / software) of each module function (such as PID leveling and image recognition for defect detection) depends on the specific application scenario and design constraints. Professional technicians can choose as needed, without exceeding the scope of this solution.

[0047] Component and unit integration In the solution, separate components such as drive motor 13, level 2, and vision sensor can be physically independent or integrated according to function; the core modules of the software control system (such as adaptive leveling and quality closed loop) can be integrated into a processing unit or exist separately, depending on the actual needs.

[0048] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0049] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A laser welding robot for wind turbine blade production, characterized in that, The system includes a mechanical body and a software control system. The mechanical body includes a base plate (1), a support assembly, a robotic arm (3), a quick-assembly structure, a horizontal fine-tuning structure, and a mounting plate (4). The support assembly is located around the bottom surface of the base plate (1) and includes a level (2), an adjustment component for adjusting the height of the base plate (1), and a drive component for driving the adjustment component. The robotic arm (3) is fixed to the middle of the top surface of the base plate (1). The quick-assembly structure includes a rotating disk (8) fixed to the end of the robotic arm (3), an abutment disk (11) at the rear end of the horizontal fine-tuning structure, and an identification component on the abutment disk (11). The front end of the rotating disk (8) is provided with a card interface (9), and the outer side of the abutment disk (11) is provided with an adapter card (10). The horizontal fine-tuning structure includes a shell (12), a drive motor (13), a transmission mechanism, and a guide rod (18). The mounting plate (4) is equipped with a laser welding head and integrates a detection component for collecting welding status data. The detection component for the welding status includes a visual sensor for collecting weld image data and a temperature sensor for collecting molten pool temperature data. The software control system is electrically connected to the drive components, level (2), identification components, detection components, and robotic arm (3) of the main mechanical body, including: The adaptive leveling control module receives data from the level (2), calculates the adjustment amount, and controls the support component drive. Specifically, the PID control algorithm is used to calculate the adjustment amount of the support component. The level status data fed back by the level instrument (2) can be monitored in real time. When the level deviation of the base plate (1) exceeds the preset threshold, the drive component is dynamically triggered to compensate for the deviation. The modular quick-change control module reads the laser welding head information of the identification component, matches the welding process parameters, and monitors the connection status between the card connector (10) and the card interface (9); the identification component is an RFID chip or a QR code tag; the modular quick-change control module has a pre-stored welding process database, which can call the appropriate laser power, welding speed and defocusing parameters from the database according to the laser welding head information read by the identification component; The multi-dimensional fine-tuning compensation module receives welding status data and end position data of the robotic arm (3), calculates the horizontal fine-tuning amount, and controls the drive motor (13). The welding quality closed-loop control module analyzes welding status data, compares it with preset quality standards, and adjusts welding process parameters. The welding quality closed-loop control module analyzes weld images through image recognition algorithms to determine weld defects and analyzes the molten pool temperature through threshold comparison to determine whether the temperature is abnormal.

2. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The adjusting component of the support assembly includes a rotating sleeve (5) rotatably connected to the bottom surface of the base plate (1), and an adjusting screw (6) engaged with the internal thread of the rotating sleeve (5). A washer (7) is fixedly connected to the bottom end of the adjusting screw (6). The driving component of the support assembly is a servo motor or a stepper motor, and is connected to the rotating sleeve (5) for transmission to drive its rotation.

3. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The identification device is an RFID chip or a QR code tag; the modular quick-change control module has a pre-stored welding process database, which can retrieve the appropriate laser power, welding speed and defocusing parameters from the database based on the laser welding head information read by the identification device.

4. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The welding status detection component includes a vision sensor for acquiring weld image data and a temperature sensor for acquiring molten pool temperature data; the welding quality closed-loop control module analyzes the weld image through an image recognition algorithm to determine weld defects, and analyzes the molten pool temperature through threshold comparison to determine whether the temperature is abnormal.

5. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The adaptive leveling control module specifically uses a PID control algorithm to calculate the adjustment amount of the support component. It can monitor the level status data fed back by the level instrument (2) in real time. When the level deviation of the base plate (1) exceeds the preset threshold, it dynamically triggers the drive component to compensate for the deviation.

6. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The transmission mechanism of the horizontal fine-tuning structure includes an active gear (14) fixed to the shaft of the drive motor (13), a driven gear (15) meshing with the active gear (14), and a lead screw (16) coaxially fixed to the driven gear (15) and rotatably connected inside the housing (12). The outer side of the lead screw (16) is fitted with a threaded seat (17). One end of the guide rod (18) is fixed to the threaded seat (17), and the other end passes through the limiting plate (19) at the front end of the housing (12) and is connected to the mounting plate (4). The multi-dimensional fine-tuning compensation module can calculate the fine-tuning amount of the horizontal X-axis and Y-axis. By controlling the forward and reverse rotation and speed of the drive motor (13), the mounting plate (4) is driven to move along the X-axis or Y-axis to compensate for the joint force of the robotic arm (3).

7. The laser welding robot for wind turbine blade production according to claim 1, characterized in that, The software control system also includes a human-computer interaction and data management module. This module provides a visual operation interface to set welding parameters and view equipment status, while storing adaptive leveling records, quick change records and welding quality data, and supports data export and historical traceability.

8. The laser welding robot for wind turbine blade production according to claim 4, characterized in that, Weld seam defects are identified by analyzing weld seam images using image recognition algorithms. The image recognition algorithm includes image preprocessing, edge enhancement, defect feature extraction, and defect determination. Image preprocessing: To address the overexposure interference in the red channel caused by welding arc light, a dynamic grayscale algorithm is used to optimize the image. The pixel values ​​of the red, green, and blue channels of the image acquired by the vision sensor are recorded as follows: Then, the arc compensation coefficient is dynamically set based on the real-time ambient light intensity. The grayscale value is calculated by combining the pixel values ​​of the red, green, and blue channels with their corresponding channel pixel weights and the arc compensation coefficient. The formula is as follows: ,in , , These represent the basic grayscale weight coefficients for the red, green, and blue channels, respectively. Edge enhancement: An improved Sobel operator is used to enhance the continuity of weld edges, with a horizontal gradient. with vertical gradient Calculate using the following formulas respectively: ; ; in, , To preprocess the grayscale image in coordinates Pixel value at; Real-time calculation of adaptive binarization threshold based on Otsu algorithm After dividing the weld seam and defect area, the core features are extracted, including defect area, defect roundness, and grayscale difference, which are calculated as follows; Through formula Calculate the defect area S, where D is the set of pixels in the defect region; Through formula Calculate the defect roundness C, where L represents the defect boundary perimeter, and , The defect boundary pixel set is defined. The defect circularity C is used to distinguish between pores and cracks. When C≈1, it is determined to be a pore, and when C≈0, it is determined to be a crack. Through formula Calculate grayscale difference , This represents the average gray level of the defective area. The average grayscale value and grayscale difference value represent the average grayscale value of the normal weld area. Used to identify incomplete fusion defects; Set a standard threshold for any feature among the core features, including an area threshold that is dynamically adjusted with the weld width. Circularity threshold Length threshold and grayscale difference threshold ; like This indicates that a defect exists; like This indicates the presence of a crack; like This indicates the presence of a non-fusion defect.

9. A laser welding robot for wind turbine blade production according to claim 4, characterized in that, The molten pool temperature is analyzed by threshold comparison to determine whether the temperature is abnormal. The specific analysis includes temperature preprocessing, dynamic threshold determination, and real-time power compensation, as implemented as follows: Temperature preprocessing: A sliding window filter is used to eliminate instantaneous fluctuations in temperature data. The filtered temperature... Calculate according to this formula Where n is the width of the sliding window, This refers to the temperature acquisition interval. Historical temperature data; Dynamic threshold determination: The temperature threshold is adjusted based on the weld thickness w, including the static temperature threshold and the temperature change rate threshold, while simultaneously monitoring the temperature change rate. Static temperature threshold: if To determine temperature anomalies; the relationship between the dynamic threshold and weld thickness is as follows: , ,in , Based on the minimum and maximum temperature thresholds, , This is the corresponding thickness correction factor; Rate of change threshold: Calculate the rate of temperature change The formula is Preset maximum allowable deformation rate ,like If so, the temperature stability is determined to be abnormal; Real-time power compensation: When the temperature is abnormal, the laser power is adjusted using a PID algorithm to obtain the adjusted laser power. Calculate according to the following formula ,in The initial laser power, To achieve the optimal molten pool temperature, , , These are the proportional, integral, and derivative coefficients of the PID controller.

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