Pipeline welding method, device and welding system based on high-frequency point laser scanning

Abstract

The invention relates to a pipeline welding method, device and system based on high-frequency point laser scanning. The welding method comprises the steps that a high-frequency point laser scanning device is controlled to scan a current welding seam high-frequency emission laser beam of a to-be-welded pipeline, and welding seam scanning information of the current welding seam of the to-be-welded pipeline is obtained; the obtained welding seam scanning information is processed and analyzed, and welding seam welding information of the to-be-welded pipeline is obtained; and the welding execution device is controlled to weld the current welding seam of the to-be-welded pipeline according to the obtained welding seam welding information. The problems that traditional manual welding is low in welding efficiency and poor in welding quality stability can be solved.

Description

Technical Field

[0001] This invention relates to the field of welding technology, and in particular to a pipe welding method, apparatus and welding system based on high-frequency spot laser scanning. Background Technology

[0002] Pipeline welding is extremely common in modern industry, with applications spanning multiple key sectors such as petrochemicals, power energy, and urban construction. For example, in the petrochemical industry, numerous pipelines need to be connected to transport various chemical raw materials and products; in urban construction, the laying of water and gas supply pipelines is fundamental to ensuring residents' daily lives. With continuous industrial development, the requirements for pipeline welding quality and efficiency are becoming increasingly stringent. High-quality welding ensures that pipelines do not leak or rupture during long-term use, thereby guaranteeing production safety and reducing maintenance costs. Conversely, efficient welding operations help accelerate project progress and improve enterprise production efficiency.

[0003] Traditional pipe welding methods, such as manual arc welding, rely heavily on the individual skills and experience of the welder. During the welding process, the welder needs to visually inspect the weld seam and manually adjust welding parameters and the welding torch position. This makes the weld quality highly dependent on the welder's condition and skill level; the quality of welds completed by different welders, and even by the same welder at different times, can vary significantly. Furthermore, manual welding is slow, making it difficult to meet the demands of large-scale, high-efficiency modern industrial production. In harsh working environments, such as high-temperature, high-humidity, and high-dust conditions, manual welding not only threatens the welder's health but also further affects the consistency of weld quality. Summary of the Invention

[0004] This invention provides a pipe welding method, apparatus, and welding system based on high-frequency laser scanning, which can solve the problems of low welding efficiency and poor welding quality stability in traditional manual welding.

[0005] To address the aforementioned technical problems, this invention provides a pipe welding method based on high-frequency laser scanning, applicable to welding systems; The welding system includes an auxiliary support mechanism, as well as a high-frequency laser scanning device and a welding execution device mounted on the auxiliary support mechanism. The welding method includes: The high-frequency laser scanning device is controlled to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. The obtained weld scan information is processed and analyzed to obtain the weld information of the pipeline to be welded; The control welding execution device performs welding on the current weld of the pipeline to be welded based on the obtained weld welding information.

[0006] In addition, the present invention also proposes a pipe welding device based on high-frequency laser scanning, which is applied to a welding system; The welding system includes an auxiliary support mechanism, as well as a high-frequency laser scanning device and a welding execution device mounted on the auxiliary support mechanism. The welding apparatus includes: The scanning control module is used to control the high-frequency laser scanning device to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam, and to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. The welding information acquisition module is used to process and analyze the obtained weld scanning information to obtain the weld information of the pipeline to be welded. The welding control module is used to control the welding execution device to weld the current weld of the pipeline to be welded based on the obtained weld welding information.

[0007] Furthermore, the present invention also proposes a welding system comprising: Auxiliary support mechanism; A high-frequency spot laser scanning device is mounted on the auxiliary support mechanism; The welding actuator is mounted on the auxiliary support mechanism; and The controller is connected to the auxiliary support mechanism, the high-frequency laser scanning device, and the welding execution device. The controller is used to implement the pipe welding method based on high-frequency laser scanning as described above.

[0008] Furthermore, the present invention also proposes a computer storage medium, characterized in that the computer storage medium includes: at least one instruction, which, when executed by a computer, implements the method steps in the pipe welding method based on high-frequency laser scanning as described above.

[0009] The beneficial effects of the technical solution provided by this invention include: A high-frequency laser scanning device can scan the current weld seam of a pipeline to be welded using a high-frequency laser beam, acquiring the welding information of the current weld seam. Then, the welding execution device can automatically weld the current weld seam based on the obtained welding information. Compared to traditional manual welding methods, this method offers higher welding speed and consistency, significantly improving welding efficiency and quality. Furthermore, by applying high-frequency laser scanning technology to automated pipeline welding, it is possible to quickly and accurately acquire three-dimensional information of the pipeline weld seam, providing accurate data support for the welding system and enabling the welding equipment to handle various complex situations in pipeline welding more intelligently and precisely. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of the steps of the pipe welding method based on high-frequency laser scanning as described in an embodiment of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of the steps of the pipe welding method based on high-frequency laser scanning as described in an embodiment of the present invention. Figure 2 ; Figure 3 This is a schematic diagram of the steps of the pipe welding method based on high-frequency laser scanning as described in an embodiment of the present invention. Figure 3 ; Figure 4 This is a simplified structural diagram of the pipe welding device based on high-frequency laser scanning as described in an embodiment of the present invention. Figure 1 ; Figure 5 This is a simplified structural diagram of the pipe welding device based on high-frequency laser scanning as described in an embodiment of the present invention. Figure 2 ; Figure 6 This is a simplified block diagram illustrating the structure of the welding system described in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0013] like Figure 1 As shown, in some embodiments, the present invention proposes a pipe welding method based on high-frequency point laser scanning, applied to welding system 10. For example... Figure 6 As shown, the welding system 10 may include an auxiliary support mechanism 100, a high-frequency laser scanning device 200, and a welding execution device 300 mounted on the auxiliary support mechanism 100. The auxiliary support mechanism 100 can be used to support and position the pipe to be welded, and also to install and position the high-frequency laser scanning device 200 and the welding execution device 300.

[0014] like Figure 1 As shown, the pipe welding method based on high-frequency laser scanning may include the following steps: S100, The high-frequency laser scanning device 200 is controlled to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. S200. Process and analyze the obtained weld scanning information to obtain the weld information of the pipeline to be welded. S300, the welding control device 300 performs welding on the current weld of the pipeline to be welded based on the obtained weld welding information.

[0015] The high-frequency laser scanning device 200 scans the current weld seam of the pipeline to be welded using a high-frequency laser beam, acquiring the weld seam information. Then, the welding execution device 300 automatically welds the current weld seam based on this information. Compared to traditional manual welding methods, this method offers higher welding speed and consistency, significantly improving welding efficiency and quality. Furthermore, by applying high-frequency laser scanning technology to automated pipeline welding, the three-dimensional information of the pipeline weld seam can be quickly and accurately acquired, providing accurate data support for the welding system and enabling the welding equipment to handle various complex situations in pipeline welding more intelligently and precisely.

[0016] Specifically, the auxiliary support mechanism 100 includes an equipment support platform 110, and a welding roller frame 120 and a pipe positioning fixture 130 mounted on the equipment support platform 110. The welding roller frame 120 and the pipe positioning fixture 130 are used to fix the welding pipe. The welding roller frame 120 can be adjusted according to the diameter of the welding pipe to ensure stable rotation of the welding pipe during the welding process. The pipe positioning fixture 130 ensures the accuracy of the welding pipe connection, laying the foundation for subsequent welding operations.

[0017] Therefore, in step S100, before controlling the high-frequency spot laser scanning device 200 to scan the current weld seam of the pipe to be welded with a high-frequency emitted laser beam, the following steps may also be included: S102. The pipe to be welded is supported by the welding roller frame 120 and positioned by the pipe positioning fixture 130. The pipe to be welded can be pre-lifted onto the welding roller frame 120, and its position and angle can be precisely adjusted using the welding roller frame 120 and the pipe positioning fixture 130 to ensure that the centerline of the pipe coincides with the rotation centerline of the welding roller frame 120, and that the misalignment and weld gap at the pipe joint meet the welding process requirements.

[0018] Furthermore, the welding roller frame 120 includes an active roller frame and a driven roller frame, as well as a roller motor drive mechanism connected to the active roller frame. The pipe to be welded can be placed on the active and driven roller frames. The roller motor drive mechanism can drive the rollers on the active roller frame to rotate, thereby rotating the pipe to be welded, facilitating initial positioning and welding position adjustment. The rollers of the active and driven roller frames can be made of high-strength polyurethane-coated material, providing sufficient friction to rotate the pipe while protecting the pipe surface from scratches. Moreover, the spacing between the multiple rollers on the active and driven roller frames is electrically adjustable to accommodate welding requirements for pipes of different lengths. In addition, the driven roller frame can be equipped with casters for easy movement and positioning, and has an automatic centering function to ensure the stability of the pipe during rotation. Limit switches and anti-collision devices can also be installed on the active and driven roller frames to prevent the pipe from shifting or colliding during rotation, ensuring welding safety. Furthermore, the pipe positioning fixture 130 may include a hydraulically driven multi-jaw self-centering pipe clamp mounted on the equipment support platform 110. This multi-jaw self-centering pipe clamp can hold pipes up to 1200mm in diameter with a repeatability of ±0.05mm. The multi-jaw self-centering pipe clamp may include multiple symmetrically distributed pipe jaws and a hydraulically driven cylinder connected to the pipe jaws. The hydraulically driven cylinder can drive the pipe jaws to quickly clamp and release the welded pipe. The surface of each pipe jaw is coated with a non-slip, wear-resistant material (such as a hard alloy coating), providing sufficient clamping force (maximum clamping force up to 50kN) while avoiding damage to the surface of the welded pipe. For pipes of different diameters, different pipe jaws can be quickly replaced via an electric adjustment mechanism, improving equipment versatility. In addition, an adjustable support base can be installed below the pipe jaws, employing a screw lifting mechanism with an adjustment range of 0-300mm, adapting to pipe installation requirements at different heights and ensuring that the coaxiality error between the pipe centerline and the rotation centerline of the welding roller frame 120 is less than 0.5mm. Furthermore, the equipment support platform 110 can be a steel structure support platform, on which the high-frequency laser scanning device 200, welding execution device 300, etc., are installed. Moreover, the steel structure support platform can be welded from Q355B steel, and its structural design has been optimized through finite element analysis to ensure that the platform's deformation is less than 1mm when bearing the weight of equipment and pipelines (maximum load capacity up to 10 tons). The surface of the steel structure support platform can be treated with anti-slip coating (e.g., sprayed with anti-slip paint) and equipped with guardrails (1.2m high) and kickboards (150mm high) to ensure operator safety. Vibration damping pads (rubber shock absorbers, natural frequency 5-10Hz) can be installed at the bottom of the steel structure support platform to reduce vibration transmission during equipment operation and lower noise interference. Cable trays and air pipe channels are installed on the platform to neatly arrange the cables and air pipes of the welding system, preventing cable tangling and air pipe wear, while improving the overall aesthetics and maintenance convenience of the equipment. In addition, the platform also has multiple reserved interfaces for easy connection to external equipment (such as dust removal systems and power cabinets).

[0019] Furthermore, the high-frequency laser scanning device 200 may include a multi-dimensional adjustment bracket 210 mounted on the equipment support platform 110 of the auxiliary support mechanism 100, and a high-frequency laser scanner 220 mounted on the multi-dimensional adjustment bracket 210. The high-frequency laser scanner 220 is the core module of the high-frequency laser scanning device 200, and it can use a semiconductor laser with a wavelength of 650nm as its light source. This light source features high stability, low power consumption, and strong anti-interference capabilities. The high-frequency laser scanner 220 is mounted on a specially designed multi-dimensional adjustment bracket 210. The main body of the multi-dimensional adjustment bracket 210 can be made of high-strength aluminum alloy, ensuring structural strength while reducing overall weight. Moreover, the multi-dimensional adjustment bracket 210 (such as a three-dimensional adjustment bracket) can flexibly adapt to welded pipes of different diameters and installation positions. In the vertical direction, the X, Y, and Z dimensions are adjustable, covering a range of common pipe diameters.

[0020] Therefore, in step S100, controlling the high-frequency laser scanning device 200 to scan the current weld seam of the pipe to be welded with a high-frequency emitted laser beam to obtain the weld seam scanning information of the current weld seam of the pipe to be welded may further include the following steps: S110 controls the multi-dimensional adjustment bracket 210 to adjust the incident angle of the laser beam of the high-frequency laser scanner 220 so that the incident angle of the laser beam reaches the preset scanning angle.

[0021] Before scanning the weld seam of the pipe to be welded using the high-frequency laser scanner 220, the angle position of the high-frequency laser scanner 220 can be precisely adjusted using the multi-dimensional adjustment bracket 210, thereby adjusting the incident angle of the laser beam of the high-frequency laser scanner 220 to obtain the best scanning effect.

[0022] S120: Control the high-frequency laser scanner 220 to emit a high-frequency scanning laser beam at a preset scanning angle to the current weld surface of the pipe to be welded, and receive the reflected laser beam of the scanning laser beam that illuminates the current weld surface.

[0023] The high-frequency spot laser scanner 220 can emit a scanning laser beam at a high frequency of 2000-3000 times per second. After being collimated by a collimating lens, the scanning laser beam is projected as a point beam onto the surface of the weld seam of the pipe to be welded. Furthermore, the high-frequency spot laser scanner 220 can perform self-scanning by a galvanometer motor, forming a fan-shaped scanning area to scan the surface of the pipe weld seam. Moreover, after the scanning laser beam irradiates the surface of the pipe weld seam, it is reflected to form a reflected laser beam. This reflected laser beam is received by the high-speed linear CCD sensor of the high-frequency spot laser scanner 220. This high-speed linear CCD sensor has a resolution of up to 2048 pixels and a sampling frequency of up to 10kHz.

[0024] S130. Based on the emitted scanning laser beam and the received reflected laser beam, obtain the three-dimensional weld model information of the current weld of the pipeline to be welded formed by the laser point cloud data, that is, obtain the weld scanning information of the current weld.

[0025] Based on the time-of-flight (TOF) principle, by precisely measuring the time difference Δt between laser emission (scanning laser beam) and reception (reflected laser beam), and combining this with the speed of light c, the distance L = c × Δt / 2 from the high-frequency laser scanner 220 to each point on the surface of the welded pipe is calculated. Simultaneously, the angle θ of the laser beams (scanning laser beam and reflected laser beam) can be recorded in real time by a high-precision angle encoder (resolution up to 0.001 degrees) inside the high-frequency laser scanner 220. Then, the distance L and angle θ of each point can be converted from polar coordinates to Cartesian coordinates using a coordinate transformation algorithm, thereby accurately obtaining the three-dimensional coordinates (X, Y, Z) of each point on the surface of the welded pipe. This generates approximately 4 million three-dimensional coordinate data points per second, constructing a high-precision three-dimensional weld model with an accuracy of ±0.05 mm.

[0026] In summary, before acquiring the three-dimensional weld model information of the current weld seam of the pipeline to be welded by scanning with the high-frequency laser scanner 220, the position and angle of the high-frequency laser scanner 220 can be automatically adjusted by the multi-dimensional adjustment bracket 210 (such as a three-dimensional adjustment bracket). Then, the high-frequency laser scanner 220 emits a scanning laser beam at an ultra-high frequency of 2000-3000 times / second to perform an all-round scan of the pipeline weld seam. Within a few seconds, the three-dimensional coordinate information of the entire annular pipeline weld seam can be acquired, generating a high-precision weld seam model (i.e., three-dimensional weld seam model information) containing more than approximately 6 million three-dimensional coordinate data points. The data information can be transmitted in real time via a high-speed fiber optic data line of approximately 10Gbps.

[0027] Furthermore, in step S200, processing and analyzing the obtained weld scanning information to obtain the weld information of the pipe to be welded may further include the following steps: S210. Preprocess the three-dimensional weld model information of the current weld of the pipeline to be welded to obtain the final weld model information.

[0028] That is, after obtaining the three-dimensional weld model information of the current weld of the pipeline to be welded by scanning with a high-frequency laser scanner 220, the three-dimensional weld model information of the current weld can be preprocessed to remove noise, abnormal points and other interference data, thereby improving data quality.

[0029] S220. Using a preset algorithm, perform weld feature recognition and extraction on the preprocessed final weld model information to obtain the geometric feature information of the current weld.

[0030] The preset algorithm can be used to extract features from the preprocessed data (i.e., the final weld model information) and identify the geometric features of the current weld, such as edge contour, center line, weld width, and bevel angle.

[0031] S230. Based on the obtained geometric feature information of the current weld, obtain the set welding parameters and set welding path applicable to the current weld, that is, obtain the weld welding information of the current weld.

[0032] The extracted geometric feature information of the current weld can be matched with the preset welding process parameter library, and combined with machine learning algorithms to calculate the accurate welding parameters suitable for the current weld (i.e., set the welding parameters), and at the same time plan the welding path (i.e. set the welding path).

[0033] Further, in step S210, the three-dimensional weld model information of the current weld of the pipeline to be welded is preprocessed to obtain the final weld model information, which may further include the following steps: S212. An outlier removal algorithm based on statistical analysis is used to remove invalid data points in the laser point cloud data of the three-dimensional weld model information of the current weld of the pipeline to be welded, so as to obtain the primary weld model information.

[0034] When denoising 3D weld seam model information, an outlier removal algorithm based on statistical analysis can be used. A threshold of 3 times the standard deviation can be set to quickly remove invalid data points in the laser point cloud data of 3D weld seam model information caused by external interference or scanning errors.

[0035] Specifically, based on statistical analysis principles, the outlier removal algorithm assumes that normal data points in the laser point cloud data of the 3D weld seam model information conform to a certain probability distribution. In practical applications, taking each data point in the laser point cloud data as the center, its distance to surrounding neighboring points (usually k nearest neighbors are selected, where k can be set according to the data density, such as k=10) is calculated. By calculating the distance statistics of all data points in the laser point cloud data of the 3D weld seam model information, the mean μ and standard deviation σ of the distance are obtained. For example, a threshold can be set as μ+3σ. If the distance between a data point in the laser point cloud data of the 3D weld seam model information and its neighboring points exceeds this threshold, the data point is determined to be an outlier and removed. For example, when scanning and acquiring the laser point cloud data of the current weld seam of a welded pipeline, erroneous data points caused by abnormal surface reflection of the current weld seam or equipment vibration are usually far from surrounding normal points. This algorithm can effectively remove them, ensuring the reliability of the data.

[0036] S214. Using a median filtering algorithm, the laser point cloud data of the primary weld model information of the current weld seam of the pipeline to be welded is smoothed to obtain the final weld seam model information.

[0037] Median filtering can smooth laser point cloud data of 3D weld seam models, allowing for a 5×5 filtering window size. This effectively eliminates noise and improves data quality. Median filtering is a non-linear filtering algorithm. For the 3D coordinate data (X, Y, Z) of a 3D weld seam model, a 5×5×5 3D filtering window can be constructed spatially around each data point (the window size can be adjusted according to data smoothing requirements). The X, Y, and Z coordinate values ​​of all data points within the window are sorted, and the median value is used as the new coordinate value for the center data point of the window. This median filtering algorithm effectively suppresses salt-and-pepper noise in the laser point cloud data of 3D weld seam models, smoothing the data while preserving important details such as weld seam edges, and avoiding subsequent feature extraction errors caused by noise.

[0038] In summary, after obtaining the three-dimensional weld model information of the current weld seam of the pipeline to be welded through scanning, the laser point cloud data of the three-dimensional weld model information can be preprocessed, that is, by using outlier removal algorithm and median filtering algorithm to quickly remove invalid data in the laser point cloud data and smooth the data, thereby improving data quality.

[0039] Furthermore, in step S210, the preprocessing of the three-dimensional weld model information of the current weld of the pipeline to be welded to obtain the final weld model information may further include the following steps: S216. The laser point cloud data of the three-dimensional weld model information of the current weld of the pipeline to be welded is optimized using the voxel filtering algorithm to obtain the optimized weld model information of the current weld.

[0040] This method not only performs noise reduction preprocessing on the laser point cloud data of the current 3D weld model of the pipeline to be welded, but also optimizes the laser point cloud data using point cloud filtering algorithms to reduce the amount of point cloud data. Specifically, considering the characteristics of laser point cloud data, a voxel filtering algorithm can be used to divide the 3D space into a uniform voxel grid. For each data point within a voxel, its centroid coordinates are calculated, and these centroid coordinates represent all data points within that voxel, thereby reducing the amount of point cloud data while maintaining the overall shape and characteristics of the laser point cloud data. This method reduces the computational load of laser point cloud data processing and improves processing efficiency without affecting the key information of the weld.

[0041] Or / and, S218, when the three-dimensional weld model information includes weld scanning information from multiple scanning perspectives, the iterative nearest point algorithm is used to fuse the laser point cloud data of multiple weld scanning information into a complete fused weld model information.

[0042] When multi-view scanning of the weld seam of a pipeline to be welded is required to obtain more comprehensive information (i.e., acquiring multiple sets of laser point cloud data), point cloud registration is necessary. Specifically, the Iterative Closest Point (ICP) algorithm can be used, selecting one set of laser point cloud data as the reference set and the other set as the set to be registered. Then, by finding the nearest data point in the reference set for each data point in the set to be registered, the transformation TR matrix (including translation and rotation) between the two sets of laser point cloud data is calculated. The transformation matrix is ​​iteratively optimized to ensure that the overlapping parts of the two sets of laser point cloud data coincide as much as possible. After registration, the multiple sets of laser point cloud data from the multi-view scans can be fused into a complete 3D weld seam model (i.e., fused weld seam model information), providing more accurate data support for accurately extracting weld seam features and planning welding paths.

[0043] Furthermore, in step S220, the preprocessed final weld model information is used to identify and extract weld features using a preset algorithm to obtain the geometric feature information of the current weld. This may further include the following steps: S222. Adaptive threshold binarization method is used to binarize the preprocessed final weld model information and extract the weld edge contour of the current weld from the final weld model information.

[0044] Before using the Canny operator to perform edge detection on the preprocessed final weld model information, binarization processing of the laser point cloud data of the final weld model information is required to highlight the weld edge information. Specifically, an adaptive threshold binarization method can be used to project the three-dimensional laser point cloud data of the final weld model information onto a two-dimensional plane (such as the XY plane). The Z coordinate value of each data point is used as the gray value. By calculating the mean and variance of the gray values ​​of data points in each local region (such as a 10×10 pixel sub-region), the threshold of that local region is dynamically determined. If the gray value of a data point is greater than the threshold, it is assigned a value of 255 (white), representing the weld edge or an important feature area; if the gray value of a data point is less than the threshold, it is assigned a value of 0 (black), representing the background area. This binarization processing method can adapt to the gray value changes of different pipe surface materials and lighting conditions, and accurately extract the potential areas of the weld edge.

[0045] S224. Based on the weld contour points of the weld edge profile of the current weld, the least squares method is used to fit the weld contour points of the weld edge profile to obtain the weld centerline of the current weld.

[0046] After obtaining the weld edge contour of the current weld, the weld contour points of the weld edge contour can be obtained. The laser coordinates of these weld contour points can then be treated as discrete data, and curve fitting can be performed on these weld contour points using the least squares method. Specifically, assuming the weld centerline of the current weld can be represented by the quadratic polynomial y=ax... 2 +bx+c represents (for curve fitting in three-dimensional space, this can be done on different planes or using a spatial curve fitting algorithm), by minimizing the sum of the squares of the vertical distances from all weld contour points to the fitted curve, solving for the coefficients a, b, and c of the polynomial, thus obtaining the mathematical expression for the weld centerline. This weld centerline can serve as an important reference for welding path planning, ensuring that the welding moves along the weld centerline and guaranteeing welding quality.

[0047] S226. The extracted weld edge contour is processed using erosion and dilation operations in morphological algorithms. The weld width, bevel angle, misalignment, and weld gap value of the current weld are calculated to obtain the geometric feature information of the current weld.

[0048] After obtaining the weld edge contour of the current weld through binarization, the improved Canny operator and morphological algorithm can be used to extract the edge contour and calculate the weld width, bevel angle, and other geometric features. Specifically, the extracted weld edge contour can be processed using the erosion and dilation operations in the improved Canny operator and morphological algorithm. Furthermore, erosion can remove burrs and small protrusions from the weld edge contour, while dilation can fill small voids within the weld edge contour, making it smoother and more regular. Moreover, by calculating the number of pixels or length of the processed weld edge contour and combining it with the actual physical size ratio of the scan, the weld width can be accurately calculated; the bevel angle can be calculated using the angle information and geometric relationships of the weld edge contour; the misalignment can be calculated by comparing the height difference between the two contour points of the current weld; and the weld gap size and other geometric features can be calculated based on the gap of the weld edge contour.

[0049] Furthermore, in step S230, based on the obtained geometric feature information of the current weld, the set welding parameters and set welding path applicable to the current weld are obtained, including: S222. Based on the obtained geometric feature information of the current weld, match it with the standard weld in the preset welding process parameter library.

[0050] The extracted geometric feature information of the current weld is matched with the preset welding process parameter library, which includes more than 20 common pipe materials such as carbon steel, stainless steel, and alloy steel, as well as the optimal welding parameter combinations for various specifications with pipe diameters of 15mm-2000mm and wall thicknesses of 1mm-50mm.

[0051] S234. When the geometric feature information of the current weld is successfully matched with the standard weld in the preset welding process parameter library, the set welding parameters and set welding path applicable to the current weld are calculated by combining machine learning algorithm, that is, the weld welding information of the current weld is obtained.

[0052] The extracted weld features are then matched with a welding process parameter library. Combined with a support vector machine regression algorithm, precise welding parameters suitable for the current weld are calculated, including setting the welding current, welding voltage, and welding speed. The optimal welding path for the welding torch 340 (i.e., the set welding path) is also planned. Specifically, through a machine learning-based parameter optimization algorithm, combined with the geometric features of the current weld, precise welding parameters are calculated, including setting the welding current (adjustment range 50-500A, accuracy ±1A), welding voltage (adjustment range 15-40V, accuracy ±0.1V), welding speed (0.1-2m / min, accuracy ±0.01m / min), wire feed speed (1-15m / min, accuracy ±0.1m / min), welding torch oscillation amplitude (0-20mm, accuracy ±0.1mm), and frequency (0-20Hz, accuracy ±0.1Hz), as well as the set welding path for welding with the welding torch 340.

[0053] Furthermore, the welding execution device 300 may include a welding power source 310, a wire feeder 320, and a robotic arm 330 mounted on an equipment support platform 110 of the auxiliary support mechanism 100, as well as a welding torch 340 mounted on the robotic arm 330. Specifically, the welding power source 310 may be a digital pulse MIG welding power source 310, model EWM JN-400-XQR-React. This welding power source 310 has a dual-pulse welding function, enabling precise control of welding current and welding voltage. Its welding current adjustment range is 40-500A, with a dynamic response time of less than 1ms, and its voltage adjustment range is 10-40V, with a control accuracy of ±0.1V. This welding power source 310 supports multiple welding modes, including short-circuit transition, spray transition, and pulse transition, and can automatically switch according to different welding materials and process requirements. Moreover, the wire feeder 320 may be a four-wheel drive wire feeder with wire feed wheels made of hard alloy and featuring V-grooves on the surface to effectively prevent wire slippage. The wire feeding device 320 uses a servo motor with high torque and low inertia. The wire feeding speed can be adjusted from 1 to 15 m / min with a control accuracy of ±0.1 m / min. The wire feeding hose of the device 320 is made of braided spring steel wire with an inner diameter of 1.2 mm and a length of 3 m, ensuring smooth wire feeding.

[0054] Furthermore, the welding torch 340 can be a water-cooled gooseneck torch 340, with the torch body made of copper and an internal circulating water flow rate of 5L / min, which can effectively reduce the temperature of the welding torch 340 and ensure stable welding over a long period of time. The nozzle diameter of the welding torch 340 can be 12mm, and the shielding gas is a mixture of 82%Ar + 18%CO2, with a gas flow rate of 15-25L / min. Furthermore, the robotic arm 330 can be a six-axis industrial robot arm, model FANUC M-20iA, with a repeatability of ±0.08mm and a maximum load of 20kg. Each joint of the robotic arm 330 can be driven by a servo motor via a high-precision harmonic reducer, achieving a joint movement speed of up to 180 degrees / second. The end effector of the robotic arm 330 can be equipped with a quick-change device for rapidly switching between different types of welding torches 340 or tools. Based on the calculated welding path, the robotic arm 330 uses an inverse kinematics algorithm to convert the path coordinates of the welding path into joint angles, driving the coordinated movement of each joint to achieve precise positioning and trajectory tracking of the welding torch 340.

[0055] Therefore, in step S300, controlling the welding execution device 300 to weld the current weld of the pipeline to be welded based on the obtained weld welding information may further include the following steps: S310. Based on the obtained weld welding information, the robotic arm 330 of the welding execution device 300 is controlled to drive the welding torch 340 to move along the set welding path at a set welding speed. S320: Based on the obtained weld welding information, the welding parameters are set, the welding power supply 310 of the welding execution device 300 is controlled to output the set welding current and the set welding current, the wire feeding device 320 is controlled to set the wire feeding speed, and the welding torch 340 is controlled to set the welding torch oscillation amplitude, so as to weld the current weld of the pipeline to be welded.

[0056] After receiving the welding instruction and welding information for the current weld seam of the pipe to be welded, the welding execution device 300 can control the welding power supply 310 to output the set welding current and set welding voltage according to the set parameters; and can control the robotic arm 330 to move the welding torch 340 to the starting welding position, while controlling the wire feeding device 320, which is a four-wheel drive wire feeder, to feed the welding wire into the welding area at the set wire feeding speed, and start the welding operation on the current weld seam of the pipe to be welded. During the welding of the current weld seam of the pipe to be welded, the pipe to be welded rotates at a constant speed under the drive of the welding roller frame 120 of the auxiliary support mechanism 100, and the robotic arm 330 of the welding execution device 300 precisely controls the welding position and welding posture of the welding torch 340 according to the set welding path, ensuring that the welding torch 340 moves along the weld seam trajectory.

[0057] In addition, such as Figure 2 As shown, in step S300, the control welding execution device 300 welds the current weld of the pipe to be welded according to the obtained weld welding information, and may also include the following steps: S400: When welding the current weld of the pipeline to be welded, detect the actual welding path and actual welding parameters of the current weld.

[0058] That is, when welding the current weld seam of the pipe to be welded, the welding process can be monitored in real time through various detection sensors. Specifically, the actual welding current and actual welding voltage can be collected in real time through welding current sensors and welding voltage sensors, and the actual wire feeding speed of the wire feeding device 320 can also be detected to detect various actual welding parameters; and the motion state of the robotic arm 330 can be monitored through encoders at the joints of the robotic arm 330, thereby detecting the actual welding path.

[0059] S500 When the actual welding path deviates from the set welding path, or / and the actual welding parameters deviate from the set welding parameters, an adaptive adjustment method is used to adjust the actual welding path and / or the actual welding parameters in real time so that the actual welding path is consistent with the set welding path, or / and the actual welding parameters are consistent with the set welding parameters.

[0060] When the actual welding parameters deviate from the set welding parameters, such as a welding current deviation exceeding ±2A, or when misalignment or changes in weld gap are detected, the output values ​​of the welding voltage and welding current of the welding power supply 310 can be automatically adjusted through the PID adjustment algorithm and the motion control algorithm of the robotic arm 330, or the wire feeding speed of the wire feeding device 320 can be adjusted, or the motion trajectory of the robotic arm 330 can be adjusted to adjust the welding path, etc., to ensure a stable welding process and that the weld quality meets the standards.

[0061] Due to the wide variety of specifications for welded pipes, they may differ in diameter, wall thickness, and material. Furthermore, the actual installation process involves complex and variable pipe joint conditions, such as misalignment and uneven weld gaps. Traditional welding methods struggle to automatically adjust welding parameters and paths to accommodate these real-time changes, leading to inconsistent weld quality and defects like weak welds, incomplete penetration, and undercut. Adaptive welding methods, however, offer a solution for achieving high-quality and efficient pipe welding, making it a hot research and application area. Specifically, adaptive welding automatically adjusts actual welding parameters—such as welding current, voltage, and speed—and the welding path based on real-time welding information, including weld position, shape, and gap size. This ensures high-quality welding even under complex conditions.

[0062] Furthermore, the welding process parameter library is the core data support for realizing the adaptive adjustment method. It is constructed by collecting the experience of senior welding experts in the industry, a large amount of welding experimental data, and actual engineering cases. The welding process parameter library covers more than 20 common pipe materials, including carbon steel, stainless steel, and alloy steel. For different specifications of pipes with diameters ranging from 20mm to 2000mm and wall thicknesses from 1.5mm to 50mm, it establishes a correspondence between various weld feature parameters, such as misalignment (0-5mm), weld gap size (0-8mm), and bevel angle (30°-90°), and welding process parameters. After extracting the misalignment, weld gap, and other features of the current weld, the welding process parameter library can be quickly retrieved using the adaptive adjustment method. For example, for a carbon steel pipe with a diameter of 500mm and a wall thickness of 10mm, if a weld misalignment of 2mm and a weld gap of 3mm are detected, the corresponding welding parameter combination can be automatically retrieved from the welding process parameter library as the initial parameter setting basis for welding the current weld. Meanwhile, the welding process parameter library has a self-learning function, which can continuously optimize and update parameter combinations based on quality feedback data in the actual welding process, thereby improving the accuracy and applicability of the welding process parameter library.

[0063] For example, when misalignment is detected in the current weld of the pipe to be welded during the welding process, an adaptive adjustment method can be used to formulate a welding current adjustment strategy based on the size of the misalignment and the welding process parameter library. If the misalignment is within the range of 0-1mm, a linear compensation algorithm can be used to gradually increase the current in small increments (e.g., 5A increase for every 0.1mm of misalignment) to ensure sufficient fusion of the weld at the misalignment. When the misalignment reaches 1-3mm, a segmented adjustment method can be used, dividing the misalignment area into multiple segments, and dynamically adjusting the actual welding current within each segment according to the changes in misalignment. For example, the current can be increased by a larger margin (e.g., 10A increase for every 0.5mm of misalignment) to fuse the root of the current weld, and then the current increment can be gradually reduced to ensure a beautiful weld surface. If the misalignment exceeds 3mm, the actual welding speed and the actual wire feed speed can be adjusted together. While appropriately increasing the actual welding current, the actual welding speed can be reduced by 10%-20%, and the actual wire feed speed can be increased by 5%-10% to ensure weld quality. During current regulation, the welding power supply 310 can achieve smooth switching of welding current through a fast-response IGBT drive circuit, with the switching time controlled within 50ms, to avoid the impact of sudden current changes on the welding process.

[0064] Furthermore, the welding current can be adjusted using a fuzzy control algorithm employed in the adaptive adjustment method to accommodate different weld gap sizes. Specifically, when the weld gap is between 0-2mm, the current adjustment amount can be determined based on the gap change rate (the amount of change in gap per unit time) and the current gap value, using fuzzy inference rules. For example, if the gap change rate is small and the gap value is close to 2mm, the current can be moderately increased (e.g., by 10-15A) to ensure full weld filling. When the gap reaches 2-5mm, a two-stage adjustment mechanism can be activated. First, a larger current (20-30A more than the set welding current) is used to quickly fill the bottom of the gap, and then the welding current is gradually reduced to near the normal range to ensure a smooth weld surface. For gaps exceeding 5mm, the wire feeder 320 can be prioritized to increase the wire feed amount, while the welding current is increased proportionally (e.g., 8A more current for every 1mm increase in gap), and this is combined with an oscillating welding process to ensure uniform weld formation. Throughout the current regulation process, the feedback data of the actual welding current and actual welding voltage can be monitored in real time. The actual welding current is finely adjusted through the PID control algorithm to ensure that the actual welding current is stable within the range of ±3A of the set value (i.e., the set welding current).

[0065] Furthermore, during spot welding, a special current waveform control strategy can be employed using an adaptive adjustment method. Specifically, a high peak current (1.5-2 times the set welding current) is first used to rapidly melt the metal in the welding area, forming the weld core. The peak current duration is controlled within 100-300 ms to ensure sufficient weld strength. Subsequently, the actual welding current rapidly decreases to the holding current (approximately 60%-80% of the set welding current), maintained for 200-500 ms, allowing the weld to maintain good metallurgical bonding during cooling and preventing defects such as shrinkage cavities and cracks. During spot welding point switching, a current ramp-up and ramp-down method can be used to avoid electromagnetic interference and spatter caused by sudden changes in welding current. The rise and fall times of the welding current can be adjusted according to the welding material and pipe diameter, generally between 50-150 ms. Simultaneously, the current output frequency of the spot welds can be automatically adjusted according to the spacing and welding speed to ensure uniform weld distribution and meet welding strength and appearance requirements.

[0066] During welding, welding parameters are controlled collaboratively. This means that the actual welding current is not adjusted alone, but rather in conjunction with other welding parameters such as welding speed, wire feed speed, and welding torch oscillation amplitude. While adjusting the actual welding current based on misalignment or weld gap, the wire feed speed is simultaneously adjusted to match the actual wire feed amount with the required deposited metal. For example, when the actual welding current increases, the actual wire feed speed is increased proportionally (the ratio of the increase in wire feed speed to the increase in current is approximately 1:1.2). Regarding the actual welding torch oscillation amplitude, when dealing with misaligned or large-gap welds, the oscillation amplitude can be automatically increased (e.g., from the normal 8mm to 12-15mm), and the actual oscillation frequency can be adjusted to ensure good fusion at the weld edges.

[0067] Moreover, such as Figure 3 As shown, in step S300, after the welding execution device 300 welds the current weld of the pipe to be welded based on the obtained weld welding information, the following steps may also be included: S600: After completing the welding of a section of the current weld seam of the pipeline to be welded, control the high-frequency laser scanner 220 to scan the welded seam and obtain the post-weld three-dimensional model data of the welded seam. S700 compares and analyzes the post-weld 3D model data with the pre-weld 3D weld model information and welding process standards to detect the weld formation quality of the welded seam.

[0068] After completing a section of welding, the weld can be scanned again using a high-frequency laser scanner 220. The resulting 3D model data is compared and analyzed with the 3D weld model information before welding and the welding process standards to check the weld formation quality, including weld width, reinforcement height, surface flatness, and the presence of defects such as porosity and cracks. If defects are detected, the defect information and optimization requirements are recorded.

[0069] Once the entire pipe to be welded is completed, the welding operation can be automatically stopped, the robotic arm 330 can be controlled to return to its initial position, and the high-frequency laser scanner 220 can be controlled to shut down. The various devices and equipment of the welding system can be cleaned and maintained, and the data generated during the welding process can be automatically stored and backed up for later review and analysis.

[0070] In addition, such as Figure 4 As shown, in other embodiments, the present invention also proposes a pipe welding device 1000 based on high-frequency point laser scanning, applied to welding system 10. For example... Figure 6 As shown, the welding system includes an auxiliary support mechanism 100, a high-frequency laser scanning device 200, and a welding execution device 300 mounted on the auxiliary support mechanism 100. The auxiliary support mechanism 100 can be used to support and position the pipe to be welded, and also to install and position the high-frequency laser scanning device 200 and the welding execution device 300.

[0071] Specifically, such as Figure 4 As shown, the pipe welding device 1000 based on high-frequency spot laser scanning may include: The scanning control module 1002 is used to control the high-frequency laser scanning device 200 to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam, and to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. The welding information acquisition module 1004 is used to process and analyze the obtained weld scanning information to obtain the weld information of the pipeline to be welded. The welding control module 1006 is used to control the welding execution device 300 to weld the current weld of the pipeline to be welded according to the obtained weld welding information.

[0072] Moreover, such as Figure 5 As shown, the pipe welding device 1000 based on high-frequency point laser scanning may further include a welding adaptive adjustment module 1008, used for: When welding the current weld of the pipeline to be welded, detect the actual welding path and actual welding parameters of the current weld. When the actual welding path deviates from the set welding path, or / and the actual welding parameters deviate from the set welding parameters, the actual welding path and / or the actual welding parameters are adjusted in real time to make the actual welding path consistent with the set welding path, or / and the actual welding parameters consistent with the set welding parameters.

[0073] Moreover, such as Figure 5 As shown, the pipe welding device 1000 based on high-frequency laser scanning may further include a welding quality inspection module 1009, used for: After the welding of a section of the current weld seam of the pipeline to be welded is completed, the high-frequency laser scanner 220 is controlled to scan the weld seam and obtain the post-weld three-dimensional model data of the weld seam. The post-weld 3D model data is compared and analyzed with the pre-weld 3D weld model information and welding process standards to detect the weld formation quality of the welded seam.

[0074] The pipe welding device 1000 based on high-frequency laser scanning described in this embodiment corresponds to the pipe welding method based on high-frequency laser scanning described above. The functions of each module in the pipe welding device 1000 based on high-frequency laser scanning in this embodiment are described in detail in the corresponding method embodiments, and will not be repeated here. Moreover, the specific structure of the welding system 10 in this embodiment is also specifically described in the above embodiments, and will not be repeated here.

[0075] In addition, such as Figure 6 As shown, in some other embodiments, the present invention also proposes a welding system 10, including an auxiliary support mechanism 100, a high-frequency laser scanning device 200 and a welding execution device 300 disposed on the auxiliary support mechanism 100, and a controller 400 connected to the auxiliary support mechanism 100, the high-frequency laser scanning device 200 and the welding execution device 300.

[0076] Furthermore, the controller 400 can be used to implement each step of the above-described pipe welding method based on high-frequency laser scanning. The specific implementation method can be found in the detailed content of the above-described pipe welding method based on high-frequency laser scanning, and will not be repeated here. Moreover, the specific structure of the welding system 10 in this embodiment has also been specifically described in the above embodiments, and will not be repeated here.

[0077] The pipeline welding solution based on high-frequency laser scanning provided by this invention can significantly improve welding quality, greatly increase welding efficiency, enhance adaptability to complex working conditions, increase the degree of intelligence, reduce production costs, and ensure stable and reliable equipment. Specifically, it is manifested in: A high-precision weld model with over 5-6 million three-dimensional coordinate data points can be quickly acquired using a high-frequency laser scanner 220 at 2000-3000 scans per second. This accurately captures subtle features such as weld misalignment and weld gap size, providing a reliable data foundation for precise welding, thus enabling precise data acquisition. The data processing and analysis unit (such as the controller 400) combines a welding process parameter library with a support vector machine regression algorithm to calculate precise welding parameters. Welding data is monitored in real time through adaptive adjustment. When the welding current deviation exceeds ±2A or weld abnormalities occur, the welding parameters and welding torch position can be dynamically adjusted in a timely manner through a PID adjustment algorithm and a robotic arm 330 motion control algorithm. This effectively avoids defects such as incomplete penetration, undercut, and porosity, significantly improving weld formation quality and weld joint strength, thus achieving intelligent parameter adjustment.

[0078] From weld seam scanning, data processing, and parameter planning to welding operations, the entire process is automated. The laser scanning module (i.e., the high-frequency laser scanning device 200) rapidly acquires data, which is efficiently calculated by the controller 400, and precisely executed by the welding execution device 300. No frequent manual intervention is required, allowing for continuous operation. In large-scale pipeline welding projects, this significantly shortens the welding cycle and increases welding output per unit time, thus automating the welding process. Real-time monitoring of the welding process via adaptive adjustment allows for rapid response and adjustment of welding parameters and welding torch trajectory upon detecting parameter deviations or weld seam changes, ensuring smooth welding and minimizing downtime caused by parameter adjustments and defect repairs. This results in rapid response and adjustment of the welding process.

[0079] It can adapt to the welding needs of more than 20 types of pipe materials, with pipe diameters ranging from 10 to 3000 mm and wall thicknesses from 0.5 to 80 mm, meeting the diverse pipe welding requirements of industries such as petrochemicals, power energy, and urban construction. It can accommodate multiple pipe specifications. Addressing common issues such as misalignment and uneven gaps during pipe butt welding assembly, it uses an adaptive adjustment method based on a welding process parameter library and special welding process control strategies, such as linear compensation, segmented adjustment, and fuzzy control algorithms, to precisely adjust parameters such as welding current and wire feed speed. This ensures stable and high-quality welding under various complex working conditions, achieving flexible assembly adaptation.

[0080] Integrating advanced sensor technology, algorithms, and intelligent control systems, the system automatically acquires weld data via a high-frequency laser scanning device 200. The controller 400 intelligently analyzes the data and generates welding parameters and paths. An adaptive adjustment method monitors and dynamically optimizes the welding process in real time. The welding quality inspection stage automatically identifies defects and plans repair welds, reducing reliance on manual experience and achieving automated decision-making. The welding system possesses self-learning capabilities, continuously optimizing its process parameter library and control strategies based on actual welding data. With increased usage time and accumulated welding cases, system performance continuously improves, adapting to new welding demands and changing operating conditions, thus achieving self-learning optimization.

[0081] Automated and intelligent welding processes reduce reliance on a large number of skilled welders, lowering the cost of manual training and labor. Simultaneously, reduced human error and rework costs due to welding quality issues further reduce labor costs. Precise control of welding parameters and high-quality welding results reduce material waste during the welding process, improve weld quality, avoid material loss due to welding defects, and lower overall production costs, thus increasing material utilization.

[0082] Each component employs high-quality hardware, such as high-precision sensors and stable laser emission units in the laser scanning module (i.e., the high-frequency laser scanning device 200), a high-performance welding power supply 310 and a precision robotic arm 330 in the welding execution device 300, a reliable PLC controller capable of adaptive adjustment, and various types of high-precision sensors, ensuring stable operation under long-term, high-intensity working conditions, thus improving hardware quality. The system possesses a comprehensive fault diagnosis and emergency handling mechanism, enabling timely detection of equipment faults and the implementation of corresponding measures to reduce equipment failure rates, minimize downtime, ensure the continuity of welding production, and reduce equipment maintenance costs and losses caused by production interruptions, thus improving fault diagnosis.

[0083] Furthermore, in other embodiments, the present invention proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement all or part of the method steps of the pipe welding method based on high-frequency laser scanning as described above.

[0084] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0085] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that runs on the processor, and the processor executes the computer program to implement all or part of the method steps described above.

[0086] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0087] Memory can be used to store computer programs and / or models. The processor performs various functions of the computer device by running or executing the computer programs and / or models stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A pipe welding method based on high-frequency spot laser scanning, applied to a welding system; The welding system includes an auxiliary support mechanism, as well as a high-frequency laser scanning device and a welding execution device mounted on the auxiliary support mechanism. Its features are, The welding method includes: The high-frequency laser scanning device is controlled to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. The obtained weld scan information is processed and analyzed to obtain the weld information of the pipeline to be welded; The control welding execution device performs welding on the current weld of the pipeline to be welded based on the obtained weld welding information.

2. The pipe welding method based on high-frequency laser scanning according to claim 1, characterized in that, The high-frequency laser scanning device includes a multi-dimensional adjustment bracket mounted on the auxiliary support mechanism, and a high-frequency laser scanner mounted on the multi-dimensional adjustment bracket. The high-frequency laser scanning device is controlled to scan the current weld seam of the pipeline to be welded using a high-frequency emitted laser beam, acquiring weld seam scanning information of the current weld seam, including: The multi-dimensional adjustment bracket is controlled to adjust the incident angle of the laser beam of the high-frequency laser scanner so that the incident angle of the laser beam reaches the preset scanning angle. The high-frequency laser scanner is controlled to emit a high-frequency scanning laser beam at a preset scanning angle onto the current weld surface of the pipe to be welded, and the reflected laser beam of the scanning laser beam that illuminates the current weld surface is received. Based on the emitted scanning laser beam and the received reflected laser beam, the three-dimensional weld model information of the current weld of the pipeline to be welded is obtained by acquiring the laser point cloud data, that is, the weld scanning information of the current weld is obtained.

3. The pipe welding method based on high-frequency laser scanning according to claim 2, characterized in that, The process of processing and analyzing the obtained weld scan information to obtain the weld information of the pipeline to be welded includes: The three-dimensional weld model information of the current weld of the pipeline to be welded is preprocessed to obtain the final weld model information; The pre-defined algorithm is used to identify and extract weld features from the pre-processed final weld model information to obtain the geometric feature information of the current weld. Based on the obtained geometric feature information of the current weld, the set welding parameters and set welding path applicable to the current weld are obtained, thus obtaining the weld welding information of the current weld.

4. The pipe welding method based on high-frequency laser scanning according to claim 3, characterized in that, The preprocessing of the three-dimensional weld model information of the current weld seam of the pipeline to be welded yields the final weld model information, including: An outlier removal algorithm based on statistical analysis is used to remove invalid data points from the laser point cloud data of the three-dimensional weld model information of the current weld of the pipeline to be welded, so as to obtain the primary weld model information. The laser point cloud data of the primary weld model information of the current weld seam of the pipeline to be welded is smoothed by the median filtering algorithm to obtain the final weld seam model information.

5. The pipe welding method based on high-frequency laser scanning according to claim 3, characterized in that, The preprocessing of the three-dimensional weld model information of the current weld of the pipeline to be welded to obtain the final weld model information also includes: A voxel filtering algorithm is used to optimize the laser point cloud data of the 3D weld model of the current weld in the pipeline to be welded, resulting in optimized weld model information; or / and, When the 3D weld model information includes weld scanning information from multiple scanning perspectives, the iterative nearest point algorithm is used to fuse the laser point cloud data of multiple weld scanning information into a complete fused weld model information.

6. The pipe welding method based on high-frequency laser scanning according to claim 3, characterized in that, The step of using a preset algorithm to identify and extract weld features from the preprocessed final weld model information to obtain the geometric feature information of the current weld includes: An adaptive threshold binarization method is used to binarize the preprocessed final weld model information and extract the weld edge contour of the current weld from the final weld model information. Based on the weld contour points of the weld edge profile of the current weld, the least squares method is used to fit the weld contour points of the weld edge profile to obtain the weld centerline of the current weld. The extracted weld edge contour is processed using erosion and dilation operations in morphological algorithms. The weld width, bevel angle, misalignment, and weld gap value of the current weld are calculated to obtain the geometric feature information of the current weld.

7. The pipe welding method based on high-frequency laser scanning according to claim 3, characterized in that, The step of obtaining the set welding parameters and set welding path applicable to the current weld based on the obtained geometric feature information of the current weld includes: Based on the obtained geometric feature information of the current weld, it is matched with the standard weld in the preset welding process parameter library; When the geometric feature information of the current weld successfully matches the standard weld in the preset welding process parameter library, the set welding parameters and set welding path applicable to the current weld are calculated by combining machine learning algorithms, thus obtaining the weld welding information of the current weld.

8. The pipe welding method based on high-frequency laser scanning according to claim 3, characterized in that, The welding execution device includes a welding power source, a wire feeding device and a robotic arm mounted on the auxiliary support mechanism, and a welding torch mounted on the robotic arm. The controlled welding execution device welds the current weld of the pipeline to be welded based on the obtained weld welding information, including: Based on the obtained weld welding information, the robotic arm of the welding execution device is controlled to move the welding torch along the set welding path at a set welding speed. Based on the obtained weld welding information, the welding parameters are set, the welding power output of the welding execution device is controlled to set the welding current and the welding current setting, the wire feeding device is controlled to set the wire feeding speed, and the welding torch is controlled to set the welding torch oscillation amplitude, so as to weld the current weld of the pipeline to be welded.

9. The pipe welding method based on high-frequency laser scanning according to claim 8, characterized in that, The controlled welding execution device performs welding on the current weld of the pipeline to be welded based on the obtained weld welding information, and further includes: When welding the current weld of the pipeline to be welded, detect the actual welding path and actual welding parameters of the current weld. When the actual welding path deviates from the set welding path, or / and the actual welding parameters deviate from the set welding parameters, the actual welding path and / or the actual welding parameters are adjusted in real time to make the actual welding path consistent with the set welding path, or / and the actual welding parameters consistent with the set welding parameters.

10. The pipe welding method based on high-frequency laser scanning according to claim 1, characterized in that, After the controlled welding execution device welds the current weld of the pipeline to be welded based on the obtained weld welding information, it further includes: After completing the welding of a section of the current weld seam of the pipeline to be welded, the high-frequency laser scanner is controlled to scan the weld seam and obtain the post-weld three-dimensional model data of the weld seam. The post-weld 3D model data is compared and analyzed with the pre-weld 3D weld model information and welding process standards to detect the weld formation quality of the welded seam.

11. A pipe welding device based on high-frequency laser scanning, applied to a welding system; The welding system includes an auxiliary support mechanism, as well as a high-frequency laser scanning device and a welding execution device mounted on the auxiliary support mechanism. Its features are, The welding apparatus includes: The scanning control module is used to control the high-frequency laser scanning device to scan the current weld seam of the pipeline to be welded with a high-frequency laser beam, and to obtain the weld seam scanning information of the current weld seam of the pipeline to be welded. The welding information acquisition module is used to process and analyze the obtained weld scanning information to obtain the weld information of the pipeline to be welded. The welding control module is used to control the welding execution device to weld the current weld of the pipeline to be welded based on the obtained weld welding information.

12. The pipe welding device based on high-frequency laser scanning according to claim 11, characterized in that, The welding apparatus further includes: The welding adaptive adjustment module is used for: When welding the current weld of the pipeline to be welded, detect the actual welding path and actual welding parameters of the current weld. When the actual welding path deviates from the set welding path, or / and the actual welding parameters deviate from the set welding parameters, the actual welding path and / or the actual welding parameters are adjusted in real time to make the actual welding path consistent with the set welding path, or / and the actual welding parameters consistent with the set welding parameters.

13. The pipe welding device based on high-frequency laser scanning according to claim 11, characterized in that, The welding apparatus further includes: The welding quality inspection module is used for: After completing the welding of a section of the current weld seam of the pipeline to be welded, the high-frequency laser scanner is controlled to scan the weld seam and obtain the post-weld three-dimensional model data of the weld seam. The post-weld 3D model data is compared and analyzed with the pre-weld 3D weld model information and welding process standards to detect the weld formation quality of the welded seam.

14. A welding system, characterized in that, include: Auxiliary support mechanism; A high-frequency spot laser scanning device is mounted on the auxiliary support mechanism; The welding execution device is mounted on the auxiliary support mechanism; as well as The controller is connected to the auxiliary support mechanism, the high-frequency laser scanning device, and the welding execution device. The controller is used to implement the pipe welding method based on high-frequency point laser scanning as described in any one of claims 1-10.

15. A computer storage medium, characterized in that, The computer storage medium includes at least one instruction that, when executed by a computer, implements the method steps of the pipe welding method based on high-frequency laser scanning as described in any one of claims 1-10.

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