Point cloud-based adaptive laser hybrid welding method and welding mechanism

Abstract

The present invention provides a point cloud-based adaptive laser hybrid welding method, comprising the following steps: step 1: using a plurality of laser radars to scan a workpiece so as to obtain point cloud data; step 2: processing the point cloud data obtained by scanning, and performing reverse modeling to obtain a point cloud model; step 3: performing position extraction and type annotation on different weld seams by means of an adaptive weld seam extraction algorithm, such that straight weld seams, arc weld seams, and cross weld seams can be identified; step 4: automatically planning a finding sequence on the basis of coordinate values of the weld seams, and sending the finding sequence to a robot; and step 5: by means of an adaptive finding algorithm, using a professional vision device to accurately position the weld seams, and by incorporating an autonomous welding process library, completing welding.

Description

An adaptive laser hybrid welding method and welding mechanism based on point cloud Technical Field

[0001] This invention relates to the field of automated welding technology, and in particular to an adaptive laser composite welding method and welding mechanism based on point cloud. Background Technology

[0002] In the welding process of assembled workpieces, weld seam identification and positioning are prerequisites for subsequent laser hybrid welding. Traditional weld seam extraction methods mainly rely on manual identification, which is inefficient and inaccurate, often requiring significant manpower. Therefore, it is necessary to develop a new weld seam identification technology to increase the automation and accuracy of the production line, thereby improving production efficiency and product quality. Summary of the Invention

[0003] To address the aforementioned challenges, this invention provides an adaptive laser composite welding method based on point clouds, which can adaptively identify the type and location of weld seams based on the three-dimensional point cloud of a small assembled workpiece, reducing reliance on manual operation.

[0004] To achieve the above functions, this invention proposes an adaptive laser composite welding method based on point clouds. This method uses several laser radars to scan the workpiece, acquire point cloud data, and performs downsampling filtering, pass-through filtering, and point cloud registration to establish a point cloud model. Then, an adaptive weld seam extraction algorithm is used to extract the position and label the type of different weld seams, enabling the identification of straight weld seams, circular arc weld seams, and cross weld seams. Simultaneously, this invention can automatically plan the positioning sequence based on the coordinate values ​​of the weld seams and send the positioning sequence to the robot. Finally, the adaptive positioning algorithm is used to accurately locate the weld seams using specialized vision equipment. Combined with an independent welding process library, welding is completed. Specifically, the method includes the following steps:

[0005] Step 1: Use several LiDAR scanners to scan the workpiece and acquire point cloud data;

[0006] Step 2: Process the point cloud data acquired by scanning and reverse model the point cloud to obtain a point cloud model;

[0007] Step 3: Using an adaptive weld extraction algorithm, extract the location and label the type of different welds;

[0008] Step 4: Plan the positioning sequence automatically based on the coordinate values ​​of the weld and send the positioning sequence to the robot;

[0009] Step 5: Using an adaptive positioning algorithm, the weld seam is precisely located with professional vision equipment, and the welding is completed in conjunction with an independent welding process library.

[0010] In step 2 of this invention, the point cloud data is subjected to downsampling filtering, pass-through filtering, and point cloud registration operations to establish a point cloud model.

[0011] In step 3 of this invention, the weld seam includes straight weld seam, circular arc weld seam, and cross weld seam. The extracted weld seam point cloud is represented using different colors.

[0012] In step 4 of this invention, after all welds have been extracted, they are sorted according to the welding sequence rules, and all welding positions are generated. After the weld positions are confirmed, the robot's reachability and interference are calculated based on the positional relationship between the welds, and the robot's welding transition points are planned; finally, the complete welding position and transition point information are sent to the robot.

[0013] In step 5 of this invention, the robot automatically reaches the welding position by passing through the transition point and completes the welding in conjunction with the welding process library. The optimal welding parameters for workpieces of various plate thicknesses and welding requirements are determined in advance through testing and saved to the welding process library.

[0014] This invention also proposes an adaptive laser composite welding mechanism based on point clouds; the scanning gantry mechanism includes: a lidar, a scanning gantry, guide rails, a welding torch, a robotic arm, a robot control cabinet, a welding power source, and a dust removal device; wherein, the scanning gantry can move by means of the guide rails on both sides; when the scanning gantry starts to move, the lidar scans the workpiece in real time.

[0015] The beneficial effects of this invention are as follows: By acquiring the point cloud of the workpiece using lidar and employing the proposed adaptive method, the weld seam can be accurately and effectively identified, and the positioning sequence can be planned automatically. Finally, through the adaptive positioning algorithm combined with an independent welding process library, laser hybrid welding is performed, achieving automation and high efficiency of the welding system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0017] Figure 1 shows the workpiece to be adaptively welded using the point cloud-based adaptive laser composite welding method proposed in this invention.

[0018] Figure 2 is a scanning gantry structure diagram of the point cloud-based adaptive laser composite welding method proposed in this invention.

[0019] Figure 3 shows the point cloud model of the adaptive laser composite welding method based on point cloud proposed in this invention.

[0020] Figure 4 shows the weld point cloud of the adaptive laser hybrid welding method based on point cloud proposed in this invention.

[0021] Figure 5 shows the weld seam positioning sequence of the point cloud-based adaptive laser composite welding method proposed in this invention. Detailed Implementation

[0022] The invention will be further described in detail below with reference to the specific embodiments and accompanying drawings. Except for the contents specifically mentioned below, the processes, conditions, and experimental methods for implementing the invention are all common knowledge and general knowledge in the art, and the invention does not have any particular limitations.

[0023] Example 1

[0024] This embodiment proposes an adaptive laser composite welding mechanism. Figure 1 shows the workpiece to be adaptively welded, and Figure 2 is a structural diagram of the scanning gantry. In Figure 2, numbers 1 to 4 indicate the positions of the four lidars, number 5 is the scanning gantry (which can move using guide rails numbered 6 on both sides), number 7 is the welding torch, number 8 is the robotic arm, number 9 is the robot control cabinet, number 10 is the welding power source, and number 11 is the dust removal device. When the gantry begins to move, the four lidars scan the workpiece in real time.

[0025] Example 2

[0026] This embodiment proposes an adaptive laser hybrid welding method based on point cloud. The method first processes the point cloud data obtained by scanning and then reverse models to obtain a point cloud model, as shown in Figure 3.

[0027] Then, the weld seam segmentation algorithm in the adaptive algorithm is used to obtain the positions of all weld seams in the point cloud. The extracted weld seams are then decomposed twice on a macroscopic scale. After segmentation, different weld seams are extracted and labeled with their types, enabling the identification of straight weld seams, circular arc weld seams, cross weld seams, etc. The extracted weld seam point cloud is represented using different colors to observe the results of the adaptive weld seam extraction algorithm, as shown in Figure 4.

[0028] After all weld seams are extracted, they are sorted according to the welding sequence rules pre-entered by the welding process engineers, and all welding positions are generated. Once the weld seam positions are confirmed, the robot's reachability and interference are calculated based on the positional relationships between the weld seams, and the robot's welding transition points are planned. Finally, the complete welding position and transition point information are sent to the robot, which automatically passes through the transition points (to avoid collisions) to reach the welding position and completes the welding in conjunction with the self-developed welding process library.

[0029] Welding process library explanation: Process engineers pre-determine the optimal welding parameters for workpieces of various plate thicknesses and welding requirements through testing, and save these parameters to the welding process library. During subsequent welding, the vision equipment automatically identifies the plate thickness and matches the customer's welding requirements. The system automatically selects the corresponding welding parameters from the process library and performs the welding.

[0030] The positioning sequence is then sent to the robot, as shown in Figure 5.

[0031] The scope of protection of this invention is not limited to the above embodiments. Any variations and advantages that can be conceived by those skilled in the art without departing from the spirit and scope of the inventive concept are included in this invention and are protected by the appended claims.

Claims

1. An adaptive laser hybrid welding method based on point clouds, characterized in that, Includes the following steps: Step 1: Use several LiDAR scanners to scan the workpiece and acquire point cloud data; Step 2: Process the point cloud data acquired by scanning and reverse model the point cloud to obtain a point cloud model; Step 3: Using an adaptive weld extraction algorithm, extract the location and label the type of different welds; Step 4: Plan the positioning sequence automatically based on the coordinate values ​​of the weld and send the positioning sequence to the robot; Step 5: Using an adaptive positioning algorithm, the weld seam is precisely located with professional vision equipment, and the welding is completed in conjunction with an independent welding process library.

2. The welding method as described in claim 1, characterized in that, The method utilizes an adaptive laser hybrid welding mechanism; the scanning gantry mechanism includes: a lidar, a scanning gantry, guide rails, a welding torch, a robotic arm, a robot control cabinet, a welding power source, and a dust removal device; wherein... The scanning gantry can be moved using guide rails on both sides; When the scanning gantry begins to move, the lidar scans the workpiece in real time.

3. The welding method as described in claim 1, characterized in that, In step 2, the point cloud data is subjected to downsampling filtering, pass-through filtering, and point cloud registration operations to establish a point cloud model.

4. The welding method as described in claim 1, characterized in that, In step 3, the weld seam includes a straight weld seam, a circular arc weld seam, and a cross weld seam.

5. The welding method as described in claim 1, characterized in that, In step 3, the extracted weld point cloud is represented using different colors.

6. The welding method as described in claim 1, characterized in that, In step 4, after all welds have been extracted, all welds are sorted according to the welding sequence rules, and all welding positions are generated.

7. The welding method as described in claim 6, characterized in that, After the weld positions are confirmed, the robot's accessibility and interference are calculated based on the positional relationship between the welds, and the robot's welding transition points are planned; finally, the complete welding position and transition point information are sent to the robot.

8. The welding method as described in claim 1, characterized in that, In step 5, the robot automatically passes through the transition point to reach the welding position and completes the welding in conjunction with the welding process library.

9. The welding method as described in claim 8, characterized in that, The optimal welding parameters for workpieces with various plate thicknesses and welding requirements are determined in advance through testing and saved to the welding process library.

10. An adaptive laser composite welding mechanism, characterized in that, include: LiDAR, scanning gantry, guide rails, welding torch, robotic arm, robot control cabinet, welding power supply, and dust removal device; among which, The scanning gantry can be moved using guide rails on both sides; When the scanning gantry begins to move, the lidar scans the workpiece in real time.

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