Method for planning welding parameters of multiple layers of welding seams according to welding seam feature information

Abstract

The invention belongs to the technical field of fusion welding, and particularly relates to a method for planning multi-layer and multi-welding-seam welding parameters according to welding seam feature information, which comprises the following steps: 1) laser emitted by a 3D visual sensor is perpendicular to a welding seam and scans along the direction of the welding seam, and the 3D visual sensor scans welding seam data and transmits the data to a robot controller in real time; (2) the robot controller processes the weld joint data, the whole weld joint is subjected to multi-layer and multi-pass planning calculation, and the number of welding layers and the number of welding passes of each layer are planned; (3) the robot controller calculates the layer number and the channel number of a welding channel needing to be welded currently, a welding gun track coordinate set on a welding path and welding parameters corresponding to welding path points; and (4) the robot controller adjusts the welding track and the welding parameters in real time according to the step (1) to the step (3) in the current welding bead welding process, so that the purpose of multi-layer and multi-pass planning self-adaptive welding of the medium-thickness plate is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of fusion welding, and specifically relates to a method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information. Background Art

[0002] In the past decade or so, with the rapid development of the Chinese economy, the growth environment and family living conditions of young people newly entering the labor market have generally improved. Among the young people growing up in the Internet environment, few are willing to work in the medium and thick plate welding industry with high labor intensity, simple repetition, and dangerous and harsh working environments. There is even a situation where it is difficult to hire welders even with high salaries. Where is the way out for welding manufacturing? Only by adopting industrial robot welding technology, changing the manufacturing mode, can we solve the problem of welder shortage while improving welding quality and reducing costs.

[0003] Currently, there are mainly two methods for programming industrial robot multi-layer and multi-pass welding procedures: manual teaching and offline programming. For medium and thick plate structural parts, large welding grooves require multiple layers of welding for each weld, and multiple passes for each layer. If manual teaching is used, it will take a lot of time to manually calibrate all the weld bead trajectories during robot welding, which will undoubtedly greatly reduce the working efficiency of the robot. At the same time, the random errors generated by multiple teachings will also have an adverse impact on welding quality. The industrial robot offline programming technology can effectively reduce the workload of manual teaching. However, the pre-planned offline programming is an ideal working state. In fact, when welding the welds of the same type of workpiece, due to the need for repeated clamping of the workpiece, the actual position of the workpiece will deviate from the ideal position of the offline programming. When processing the same type of workpiece, the groove angle and assembly gap will also deviate. And in actual production, the same type of weld is often made with grooves of different angles to meet different production requirements. For the same type of weld with different groove angles, due to the change in the filling amount, a new offline program needs to be re-programmed, which is time-consuming and laborious. Summary of the Invention

[0004] The object of the present invention is to provide a method for planning welding parameters of multi-layer and multi-pass welds based on weld feature information. Based on the scanning results of a 3D vision sensor, the method for planning welding parameters of multi-layer and multi-pass welds. During the process of welding butt double-V and butt single-V fillet welds of medium-thick plates, the laser emitted by the 3D vision sensor is perpendicular to the weld and scans along the weld direction. The weld data scanned by the 3D vision sensor is transmitted to the robot in real time. After being processed by the robot, the entire weld is subjected to multi-layer and multi-pass planning calculations to plan the number of welding layers and the number of welding passes per layer, and calculate the layer number, pass number of the weld bead to be welded currently, the set of welding torch trajectory coordinates on the welding path, the welding parameters corresponding to the welding path points, etc. During the process of welding the current weld bead, the welding trajectory and welding parameters are adjusted in real time according to the above data to achieve the purpose of adaptive welding for multi-layer and multi-pass planning of medium-thick plates. Compared with the original method, the welding quality and welding efficiency are significantly improved.

[0005] The technical solution adopted by the present invention to achieve the above object is: a method for planning welding parameters of multi-layer and multi-pass welds based on weld feature information, including the following steps:

[0006] 1) The laser emitted by the 3D vision sensor is perpendicular to the weld and scans along the weld direction. The weld data scanned by the 3D vision sensor is transmitted to the robot controller in real time;

[0007] 2) The robot controller processes the weld data, and performs multi-layer and multi-pass planning calculations on the entire weld to plan the number of welding layers and the number of welding passes per layer;

[0008] 3) The robot controller calculates the layer number, pass number of the weld bead to be welded currently, the set of welding torch trajectory coordinates on the welding path, and the welding parameters corresponding to the welding path points;

[0009] 4) The robot controller adjusts the welding trajectory and welding parameters in real time according to steps 1) to 3) during the process of welding the current weld bead to achieve the purpose of adaptive welding for multi-layer and multi-pass planning of medium-thick plates.

[0010] The 3D vision sensor is fixed on the welding end of the robot and moves with the welding torch; the scanning direction of the 3D vision sensor is consistent with the welding direction.

[0011] The weld data includes: the top layer weld width, bottom layer weld width, and weld depth of the weld cross-section.

[0012] Step 2) includes the following steps:

[0013] 2-1) The robot controller calculates the weld cross-section as a trapezoid based on the top layer weld width, bottom layer weld width, and weld depth of the weld cross-section, sets the average width and filling height of the internal weld beads per layer to be the same, and obtains the planned number of layers according to the cross-section data at the maximum weld depth and the widest bottom layer weld width, that is:

[0014]

[0015] Among them, C is the planned number of layers, t d is the weld depth, and h0 is the set filling height;

[0016] 2-2) The robot controller calculates the bottom edge width of each layer according to the planned number of layers obtained in step 2-1), that is:

[0017]

[0018] Among them, W n is the bottom edge width of a certain layer, w h is the top seam width of the weld seam feedback by the sensor, w b is the bottom seam width of the weld seam feedback by the sensor, c h is the total height of the previously filled layers when welding to this layer, which is obtained by accumulating the filling height of each previous layer, t d is the weld depth;

[0019] 2-3) According to the bottom edge width of each layer, obtain the number of filling weld beads for each layer to ensure the number of filling weld beads for each layer; then obtain the number of filling weld beads for each layer, that is:

[0020]

[0021] Among them, W cn is the number of weld beads for the current layer, and w0 is the maximum set weld bead width.

[0022] The said step 3) includes the following steps:

[0023] 3-1) The planned number of layers and the number of weld beads per layer, through the trapezoidal model composed of the weld seam data scanned by the 3D vision sensor, obtain the width and height of a specific weld bead of a specific layer to be welded currently and the trapezoidal model of this weld bead;

[0024] 3-2) According to the trapezoidal area calculation method, obtain the filling cross-sectional area of the current weld bead;

[0025] 3-3) The robot controller collects the wire feeding speed of the welding machine and the wire diameter, and obtains the welding filling speed, the welding oscillation amplitude value and the actual welding frequency;

[0026] 3-4) According to the welding filling speed, the welding oscillation amplitude value and the actual welding frequency, obtain the welding parameters and welding trajectory coordinates of the corresponding weld bead and save them in a list.

[0027] The said step 3-3) is specifically:

[0028] a. The robot controller obtains the welding filling speed according to the wire feeding speed and the wire radius, that is:

[0029]

[0030] where V is the current welding speed, r is the wire radius, s p is the wire feeding speed, and s is the cross-sectional area of the current weld bead;

[0031] b. Obtain the welding oscillation amplitude value, that is:

[0032]

[0033] where A mpl is the actual welding oscillation amplitude, gap is the width of the current weld bead, and c0 is the set oscillation amplitude adjustment parameter;

[0034] c. Obtain the actual welding frequency, that is:

[0035]

[0036] where F req is the actual welding frequency, A mpl is the actual welding oscillation amplitude, F req0 is the set standard welding frequency, and A npl0 is the set standard welding oscillation amplitude.

[0037] In step 3-4), the obtaining of the welding parameters and welding trajectory coordinates of the corresponding weld bead is specifically as follows:

[0038] a) The welding parameters of the corresponding weld bead are calculated by the formula steps in item 6, and the welding trajectory coordinates are considered as the coordinates of the midpoint between the two endpoints of the bottom edge of the trapezoidal model of the corresponding weld bead;

[0039] where, the obtaining of the welding trajectory coordinates, that is:

[0040]

[0041]

[0042]

[0043] where Pos is the welding trajectory coordinates in the space rectangular coordinate system, p1 is the coordinate value of endpoint 1 of the bottom edge of the current weld bead trapezoidal model in the space rectangular coordinate system, and p2 is the coordinate value of endpoint 2 of the bottom edge of the current weld bead trapezoidal model in the space rectangular coordinate system;

[0044] b) Store the above welding parameters and welding trajectory coordinates in a data structure, and store them as a set of data units obtained by one scan calculation of the weld bead in the data list.

[0045] Step 4) is specifically as follows:

[0046] 4-1) During the swing welding process of the robot, by comparing the distance relationship between the current position of the robot and the welding trajectory position in the data list;

[0047] 4-2) Each time a comparison is made to find the nearest welding trajectory coordinate, and it is considered that the set of data units where this coordinate is located is the currently matched welding data;

[0048] 4-3) By changing the robot system variables, input the matched welding trajectory coordinates and welding parameters into the robot to change the robot's motion trajectory and welding parameters;

[0049] 4-4) During the welding process, repeatedly perform steps 1) to 3) to achieve the purpose of continuously adjusting the robot's welding trajectory and welding parameters, and realize adaptive welding.

[0050] The present invention has the following beneficial effects and advantages:

[0051] 1. The present invention uses a 3D vision sensor to cooperate with the robot for multi-layer and multi-pass planning adaptive welding. Compared with the previous method of manual multi-segment teaching, it improves the welding efficiency, welding quality, and welding quality stability.

[0052] 2. The present invention enables the industrial robot to have the ability to autonomously sense changes in welding conditions and make corresponding feedback, expands the adaptability of the welding robot to multi-layer and multi-pass welding, and improves the automation and intelligence level of medium and thick plate robot welding.

[0053] 3. The present invention reduces the labor intensity of operators and lowers the technical level requirements for operators. Brief Description of the Drawings

[0054] Figure 1 is the method flow block diagram of the present invention;

[0055] Figure 2 is the weld data flow diagram of the robot controller of the present invention;

[0056] Figure 3 is the welding parameter corresponding to the welding torch trajectory coordinate set and welding path point of the robot controller of the present invention;

[0057] Figure 4 is the control flow diagram for real-time adjustment of the welding trajectory and welding parameters of the present invention;

[0058] Figure 5 is the welding example diagram of this embodiment. Detailed Embodiment

[0059] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0060] As Figure 1 shown, it is a flowchart of the method of the present invention. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information of the present invention includes the following steps:

[0061] 1) The laser emitted by the 3D vision sensor is perpendicular to the weld and scans along the weld direction. The weld data scanned by the 3D vision sensor is transmitted to the robot controller in real time;

[0062] 2) The robot controller processes the weld data, performs multi-layer and multi-pass planning calculations on the entire weld, and plans the number of welding layers and the number of welding passes per layer;

[0063] 3) The robot controller calculates the weld bead layer number, bead number, set of torch trajectory coordinates on the welding path, and welding parameters corresponding to the welding path points that need to be welded currently;

[0064] 4) During the process of welding the current weld bead, the robot controller adjusts the welding trajectory and welding parameters in real time according to steps 1) to 3) in real time to achieve the purpose of adaptive welding for multi-layer and multi-pass planning of medium and thick plates.

[0065] The 3D vision sensor is fixed on the welding end of the robot and moves with the torch; the scanning direction of the 3D vision sensor is the same as the welding direction.

[0066] The weld data includes: top layer weld width, bottom layer weld width, and weld depth of the weld cross-section.

[0067] As Figure 2 shown, it is the flowchart of the weld data by the robot controller of the present invention;

[0068] Specifically applied to step 2), it includes the following steps:

[0069] 2-1) The robot controller calculates the weld cross-section as a trapezoid according to the top layer weld width, bottom layer weld width, and weld depth of the weld cross-section, sets the average width and filling height of the inner weld beads per layer to be the same, and obtains the planned number of layers according to the cross-section data at the maximum weld depth and the widest bottom layer weld width, that is:

[0070]

[0071] Among them, C is the planned number of layers, t d is the weld depth, and h0 is the set filling height;

[0072] 2-2) The robot controller calculates the bottom edge width of each layer according to the planned number of layers obtained in step 2-1), that is:

[0073]

[0074] Among them, W n is the bottom width of a certain layer, w h is the top seam width of the weld seam feedback by the sensor, w b is the bottom seam width of the weld seam feedback by the sensor, c h is the total height of the previously filled layers when welding to this layer, which is obtained by accumulating the filling height of each previous layer, t d is the weld depth;

[0075] 2-3) According to the bottom edge width of each layer, obtain the number of filling weld beads for each layer to ensure the number of filling weld beads for each layer; then obtain the number of filling weld beads for each layer, that is:

[0076]

[0077] Among them, W cn is the number of weld beads in the current layer, and w0 is the maximum set weld bead width.

[0078] As Figure 3 shown, it is the set of welding torch trajectory coordinates of the robot controller of the present invention and the welding parameters corresponding to the welding path points;

[0079] Specifically applied to step 3), it includes the following steps:

[0080] 3-1) Plan the number of layers and the number of weld beads per layer. Through the trapezoidal model composed of the weld seam data scanned by the 3D vision sensor, obtain the width and height of a specific weld bead of a specific layer to be welded currently and the trapezoidal model of this weld bead;

[0081] 3-2) According to the trapezoidal area calculation method, obtain the filling cross-sectional area of the current weld bead;

[0082] 3-3) The robot controller collects the wire feeding speed of the welding machine and the wire diameter, and obtains the welding filling speed, the welding oscillation amplitude value and the actual welding frequency;

[0083] 3-4) According to the welding filling speed, the welding oscillation amplitude value and the actual welding frequency, obtain the welding parameters and welding trajectory coordinates of the corresponding weld bead, and save them in a list.

[0084] The said step 3-3) is specifically:

[0085] a. The robot controller obtains the welding filling speed according to the wire feeding speed and the wire diameter, that is:

[0086]

[0087] Among them, V is the current welding speed, r is the wire diameter, s p is the wire feeding speed, and s is the cross-sectional area of the current weld bead;

[0088] b. Obtain the welding oscillation amplitude value, i.e.:

[0089]

[0090] where A mpl is the actual welding oscillation amplitude, gap is the current weld bead width, and c0 is the set oscillation amplitude adjustment parameter;

[0091] c. Obtain the actual welding frequency, i.e.:

[0092]

[0093] where F req is the actual welding frequency, A mpl is the actual welding oscillation amplitude, F req0 is the set standard welding frequency, and A mpl0 is the set standard welding oscillation amplitude.

[0094] In step 3-4), the obtaining of the welding parameters and welding trajectory coordinates of the corresponding weld bead is specifically as follows:

[0095] a) The welding parameters of the corresponding weld bead are calculated by the formula steps in item 6, and the welding trajectory coordinates are considered to be the coordinates of the midpoint between the two end points of the bottom edge of the trapezoidal model of the corresponding weld bead;

[0096] where, obtaining the welding trajectory coordinates, i.e.:

[0097]

[0098]

[0099]

[0100] where Pos is the welding trajectory coordinates in the space rectangular coordinate system, p1 is the coordinate value of the end point 1 of the bottom edge of the current weld bead trapezoidal model in the space rectangular coordinate system, and p2 is the coordinate value of the end point 2 of the bottom edge of the current weld bead trapezoidal model in the space rectangular coordinate system;

[0101] b) Store the above welding parameters and welding trajectory coordinates in a data structure, and store them as a set of data units obtained by one scan calculation of the weld bead in a data list.

[0102] As Figure 4 shown, it is the flowchart of the method for continuously adjusting the robot welding trajectory and welding parameters of the present invention, where step 4) is specifically:

[0103] 4-1) During the oscillating welding process of the robot, by comparing the distance relationship between the current position of the robot and the welding trajectory position in the data list;

[0104] 4-2) Each comparison finds the coordinates of the nearest welding trajectory, and it is considered that the set of data units where these coordinates are located is the currently matched welding data;

[0105] 4-3) By changing the robot system variables, the matched welding trajectory coordinates and welding parameters are input into the robot to change the robot's motion trajectory and welding parameters;

[0106] 4-4) Steps 1) to 3) are repeatedly performed during the welding process to achieve the purpose of continuously adjusting the robot's welding trajectory and welding parameters and realize adaptive welding.

[0107] Example 1:

[0108] Taking the process of welding butt double-V and single-V fillet welds of medium-thick plates as an example, in this example, the medium-thick plate is made of Q235 with a thickness of 20 - 30 mm. The groove angle of the two plates for the double-V type is 50 - 90 degrees, the groove angle of the single-V type plate is 40 - 45 degrees, the gap of the medium-thick plate is 0 - 10 mm, and the root face height is 0 - 2 mm.

[0109] The welding robot selected: The Fanuc robot model is M20ID-25, the firmware version is v9.1, and the system support packages are karel (R632), R7398 (Dyn Path Modifier); the welding current is 280 - 330 A, the welding voltage is 28 - 34 V, the welding speed is 4 - 13 mm / s, the oscillation frequency is 1 - 4 HZ, and the oscillation amplitude is 0.1 - 10 mm. During the welding process, the shielding gas is output coaxially from the wire electrode nozzle, and a mixed gas of 80% argon and 20% carbon dioxide is used, with a gas flow rate of 20 L / min.

[0110] Among them, the 3D vision sensor suitable for scanning the weld seam uses a full-view sensor, the model is FV-240-WD sensor, the double-V groove feature recognition uses the double-V mode, and the single-V groove feature recognition uses the mode of a circular tube tangent to a vertical plane. Specifically as Figure 5 shown;

[0111] The 3D vision sensor scans the weld seam data and transmits it to the robot in real time. After being processed by the robot, multi-layer and multi-pass planning calculations are carried out for the entire weld seam to plan the number of welding layers and the number of welding passes per layer, and calculate the weld bead layer number, bead number, the set of torch trajectory coordinates on the welding path, the welding parameters corresponding to the welding path points, etc. During the process of welding the current weld bead, the welding trajectory and welding parameters are adjusted in real time according to the above data to achieve the purpose of multi-layer and multi-pass planning adaptive welding of medium-thick plates.

[0112] The 3D vision sensor scans the weld seam and calculates parameters such as the top seam width, bottom seam width, and weld depth of the weld cross-section, and transmits the data to the robot. The robot calculates the weld cross-section as a trapezoid based on the above data, assuming that the average width and filling height of each inner pass are consistent. For all the scanning results of the weld seam, the cross-section data at the maximum weld depth and the widest bottom seam width are used as the planning basis. The planned number of layers is calculated by dividing the weld depth by the set filling height. Then, the bottom edge width of each layer is calculated based on the planned number of layers, and the number of filling passes for each layer is calculated by dividing the bottom edge width of each layer by the set maximum filling width value of the pass, ensuring that the calculated average pass width does not exceed the set maximum width value. In this way, the number of multi-layer and multi-pass of the current weld seam is planned. With the planned number of layers and the number of passes per layer, the width and height of a specific pass in a specific layer to be welded currently and the trapezoidal model of this pass can be calculated through the trapezoidal model formed by the weld seam data scanned by the 3D vision sensor. Then, according to the trapezoidal area calculation method, the filling cross-sectional area of this pass can be calculated. Combining the wire feeding speed and wire radius of the welding machine, the welding filling speed can be calculated. The current welding speed is calculated by dividing the welding filling speed by the cross-sectional area of the current pass. The bottom edge width of the trapezoidal data model is used as the pass width, and the pass width is divided by 2 to calculate the welding swing amplitude value. Then, the ratio of the difference between the welding swing amplitude value and the standard swing amplitude value is calculated and multiplied by the preset standard frequency value to obtain the actual welding frequency. The position coordinates of the midpoint of the bottom edge of the trapezoidal data model are used as the welding trajectory coordinates. The welding parameters and welding trajectory coordinates of each corresponding pass of the entire weld seam scan result are calculated and saved in a list.

[0113] The above description is only the implementation mode of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, expansions, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information, characterized in that It includes the following steps: 1) The laser emitted by the 3D vision sensor is perpendicular to the weld seam and scans along the weld seam direction. The weld seam data scanned by the 3D vision sensor is transmitted to the robot controller in real time; 2) The robot controller processes the weld seam data, conducts multi-layer and multi-pass planning calculations for the entire weld seam, and plans the number of welding layers and the number of welding passes per layer; 3) The robot controller calculates the layer number, pass number of the weld pass to be welded currently, the set of torch trajectory coordinates on the welding path, and the welding parameters corresponding to the welding path points; 4) During the process of welding the current weld pass, the robot controller adjusts the welding trajectory and welding parameters in real time according to steps 1) to 3) in real time to achieve the purpose of adaptive multi-layer and multi-pass welding of medium and thick plates.

2. The method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 1, wherein The 3D vision sensor is fixed on the welding end of the robot and moves with the torch; the scanning direction of the 3D vision sensor is the same as the welding direction.

3. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 1, wherein The weld seam data includes: the top seam width, bottom seam width, and weld depth of the weld seam cross-section.

4. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 1, wherein, Step 2) includes the following steps: 2-1) The robot controller calculates the weld seam cross-section as a trapezoid based on the top seam width, bottom seam width, and weld depth of the weld seam cross-section, sets the average width and filling height of the internal weld passes per layer to be consistent, and obtains the planned number of layers according to the cross-section data at the maximum weld depth and the widest bottom seam width, that is: Among them, C is the planned number of layers, and t d is the weld depth, and h0 is the set filling height; 2-2) The robot controller calculates the bottom edge width of each layer according to the planned number of layers obtained in step 2-1), that is: Among them, W n is the bottom width of a certain layer, w h is the top seam width of the weld seam feedback by the sensor, w b is the bottom seam width of the weld seam feedback by the sensor, c h is the total height of the previously filled layers when welding to this layer, obtained by accumulating the filling height of each previous layer, t d is the weld depth; 2-3) According to the bottom edge width of each layer, obtain the number of filling weld passes for each layer to ensure the number of filling weld passes for each layer; then obtain the number of filling weld passes for each layer, that is: Among them, W cn is the number of weld beads in the current layer, and w0 is the maximum set weld bead width.

5. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 1, characterized in that, Step 3) includes the following steps: 3-1) Based on the planned number of layers and the number of weld passes per layer, through the trapezoidal model formed by the weld seam data scanned by the 3D vision sensor, obtain the width and height of a specific weld pass in a specific layer to be welded currently and the trapezoidal model of this weld pass; 3-2) According to the trapezoidal area calculation method, obtain the filling cross-sectional area of the current weld pass; 3-3) The robot controller collects the wire feeding speed of the welding machine and the wire radius, and obtains the welding filling speed, welding oscillation amplitude value, and actual welding frequency; 3-4) According to the welding filling speed, welding oscillation amplitude value, and actual welding frequency, obtain the welding parameters and welding trajectory coordinates of the corresponding weld pass and save them in a list.

6. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 5, characterized in that, Step 3-3) is specifically: a. The robot controller obtains the welding filling speed according to the wire feeding speed and the wire radius, that is: Wherein, V is the current welding speed, r is the wire radius, s p is the wire feeding speed, and s is the cross-sectional area of the current weld bead; b. Obtain the welding oscillation amplitude value, that is: Among them, A mpl is the actual welding oscillation amplitude, gap is the current bead width, and c0 is the set oscillation amplitude adjustment parameter; c. Obtain the actual welding frequency, that is: Among them, F req is the actual welding frequency, A mpl is the actual welding swing amplitude, F req0 is the set standard welding frequency, A mpl0 is the set standard welding swing amplitude.

7. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 5, characterized in that, In step 3-4), the obtaining of the welding parameters and welding trajectory coordinates of the corresponding weld pass is specifically: a) The welding parameters of the corresponding weld pass are calculated by the formula steps in item 6, and the welding trajectory coordinates are considered to be the midpoint coordinates between the two endpoints of the bottom edge of the trapezoidal model of the corresponding weld pass; Among them, obtaining the welding trajectory coordinates, that is: Among them, Pos is the welding trajectory coordinate in the space rectangular coordinate system, p1 is the coordinate value of endpoint 1 of the bottom edge of the current weld pass trapezoidal model in the space rectangular coordinate system, and p2 is the coordinate value of endpoint 2 of the bottom edge of the current weld pass trapezoidal model in the space rectangular coordinate system; b) Store the above welding parameters and welding trajectory coordinates in a data structure, and store a set of data units obtained from one scan calculation of this weld bead in a data list.

8. A method for planning welding parameters of multi-layer and multi-pass welds according to weld feature information as claimed in claim 1, wherein The specific steps of step 4) are as follows: 4-1) During the oscillating welding process of the robot, compare the distance relationship between the current position of the robot and the welding trajectory position in the data list; 4-2) Each time a comparison is made, find the nearest welding trajectory coordinate, and consider that the set of data units where this coordinate is located is the currently matched welding data; 4-3) By changing the robot system variables, input the matched welding trajectory coordinates and welding parameters into the robot to change the robot motion trajectory and welding parameters; 4-4) Repeatedly perform steps 1) to 3) during the welding process to achieve the purpose of continuously adjusting the robot welding trajectory and welding parameters, and realize adaptive welding.

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