CN111230869A - Complex space curve weld joint movement track and welding process collaborative planning method - Google Patents
Complex space curve weld joint movement track and welding process collaborative planning method Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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Abstract
The invention relates to a complex space curve weld joint movement track and welding process collaborative planning method which comprises the following steps of (1) extracting a complex product weld joint outline; (2) planning the three-dimensional contour of the welding seam in a partition manner; (3) planning the welding sequence of the welding seams in each area; (4) performing motion simulation and multi-robot cooperative motion simulation on the formed robot motion track; (5) planning welding process parameters; (6) and (4) data distribution and execution. The robot motion path and process collaborative planning method is innovatively used in the field of automatic welding of complex space curve welding seams, achieves intelligent partition of the space curve welding seams and sequential planning of the welding seams, achieves adaptive collaborative planning of the motion path and process of the robot with the variable attitude and curvature welding seams, and achieves intelligent collaborative planning of the motion path and process of the robot with the space curve welding seams.
Description
Technical Field
The invention belongs to the technical field of mechanical engineering, and relates to a method for cooperatively planning a motion trail of a weld joint and a welding process by using a complex space curve.
Background
In the structure of a large-scale spacecraft, a large number of welding seams in complex forms exist, such as intersecting lines formed by a spherical shell, a cylindrical shell, a circular truncated cone shell and a circular flange and a special-shaped flange. Cabin structures such as manned spacecrafts, celestial palace series and lunar exploration aircrafts involve the welding problem of welding seams in the forms to different degrees. The spatial pose of a welding seam to be welded in a large complex thin-wall welding structure in aerospace is variable, so that welding process parameters also need to be changed in real time to adapt to different poses.
At present, the motion trajectory planning of a welding robot has been developed from a traditional teaching mode to an offline programming technology, the offline programming technology is a screen teaching method based on three-dimensional graphics, but offline programming software owned by a robot company, such as the offline programming software of the welding robot of ABB and KUKA company, can only plan and simulate the motion of the robot path, lacks the function of system planning, and welding process parameters still need to be set from a welding machine, and when a welding task is performed on a complex space curve welding seam, the welding process parameters need to be frequently modified, which becomes a bottleneck of automatic welding application.
Disclosure of Invention
The technical problem solved by the invention is as follows: the method overcomes the defects of the prior art, provides a method for cooperatively planning the motion trail of the complex space curve welding seam and the welding process, realizes the robot automatic welding of the deflection posture and curvature changing space curve welding seam, and has unique advantages particularly for the welding and manufacturing of various small-batch complex products of spacecrafts.
The technical scheme of the invention is as follows:
a complex space curve weld joint movement track and welding process collaborative planning method comprises the following steps:
(1) extracting the weld seam outline of the complex product: when the deviation of the weld seam outline of the actual complex product and the theoretical model is not more than 2mm, selecting a three-dimensional outline curve of the weld seam to be welded from the theoretical model; when the deviation of the actual complex product from the weld contour of the theoretical model is larger than 2mm, optimizing measurement parameters by using a global vision measurement method to obtain three-dimensional space point cloud data, and obtaining a three-dimensional contour curve of the actual weld after data processing;
(2) and (3) planning the three-dimensional profile of the welding seam in a partition manner: dividing the three-dimensional contour curve into at least 2 areas according to the applicability of the machining process, wherein the welding robot in each area is not interfered with a product and a tool, namely, a welding gun at the tail end of the robot can reach each welding line and cannot collide with the product and the tool in the moving process; the curve of the new welding line formed by cutting is continuous and smooth, so that the stability of the motion process of the robot is ensured; planning the welding sequence of the divided areas, and adopting the sequence of the positions of the symmetrical areas or the sequence of simultaneous welding to avoid the welding quality influenced by large welding seam clearance caused by the position deviation of the skin caused by the accumulation of welding deformation caused by the continuous welding of two adjacent areas;
(3) planning the welding sequence of the welding seams in each area: performing tack welding on the welding seams in each area in advance, sequentially or simultaneously welding the welding seams at the symmetrical positions, performing sectional backing welding on the long welding seams, and forming a robot motion track;
(4) performing motion simulation and multi-robot cooperative motion simulation on the formed robot motion track, wherein the motion simulation and the multi-robot cooperative motion simulation comprise virtual operation of a single robot, multi-robot cooperative virtual operation is required to be performed when a plurality of robots operate simultaneously, the robot motion track is corrected, and motion interference and singular points, interference points and unreachable points in the operation process are avoided;
(5) planning welding process parameters: welding process parameters of different materials and positions are obtained according to a process test, welding process signals including electric signals, temperature field signals and image signals are collected through a sensor and fused based on multi-source information, after data processing such as denoising and cleaning is carried out on the multi-channel information, data mining and modeling are carried out on data related to the welding process, including process parameters, geometric characteristics of a molten pool, geometric dimensions of an assembly gap, geometric dimensions of a welding seam and types of welding defects, the implicit relation between each parameter and the quality of the welding seam is revealed by an artificial intelligence means to form a welding process knowledge base, taking the space pose information of the welding line profile of the complex product as an input condition, and outputting welding process parameters including welding current, welding voltage, welding speed, welding gun inclination angle and shielding gas flow under the space poses of different welding line profiles according to a welding process knowledge base;
(6) data distribution and execution: and (4) transmitting the robot motion trail corrected in the step (4) and the welding process parameter information acquired in the step (5) to the robot, executing the robot motion trail by the robot body, transmitting the welding process parameters to the welding machine by the robot controller, executing the welding process parameters by the welding machine, and realizing the automatic welding of the complex welding seam.
Further, the global vision measuring method in the step (1) comprises the following steps: according to a thin-wall structure model with a space curve welding line, working out a running track of an industrial robot, manufacturing a profiling welding tool, and assembling a thin-wall structure on the surface of the tool; the method comprises the following steps that 1 surface structure light measuring head and 2 industrial cameras are packaged to form a visual measuring head which is installed at the tail end of an industrial robot, the optical coordinate of the visual measuring head and the tool coordinate of the industrial robot are calibrated, the measuring angle, the light field intensity and the running track and speed of the visual measuring head are optimized, the measuring head is carried by the industrial robot, the measurement of a space curve welding seam of a thin-wall structure is carried out, three-dimensional space point cloud data of the thin-wall structure are obtained, the three-dimensional space point cloud data are sent to an external data processing module, the data processing module firstly carries out cleaning, denoising and splicing on the data, and complete three-dimensional space point cloud; and then, the data processing module carries out welding seam position identification and characteristic extraction, and finally obtains the three-dimensional profile curve of the actual welding seam.
Furthermore, the three-dimensional contour of the partition planning welding line in the step (2) is a space three-dimensional contour or a two-dimensional plane contour, newly added welding lines need to be reduced as much as possible during partition planning, the size of the area needs to meet the breadth size of skin raw materials and the motion range factor of the robot at the same time, and the size of the divided areas is guaranteed to be consistent.
Further, in the motion track of the robot in the step (3), the welding gun is perpendicular to the plane of the welding line and forms an included angle of 105-110 degrees with the welding walking direction.
Furthermore, in the step (5), an analog quantity is defined in the motion track of the robot, including variable information of each welding parameter, and a welding arc starting instruction, a welding arc receiving instruction and a welding parameter instruction are inserted into the motion track coordinates of the robot to form collaborative planning instruction information of the motion track of the welding seam and the welding process.
And further, correcting the welding gun inclination angle of the robot running track according to the welding gun inclination angle information output in the step (5) to form a collaborative plan of the welding seam motion track and the welding process. .
Further, in the step (5), the welding process knowledge base is perfected by mining and optimizing welding process data and continuously updating and iterating in actual production.
Further, in the step (5), the welding process knowledge base carries out reasoning and prejudging on the welding seam result according to the model of the process knowledge base through welding state information obtained by on-line sensing in the welding process, so that quality problems such as instability of a molten pool and the like are found in advance and timely adjusted.
Furthermore, the welding method applicable to the method comprises GTAW, GMAW, solid phase welding, resistance spot welding and high-energy beam welding, and different tracks and process parameter control are carried out according to the characteristics of the welding method when the specific method is applied.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention is innovatively used in the field of automatic welding of complex space curve welding seams, realizes intelligent partition of the space curve welding seams and sequential planning of the welding seams, and realizes self-adaptive collaborative planning of the movement track and the process of a robot for the welding seams with variable postures and variable curvatures so as to realize collaborative intelligent planning of the movement track and the process of the robot for the welding seams with the space curves;
(2) aiming at the complex space curve welding seam, partitioning the welding seam by combining a deformation prediction analysis means and a deformation inhibition and elimination means, developing an autonomous collision avoidance planning algorithm by adopting a virtual environment cooperatively controlled by multiple robots, automatically identifying the welding seam, and performing path discrete interpolation based on a spline interpolation algorithm, so that the smooth motion track is realized, and the thin-wall complex curve welding seam is better adapted;
(3) and (3) collaborative planning innovation: firstly, establishing a welding specification intelligent prediction model adapting to the change of the position and the assembly precision of a welding seam, dynamically and discretely planning the arc welding process specification according to the change of the position and the assembly precision of the welding seam space while finishing the planning of the welding seam track, and taking the position and the position of the welding seam space as input parameters and the welding process parameters as an output path-process collaborative planning model based on a welding process knowledge base.
Drawings
FIG. 1 is a diagram of the movement locus and the process cooperation planning of a robot with a complex structure;
FIG. 2 is a sectional layout of a weld of a complex structure.
Detailed Description
The invention is further illustrated by the following examples.
The invention comprises two main modules of robot welding motion track planning and process specification planning, and has the functions of complex welding seam extraction, partition planning, welding sequence planning, welding process planning and the like, as shown in figures 1 and 2, the specific technical scheme is as follows:
a complex space curve weld joint movement track and welding process collaborative planning method comprises the following steps:
(1) extracting the weld seam outline of the complex product: when the deviation of the weld seam outline of the actual complex product and the theoretical model is not more than 2mm, selecting a three-dimensional outline curve of the weld seam to be welded from the theoretical model; when the deviation of the actual complex product from the weld contour of the theoretical model is larger than 2mm, optimizing measurement parameters by using a global vision measurement method to obtain three-dimensional space point cloud data, and obtaining a three-dimensional contour curve of the actual weld after data processing;
(2) and (3) planning the three-dimensional profile of the welding seam in a partition manner: dividing the three-dimensional contour curve into at least 2 areas according to the applicability of the machining process, wherein the welding robot in each area is not interfered with a product and a tool, namely, a welding gun at the tail end of the robot can reach each welding line and cannot collide with the product and the tool in the moving process; the curve of the new welding line formed by cutting is continuous and smooth, so that the stability of the motion process of the robot is ensured; planning the welding sequence of the divided areas, and adopting the sequence of the positions of the symmetrical areas or the sequence of simultaneous welding to avoid the welding quality influenced by large welding seam clearance caused by the position deviation of the skin caused by the accumulation of welding deformation caused by the continuous welding of two adjacent areas;
(3) planning the welding sequence of the welding seams in each area: performing tack welding on the welding seams in each area in advance, sequentially or simultaneously welding the welding seams at the symmetrical positions, performing sectional backing welding on the long welding seams, and forming a robot motion track;
(4) performing motion simulation and multi-robot cooperative motion simulation on the formed robot motion track, wherein the motion simulation and the multi-robot cooperative motion simulation comprise virtual operation of a single robot, multi-robot cooperative virtual operation is required to be performed when a plurality of robots operate simultaneously, the robot motion track is corrected, and motion interference and singular points, interference points and unreachable points in the operation process are avoided;
(5) planning welding process parameters: welding process parameters of different materials and positions are obtained according to a process test, welding process signals including electric signals, temperature field signals and image signals are collected through a sensor and fused based on multi-source information, after data processing such as denoising and cleaning is carried out on the multi-channel information, data mining and modeling are carried out on data related to the welding process, including process parameters, geometric characteristics of a molten pool, geometric dimensions of an assembly gap, geometric dimensions of a welding seam and types of welding defects, the implicit relation between each parameter and the quality of the welding seam is revealed by an artificial intelligence means to form a welding process knowledge base, taking the space pose information of the welding line profile of the complex product as an input condition, and outputting welding process parameters including welding current, welding voltage, welding speed, welding gun inclination angle and shielding gas flow under the space poses of different welding line profiles according to a welding process knowledge base;
(6) data distribution and execution: and (4) transmitting the robot motion trail corrected in the step (4) and the welding process parameter information acquired in the step (5) to the robot, executing the robot motion trail by the robot body, transmitting the welding process parameters to the welding machine by the robot controller, executing the welding process parameters by the welding machine, and realizing the automatic welding of the complex welding seam.
The global vision measuring method in the step (1) comprises the following steps: according to a thin-wall structure model with a space curve welding line, working out a running track of an industrial robot, manufacturing a profiling welding tool, and assembling a thin-wall structure on the surface of the tool; the method comprises the following steps that 1 surface structure light measuring head and 2 industrial cameras are packaged to form a visual measuring head which is installed at the tail end of an industrial robot, the optical coordinate of the visual measuring head and the tool coordinate of the industrial robot are calibrated, the measuring angle, the light field intensity and the running track and speed of the visual measuring head are optimized, the measuring head is carried by the industrial robot, the measurement of a space curve welding seam of a thin-wall structure is carried out, three-dimensional space point cloud data of the thin-wall structure are obtained, the three-dimensional space point cloud data are sent to an external data processing module, the data processing module firstly carries out cleaning, denoising and splicing on the data, and complete three-dimensional space point cloud; and then, the data processing module carries out welding seam position identification and characteristic extraction, and finally obtains the three-dimensional profile curve of the actual welding seam.
And (3) the three-dimensional contour of the partition planning welding line in the step (2) is a space three-dimensional contour or a two-dimensional plane contour, newly added welding lines need to be reduced as much as possible during partition planning, the size of the area needs to meet the factors of the breadth size of the skin raw material and the motion range of the robot at the same time, and the size of the divided areas is guaranteed to be consistent.
In the motion track of the robot in the step (3), the welding gun is vertical to the plane of the welding line and forms an included angle of 105-110 degrees with the welding walking direction.
In the step (5), analog quantity is defined in the motion track of the robot, including variable information of each welding parameter, and the joint motion track and the collaborative planning instruction information of the welding process are formed by inserting a welding arc starting instruction, a welding arc receiving instruction and a welding parameter instruction into the motion track coordinates of the robot.
And (5) correcting the welding gun inclination angle of the robot running track according to the welding gun inclination angle information output in the step (5), and forming a collaborative plan of the welding seam motion track and the welding process.
And (5) in the actual production, the welding process knowledge base is perfected by mining and optimizing welding process data and continuously updating and iterating.
And (5) in the welding process, the welding process knowledge base carries out reasoning and prejudgment on the welding seam result according to the model of the process knowledge base through welding state information obtained by on-line sensing, finds out quality problems such as instability of a molten pool in advance and carries out timely adjustment.
The welding method applicable to the method comprises GTAW, GMAW, solid phase welding, resistance spot welding and high-energy beam welding, and different tracks and process parameter control are carried out according to the characteristics of the welding method when the specific method is applied.
The method is innovative: the traditional off-line programming aims at simple and single welding seams, the same welding process parameters and welding gun postures are only simply set in the full track, the method is innovatively used in the field of automatic welding of the welding seams with complex space curves, intelligent partition of the welding seams with the space curves and sequential planning of the welding seams are realized, self-adaptive collaborative planning of the movement track and the process of the robot with the variable postures and the variable curvature welding seams is realized, and the intelligent planning of the movement track and the process of the robot with the space curves and the welding seams is realized.
Partitioning and path innovation: aiming at the complex space curve welding seam, the partitioning of the welding seam is completed by combining a deformation prediction analysis means and a deformation inhibition and elimination means, an autonomous collision avoidance planning algorithm is developed by adopting a virtual environment cooperatively controlled by multiple robots, the welding seam is automatically identified, the discrete interpolation of the path is carried out based on a spline interpolation algorithm, the smoothness of the motion track is realized, and the thin-wall complex curve welding seam is better adapted.
And (3) collaborative planning innovation: firstly, establishing a welding specification intelligent prediction model adapting to the change of the position and the assembly precision of a welding seam, dynamically and discretely planning the arc welding process specification according to the change of the position and the assembly precision of the welding seam space while finishing the planning of the welding seam track, and taking the position and the position of the welding seam space as input parameters and the welding process parameters as an output path-process collaborative planning model based on a welding process knowledge base.
The method mainly aims at the requirement that different welding process parameters are required to be adopted according to different motion tracks of the three-dimensional thin-wall aluminum alloy structural welding seam, and provides a method for collaborative planning of the motion tracks and the welding process of the space curve welding seam. The main process comprises intelligent partitioning of a large-area complex welding seam, global path planning of the welding seam, and a robot path and welding specification parameter collaborative planning data interaction model.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Claims (9)
1. A complex space curve weld joint movement track and welding process collaborative planning method is characterized by comprising the following steps:
(1) extracting the weld seam outline of the complex product: when the deviation of the weld seam outline of the actual complex product and the theoretical model is not more than 2mm, selecting a three-dimensional outline curve of the weld seam to be welded from the theoretical model; when the deviation of the actual complex product from the weld contour of the theoretical model is larger than 2mm, optimizing measurement parameters by using a global vision measurement method to obtain three-dimensional space point cloud data, and obtaining a three-dimensional contour curve of the actual weld after data processing;
(2) and (3) planning the three-dimensional profile of the welding seam in a partition manner: dividing the three-dimensional contour curve into at least 2 areas according to the applicability of the machining process, wherein the welding robot in each area is not interfered with a product and a tool, namely, a welding gun at the tail end of the robot can reach each welding line and cannot collide with the product and the tool in the moving process; the curve of the new welding line formed by cutting is continuous and smooth, so that the stability of the motion process of the robot is ensured; planning the welding sequence of the divided areas, and adopting the sequence of the positions of the symmetrical areas or the sequence of simultaneous welding to avoid the welding quality influenced by large welding seam clearance caused by the position deviation of the skin caused by the accumulation of welding deformation caused by the continuous welding of two adjacent areas;
(3) planning the welding sequence of the welding seams in each area: performing tack welding on the welding seams in each area in advance, sequentially or simultaneously welding the welding seams at the symmetrical positions, performing sectional backing welding on the long welding seams, and forming a robot motion track;
(4) performing motion simulation and multi-robot cooperative motion simulation on the formed robot motion track, wherein the motion simulation and the multi-robot cooperative motion simulation comprise virtual operation of a single robot, multi-robot cooperative virtual operation is required to be performed when a plurality of robots operate simultaneously, the robot motion track is corrected, and motion interference and singular points, interference points and unreachable points in the operation process are avoided;
(5) planning welding process parameters: welding process parameters of different materials and positions are obtained according to a process test, welding process signals including electric signals, temperature field signals and image signals are collected through a sensor and fused based on multi-source information, after data processing such as denoising and cleaning is carried out on the multi-channel information, data mining and modeling are carried out on data related to the welding process, including process parameters, geometric characteristics of a molten pool, geometric dimensions of an assembly gap, geometric dimensions of a welding seam and types of welding defects, the implicit relation between each parameter and the quality of the welding seam is revealed by an artificial intelligence means to form a welding process knowledge base, taking the space pose information of the welding line profile of the complex product as an input condition, and outputting welding process parameters including welding current, welding voltage, welding speed, welding gun inclination angle and shielding gas flow under the space poses of different welding line profiles according to a welding process knowledge base;
(6) data distribution and execution: and (4) transmitting the robot motion trail corrected in the step (4) and the welding process parameter information acquired in the step (5) to the robot, executing the robot motion trail by the robot body, transmitting the welding process parameters to the welding machine by the robot controller, executing the welding process parameters by the welding machine, and realizing the automatic welding of the complex welding seam.
2. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: the global vision measuring method in the step (1) comprises the following steps: according to a thin-wall structure model with a space curve welding line, working out a running track of an industrial robot, manufacturing a profiling welding tool, and assembling a thin-wall structure on the surface of the tool; the method comprises the following steps that 1 surface structure light measuring head and 2 industrial cameras are packaged to form a visual measuring head which is installed at the tail end of an industrial robot, the optical coordinate of the visual measuring head and the tool coordinate of the industrial robot are calibrated, the measuring angle, the light field intensity and the running track and speed of the visual measuring head are optimized, the measuring head is carried by the industrial robot, the measurement of a space curve welding seam of a thin-wall structure is carried out, three-dimensional space point cloud data of the thin-wall structure are obtained, the three-dimensional space point cloud data are sent to an external data processing module, the data processing module firstly carries out cleaning, denoising and splicing on the data, and complete three-dimensional space point cloud; and then, the data processing module carries out welding seam position identification and characteristic extraction, and finally obtains the three-dimensional profile curve of the actual welding seam.
3. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: and (3) the three-dimensional contour of the partition planning welding line in the step (2) is a space three-dimensional contour or a two-dimensional plane contour, newly added welding lines need to be reduced as much as possible during partition planning, the size of the area needs to meet the factors of the breadth size of the skin raw material and the motion range of the robot at the same time, and the size of the divided areas is guaranteed to be consistent.
4. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: in the motion track of the robot in the step (3), the welding gun is vertical to the plane of the welding line and forms an included angle of 105-110 degrees with the welding walking direction.
5. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: in the step (5), analog quantity is defined in the motion track of the robot, including variable information of each welding parameter, and the joint motion track and the collaborative planning instruction information of the welding process are formed by inserting a welding arc starting instruction, a welding arc receiving instruction and a welding parameter instruction into the motion track coordinates of the robot.
6. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: and (5) correcting the welding gun inclination angle of the robot running track according to the welding gun inclination angle information output in the step (5), and forming a collaborative plan of the welding seam motion track and the welding process. .
7. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: and (5) in the actual production, the welding process knowledge base is perfected by mining and optimizing welding process data and continuously updating and iterating.
8. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: and (5) in the welding process, the welding process knowledge base carries out reasoning and prejudgment on the welding seam result according to the model of the process knowledge base through welding state information obtained by on-line sensing, finds out quality problems such as instability of a molten pool in advance and carries out timely adjustment.
9. The method for the collaborative planning of the weld movement locus and the welding process of the complex space curve according to claim 1, is characterized in that: the welding method applicable to the method comprises GTAW, GMAW, solid phase welding, resistance spot welding and high-energy beam welding, and different tracks and process parameter control are carried out according to the characteristics of the welding method when the specific method is applied.
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