CN114309886A - Curved surface welding equipment control method and system based on cooperative self-adaptation - Google Patents
Curved surface welding equipment control method and system based on cooperative self-adaptation Download PDFInfo
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- 238000003466 welding Methods 0.000 title claims abstract description 274
- 238000000034 method Methods 0.000 title claims abstract description 102
- 230000036544 posture Effects 0.000 claims abstract description 23
- 238000004891 communication Methods 0.000 claims abstract description 19
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 230000001360 synchronised effect Effects 0.000 claims abstract description 7
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 210000004258 portal system Anatomy 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 3
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Abstract
A curved surface welding equipment control method and system based on cooperative self-adaptation comprises the steps of S1) constructing a welding process database based on a robot, wherein the data of the database comprises welding process parameters and robot position and posture parameters; step S2) importing a curved surface welding task; step S3), constructing an external shaft, a welding gun and an electric control system of the robot, acquiring equipment data in real time in an industrial bus mode, and deploying a robot motion control communication link; step S4), real-time self-adaptive adjustment of process parameters, welding postures and operation sequences is carried out according to the welding operation task, the welding seam type characteristics and the welding process method; step S5), cooperatively controlling the two robot mechanisms to carry out welding operation tasks according to the matched parameters; the invention realizes the control of the curved surface welding equipment by constructing the cooperative adaptive control unit and aiming at the processing links of the curved surface welding equipment, such as process parameter matching, operation task optimization, equipment synchronous control and the like.
Description
Technical Field
The invention relates to the technical field of curved surface welding, in particular to a method and a system for controlling curved surface welding equipment based on cooperative self-adaptation.
Background
The welding is divided into plane welding and curved surface welding, the plane welding technology is mature and applied in various shipyards at present, the curved surface welding is not well applied, the welding object of the curved surface welding is a curved welding line, the used welding process and welding method are different from a straight welding line, and the welding process has higher requirements.
The robot welding is the main technical direction of the current equipment manufacturing, and the motion control and welding process control methods of all actuating mechanisms of the curved surface welding equipment have great influence on the whole welding effect; at present, the curved surface welding process is judged mainly by subjective experience of an operator, the evaluation means is original and inaccurate, the welding process of each welding line is not accurately matched by comprehensively considering manufacturing factors such as an assembly object, assembly equipment, an assembly process method and the like, and certain tested welding process parameters are often ineffective for welding similar workpieces or the welding effect is poor, so that the test process of the curved surface welding process is long in time and does not have standard and uniform process data.
In addition, the control method of the curved surface welding equipment to the robots is mainly separated control, each robot has no data association and no motion coordination, and equipment robot control and posture adjustment cannot be automatically carried out.
Disclosure of Invention
The invention aims to provide a method and a system for controlling curved surface welding equipment based on cooperative self-adaptation.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a curved surface welding equipment control method based on cooperative self-adaptation is characterized by comprising the following steps,
step S1), a welding process database based on the robot is constructed, and the data of the welding process database comprises welding process parameters and robot position and posture parameters;
step S2), importing a curved surface welding task, wherein the task comprises the length of a welding seam, the direction of the welding seam, the type of the welding seam and the name of the operation;
step S3), constructing an external shaft, a welding gun and an electric control system of the robot, acquiring equipment data in real time in an industrial bus mode, and deploying a robot motion control communication link;
step S4), real-time self-adaptive adjustment of process parameters, welding postures and operation sequences is carried out according to the welding operation task, the welding seam type characteristics and the welding process method;
step S5), according to the matched parameters, the two robot mechanisms are cooperatively controlled to carry out welding operation tasks.
Further, in step S3, deploying the robot motion control communication link specifically includes:
step S31), constructing a communication link controlled by a portal shaft;
step S32) constructing a communication link for controlling the transverse axis;
step S33), constructing a communication link controlled by the longitudinal axis;
step S34) constructs a communication link for the torch control.
Further, in the step S1, the welding process library information includes a weld position, a fillet height, a welding current, a welding voltage, a welding speed, a welding angle, a welding wire material, a welding wire specification, a shielding gas type, a lap joint form, a multi-layer multi-pass number, a weld gap, a welding material model, a welding material specification, and a welding method.
Further, the step S4 includes constructing a welding material regurator, and performing rule judgment on a welding seam starting point, a welding seam end point, a welding seam type, a joint form, a welding seam gap, a welding material model and a welding material specification, wherein the welding material regurator performs one-to-one, one-to-many and many-to-many rule matching according to the welding material type, the welding seam form and welding seam data parameters to obtain relevant welding material process parameters and inputs the relevant welding material process parameters into the operation regurator.
Further, the step S4 includes, by constructing a welding wire regurator, performing regular judgment on a welding wire starting point, a welding wire end point, a welding wire type, a joint form, a welding wire gap, a welding wire specification, and a welding wire material, wherein the welding wire regurator performs digital quantization on a welding wire model, a welding wire use direction, and a welding quality index, quantizes a welding process index, obtains a relevant process parameter of the welding wire, and inputs the relevant process parameter into the operation regurator.
Further, the step S4 includes constructing a welding rule device, and performing rule judgment on a welding seam starting point, a welding seam end point, a welding seam type, a joint form, a welding seam gap and joint form, a shielding gas type, and a welding height, where the welding rule device obtains relevant welding process parameters by matching welding postures and welding angle parameters with a welding task as a target and inputs the relevant welding process parameters into the operation rule device.
Further, the step S4 includes performing rule judgment on the operation task, the welding material process parameter, the welding wire process parameter, and the welding process parameter by constructing an operation rule device, where the operation rule device calculates time consumption of a welding line operation path with respect to an operation process, and obtains a welding current, a welding voltage, a welding speed, a welding torch attitude, and a welding sequence required by the curved surface welding device by using a minimum optimization method.
Further, the step S4 includes inputting the output of the task rule device as a task feedback, and synchronously adjusting the task and the weld information to be processed into the task rule device.
Further, the step S5 includes,
and optimizing the path of the portal frame shaft, the transverse shaft, the longitudinal shaft, the robot posture and the welding gun, and performing normalization calculation on the portal frame shaft, the transverse shaft, the longitudinal shaft, the robot posture and the welding gun operation time.
Further, the step S5 includes,
synchronously controlling the portal frame shafts, the transverse shafts, the longitudinal shafts, the robot postures and the welding guns to realize real-time synchronization of the operation of the two portal frame systems; and carrying out decision judgment on the portal shaft, the transverse shaft, the longitudinal shaft, the robot posture, the operation sequence and the priority of the welding gun, and realizing the optimal operation cooperation of the two portal systems.
The control system is used for controlling two groups of gantry systems, each gantry system comprises a gantry shaft, a transverse shaft, a longitudinal shaft, a robot and a welding gun, and the control system comprises a path optimization unit, a time optimization unit, a task optimization unit, a synchronous control unit and an optimal decision unit.
According to the invention, by constructing the cooperative adaptive control unit, the control of the curved surface welding equipment is realized aiming at the processing links of the curved surface welding equipment, such as process parameter matching, operation task optimization, equipment synchronous control and the like, the problem that the curved surface process depends on subjective manual modification is solved, the automatic matching of the process parameters is realized, the optimal operation cooperation of two gantry systems is realized, and no task interference and equipment collision are generated.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of the adaptive control method of step S4 according to the present invention;
FIG. 3 is a schematic diagram of the system of the present invention.
Reference numerals:
a path 1 optimizing unit, a time 2 optimizing unit, a task 3 optimizing unit, a synchronous control unit 4,
5 an optimal decision unit.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a curved surface welding equipment control method based on cooperative self-adaptation, which is characterized by comprising the following steps of,
step S1), a welding process database based on the robot is constructed, and the data of the welding process database comprises welding process parameters and robot position and posture parameters;
the welding process library information includes weld position, leg height, welding current, welding voltage, welding speed, welding angle, welding wire material, welding wire specification, shielding gas type, lap joint form, number of layers, weld gap, weld material type, weld material specification and welding method.
Step S2), a curved surface welding task is imported, and the task comprises the length of a welding seam, the direction of the welding seam, the type of the welding seam and the name of the operation.
Step S3), constructing an external shaft, a welding gun and an electric control system of the robot, acquiring equipment data in real time in an industrial bus mode, and deploying a robot motion control communication link;
in this step, deploying the robot motion control communication link specifically includes:
step S31), constructing a communication link controlled by a portal shaft;
step S32) constructing a communication link for controlling the transverse axis;
step S33), constructing a communication link controlled by the longitudinal axis;
step S34) constructs a communication link for the torch control.
Step S4) real-time self-adaptive adjustment process parameters, welding postures and operation sequences according to the welding operation tasks, the welding seam type characteristics and the welding process method.
The method comprises the steps of constructing a welding material regurator, carrying out rule judgment on a welding seam starting point, a welding seam end point, a welding seam type, a joint form, a welding seam gap, a welding material model and a welding material specification, carrying out one-to-one, one-to-many and many-to-many rule matching on the welding material type, the welding seam form and welding seam data parameters by the welding material regurator, obtaining relevant process parameters of the welding material, and inputting the relevant process parameters into an operation regurator.
The welding wire regurator is constructed to carry out regular judgment on a welding seam starting point, a welding seam end point, a welding seam type, a joint form, a welding seam gap, a welding wire specification and a welding wire material, and carries out digital quantification on a welding wire model, a welding wire using direction and a welding quality index, a welding process index is quantified, and relevant welding wire process parameters are obtained and input into the operation regurator.
For example, for CO2 gas, the diameter of the welding wire is 1.2mm, the welding current is in the range of 130-150A, the arc voltage is 20-22V, the welding speed is 10-14m/n, the gas flow is 5-15L/min, the extension length of the welding wire is 15-20mm, and the rule device adjusts the parameters in the range according to actual experience to obtain the optimal welding process parameters.
And (3) carrying out rule judgment on a welding seam starting point, a welding seam end point, a welding seam type, a joint form, a welding seam gap and joint form, a shielding gas type and a welding height by constructing a welding rule device, and obtaining relevant welding process parameters by matching welding attitude and welding angle parameters by taking a welding task as a target and inputting the relevant welding process parameters into an operation rule device.
And (3) carrying out rule judgment on the operation task, welding material process parameters, welding wire process parameters and welding process parameters by constructing an operation rule device, calculating the time consumption of a welding line operation path by taking the operation process as an object, and obtaining the welding current, the welding voltage, the welding speed, the welding gun posture and the welding sequence required by the curved surface welding equipment by taking the minimum optimization method.
The output of the operation regurator is used as the operation task feedback, the operation task and the welding line processing information are synchronously adjusted and input into the operation regurator, the problem that the curved surface process depends on subjective manual modification is solved, and the automatic matching of the process parameters is realized.
And taking the output of the operation regurator as operation task feedback, synchronously adjusting the operation task and processing weld joint information, and inputting the operation task and the processing weld joint information into the operation regurator.
Step S5), according to the matched parameters, the two robot mechanisms are cooperatively controlled to carry out welding operation tasks.
And decomposing and combining the welding operation tasks, constructing a task combination method, and performing task optimization on the welding operation tasks.
Optimizing paths of the portal frame shafts, the transverse shafts, the longitudinal shafts, the robot postures and the welding guns, and realizing path interlocking and path optimizing of two portal frame systems; and carrying out normalization calculation on the portal frame shaft, the transverse shaft, the longitudinal shaft, the robot posture and the welding gun operation time, and optimizing the operation time of each component.
Synchronously controlling the portal frame shafts, the transverse shafts, the longitudinal shafts, the robot postures and the welding guns to realize real-time synchronization of the operation of the two portal frame systems; decision-making judgment is carried out on the portal shaft, the transverse shaft, the longitudinal shaft, the robot posture, the operation sequence and the priority of the welding gun, optimal operation cooperation of the two portal systems is realized, and task interference and equipment collision are avoided.
The invention also discloses a control system of the curved surface welding equipment based on cooperative self-adaptation, wherein the control system is used for controlling two groups of portal systems, each portal system comprises a portal shaft, a transverse shaft, a longitudinal shaft, a robot and a welding gun, the control system comprises a path optimization unit 1, a time optimization unit 2, a task optimization unit 3, a synchronous control unit 4 and an optimal decision unit 5, and the path optimization unit 1, the time optimization unit 2, the task optimization unit 3, the synchronous control unit 4 and the optimal decision unit 5 are all applied to the step S5.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A curved surface welding equipment control method based on cooperative self-adaptation is characterized by comprising the following steps,
step S1), a welding process database based on the robot is constructed, and the data of the welding process database comprises welding process parameters and robot position and posture parameters;
step S2), importing a curved surface welding task, wherein the task comprises the length of a welding seam, the direction of the welding seam, the type of the welding seam and the name of the operation;
step S3), constructing an external shaft, a welding gun and an electric control system of the robot, acquiring equipment data in real time in an industrial bus mode, and deploying a robot motion control communication link;
step S4), real-time self-adaptive adjustment of process parameters, welding postures and operation sequences is carried out according to the welding operation task, the welding seam type characteristics and the welding process method;
step S5), according to the matched parameters, the two robot mechanisms are cooperatively controlled to carry out welding operation tasks.
2. The control method according to claim 1, wherein in step S3, deploying a robot motion control communication link specifically comprises:
step S31), constructing a communication link controlled by a portal shaft;
step S32) constructing a communication link for controlling the transverse axis;
step S33), constructing a communication link controlled by the longitudinal axis;
step S34) constructs a communication link for the torch control.
3. The control method according to claim 1, wherein in the step S1, the welding process library information includes a weld position, a fillet height, a welding current, a welding voltage, a welding speed, a welding angle, a welding wire material, a welding wire specification, a shielding gas type, a lap joint form, a multi-layer multi-pass number, a weld gap, a welding material model, a welding material specification, and a welding method.
4. The control method according to claim 1, wherein the step S4 includes constructing a welding material regurator, and performing rule judgment on a welding seam starting point, a welding seam ending point, a welding seam type, a joint form, a welding seam gap, a welding material model and a welding material specification, wherein the welding material regurator performs one-to-one, one-to-many and many-to-many rule matching according to the welding material type, the welding seam form and welding seam data parameters to obtain relevant welding material process parameters and inputs the relevant welding material process parameters into the operation regurator.
5. The control method according to claim 4, wherein the step S4 includes, by constructing a welding wire regurator, performing regular judgment on a welding line starting point, a welding line end point, a welding line type, a joint form, a welding line gap, a welding wire specification and a welding wire material, digitally quantizing the welding wire regurator according to a welding wire model, a welding wire using direction and a welding quality index, quantizing the welding process index, obtaining relevant welding wire process parameters, and inputting the relevant welding wire process parameters into the operation regurator.
6. The control method according to claim 5, wherein the step S4 includes a step of performing rule judgment on a weld starting point, a weld end point, a weld type, a joint form, a weld gap and joint form, a shielding gas type and a welding height by constructing a welding rule device, wherein the welding rule device takes a welding task as a target, and obtains and inputs welding related process parameters into an operation rule device by matching welding attitude and welding angle parameters.
7. The control method according to claim 6, wherein the step S4 includes determining the task and the welding material process parameters, the welding wire process parameters, and the welding process parameters by constructing an operation regulartor, wherein the operation regulartor calculates the time consumption of the welding seam operation path by taking the operation process as an object, and obtains the welding current, the welding voltage, the welding speed, the welding torch attitude, and the welding sequence required by the curved surface welding equipment by taking the minimum as an optimization method.
8. The control method according to claim 7, wherein the step S4 includes inputting the output of the task modulator as the task feedback, and synchronously adjusting the task and the weld information to be processed into the task modulator.
9. The authentication method according to claim 1, wherein said step S5 includes,
optimizing paths of the portal frame shaft, the transverse shaft, the longitudinal shaft, the robot posture and the welding gun, and performing normalization calculation on the portal frame shaft, the transverse shaft, the longitudinal shaft, the robot posture and the welding gun operation time;
synchronously controlling the portal frame shafts, the transverse shafts, the longitudinal shafts, the robot postures and the welding guns to realize real-time synchronization of the operation of the two portal frame systems;
and carrying out decision judgment on the portal shaft, the transverse shaft, the longitudinal shaft, the robot posture, the operation sequence and the priority of the welding gun, and realizing the optimal operation cooperation of the two portal systems.
10. The control system is used for controlling two groups of gantry systems, each gantry system comprises a gantry shaft, a transverse shaft, a longitudinal shaft, a robot and a welding gun, and the control system comprises a path optimization unit, a time optimization unit, a task optimization unit, a synchronous control unit and an optimal decision unit.
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