Elevator car bottom welding path field debugging method based on off-line programming

Abstract

The invention relates to the technical field of industrial robot welding control, and provides an off-line programming-based elevator car bottom welding path field debugging method, which comprises the following steps of: constructing a three-dimensional process simulation model of a welding robot and an elevator car bottom, and establishing a geometric mapping relationship between the model and a physical scene; virtual welding scene parameters and robot body parameters are configured in the offline programming system; generating an initial welding track based on the 45-degree inclined welding surface, and executing multi-dimensional collision detection and kinematics verification; the optimized welding track is coded into an industrial robot instruction in an ANSI / UTF-8 format, and the industrial robot instruction is deployed to a physical welding system; collecting multi-source sensing data in the welding process in real time, wherein the multi-source sensing data comprise six-axis attitude parameters, process parameters and environmental parameters; and a dynamic comparison mechanism of the actual welding data and the simulation prediction data is established, and when the deviation exceeds a preset threshold value, an iterative optimization closed loop is triggered. The method has the beneficial effects that the efficiency is improved, the precision is guaranteed, the process stability is enhanced, and the compatibility is expanded.

Description

Technical Field

[0001] The invention relates to the technical field of industrial robot welding control, and in particular to an on-site debugging method for an elevator car bottom welding path based on off-line programming. Background Art

[0002] In the welding process of the elevator car bottom, the welds are often short and mostly welded on site. Due to the structural complexity and safety requirements of the elevator car bottom, welding quality is crucial. Therefore, it is necessary to adopt efficient and accurate welding methods and strictly control the welding process to ensure welding quality and the safety of the elevator car bottom.

[0003] The programming of welding robots can be divided into two methods: teaching programming and offline programming. Teaching programming means that the operator uses the teaching box to control the movement of the robot, so that the welding gun reaches the required posture to complete the welding operation, and records the posture data of each teaching point. Then the robot can complete the welding of this weld in the "reproduction" state. Offline programming uses the results of three-dimensional graphics to establish a model of the robot and its working environment in the professional software of the computer, control and operate the graphics through software functions, and then transmit the final data program to the robot control system for direct use after running the computer simulation.

[0004] In the elevator car bottom welding process, the traditional method relies on manual teaching and on-site trial and error debugging, which has the following defects: high risk of collision: welding robots are prone to interfere with workpieces or fixtures in complex spatial trajectories, which is very time-consuming and labor-intensive; long debugging cycle: the robot posture and welding parameters need to be adjusted repeatedly, and the cost of manual intervention is high. Therefore, how to design an on-site debugging method for elevator car bottom welding path based on offline programming technology is a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0005] The invention provides an on-site debugging method for an elevator car bottom welding path based on offline programming, aiming to solve the problems of high collision risk and long debugging cycle in the existing elevator car bottom welding process.

[0006] The present invention is implemented as follows: an on-site debugging method for an elevator car bottom welding path based on offline programming, the on-site debugging method for an elevator car bottom welding path based on offline programming comprises:

[0007] S100: Build a three-dimensional process simulation model of the welding robot and the elevator car bottom, and establish a geometric mapping relationship between the model and the physical scene;

[0008] S200: respectively configuring virtual welding scene parameters and robot body parameters in the offline programming system;

[0009] S300: Generate the initial welding trajectory based on the 45° inclined welding surface, perform multi-dimensional collision detection and kinematic verification, and when the trajectory abnormality is detected: return to S100 for model correction for abnormal robot body posture; return to S200 for parameter reset for abnormal sensor or PLC communication; activate the trajectory output interface after verification;

[0010] S400: Encode the optimized welding trajectory into industrial robot instructions in ANSI / UTF-8 format and deploy it to the physical welding system;

[0011] S500: Real-time collection of multi-source sensor data during the welding process, including six-axis posture parameters, process parameters and environmental parameters;

[0012] S600: Establish a dynamic comparison mechanism between actual welding data and simulation prediction data. When the deviation exceeds the preset threshold, it triggers an iterative optimization closed loop, including error source analysis, trajectory parameter tuning, secondary simulation verification, and physical system redeployment.

[0013] Preferably, the three-dimensional process simulation model integrates a welding thermodynamic model, a material deformation model and a robot kinematic model to achieve multi-physical field coupling simulation.

[0014] Preferably, the S200 includes:

[0015] S210: establishing a bidirectional data channel between the PLC signal and the virtual sensor through an industrial communication protocol interface;

[0016] S220: Perform communication quality diagnosis based on protocol handshake feedback;

[0017] S230: Complete the topological reconstruction of the digital twin scene.

[0018] Preferably, the S200 further includes:

[0019] S240: Analyze the robot manufacturer SDK to obtain motion control characteristic parameters;

[0020] S241: Construct a composite coordinate system including TCP coordinate system, workpiece coordinate system and world coordinate system;

[0021] S242: Define the time and space constraints of the welding process sequence.

[0022] Preferably, the process parameter matrix monitored in S500 includes: an arc characteristic parameter group {I, U, V}, a heat input parameter group {T, Q} and a kinematic parameter group {θ i ,v i ,a i,i∈[1,6]}, where I represents the current of the welding arc, U represents the welding arc voltage, V represents the welding speed, T represents the welding temperature, Q represents the welding heat, i represents the i-th joint of the robot, θ i represents the angle of the robot's i-th joint, v i represents the moving speed of the robot's i-th joint, a i represents the acceleration of the i-th joint of the robot.

[0023] Preferably, the calculation formula of the welding heat Q is:

[0024]

[0025] Among them, η is the thermal welding efficiency, I is the welding current, U is the welding voltage, and V is the welding speed.

[0026] Preferably, in step S600, the allowable range of the data gap is 0-0.5 cm.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. Improved efficiency: Reduce on-site debugging time through digital twin technology;

[0029] 2. Accuracy guarantee: The dynamic comparison mechanism controls the welding trajectory deviation within 0.5cm;

[0030] 3. Enhanced process stability: The prediction accuracy of multi-physics field coupling simulation model is >95%;

[0031] 4. Compatibility expansion: supports ANSI / UTF-8 dual format instructions and is compatible with mainstream industrial robot controllers. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 is a method flow chart of an embodiment of the present invention;

[0034] Figure 2 is an overall principle block diagram of an embodiment of the present invention; DETAILED DESCRIPTION

[0035] In order to more fully understand the technical content of the present invention, the technical solution of the present invention is further introduced and illustrated in conjunction with specific embodiments below, but is not limited thereto. The technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0036] refer to Figure 1 The on-site debugging method of the elevator car bottom welding path based on offline programming includes:

[0037] 3D process simulation modeling:

[0038] S100: Build a 3D process simulation model of the welding robot and the elevator car bottom, and achieve accurate matching of the virtual scene and the physical scene through geometric mapping. Integrate the welding thermodynamics model, material deformation model and robot kinematics model to simulate the multi-physical field coupling effect.

[0039] Virtual scene and robot parameter configuration:

[0040] S200: respectively configuring virtual welding scene parameters and robot body parameters in the offline programming system;

[0041] Communication configuration: Establish a bidirectional data channel between PLC signals and virtual sensors through industrial protocols (such as OPC UA and Profinet), diagnose communication quality based on protocol handshake feedback, and reconstruct the digital twin scenario topology.

[0042] Robot parameterization: Analyze the manufacturer's SDK to obtain motion control parameters, build a composite coordinate system including the TCP coordinate system, workpiece coordinate system and world coordinate system, and define the time and space constraints of the welding process.

[0043] Welding trajectory generation and verification:

[0044] S300: Generates the initial welding trajectory based on the 45° inclined welding surface, and performs multi-dimensional collision detection (geometric interference, kinematic singularity) and posture compliance verification.

[0045] When a trajectory abnormality is detected, the following operations are performed: Model correction: When the body posture is abnormal, return to S100 to optimize the model geometry parameters; Parameter reset: When the sensor or PLC is abnormal, return to S200 to adjust the communication protocol or signal mapping; After verification, activate the trajectory output interface and generate executable instructions.

[0046] Physical system deployment and real-time monitoring:

[0047] S400: Encode the optimized welding trajectory into industrial robot instructions in ANSI / UTF-8 format and deploy it to the physical welding system;

[0048] S500: Real-time collection of multi-source sensor data during welding, including: Six-axis posture parameters: The robot's joint angles θ i , the velocity v of the robot's i-th joint i , the acceleration a of the robot's i-th joint i (i∈[1,6]), i represents the i-th joint of the robot; process parameter matrix: arc characteristics (welding current I, welding voltage U, welding speed V), heat input (welding temperature T, welding heat Q), wire feeding speed; environmental parameters: welding fume concentration, fixture displacement;

[0049] The calculation formula of welding heat Q is:

[0050]

[0051] Among them, η is the thermal welding efficiency, I is the welding current, U is the welding voltage, and V is the welding speed.

[0052] Dynamic comparison and iterative optimization:

[0053] S600: Establish a dynamic comparison mechanism between actual welding data and simulation prediction data, and set the deviation threshold:

[0054] Allowable range: track position deviation ≤ 0.5cm, current / voltage deviation ≤ 5%;

[0055] When the deviation exceeds the preset threshold, the following closed-loop optimization process is triggered:

[0056] Error source analysis: locate abnormal sources, including model errors, sensor drift or process parameter mismatch;

[0057] Trajectory parameter tuning: adjust welding speed, entry and exit points or path curvature;

[0058] Secondary simulation verification: rerun the simulation in the updated digital twin model;

[0059] System redeployment: Push optimization instructions to the physical welding system.

[0060] The present invention provides a specific implementation case below:

[0061] First, use SolidWorks to build a 3D model of the elevator car bottom, import it into RobotStudio to build a robot kinematic model, and match the physical scene feature points through the geometric hash algorithm. Connect the PLC and virtual sensor through the Profinet protocol, and detect that the signal transmission delay is ≤10ms; parse the ABB robot SDK to obtain the IRC5 controller parameters, and define the origin of the TCP coordinate system at the end of the welding gun. Generate a spiral welding trajectory with an inclination of 45°, and detect that the posture of the third joint is out of limit (θ 3 >120°), return to S100 to adjust the model joint limit parameters. The actual welding current deviation reached 8%, triggering error tracing analysis, and found that the friction coefficient of the wire feeding mechanism was not modeled in the simulation; after updating the thermodynamic model, the trajectory was regenerated, and the current deviation was reduced to 3% after secondary deployment.

[0062] The embodiments described above are only part of the embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any ordinary technician in the field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, and they should be covered by the protection scope of the present invention.

Claims

1. A method for on-site debugging of elevator car bottom welding path based on offline programming, characterized in that: The method includes: S100: Build a three-dimensional process simulation model of the welding robot and the elevator car bottom, and establish a geometric mapping relationship between the model and the physical scene; S200: respectively configuring virtual welding scene parameters and robot body parameters in the offline programming system; S300: Generates the initial welding trajectory based on the 45° inclined welding surface, and performs multi-dimensional collision detection and kinematic verification; S400: Encode the optimized welding trajectory into industrial robot instructions in ANSI / UTF-8 format and deploy it to the physical welding system; S500: Real-time collection of multi-source sensor data during the welding process, including six-axis posture parameters, process parameters and environmental parameters; S600: Establish a dynamic comparison mechanism between actual welding data and simulation prediction data, and trigger an iterative optimization closed loop when the deviation exceeds the preset threshold.

2. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1 is characterized in that: The three-dimensional process simulation model integrates a welding thermodynamic model, a material deformation model and a robot kinematic model to achieve multi-physical field coupling simulation.

3. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1 is characterized in that: The S200 includes: S210: establishing a bidirectional data channel between the PLC signal and the virtual sensor through an industrial communication protocol interface; S220: Perform communication quality diagnosis based on protocol handshake feedback; S230: Complete the topological reconstruction of the digital twin scene.

4. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1 is characterized in that: The S200 further includes: S240: Analyze the robot manufacturer SDK to obtain motion control characteristic parameters; S241: Construct a composite coordinate system including TCP coordinate system, workpiece coordinate system and world coordinate system; S242: Define the time and space constraints of the welding process sequence.

5. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1, characterized in that: In step S300, when a trajectory abnormality is detected: return to S100 for model correction for abnormal robot body posture; return to S200 for parameter reset for sensor or PLC communication abnormality; activate the trajectory output interface after verification.

6. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1, characterized in that: The process parameter matrix monitored in S500 includes: arc characteristic parameter group {I, U, V}, heat input parameter group {T, Q} and kinematic parameter group {θ i ,v i ,a i ,i∈[1,6]}, where I represents welding current, U represents welding voltage, V represents welding speed, T represents welding temperature, Q represents welding heat, i represents the i-th joint of the robot, θ i Represents the joint angles of the robot, v i represents the moving speed of the robot's i-th joint, a i represents the acceleration of the i-th joint of the robot.

7. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 6 is characterized in that: The calculation formula of the welding heat Q is: Among them, η is the thermal welding efficiency, I is the welding current, U is the welding voltage, and V is the welding speed.

8. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1, characterized in that: In step S600, the allowable range of the data gap is 0-0.5 cm.

9. The on-site debugging method for elevator car bottom welding path based on offline programming according to claim 1, characterized in that: In step S600, the iterative optimization closed loop includes: error source analysis, trajectory parameter tuning, secondary simulation verification and physical system redeployment.

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