A force-position hybrid control method for robotic friction stir welding

Abstract

The application discloses a kind of robot friction stir welding force-position hybrid control method, it is related to robot welding control technical field, comprising the following steps: step S10 establishes the hybrid strategy model based on constant displacement control and constant pressure control, the trajectory deviation caused by flexible deformation is compensated by robot dynamic offset function;The constant displacement control monitors welding depth in real time by laser sensor, the laser sensor is installed in 45° oblique direction to the outer wall of motorized spindle, and the collection point is located in the front side of the welding advancing direction, to avoid the interference of flash;The constant pressure control is collected axial upset force by six-dimensional force sensor, and force feedback type impedance control strategy is adopted.The robot friction stir welding force-position hybrid control method compensates the flexible deformation of robot by dynamic offset function, combined with the hybrid strategy of constant displacement and constant pressure control, solves the problem of uneven weld forming under single control mode.

Description

Technical Field

[0001] This invention relates to the field of robot welding control technology, specifically to a force-position hybrid control method for robot friction stir welding. Background Technology

[0002] Friction stir welding (FSW), as a disruptive solid-state joining technology for lightweight alloys such as aluminum alloys, has demonstrated significant advantages in aerospace, rail transportation, and other fields. With the surge in demand for welding complex three-dimensional components, industrial robots, due to their high flexibility and multi-degree-of-freedom characteristics, have become key equipment for realizing FSW on spatial curved surfaces. However, during the welding process, the axial upsetting force easily induces flexible deformation of the robotic arm, causing the actual trajectory of the stirring head to deviate from the preset path, resulting in fluctuations in weld depth and imbalance of upsetting force, severely affecting weld density and joint strength. Traditional single control modes have inherent defects: while constant displacement control compensates for position deviations in real time through laser sensors, it cannot suppress pressure fluctuations caused by uneven material density; constant pressure control relies on six-dimensional force sensors to adjust the downward pressure, but it is difficult to cope with system oscillations caused by multiple trajectory jumps. Furthermore, existing dynamic offset functions mostly adopt absolute correction modes, and offset resets easily lead to trajectory discontinuities and the risk of sudden equipment stops; while the trajectory generated by offline programming lacks deep coupling with the actual welding control strategy, making it difficult to guarantee the reachability and correction continuity under positioner linkage.

[0003] Domestic and international research largely focuses on single-dimensional optimization. For example, patent CN113020976A proposes a dynamic offset compensation method based on the tool coordinate system, but it does not solve the problem of offset superposition during continuous correction of multiple trajectory segments. The literature "Research on Force Control Strategy for Robot Friction Stir Welding" (Welding Journal, 2021) uses force feedback-type impedance control to achieve closed-loop pressure regulation, but lacks active compensation for welding depth deviation. Although domestic equipment development has made progress (such as the Wanzhou H1K500 robot), its force-position coordinated control accuracy is still limited by sensor feedback delay and the lack of a hybrid strategy. Experiments show that uneven heat input in the weld under constant pressure control leads to grain coarsening, while constant displacement control is prone to flash defects due to workpiece thermal softening. In existing technologies, problems such as fixed process coordinate system correction direction and disconnect between offline trajectory and online force-position control seriously restrict the welding quality and efficiency of complex curved surfaces. Therefore, there is an urgent need for a force-position hybrid control method that integrates multi-source sensor feedback, dynamic offset coordination, and priority decision-making to overcome the technical bottleneck of high-precision control in robot friction stir welding. Summary of the Invention

[0004] The purpose of this invention is to provide a force-position hybrid control method for robotic friction stir welding, in order to solve the problems mentioned in the background art, such as trajectory deviation caused by the flexible deformation of the robotic arm, mismatch between axial upsetting force and welding depth, and discontinuity in trajectory correction of complex curved surfaces.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a force-position hybrid control method for robot friction stir welding, comprising the following steps: Step S10: establishing a hybrid strategy model based on constant displacement control and constant pressure control, and compensating for trajectory deviation caused by flexible deformation through the robot's dynamic offset function; the constant displacement control monitors the welding depth in real time through a laser sensor, the laser sensor being installed at a 45° angle on the outer wall of the electric spindle, with the acquisition point located on the front side of the welding forward direction to avoid flash interference; the constant pressure control acquires the axial upsetting force through a six-dimensional force sensor, adopts a force feedback type impedance control strategy, and achieves closed-loop pressure regulation by adjusting the downward pressure of the stirring head; in the hybrid control strategy, displacement control has a higher priority than pressure control, and the dynamic offset is output through formula (1), where α>β.

[0006] ΔZ=[αβ][ΔZ1ΔZ2] Τ (1)

[0007] Step S20 performs dynamic offset correction based on the process coordinate system (CCS), which has the welding forward direction as the X-axis and the Z-axis perpendicular to the workpiece surface, and uses a relative correction mode to superimpose the offset; in multi-segment trajectory welding, the rear trajectory inherits the front offset to avoid trajectory jumps and system oscillations.

[0008] Step S30 constructs a closed-loop control process, collects welding depth and axial pressure data in real time, and generates dynamic offset commands; the laser sensor voltage signal is input to the controller after AD conversion, and the six-dimensional force sensor data is transmitted through the TCP protocol.

[0009] Step S40 uses offline programming software to generate complex curved surface welding trajectories, integrating the positioner's dynamic coordinate system and Z-axis tilt compensation function; in trajectory editing, the Z-axis is fixed and the step size is ≤1mm, and the post-code is associated with the dynamic coordinate system to ensure the robot's posture reachability.

[0010] Furthermore, in step S10: the Dirichlet integral model uses welding time as the integration factor, with a step accuracy of 0.03-0.05 mm; the PI regulator is experimentally tuned to the proportional coefficient K. p With integration time T i This eliminates overshoot. Formula (2) indicates that the stirring head tip is controlled at a set depth position:

[0011]

[0012] Furthermore, in step S20: the dynamic offset function monitors the trajectory deviation in real time through an external sensor, abandoning the absolute correction mode and only using relative correction;

[0013] The process coordinate system (CCS) is updated synchronously with the trajectory to ensure that the correction direction is consistent with the welding advance direction.

[0014] Furthermore, in step S30: the calibration voltage of the laser sensor is linearly related to the indentation depth, and the proportional coefficient and offset are determined experimentally; the axial pressure collected by the six-dimensional force sensor is linearly related to the indentation depth of the stirring head, and the stiffness coefficient is calibrated based on material properties.

[0015] Furthermore, in step S40: the offline programming software is PQArt, which defines the robot, positioner and workpiece models, generates edge trajectories and adds tilt angle compensation; the tilt angle range is 0-15°; after the trajectory is compiled, it is associated with a dynamic coordinate system to realize eight-axis synchronous motion and positioner linkage control.

[0016] Furthermore, in step S20, the single correction amount is ≤0.05mm, which is rapidly approximated over a wide range by the Dirichlet integral model, and the PI regulator eliminates steady-state error over a small range.

[0017] Furthermore, in step S30, the controller adjusts the robot's motion trajectory according to the hybrid control strategy to achieve coordinated control of depth and pressure.

[0018] Furthermore, in step S40, the effect of continuous trajectory correction is verified through simulation to avoid the risk of sudden stop.

[0019] This invention provides a force-position hybrid control method for robotic friction stir welding, which has the following beneficial effects: This invention integrates a dynamic offset mechanism of the process coordinate system (CCS), laser sensing depth feedback, and a six-dimensional force sensing pressure closed loop. By establishing a force-position hybrid control model based on priority decision-making, and combining a collaborative algorithm of Dirichlet integral fast approximation and PI-adjusted steady-state optimization, it achieves active compensation for welding depth deviation and adaptive suppression of upsetting force fluctuations. This method includes the design of a dynamic correction strategy for the process coordinate system (CCS), the construction of a multi-source sensing closed-loop feedback architecture, a force-position hybrid weighted output model, and offline programming trajectory-control coupling verification. It can effectively eliminate system oscillations caused by multi-segment trajectory jumps, ensure trajectory continuity and heat input uniformity during complex curved surface welding, and significantly improve the weld density and fatigue strength of aluminum alloy thin plates and dissimilar material joints. By compensating for robot flexibility deformation through dynamic offset function, and combining a hybrid strategy of constant displacement and constant pressure control, it solves the problem of uneven weld formation under a single control mode. By employing priority partitioning and dynamic matrix coefficient adjustment, it achieves synergistic optimization of welding depth and upsetting force, improving weld quality and system stability. The integration of offline programming and custom process instructions further improves the accuracy and efficiency of welding complex curved surfaces. Experiments show that this method can control the upsetting force fluctuation within ±5% and the welding depth error less than ±0.05mm, which is significantly better than traditional control methods. Attached Figure Description

[0020] Figure 1 This is a flowchart of the present invention;

[0021] Figure 2 This is the process coordinate system of the present invention;

[0022] Figure 3 This invention provides real-time offset compensation.

[0023] Figure 4 This is the offset for continuous trajectory correction in this invention;

[0024] Figure 5 This is a flowchart of the human-machine closed-loop control of the present invention;

[0025] Figure 6 This is a block diagram of the human-machine closed-loop control of the present invention;

[0026] Figure 7 This invention employs a PI controller to eliminate steady-state error control model on a limited scale.

[0027] Figure 8 This invention collects welding advance side position information;

[0028] Figure 9 This invention employs a PI controller for fine adjustment on a small scale.

[0029] Figure 10 The welding space is divided according to the present invention;

[0030] Figure 11 This is a flowchart of the open robot control platform based on the present invention;

[0031] Figure 12 The hybrid control model of this invention uses dynamic matrix coefficients. Detailed Implementation

[0032] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0033] like Figure 1 As for Figure 11 As shown, a force-position hybrid control method for robotic friction stir welding includes the following steps:

[0034] Firstly, the robot's dynamic offset function modeling and position correction strategy are as follows: Based on the dynamic offset function of the robot friction stir welding system, spatial offset models of the tool coordinate system (TTS) and process coordinate system (CCS) are established. The tool coordinate system is based on the robot's base coordinate system, and the center point of the tool end is determined through joint matrix operations, with the Z-axis perpendicular to the workpiece surface; the process coordinate system consists of the trajectory tangent, the +Z direction of the tool coordinate system, and the normal vector (e.g., ...). Figure 2 (As shown). Dynamic offset uses a relative correction mode, performing real-time offset compensation with the current position as a reference (e.g., Figure 3 In multi-segment trajectory welding, a continuous trajectory correction scheme is adopted to ensure that the offsets are superimposed and the motion is smooth (e.g., Figure 4 This helps avoid sudden robot stops and weld defects.

[0035] The second aspect involves human-machine closed-loop welding control: During welding, the operator monitors the welding trajectory and depth in real time via a teach pendant and sends dynamic offset commands to the robot based on experience. The system performs position corrections using a process coordinate system (X for welding direction, Y for lateral direction, and Z for vertical direction). The amount of each correction is determined by the position error, forming a human-machine closed-loop control (e.g., ...). Figure 5 , Figure 6 (As shown).

[0036] The third constant displacement control strategy: A dynamic offset model based on the TCP points of the tool coordinate system is established by acquiring welding depth voltage signals through a laser sensor. The welding depth is divided into a large range (±0.5mm) and a small range (±0.3mm). The large range is approximated by a Dirichlet function integral model, expressed as:

[0037]

[0038] To eliminate steady-state error in a small range, a PI controller is used, and the control model is: (e.g.) Figure 7 (As shown).

[0039]

[0040] The laser sensor is mounted at a 45° angle on the outer wall of the electric spindle to collect position information on the welding advance side (e.g., Figure 8 To avoid interference from flying edges.

[0041] The fourth constant pressure control strategy: Based on the acquisition of upsetting force signals by a six-dimensional force sensor, a force feedback impedance control model is adopted, and closed-loop pressure control is achieved by adjusting the depth of the stirring head. The pressure range is divided into a large range (±30% of the preset value) and a small range (±5% of the preset value). The large range is rapidly approximated using a Dirichlet integral model, while the small range is finely adjusted using a PI controller (e.g., ...). Figure 9 The displacement output is dynamically adjusted according to the pressure deviation to ensure stable upsetting force.

[0042] Fifth Force Position Hybrid Control Strategy Implementation

[0043] Combining the priorities of constant displacement and constant pressure control, displacement control is prioritized over pressure control. The welding space is divided into a large area (dominated by constant displacement), a transition zone (mixed force and position), and a small area (dominated by constant pressure) (e.g., Figure 10 Hybrid control models utilize dynamic matrix coefficients ( Figure 12 The decision displacement output is expressed as:

[0044] ΔZ=[αβ][ΔZ1ΔZ2] Τ

[0045] Here, α>β indicates a higher weight for displacement control. To address the issue of non-coincidence of control boundaries due to different material densities, a dynamic coefficient adjustment strategy is adopted to ensure coordinated optimization of welding depth and upsetting force.

[0046] Sixth, software system integration and offline trajectory planning: Based on the open robot control platform (GTRobot), develop electric spindle control, force and position control, and human-in-the-loop functional modules (such as...). Figure 11 The system achieves real-time transmission of sensor data via TCP / IP protocol and encapsulates custom welding process instructions, supporting one-click welding start. It utilizes PQArt offline programming software to generate complex curved surface welding trajectories, ensuring robot posture and trajectory accuracy through trajectory translation, rotation, and linked coordinate system settings.

[0047] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A force-position hybrid control method for robotic friction stir welding, characterized in that, Includes the following steps: Step S10 establishes a hybrid strategy model based on constant displacement control and constant pressure control, and compensates for trajectory deviation caused by flexible deformation through the robot's dynamic offset function; the constant displacement control monitors the welding depth in real time through a laser sensor, which is installed at a 45° angle on the outer wall of the electric spindle, with the acquisition point located on the front side of the welding forward direction to avoid flash interference; the constant pressure control acquires the axial upsetting force through a six-dimensional force sensor, adopts a force feedback impedance control strategy, and achieves closed-loop pressure regulation by adjusting the downward pressure of the stirring head; in the hybrid control strategy, displacement control has a higher priority than pressure control, and the dynamic offset is output through formula (1), where α>β; （1） Step S20: Perform dynamic offset correction based on the process coordinate system, where the process coordinate system has the welding forward direction as the X-axis and the Z-axis perpendicular to the workpiece surface, and uses a relative correction mode to superimpose the offset; in multi-segment trajectory welding, the rear trajectory inherits the front offset to avoid trajectory jumps and system oscillations. Step S30: Construct a closed-loop control process, collect welding depth and axial pressure data in real time, and generate dynamic offset commands; the laser sensor voltage signal is input to the controller after AD conversion, and the six-dimensional force sensor data is transmitted through TCP protocol; Step S40 uses offline programming software to generate complex curved surface welding trajectories, integrating the positioner's dynamic coordinate system and Z-axis tilt angle compensation function; in trajectory editing, the Z-axis is set to be fixed and the step size ≤1mm, and the post-code is associated with the dynamic coordinate system to ensure the robot's posture reachability; In step S10: Constant displacement control: The welding depth voltage signal is acquired by a laser sensor, and a dynamic offset model based on the TCP point of the tool coordinate system is established. The welding depth is divided into a large range (±0.5mm) and a small range (±0.3mm). The large range is approximated by the Dirichlet function integral model, and the expression is: To eliminate steady-state error in a small range, a PI controller is used, and the control model is as follows: ； In step S20: the trajectory deviation is monitored in real time by external sensors, and the absolute correction mode is abandoned, with only relative correction being used; The process coordinate system and trajectory are updated synchronously to ensure that the correction direction is consistent with the welding advance direction.

2. The force-position hybrid control method for robotic friction stir welding according to claim 1, characterized in that, In step S30: the calibration voltage of the laser sensor is linearly related to the indentation depth, and the proportional coefficient and offset are determined experimentally; the axial pressure collected by the six-dimensional force sensor is linearly related to the indentation depth of the stirring head, and the stiffness coefficient is calibrated through material properties.

3. The force-position hybrid control method for robotic friction stir welding according to claim 1, characterized in that, In step S40: the offline programming software is PQArt, which defines the robot, positioner and workpiece models, generates edge trajectories and adds tilt angle compensation; the tilt angle range is 0-15°; after the trajectory is compiled, it is associated with a dynamic coordinate system to realize eight-axis synchronous motion and positioner linkage control.

4. The force-position hybrid control method for robotic friction stir welding according to claim 1, characterized in that, In step S20, the single correction amount is ≤0.05mm. The Dirichlet integral model is used to quickly approximate the error over a large range, and the PI regulator eliminates the steady-state error over a small range.

5. The force-position hybrid control method for robotic friction stir welding according to claim 1, characterized in that, In step S30, the controller adjusts the robot's motion trajectory according to the hybrid control strategy to achieve coordinated control of depth and pressure.

6. The force-position hybrid control method for robotic friction stir welding according to claim 1, characterized in that, In step S40, the effect of continuous trajectory correction is verified through simulation to avoid the risk of sudden stop.

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