A Study on the Force/Position Hybrid Control Strategy for Eight-Axis Robotic Friction Stir Welding

Abstract

In aerospace and new-energy vehicle manufacturing, there is an increasing demand for the high-quality joining of large, curved aluminum alloy structures. This study presents a robotic friction stir welding (RFSW) system employing a force/position hybrid control. An eight-axis linkage platform integrates an electric spindle, multidimensional force sensors, and a laser displacement sensor, ensuring trajectory coordination between the robot and the positioner. By combining long-range constant displacement with small-range constant pressure—supplemented by an adaptive transition algorithm—the system regulates the axial stirring depth and downward force. The experimental results confirm that this approach effectively compensates for robotic flexibility, keeping weld depth and pressure deviations within 5%, significantly improving seam quality. Further welding verification was performed on typical curved panels for aerospace applications, and the results demonstrated strong adaptability under high-load, multi-DOF conditions, without crack formation. This research could advance the field toward more robust, automated, and adaptive RFSW solutions for aerospace, automotive, and other high-end manufacturing applications.

1. Introduction

Friction Stir Welding (FSW) is characterized by solid-state joining, low heat input, a low defect rate, and excellent mechanical properties. It has rapidly become a mainstream technology for joining aluminum alloys and dissimilar materials [1,2,3]. In high-end manufacturing sectors such as aerospace [4], rail transportation [5], shipbuilding [6], and automotive industries [7], FSW has demonstrated broad applicability for large thin-walled panels, high-strength alloys, and multi-material joints [8]. However, the increasing demand for lightweight, high-strength products in spacecraft, ships, and new-energy vehicles has increased the complexity of welding curved or multidimensional structures. Traditional FSW machines, with limited degrees of freedom, often fail to achieve high-precision welding on three-dimensional curved workpieces over large spatial ranges [9]. To meet the high-quality requirements of multi-angle, multi-curved, and large-scale workpieces, both academia and industry have begun integrating industrial robots with FSW. This approach, termed Robotic FSW (RFSW), offers enhanced flexibility and scalability, improving welding automation and adaptability to complex workpieces, thus providing new solutions for high-end equipment manufacturing [10].

In RFSW systems, the core assembly includes the robot body, the stirring spindle (comprising the electric spindle, stirring pin, and tool holder), and the welding fixtures [11,12]. The robot body typically withstands axial pressures ranging from several thousand to tens of thousands of newtons, leading to the frequent selection of high-load serial or parallel/hybrid robots to meet stiffness requirements [13,14]. For example, ABB developed the IRB 7600 and ESAB Rosio series, which are capable of handling loads up to 500 kg, welding plates up to 7 mm thick, and integrating force-sensing capabilities [15]. Additionally, parallel or hybrid robots such as the Neos Tricept and IRB 940 provide high stiffness and effective load capacities exceeding 1300 kg, enabling the welding of thick plates [16,17]. However, parallel robots face constraints in terms of flexibility and reachable workspace. Similarly, FANUC integrated the CRIO spindle system with its M-900i series of serial robots to develop an intelligent RFSW production line, further refining the load capacity and system integration with the M-900iB and KR500MT models [18]. KUKA employed its KR500 series serial robots for FSW tasks involving large flat panels and moderately curved components in the automotive and aerospace industries. Kolegain et al. improved welding accuracy for serial robots on three-dimensional seams through offline trajectory planning (using B-spline curves) and static deformation compensation [19,20]. Qin proposed a real-time error compensation method based on a nonlinear high-gain observer to address control challenges in six-axis industrial robots with joint flexibility in FSW and machining. This approach integrates dynamic modeling, stiffness identification, and robotic control simulation to enhance state estimation accuracy and positioning control, enabling high-precision welding and machining [21]. Gao introduced a wireless multi-dimensional force sensing system for hybrid FSW robots, integrating an elastomer with eight pairs of strain gauges to achieve high-precision force measurement [22]. This system expands the measurement range, reduces cross-talk, and improves the controllability and stability of the welding process. Overall, multi-degrees-of-freedom industrial robots in RFSW offer high flexibility and a broad working range. However, end-effector stiffness and joint flexibility remain key limitations in high-load solid-state welding.

In robotic FSW, offline compensation is widely adopted to reduce deformation and positioning deviations. A stiffness and thermo-mechanical coupling model of the robotic system is typically established to estimate joint forces and thermal deformations, facilitating reverse compensation in seam trajectory planning and improving welding precision [15]. For example, Beijing Sifoster company employed pre-weld spatial trajectory simulations to mitigate flexibility-induced errors in weld formation [23]. Xiao et al. proposed a constant plunge depth control method based on online trajectory generation, incorporating real-time pose measurement, deformation compensation modeling, and dynamic trajectory optimization to reduce vibration and welding defects, thereby improving weld quality in RFSW [24]. Kolegain et al. introduced a feed-forward compensation technique, integrated with offline path planning using Bézier curves, to minimize end-effector deviations in RFSW and achieve high positional and orientational accuracy despite the limited stiffness of serial robots [25]. Mario et al. applied real-time lateral deviation compensation using an embedded elastostatic model and real-time path correction, mitigating lateral tool deviation during welding.

Zeng et al. [26,27] established an interpolation compensation method based on the similarity characteristics of robotic errors. By analyzing the correlation between joint angles and positioning errors using a variogram function, they applied a linear unbiased optimal estimation model to interpolate and compensate for target points. Zhu et al. [28] proposed a bilinear interpolation method, estimating target point errors using the collected boundary point errors to achieve error compensation. Bai [29] introduced a fuzzy interpolation method, constructing a fuzzy interpolation model based on collected data points to predict target positions, demonstrating higher accuracy in simulation environments compared to kinematic calibration models. Cai et al. [30] developed an industrial robot error model based on the ordinary Kriging method, achieving better compensation accuracy than the simple Kriging method [26]. However, offline models often exhibit insufficient predictive accuracy when confronted with uncertain curved workpieces, real-time thermal softening, or material fluctuations. They also cannot adapt if deviations occur during the welding process.

To improve real-time adaptability, force/position sensors and advanced control algorithms are increasingly integrated into robotic FSW to establish online closed-loop regulation [31]. A common approach is constant pressure control, wherein force sensors measure the axial load on the stirring head while a PID-based algorithm adjusts the Z-axis position. This method ensures that the downward force remains near the target value [20]. For instance, Mendes et al. combined force control with motion control to maintain suitable welding forces, thereby ensuring sufficient plastic flow [32]. Longhurst et al. examined how welding speed, rotational speed, and axial force affect weld quality, determining that pressure control significantly improves seam consistency in automated FSW systems [25,33]. However, thermal softening may cause a continuous buildup of downward force, leading to edge spattering or end deformation. Another method is constant displacement control, which employs high-precision distance or displacement sensors (e.g., laser displacement sensors or LVDTs) to monitor the stirring head’s insertion depth. Dynamic adjustments are then performed to maintain a stable welding depth. Although this approach stabilizes weld-seam dimensions, it remains sensitive to fluctuations in frictional heat resulting from uneven material temperatures. As a result, “hard” or “soft” zones with non-uniform properties may appear in the weld.

To overcome the limitations of constant pressure and constant displacement control, force/position hybrid control was studied. This strategy uses closed-loop control to regulate both the downward force and the stirring head’s relative position, thereby balancing plastic flow and geometric uniformity in the weld seam. Kamm et al. proposed a force control method for robotic friction stir welding (RFSW) based on an open control architecture, comparing admittance control, parallel force control, and conventional external force control [34]. YAVUZ et al. proposed a function-oriented RFSW design concept based on the process requirements of friction stir welding [35]. They developed a robotic FSW system incorporating both displacement control and pressure control. Raibert originally introduced force/position hybrid control in robotic arm operations, and Smith et al. later applied it to FSW, demonstrating that hybrid control significantly reduces weld defects compared to pure position control [36]. Fehrenbacher et al. embedded thermo-couples in the tool to integrate temperature sensing with force sensing, thus coordinating thermal and force parameters [37]. Mendes et al. designed a robotic FSW system based on force/motion hybrid control. Experimental results demonstrated that the axial force on the robot remained stable, and the pressure curve exhibited minimal fluctuation [38]. Fehrenbacher et al. developed a temperature and force hybrid closed-loop control method to regulate the robotic FSW process. Although the control system’s response was slow due to hardware limitations, it still showed potential application value [12]. Longhurst et al. implemented an optimized PID control method on an FSW system modified from a milling machine, designing a pressure control system that adjusted pressure based on joint temperature measurements, effectively reducing fluctuations in the pressure curve during welding [39]. Smith et al. conducted comparative welding experiments with and without force control and found that robotic FSW systems using force control achieved better weld surface formation quality [37].

Overall, force/position hybrid control is regarded as a key direction for robotic FSW but still requires more comprehensive hardware/software integration and greater adaptability to complex curved-surface welding scenarios.

In light of these challenges, a robotic FSW system based on force/position hybrid control is proposed in this study to achieve high-precision welding of large-scale curved structures. An integrated robotic FSW setup is presented, and the coupling mechanisms between robotic joint deformation and weld seam plastic flow under high loads are analyzed. A force/position hybrid control strategy incorporating real-time weld-seam monitoring is introduced and validated, allowing synchronized adjustment of both welding depth and downward force. Experimental validation on planar and spatially curved surfaces is then conducted to assess the effectiveness of this strategy in enhancing welding precision, quality, and automation. This study thus provides new theoretical insights and technological solutions for complex curved-structure welding and expands the application of industrial robots in high-load, multi-degree-of-freedom tasks.

2. Design of the Robot Friction Stir Welding System

The robotic friction stir welding (FSW) system is designed with an integrated control framework to meet the demands of high-precision welding tasks. As shown in Figure 1, the system comprises several key components: an eight-axis linkage mechanism, a spindle subsystem, a sensor subsystem, and a control subsystem. The core of the system is the eight-axis linkage mechanism, which consists of a six-degree-of-freedom industrial robot and two positioners. This configuration extends the robot’s operational range and flexibility. It enables high-precision movement along complex welding trajectories. The spindle subsystem integrates an electric spindle and its frequency converter with the robot’s control system, ensuring precise energy input during the welding process. Meanwhile, the sensor subsystem uses a six-dimensional force sensor and a laser displacement sensor to monitor real-time data and provide continuous feedback for the force–position control strategy.

The control system of the robotic FSW system is divided into four main modules: the human–machine interface (HMI) module, the robot system layer, the execution unit, and the sensing unit. The HMI module facilitates information exchange between the operator and the system through input/output interfaces, displaying welding process parameters and real-time feedback data. This design ensures that operators can monitor and adjust the process in a timely manner. Figure 2 illustrates the system’s control architecture, highlighting the interactions between the modules. In this architecture, the robot system layer serves as the control core and is responsible for motion control, process control, external communication, and safety functions (e.g., emergency stop). The GOGO R688 controller used in this system supports multi-axis synchronous control, enabling real-time planning of the welding robot’s trajectory and data transmission to upper-level applications. It also supports various communication protocols (e.g., ModBus TCP).

Furthermore, the system design is characterized by openness and modularity, allowing for flexible adjustments based on specific task requirements. The control software provides real-time monitoring and supports the switching of various control modes, such as constant force control, constant displacement control, and spindle speed control, through integrated plug-in modules. The open and reconfigurable GTRobot control platform (V1.2.0) serves as the backbone of the system, facilitating the integration of external devices and sensors. For example, the electric spindle is not only directly controlled by the central controller but also seamlessly integrated into the force–position hybrid control loop. Additionally, the system features RSI correction functionality, enabling dynamic real-time trajectory adjustments based on sensor feedback, thereby ensuring the stability and precision of the welding process.

The eight-axis linkage system is a critical component of the robotic FSW setup, designed to accommodate complex welding trajectories and dynamic operational requirements. This system integrates a six-degree-of-freedom robot arm mounted on a base with two auxiliary positioners for workpiece orientation. Through the synchronous coordination of the eight axes, the system achieves spatial postures and positions that a single robot cannot accomplish, ensuring that the welding tool maintains the correct angle and contact state while moving along curved or complex weld seams. Figure 3 shows the offline model of the eight-axis linkage system, depicting the coordinated configuration of the robot and the dual positioners. As shown in Figure 3, the joint labels J1–J9 represent: J1–J6 correspond to the six joints of the robotic arm, enabling full six-DOF movement; J7 and J8 are the two rotational axes of the dual-positioner system, responsible for orienting the workpiece; J9 represents the base rotation or an additional axis of the fixture frame, enhancing overall flexibility. Before actual operation, offline programming and simulation (as shown in Figure 3) were used to verify the motion of the robot and positioners, optimizing joint movements and avoiding singular positions or collisions. The hardware components of the eight-axis system include the robot body, positioner units, a motion controller (GOGO R688), and servo drives for each axis. The motion controller communicates with all axes in real time via a high-speed bus (gLink bus protocol), supporting precise multi-axis synchronous control. This integrated hardware configuration ensures the system’s flexibility and accuracy during the welding process, effectively expanding the robot’s degrees of freedom and enhancing its operational capabilities.

In this design, the spindle subsystem is fully integrated into the robot’s control framework, which differs from traditional systems in which the spindle operates independently. By incorporating the spindle’s operation into the main control loop, the system structure is simplified, and the response speed is improved. As shown in Figure 4, the electric spindle used for FSW (Friction Stir Welding) is mounted on the robot’s end and is equipped with an internal water-cooling system. The integration of the electric spindle with the frequency converter allows precise control of its speed and torque, which is crucial for maintaining stable and consistent welding conditions. The water-cooling system prevents the spindle from overheating during prolonged welding processes, ensuring stable performance. The spindle’s rated speed reaches 5000 r/min, with a power output of 29 kW. It is designed to withstand a maximum axial load of approximately 5000 N and is capable of handling the significant forging forces generated during FSW. The cooling system’s configuration ensures long-term stability under heavy-load conditions.

The sensor subsystem is critical for monitoring key process parameters and achieving closed-loop control. Among these, the six-axis force sensor and the laser displacement sensor work in tandem to acquire essential data such as welding force and insertion depth. The six-axis force sensor can measure forces and moments in all directions, with the axial force (F_z) being particularly important, as the largest load during FSW is generated along this axis. The force sensor selected for this system has a range of 0–15 kN in the Z-axis direction, with an overload capacity of up to 30 kN, covering the entire range of forces that may occur during welding. The sensor is connected to a high-resolution data acquisition module (24-bit A/D) via a high-speed interface (Ethernet, with a refresh rate of up to 2000 Hz), ensuring high precision and minimal delay in the force measurement. To mechanically integrate the force sensor, it is installed between the robot’s sixth axis and the electric spindle, as shown in Figure 5. Figure 5 provides an assembly diagram of the welding head, illustrating the installation method of the force sensor and how the load is transmitted through the sensor. The circular arrows in Figure 5 represent the directions of the forces and moments acting on the sensor, including the axial force (Fz) and the moments (Mx, My, Mz) around the respective axes. With this configuration, the axial force applied to the tool can be transmitted to the sensor, enabling real-time monitoring of the welding force. However, due to additional factors such as the weight of the electric spindle and its connecting components, the raw readings from the force sensor include not only the forces generated during the process but also the gravitational force of the spindle-tool assembly and the preload-induced force bias. Therefore, a calibration procedure is necessary to ensure the measurement accuracy.

To accurately measure the forces during welding, the force sensor system must be calibrated to eliminate any static bias effects. In the static calibration test, the contributions of the installation preload and the spindle assembly’s weight to the sensor readings were quantified. First, with only the force sensor and initial connecting components installed, the sensor displays a small force value of 327 N, recorded as F₁, which arises from the mechanical preload bias caused by the installation. Subsequently, when the electric spindle and the welding tool are mounted on the sensor, the sensor reading increases to 1406 N. The difference between these two readings, 1079 N, corresponds to the gravitational force (F_G) of the spindle, tool, and connecting components. The measured values (327 N and 1406 N) confirm the presence of the static bias forces in the system. By compensating for these bias forces (F₁ and F_G) in the controller, the actual dynamic forging force (F_D) measured by the sensor during welding can be accurately calculated using the following relationship:

$$F_D=F_S-(F_1+F_G)$$

(1)

where F_S is the raw force value from the sensor. This calibration process verifies the accuracy of the force values provided by the sensor after deducting known biases and demonstrates the system’s measurement precision: after calibration, the force sensor can reliably measure the true axial welding force with minimal error. In addition to eliminating the static bias, the high resolution and high sampling rate of the sensor system also help reduce the measurement noise and error, allowing even subtle fluctuations in the welding force to be finely captured.

After the calibration of the force sensor was completed, the system shifted its focus to the precise measurement of the welding depth. The laser displacement sensor played a pivotal role in monitoring the tool insertion depth during the welding process. In this system, the CMOS analog laser displacement sensor IL-300 from Keyence was selected. The sensor has a measurement range of 160 mm to 450 mm, with a reference distance of 300 mm. Its display resolution is as high as 10 µm, and the repeatability is 30 µm, which meets the precision requirements for FSW depth control. To ensure that the laser sensor accurately captured the tool’s position, its mounting on the robot was meticulously designed. As shown in Figure 6, the sensor is fixed on the outside of the spindle through a mounting bracket and is aligned with the workpiece surface at a 45° tilt angle. This inclined mounting method was chosen to reduce measurement errors. If the sensor were vertically mounted directly above the weld seam, the laser spot would not fully coincide with the actual welding point (due to the spindle radius, the spot would fall slightly ahead of the tool), and the readings would be susceptible to minor surface irregularities of the workpiece. In contrast, the 45° tilt installation ensures that the laser spot intersects the weld line at the tool–workpiece contact point, effectively eliminating the deviation between the measurement point and the actual welding point. This configuration enhances the accuracy of the depth measurement by ensuring that the sensor always targets a fixed relative position on the workpiece, even during tool advancement. Additionally, this mounting method ensures that the laser sensor operates within its optimal measurement range throughout the process. Through the optimization of the mounting method, the potential error sources related to the laser sensor were mitigated.

To convert the laser sensor readings into meaningful displacement information, the calibration of the laser sensor was essential. The sensor outputs an analog voltage of 0–5 V, corresponding to the target distance. A linear calibration model was established to map this voltage to the actual tool insertion depth. Figure 7 illustrates the calibration model of the laser sensor: two reference points were used to define the linear relationship between the voltage and depth. During the calibration process, the laser sensor was first set to its initial state. Subsequently, the tool was moved to a known deeper position (e.g., the shoulder pressed into the workpiece by 4 mm), and the sensor’s output voltage was recorded (set to 5 V). Using these two calibration points (e.g., 0 mm insertion depth corresponding to 0 V, and 4 mm insertion depth corresponding to 5 V), a linear model was established where the voltage changes proportionally with depth. The dashed line in Figure 7 represents the reference depth or position against which the sensor measures the actual tool insertion depth. The dashed arrows indicate the movement or variation in the tool’s position or depth during the welding process. The letters ‘a’ and ‘b’ denote the specific positions of the tool relative to the reference line, which the system uses to monitor and control the tool’s depth. This linear calibration relationship, as shown in Figure 7, ensures that any voltage reading within this range can be converted into an exact displacement value. After establishing the calibration model, the sensor was calibrated according to the designed procedure, with the calibration formula set as follows:

$$0.5\le {\displaystyle \frac{\mathrm{D}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{d} \mathrm{d}\mathrm{i}\mathrm{s}\mathrm{p}\mathrm{l}\mathrm{a}\mathrm{y}\mathrm{e}\mathrm{d} \mathrm{R}\mathrm{V}\left[2\right]-\mathrm{P}\mathrm{r}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{R}\mathrm{V}\left[1\right]}{\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{t} \mathrm{c}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{P}\mathrm{V}\left[2\right]-\mathrm{P}\mathrm{r}\mathrm{e} \mathrm{c}\mathrm{o}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{P}\mathrm{V}\left[1\right]}}\le 2$$

(2)

During actual welding operations, the calibrated sensor provides real-time feedback on the tool insertion depth to the control system. The control algorithm uses this feedback to compare the current depth with the desired setpoint. If a deviation is detected, the control system adjusts the robot’s vertical position through the RSI interface to maintain the target depth. Therefore, the calibration of the laser sensor is crucial for the accuracy of the depth measurement and lays the foundation for implementing a closed-loop constant displacement control strategy.

To address potential error sources, multiple measures were implemented in the design. For the force sensor, errors could arise from zero drift, off-axis loading, signal noise, or drift. These error factors were minimized through comprehensive calibration and the use of high-resolution, high-frequency data acquisition. The mechanical compliance of the robot under force was another consideration: under high forging pressure, the robot arm might exhibit slight elastic deflection, causing the tool to deviate from the commanded trajectory and introducing positional errors. To address this issue, the system relies on feedback from the force and displacement sensors to detect such deviations; the control system compensates in real-time through the RSI interface, correcting the tool’s position and applied force. For the laser sensor, the measurement error sources include sensor alignment errors and variations in the workpiece surface reflectivity or angle. By mounting the sensor at a 45° tilt angle and performing precise calibration, misalignment errors were minimized, and the sensor’s sensitivity to surface irregularities was reduced. The sensor’s high resolution (10 µm), combined with proper calibration, ensures that even subtle changes in the insertion depth can be detected. Additionally, environmental factors affecting the measurements, such as electrical noise in the sensor signal or changes in ambient conditions, were considered; shielding measures and appropriate sensor placement reduced electrical interference, and the system typically operates in a stable environment to avoid measurement drift caused by temperature variations.

3. Force/Position Control Strategy for Robotic Friction Stir Welding

During the welding process, the robot is prone to flexible deformation due to the forging forces, which leads to positional deviations and affects the weld quality. To address this issue, force/position control strategies were employed to compensate for these deviations, ensuring precision and stability in the welding process. Accurate force/position detection is crucial for online control. Multi-dimensional force sensors (such as piezoelectric quartz sensors or strain-based sensors) can be installed on the workbench or at the spindle’s front end. Alternatively, the robot’s internal bus (RSI), combined with the EtherCAT protocol, can be used to acquire external force sensing data, enabling closed-loop regulation at millisecond cycles. For curved surface welding, laser displacement or vision sensors are often employed to detect downward pressure, lateral deviation, and workpiece surface posture, facilitating dynamic trajectory correction. However, the simultaneous demands of high load, multi-degree-of-freedom motion, and real-time high-speed data acquisition pose challenges in terms of the integration and control software development.

3.1. Robotic Dynamic Offset Function and Position Correction Schemes

During the welding process, the robot’s stirring head contacts the workpiece, which causes flexible deformation of the robot’s arm due to the forging force. This deformation leads to positional deviations, causing the robot’s trajectory to shift. To resolve this, the dynamic offset function is integrated into the robot’s control system. This function allows for real-time spatial position correction based on a dynamic coordinate system. The core concept of the dynamic offset is the real-time adjustment of the robot’s motion trajectory using a dynamic coordinate system, as shown in Figure 8.

The Tool Coordinate System (TTS) is an extension of the robot’s base coordinate system, calculated using the joint matrix, with its origin located at the tool’s end center and the Z-axis perpendicular to the work surface. The Process Coordinate System (CCS), on the other hand, references the robot’s forward direction and is synchronized with the welding trajectory to ensure accuracy during position correction.

To ensure effective position correction during the welding process, this study proposes a dynamic offset correction scheme based on the Process Coordinate System (CCS). This scheme adjusts the position based on directional changes in the robot movement during welding, as shown in Figure 9. The labels ①, ②, and ③ in Figure 9 correspond to the following: ① represents the starting point of the trajectory progress, ② indicates the point where the trajectory is being corrected, and ③ denotes the end point where the corrected trajectory reaches, showing the maximum correction amount. It is divided into two modes: the trajectory discontinuous correction scheme and the trajectory continuous correction scheme.

(1)

Trajectory Discontinuous Correction Scheme: The correction applied to the previous segment of the trajectory will be ignored in the subsequent trajectory, and the later trajectory will be executed according to the taught point coordinates.

(2)

Trajectory Continuous Correction Scheme: During the execution of multiple trajectory segments, the correction applied to the previous segment will be carried over to the subsequent trajectory, resulting in a cumulative offset.

Friction stir welding involves a strong bilateral physical connection, and discontinuous welding trajectories can cause significant system oscillations, leading to weld defects. In the initial design phase, two schemes were tested. In the first scheme, after the robot corrected the trajectory at the front end and switched direction, positional jumps occurred, causing system vibrations. The current in the motor driver surged, triggering a danger warning and an emergency stop. In contrast, the second scheme demonstrated a smooth multi-segment trajectory execution with system stability. The final coordinates at the endpoint were the sum of the original interpolation point coordinates and the offset. Based on a comprehensive analysis, the second scheme with a continuous multi-segment trajectory offset was determined to be optimal, while the first scheme was discarded.

3.2. Research on the Constant Displacement Welding Control Strategy and the Constant Pressure Welding Control Strategy

The core objective of the constant displacement control strategy is to maintain a constant plunge depth of the stirring tool during welding, ensuring the stability of the welding process. During friction stir welding, the robotic arm is prone to flexible deformation, causing variations in the plunge depth of the stirring tool, which directly impacts the quality of the weld seam. Studies have shown that maintaining a constant axial shoulder plunge depth is crucial for ensuring the welding quality. Therefore, a constant displacement control strategy is employed to achieve this goal.

Based on the robot’s DH model, constant displacement control is achieved through two approaches: the base coordinate origin offset scheme and the tool coordinate system offset scheme. The base coordinate origin offset scheme controls the robot’s end position by dynamically adjusting the origin of the base coordinate system, while the tool coordinate system offset scheme directly controls the plunge depth by adjusting the position of the tool center point (TCP).

Figure 10 illustrates the analysis of the robot’s end position and the selection of position acquisition points, ensuring measurement accuracy and real-time performance. Points 1, 2, 3, and 4 represent the laser irradiation points located on the front, rear, left, and right sides, respectively, toward advance. Based on the welding process theory, point 2, at the rear side of the weld point, irradiates the already-welded seam. This often results in a concave surface, which is inconsistent with the height of the unwelded seam and is not suitable for reference. Point 3, on the left side of the weld point, causes spatter accumulation and burr formation when the spindle rotates counterclockwise. Conversely, point 4, on the right side when the spindle rotates clockwise, experiences similar issues. Both points 3 and 4 lead to inaccurate position feedback due to spatter interference. In contrast, point 1, which avoids these issues, accurately represents the weld seam information and provides forward feedback, thus meeting the requirements for constant displacement control. Therefore, point 1 was selected as the reference for data collection. Effective position deviation correction is achieved through the collaboration of a laser sensor and a position signal controller.

Based on the above control strategy, a constant displacement control system is designed with the hardware structure shown in Figure 11. The system consists of key components such as a laser sensor, robot controller, positioner, and electric spindle. Dynamic offset control is achieved through the feedback of position signals via a signal controller.

In the constant displacement control system, the robot’s end-effector displacement is controlled using a combination of a PI controller and a Dirichlet integral model. The Dirichlet integral model is used for displacement adjustment over a large range, while the PI controller is employed for finer control within a smaller range. The control model, shown in Figure 12, ensures the stability and efficiency of the welding process.

The constant pressure control strategy plays a crucial role in robotic friction stir welding, especially when variations in the axial forging force affect the welding quality. Constant pressure control ensures the mechanical properties and quality of the weld seam by regulating the axial pressure. For constant pressure control, force feedback-based impedance control is widely used. This approach uses force sensors to monitor the axial pressure in real-time and feeds the data back to the control system. The system then adjusts the robot’s end-effector position and plunge depth to maintain the stability of the pressure. As shown in Figure 13, the constant pressure control system consists of a six-dimensional force sensor, a data acquisition card, a robot controller, and an electric spindle. Data are transmitted via the TCP protocol, processed by the robot control system, and used to dynamically adjust the welding pressure. The control model, shown in Figure 14, employs a PID control algorithm to ensure the stability of the welding pressure.

3.3. Research on the Force/Position Hybrid Control Strategy

The force/position hybrid control strategy combines the advantages of constant displacement control and constant pressure control. It allows for the flexible adjustment of depth and pressure during the processing of complex welded workpieces, meeting varying welding requirements. It adjusts the priority between force and position control, ensuring constant displacement control over a large range while employing constant pressure control in small ranges to maintain the welding quality. The force/position hybrid control divides the control areas reasonably, combining the PID regulation and Dirichlet integral models. This approach not only ensures welding stability but also improves the quality of the weld seam.

In this study, the hybrid control strategy prioritizes constant displacement control, ensuring that the stirring tool tip remains within the designated displacement control range throughout the welding process. Global control is achieved through the dynamic robot offset to adjust the depth, while local control fine-tunes the axial forging pressure by adjusting the tool tip position within a smaller control region. The overall strategy follows a structured spatial division: in large-scale regions, constant displacement control ensures proper weld formation; in localized regions, constant pressure control maintains weld quality; and in boundary zones, hybrid control stabilizes the process. The space division is shown in Figure 15.

The target depth position is set as the Z-axis coordinate zero point, with constant displacement control applied over a large range. Outside the defined critical point, displacement control is used, while within the critical range, constant displacement control is disabled. The small-range interval for constant displacement is set within ±0.3 mm on the Z-axis. The large range is divided into multiple stages, each controlled by a Dirichlet integral model. In small intervals, a constant pressure control model is applied, with the pressure adjusted within a target range of 90–110% using a PI controller. However, during actual welding, the boundaries for the constant pressure control and constant displacement control do not overlap, requiring hybrid control. Therefore, a classification-based approach is necessary for this case.

As shown in Figure 16, the upper boundary point of the small pressure range is A, with a pressure value of 90% a N. The corresponding pressure at the upper boundary of the small displacement range (0.3 mm) is b N. Theoretically, due to the inhomogeneity of the material density in the weld, two cases (Q1 and Q2) are discussed. In the Q1 region, where the pressure at point A is greater than b N, a hybrid control method combining force and displacement is used. The output formula is shown in Equation (3), where Z1 and Z2 represent the dynamic offsets for the displacement and pressure control, and α and β are the control coefficients. Because displacement control takes priority over pressure control, α > β. In the Q2 region, where the pressure at point A is less than b N, the hybrid control scheme is still applied, ensuring that the welding position remains within the small displacement range. Output coefficients α and β can be adjusted accordingly. The same approach is applied to regions Q3 and Q4, which are not analyzed further.

$$\mathsf{\Delta }Z=\left[\begin{array}{cc}\alpha & \beta \end{array}\right]{\left[\begin{array}{cc}\mathsf{\Delta }{Z}_1& \mathsf{\Delta }{Z}_2\end{array}\right]}^T$$

(3)

The control strategy for the hybrid system combines the constant displacement system and the constant pressure system. The displacement output coefficients are designed as shown in

Table 1. Displacement output dynamic matrix of the hybrid control system.

	Number	(−100%, −8%)	[−8%, −5%)	[−5%, 5%)	[−5%, 8%)	(5%, 8%)	(8%, +∞)
(0.5, +∞)	1	X11	X12	X13	X14	X15	X16
	2	Y11	Y12	Y13	Y14	Y15	Y16
(0.3,0.5]	3	X21	X22	X23	X24	X25	X26
	4	Y21	Y22	Y23	Y24	Y25	Y26
[−0.3,0.3]	5	X31	X32	X33	X34	X35	X36
	6	Y31	Y32	Y33	Y34	Y35	Y36
[−0.5, −0.3)	7	X41	X42	X43	X44	X45	X46
	8	Y41	Y42	Y43	Y44	Y45	Y46
(−∞, −0.5)	9	X51	X52	X53	X54	X55	X56
	10	Y51	Y52	Y53	Y54	Y55	Y56

, where the displacement output coefficient for the constant displacement system is X_ij (odd-numbered rows), and for the constant pressure system, it is Y_ij (even-numbered rows). During the hybrid control welding process, both the pressure and position control systems operate simultaneously, with the displacement output being a superposition of both. The expression is given in Equation (4), where x and a are the correction coefficients and step sizes for the displacement control, and y and b are the correction coefficients and step sizes for the pressure control.

$$\mathsf{\Delta }z=\left[x,y\right]{\left[a,b\right]}^T$$

(4)

The hybrid control system uses a long-range constant displacement control, small-range constant pressure control, and a force–position combined control strategy in the transition region. Based on the shoulder plunge depth and top forging pressure changes during welding, the constant displacement system controls large ranges in the intervals (0.5, +∞) and (−∞, −0.5), while the transition zone for force–position control is in the intervals (0.3, 0.5] and [−0.5, −0.3]. The small range is controlled in the [−0.3, 0.3] interval. The constant pressure system controls large ranges in the intervals (−100%, −8%) and (8%, +∞), with the transition zones being [−8%, −5%) and (5%, 8%], and the small range being [−5%, 5%].

For example, when the welding position is in the (0.3, 0.5] range and the pressure is in the [−8%, −5%) range, the output displacement can be expressed by Equation (5), realizing hybrid control during the welding process.

$$\mathsf{\Delta }\mathrm{z}=X_{22}.a+Y_{22}.b$$

(5)

4. Robotic Friction Stir Welding Control System Software Design

To meet the requirements of robotic friction stir welding (FSW), this research develops an open, scalable control software system based on the GTRobot platform. The system integrates core modules, including electric spindle control, human-in-the-loop control, constant displacement, constant pressure, and force–position hybrid control, providing a complete hardware–software solution for large, thin-walled, complex curved surface welding. Offline programming methods are employed for welding trajectory design and simulation, enabling the precise planning and visualization of 3D welds.

The open robotic control system supports secondary development and multi-mode motion control. According to the IEEE definitions, such a system should feature expandability, portability, interoperability, and modularity. However, many industrial robot controllers on the market are specialized and lack these capabilities.

To address the challenges in welding large curved surfaces, the GTRobot open, reconfigurable control platform (Figure 17) was employed. Built on the Windows CE operating system with a DSP/ARM motion control layer, it ensures high real-time performance and reliability. The platform supports expansion through external I/O modules and various communication protocols, enabling seamless integration of external sensors and robotic motion.

The GTRobot control system architecture is composed of three layers: the platform application layer (CPU), the motion control layer (DSP/ARM), and the drive layer. The platform application layer, implemented in C/C++, handles human–machine interaction (UI/HMI), task management, and communication processes. The motion control layer executes high-real-time calculations such as trajectory planning and kinematics/dynamics. The drive layer ensures stable robot movement under high load by controlling the power output and joint motors.

Secondary development is focused on the platform application layer, using tools such as VS2008, QT4.7.3, and GUCN455CE6SDK to compile and debug code. Custom plugins (dll) are integrated into the main program (GTRobot.exe), with the process involving WinCE environment configuration, project creation, library/resource setup, and C++ programming.

Force–position control is critical for friction stir welding, enabling real-time adjustments of parameters such as “upset force” and “downward displacement”. The constant pressure control module uses force sensors to measure the axial force and compare it to the set value. If deviations occur, the system issues position offset commands to maintain a constant upset force. The backend UML structure, where the PLC periodically queries the sensor, parses the data, and adjusts the displacement through the pressure control function, forms a closed-loop process. The software control process of the constant-voltage system is shown in Figure 18.

The constant displacement module uses a laser sensor’s voltage signal (0–5 V) to monitor the displacement, and the robot offsets ensure a consistent insertion force. The frontend interface includes voltage collection, board thickness settings, and calibration controls. The backend reads the voltage via GTR_GetAiValue (double &value, const short index, short type) and compensates based on the displacement model (DoOffset).

Hybrid control combines pressure and position signals to maintain the weld pool flow and consistent heat input in complex scenarios. The frontend interface offers settings for the force–position ratio and coordinate correction. The backend registers two PLC threads to collect real-time pressure and position data using a dynamic matrix algorithm (pressure_control + position_control) for decision-making and corrections, ensuring system flexibility and stability. After comprehensive analysis, the back-end program operation of the force–position hybrid system is shown in Figure 19.

For spatial surface welding, which is difficult due to teaching programming challenges, the PQArt 2024 offline programming software is used for modeling and trajectory planning. In PQArt 2024, several coordinate systems must be defined, and welding paths are generated with options like “edge trajectory”. Parameters such as step size, fixed Z-axis, and tilt angle were added. Figure 20 illustrates the specification of trajectory speed, instruction type, and path optimization. Additionally, it presents a flowchart of the constant displacement control algorithm, demonstrating the interaction between calibrated sensor data and control logic. The system continuously compares real-time welding depth data with the target value and adjusts the tool position accordingly.

Trajectory editing includes operations such as translation, rotation, Z-axis fixation, and coordinated motion, effectively addressing issues like “inaccessibility” and “axis over-limit” while enabling coordinate mapping for positioner-linked scenarios. After editing, the robot motion can be simulated within the software. Figure 21 illustrates the robot’s posture in the offline simulation. Once the simulation is collision free, an executable file can be generated and downloaded to the robot system for execution.

5. Experimental Results

In this chapter, welding experiments were conducted using the developed robotic friction stir welding system to validate its control performance, including both planar straight-line trajectories and spatially complex curved trajectories. The planar trajectory welding experiments compared the welding quality and stability under different control modes. In addition, complex surface trajectory welding experiments were performed on a satellite radiator workpiece, demonstrating the system’s capability in tracking spatially complex paths.

5.1. Plane Linear Trajectory Process Welding Experiment

5.1.1. Subsubsection

The planar straight-line trajectory welding experiment evaluates the performance of the robot’s force–position hybrid control. The experimental setup, as shown in Figure 22, includes a heavy-duty robot, a high-precision rotary positioning table, an electric spindle with a stir pin tool, and pressure and laser displacement sensors. For a 5 mm-thick 2219 aluminum alloy plate, the welding trajectory was obtained through robot teaching, and the plunge depth was compensated during path interpolation to eliminate the insufficient plunge caused by the robot deformation under force.

This experiment employed a control variable method to compare four welding modes: (1) No control mode, where no force or position intervention was applied during welding, and the robot executed the taught trajectory in an open-loop manner; (2) Constant force control, where the axial forging force was stabilized at the target value of 4500 N; (3) Constant displacement control, where the tool shoulder position was maintained at the set depth, corresponding to a position sensor voltage of 1.1 V when the shoulder was plunged 0.1 mm after calibration; (4) Force–position hybrid control, combining both force and position control. All experiments shared the same basic process parameters: the welding plate was a 5 mm-thick 2219 aluminum alloy plate, with a welding speed of 2 mm/s, spindle speed of 1500 rpm, and plunge feed rate of 0.25 mm/s.

Table 2. Constant pre-pressure control process parameter table.

Number	Plate Material	Welding Speed	Spindle Speed	Pressing Speed	Nominal Pressure	Control Depth	Displacement Voltage
1	2219	2 mm/s	1500 r/min	0.25 mm/s
2	2219	2 mm/s	1500 r/min	0.25 mm/s
3	2219	2 mm/s	1500 r/min	0.25 mm/s	4500 N
4	2219	2 mm/s	1500 r/min	0.25 mm/s	4500 N
3	2219	2 mm/s	1500 r/min	0.25 mm/s		7 mm	1.1 V
4	2219	2 mm/s	1500 r/min	0.25 mm/s		10 mm	1.1 V
7	2219	2 mm/s	1500 r/min	0.25 mm/s	4500 N	7 mm	1.1 V

lists the process parameters for each control mode (supplemented table for all four modes). Modes (1)–(3) were compared with plunge depth compensations of 7 mm and 10 mm, while mode (4), the force–position hybrid control, used an initial plunge depth of 7 mm.

5.1.2. Experimental Results and Analysis

Using the control variable method, we compared three modes: constant preload control, constant displacement control, and force–position hybrid control. Welding data were collected and analyzed to evaluate the effectiveness of the system. Depth compensation was used during the trajectory interpolation because the potential deformation of the robot affects the depth of machining. Comparative experiments were conducted for weld depths of 7 mm and 10 mm with the process parameters shown in

Table 3. Hybrid force–position control process parameter table.

Workpiece Material	Plate Thickness	Welding Speed	Spindle Speed	Control Pressure	Control Depth	Trajectory Planning
5A06-T6	4 mm	Segment Control	1800 r/min	4500 N	Segment Control	Offline Programming

. As shown in Figure 23a, the pressure was varied between 4546 N and 4409 N with a maximum control error of 2.02%.

• Axial force and position control performance.

In the constant force control mode, the axial pressure remained stable within the set range during welding. As shown in Figure 23a, for the 7 mm plunge experiment, the forging pressure fluctuated between approximately 4409 N and 4546 N during the stable welding stage, with a maximum deviation of 2.02% from the set value of 4500 N; for the 10 mm plunge experiment, the pressure varied between 4401 N and 4543 N, with a maximum error rate of about 2.21%. These results meet the design requirement of controlling the pressure fluctuations within ±5%. In the constant displacement control mode, the system also achieved high precision in the tool depth position control. As indicated by the position sensor voltage in Figure 23b, for the 7 mm initial plunge experiment, the voltage ranged from 0.82 V to 1.32 V, corresponding to a maximum position error of about 0.22 mm; for the 10 mm plunge experiment, the voltage ranged from 0.81 V to 1.38 V, with a maximum error of about 0.29 mm. All errors were within the control threshold of ±0.3 mm, meeting the position control accuracy requirement of less than 0.3 mm. As shown in Figure 23c, in the force–position hybrid control mode, both axial force and position remained within the required ranges: the maximum pressure fluctuation was 4597 N, with an error rate of 2.21%, and the maximum displacement voltage fluctuation was 0.86 V, with an error rate of 0.24 mm, satisfying the control accuracy indicators of 5% for pressure and 0.3 mm for position. Comprehensive analysis shows that the hybrid control system’s performance meets the control requirements.

2.

Weld formation quality.

The weld surface in the no-control mode exhibited moderate smoothness, with uneven flash and noticeable concavity under deeper plunges. As shown in Figure 24, the 7 mm plunge weld in the no-control mode had slight burrs and uneven flash, whereas the 10 mm plunge weld showed significant depression, uneven flash distribution, and a deep keyhole mark left by the stir pin extraction. On the weld backside, the 7 mm no-control weld appeared light-colored, indicating insufficient heat input and surface grooves; the 10 mm no-control weld backside was darker, with material adhesion, suggesting that excessive plunge caused backside sticking.

Compared to the no-control welds, the welds under constant force control exhibited smoother and more uniform surfaces, with significantly improved overall formation and minimal burrs. As shown in Figure 25, for the 7 mm plunge depth, the weld surface was smooth with very slight burrs and minimal concavity; the welding end stage showed minor uneven flash, but the keyhole mark was shallow. For the 10 mm plunge, the weld start stage had a slight and localized flash, but the subsequent weld formation was uniform and smooth, with a surface quality similar to that of the 7 mm test. Both constant force welds showed only slight material adhesion on the backside, indicating an appropriate plunge depth that ensured sufficient fusion without excessive material extrusion.

Compared to the no-control welds, the welds under constant displacement control exhibited even smoother surfaces, with uniform weld width and no visible defects such as grooves or excessive flash. As shown in Figure 26, the weld surface from the 7 mm initial plunge was flat, with slight concavity within an acceptable range; for the 10 mm initial plunge, the weld start segment showed slightly more noticeable flash, but the subsequent weld formation was uniform and smooth. Both constant displacement control welds exhibited minimal backside adhesion, far less than that in the no-control mode. This indicates that maintaining a constant plunge depth improves the weld consistency and reduces defects.

In the force–position hybrid control mode, the overall weld formation quality was the best. As shown in Figure 27, the weld surface under the hybrid control was smooth and flat, with minimal concavity and an evenly distributed flash. The weld edges were clean, with no excessive material overflow, and the weld width was uniform along the joint. The weld backside showed no adhesion between the workpiece and the tool, indicating that the optimized plunge depth control achieved sufficient material fusion without causing tool drag or excessive flash. Compared with the no-control welds, the hybrid control welds showed significant improvements in surface smoothness and consistency. The successful combination of force and position control not only maintained the process stability but also enhanced the forging and flow of the material in the stir zone, resulting in dense and high-quality welds.

Due to the thermomechanical effects of the friction stir welding, the material in the weld zone under hybrid control underwent intense plastic flow and dynamic recrystallization, forming a fine and uniform recrystallized microstructure in the weld nugget. No defects such as voids or cracks were observed within the weld, further demonstrating that the force–position hybrid control strategy can produce high-quality welded joints that meet the quality requirements.

5.2. Complex Curved Trajectory Welding Experiment

5.2.1. Trajectory Planning and Control Method

To verify the system’s performance on complex spatial trajectories, a welding experiment was conducted on a complex curved panel—a satellite radiator. The workpiece is a typical thin-walled structure with a complex curvature, made of 5A06-T6 aluminum alloy with a thickness of 4 mm. The welding fixture and the preset trajectory are shown in Figure 28. This spatial curve welding experiment faced three challenges: (1) The robot must strictly follow the three-dimensional weld trajectory to avoid positional deviations; (2) Real-time control of the plunge depth is required during trajectory execution to adapt to curvature changes; (3) Maintaining a stable orientation of the welding tool relative to the weld, with a tool tilt angle of approximately 2.5°, to ensure a defect-free welding process. Since teaching alone is insufficient to precisely achieve a curved trajectory while maintaining a constant tilt angle, this experiment utilized offline programming to generate the welding path and tool orientation, which were then imported into the robot control system for execution.

The process parameters for complex curved welding are listed in Table 3. Based on the workpiece geometry and weld seam curvature, the trajectory was divided into 17 segments, classified into two categories: small-curvature segments (1, 3, 5, 7, 9, 11, 13, 15, 17) and large-curvature segments (2, 4, 6, 8, 10, 12, 14, 16). A force–position control strategy was developed for each segment (Figure 29) to ensure welding quality. Welding speed (X-direction) was adjusted according to curvature, reducing speed for high-curvature sections. In the Y-direction, manual intervention corrected trajectory deviations. In the Z-direction, a force–position hybrid control method dynamically adjusted position calibration voltage to maintain proper tool plunge depth.

During the welding process, the pre-planned welding trajectory and control parameters for each segment are imported into the robot control system, ensuring compatibility with the robot controller and positioner. The robot then executes the welding along the offline-taught path while integrating real-time monitoring and adjustments. As the robot enters each trajectory segment, the system applies the corresponding control parameters (force or position settings) as planned. Operators monitor the welding process on-site and make minor Y-direction adjustments if necessary. In high-curvature arc segments, potential lateral deviations may occur; to address this, welding speed is reduced, and slight manual corrections are applied to ensure the tool follows the correct trajectory.

5.2.2. Experimental Results and Analysis

• Weld Formation Quality

The robotic system successfully completed the welding of the curved panel, achieving sound joint quality. Figure 30 presents the macroscopic morphology of the 4 mm-thick satellite radiator curved welds. The numbers 1 to 9 in Figure 30 represent nine representative cross-sectional specimens extracted from different characteristic locations along the weld path. These specimens were taken for metallographic analysis to evaluate the internal weld integrity. Overall, the welds exhibited favorable formation characteristics with smooth surfaces and uniform widths. Minor edge defects were observed only in a few high-curvature segments, primarily attributable to transient effects caused by rapid directional changes and contact condition variations during high-curvature welding. To evaluate the internal weld integrity, nine representative cross-sectional specimens were extracted from characteristic locations along the weld path for metallographic analysis. Figure 31 displays the microstructural images of the weld cross-sections at the sampling points, revealing fully dense stir zones without internal porosity or cracks. The microstructural images in Figure 31 correspond to nine cross-sectional samples taken from various locations along the weld path. Each subfigure (1–9) represents a different sampling point, where the weld’s internal characteristics were analyzed. The images show the weld’s microstructure, including the stir zones, which are fully dense without internal porosity or cracks, confirming the high quality of the welds at each sampling point. These results confirm that the force–position-controlled friction stir welding system can produce high-quality welds on complex curved surfaces.

Subsequent welding experiments were conducted on 2 mm-thick satellite radiator panels on further validate the system capabilities. As shown in Figure 32, the thin-plate welds demonstrated comparable formation quality with uniform profiles and no visible defects, indicating the control system’s adaptability to varying material thicknesses. This outcome suggests that parameter optimization enables the welding system to maintain superior weld formation across different material thicknesses in geometrically complex workpieces.

2.

Trajectory Tracking Accuracy and System Performance

The complex trajectory welding experiments demonstrated the system’s capacity to maintain both welding quality and precise spatial trajectory following. During the satellite radiator curved panel welding, the robotic tool path exhibited excellent conformity with the preprogrammed trajectories. Through segmented trajectory planning and parameter-specific control strategies, initial path deviations were effectively corrected, maintaining tool tracking accuracy within ±0.3 mm—fully compliant with the design specifications. Post-weld inspections revealed no significant path deviations or weld defects, with continuous central alignment along the joint line verifying the trajectory tracking reliability. Timely manual adjustments in the high-curvature segments further ensured the path accuracy. Moreover, the tool tilt angle remained stable at approximately 2.5° throughout the process, with neither excessive force nor positional errors exceeding the preset thresholds. These observations validate the real-time adaptability of the force–position hybrid control algorithm in complex curved trajectory welding, demonstrating the developed system’s robustness and practical applicability.

6. Conclusions

This study aimed at welding complex curved components in aerospace space capsules and analyzed the heat generation mechanism in robotic friction stir welding. A multi-degree-of-freedom robotic friction stir welding (RFSW) system was designed, integrated with a force/position hybrid control strategy, and effectively addressed the challenges of welding large-scale curved aluminum alloy panels. Based on the system configuration, an eight-axis linkage system was studied, and the electric spindle was integrated into the welding control system to achieve an integrated control solution.

The research also explored the application of external sensing devices and the secondary development of the control system within the robotic control framework to develop a force–position control strategy for robotic friction stir welding. The proposed control system illustrated that combining constant displacement control for large ranges with constant pressure control for small ranges significantly improved the weld seam uniformity and consistency.

With these functions in place, welding along complex spatial curved trajectories was successfully completed. Experimental validation on both planar and complex curved surfaces showed that (1) flexible deformation and positioning deviations were effectively compensated; (2) the forging pressure and welding depth remained within a controllable tolerance (<5%); and (3) the resulting weld seams exhibited a uniform morphology and reliable mechanical properties. This research supports the development of high-precision and highly reliable intelligent welding robotic systems, promoting the advancement of automated welding technology for complex curved structures in the aerospace manufacturing industry.