Digital Twin Virtual Welding Approach of Robotic Friction Stir Welding Based on Co-Simulation of FEA Model and Robotic Model

Abstract

Robotic friction stir welding has become an important research direction in friction stir welding technology. However, the low stiffness of serial industrial robots leads to substantial, difficult-to-measure end-effector deviations under the welding forces during the friction stir welding process, impacting the welding quality. To more effectively measure the deviations in the end-effector, this study introduces a digital twin model based on the five-dimensional digital twin theory. The model obtains the current data of the robot and six-axis force sensor and calculates the real-time end deviations using the robot model. Based on this, a virtual welding model was realized by integrating the FEA model with the digital twin model using a co-simulation approach. This model achieves pre-process simulation by iteratively cycling through the simulated force from the FEA model and the end displacement from the robot model. The virtual welding model effectively predicts the welding outcomes with a mere 6.9% error in lateral deviation compared to actual welding, demonstrating its potential in optimizing welding parameters and enhancing accuracy and quality. Employing digital twin models to monitor, simulate, and optimize the welding process can reduce risks, save costs, and improve efficiency, providing new perspectives for optimizing robotic friction stir welding processes.

Active Vibration Avoidance Method for Variable Speed Welding in Robotic Friction Stir Welding Based on Constant Heat Input

“…The stiffness along the welding direction and the Cartesian stiffness at the robot’s end-effector can be calculated using the joint stiffness data shown in Table 1 , combined with the robot’s pose [ 37 ]. Since the dynamic model simulated the vibration conditions of the FSW robot near its working position, the calculations were performed using the joint angles at the working pose, which are (−83.60°, 22.12°, 10.37°, 0°, 57.51°, −83.60°).…”

“…where F C a is the resistance experienced by the stirring tool; v is the speed of the stirring tool's movement. The stiffness along the welding direction K a and the Cartesian stiffness K c at the robot's end-effector can be calculated using the joint stiffness data shown in Table 1, combined with the robot's pose [37]. Since the dynamic model simulated the vibration conditions of the FSW robot near its working position, the calculations were performed using the joint angles at the working pose, which are (−83.60 • , 22.12 • , 10.37 To investigate the effects of different stirring tool eccentric errors and spindle speeds on the vibrations during welding, the handle of the stirring tool, as shown in Figure 5a, was ground to achieve different eccentric errors.…”

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

“…Based on the MDH model and the parameters of the FSW robot, the DH parameters, the joint stiffness, and joint range limits are shown in Table 1. The homogeneous transformation matrix for adjacent link coordinate systems in the MDH model is defined in Equation (1) [30]:…”

“…MDH parameters and joint stiffness of the FSW robot. The homogeneous transformation matrix for adjacent link coordinate systems in the MDH model is defined in Equation (1) [30]:…”