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Friction stir welding (FSW) is a novel method for joining materials without using consumables and without melting the materials. The purpose of this paper is to present the state of the art in robotic FSW and outline important steps for its implementation in industry and specifically the automotive industry.

This study focuses on the robot deflections during FSW, by relating process forces to the deviations from the programmed robot path and to the strength of the obtained joint. A robot adapted for the FSW process has been used in the experimental study. Two sensor‐based methods are implemented to determine path deviations during test runs and the resulting welds were examined with respect to tensile strength and path deviation.

It can be concluded that deflections must be compensated for in high strengths alloys. Several strategies can be applied including online sensing or compensation of the deflection in the robot program. The welding process was proven to be insensitive for small deviations and the presented path compensation methods are sufficient to obtain a strong and defect‐free welding joint.

This paper demonstrates the effect of FSW process forces on the robot, which is not found in literature. This is expected to contribute to the use of robots for FSW. The experiments were performed in a demonstrator facility which clearly showed the possibility of applying robotic FSW as a flexible industrial manufacturing process.

The weld joint of large thin-wall metal parts which deforms in manufacturing and clamping processes is very difficult to manufacture for its shape is different from the initial model; thus, the space normals of the part surface are uncertain.

In this paper, an effective method is presented to calculate cutter location points and to estimate the space normals by measuring some sparse discrete points of weld joint. First, a contact-type probe fixed in the end of friction stir welding (FSW) robot is used to measure a series of discrete points on the weld joint. Then, a space curve can be got by fitting the series of points with a quintic spline. Second, a least square plane (LSP) of the measured points is obtained by the least square method. Then, normal vectors of the plane curve, which is the projection of the space curve on the LSP, are used to estimate the space normals of the weld joint curve. After path planning, a post-processing method combing with FSW craft is elaborated.

Simulation and real experiment demonstrate that the proposed strategy, which obtains cutter locations of welding and normals without measuring the entire surface, is feasible and effective for the FSW of large thin-walled complex surface parts.

This paper presents a novel method which makes it possible to accurately weld the large thin-wall complex surface part by the FSW robot. The proposed method might be applied to any multi-axes FSW robot similar to the robot studied in this paper.

This study aims to enable robotic friction stir welding (FSW) in practice. The use of robots has hitherto been limited, because of the large contact forces necessary for FSW. These forces are detrimental for the position accuracy of the robot. In this context, it is not sufficient to rely on the robot’s internal sensors for positioning. This paper describes and evaluates a new method for overcoming this issue.

A closed-loop robot control system for seam-tracking control and force control, running and recording data in real-time operation, was developed. The complete system was experimentally verified. External position measurements were obtained from a laser seam tracker and deviations from the seam were compensated for, using feedback of the measurements to a position controller.

The proposed system was shown to be working well in overcoming position error. The system is flexible and reconfigurable for batch and short production runs. The welds were free of defects and had beneficial mechanical properties.

In the experiments, the laser seam tracker was used both for control feedback and for performance evaluation. For evaluation, it would be better to use yet another external sensor for position measurements, providing ground truth.

These results imply that robotic FSW is practically realizable, with the accuracy requirements fulfilled.

The method proposed in this research yields very accurate seam tracking as compared to previous research. This accuracy, in turn, is crucial for the quality of the resulting material.

This paper aims to present a deflection model to improve positional accuracy of industrial robots. Earlier studies have demonstrated the lack of accuracy of heavy-duty robots when exposed to high external forces. One application where the robot is pushed to its limits in terms of forces is friction stir welding (FSW). This process requires the robot to deliver forces of several kilonewtons causing deflections in the robot joints. Especially for robots with serial kinematics, these deflections will result in significant tool deviations, leading to inferior weld quality.

This paper presents a kinematic deflection model, assuming a rigid link and flexible joint serial kinematics robot. As robotic FSW is a process which involves high external loads and a constant welding speed of usually below 50 mm/s, many of the dynamic effects are negligible. The model uses force feedback from a force sensor, embedded on the robot, and predicts the tool deviation, based on the measured external forces. The deviation is fed back to the robot controller and used for online path compensation.

The model is verified by subjecting an FSW tool to an external load and moving it along a path, with and without deviation compensation. The measured tool deviation with compensation was within the allowable tolerance for FSW.

The model can be applied to other robots with a force sensor.

The presented deflection model is based on force feedback and can predict and compensate tool deviations online.

Presents an approach to improved performance and flexibility in industrial robotics by means of sensor integration and feedback control in task‐level programming and task execution. Also presents feasibility studies in support of the ideas. Discusses some solutions to the problem using six degrees of freedom force control together with the ABB S4CPlus system as an illustrative example. Consider various problems in the design of an open sensor interface for industrial robotics and discusses possible solutions. Finally, presents experimental results from industrial force controlled grinding.

The purpose of this research is to develop closed‐loop control of robotic welding processes.

The approach being developed is the creation of three‐dimensional models of the weld pool using stereo imagining. These models will be used in a model‐based feedback control system. Fusion of more than one sensor type in the controller is used.

Three‐dimensional images can be produced from stereo images of GMAW‐p weld pools. This requires coordinating the image capture with the arc pulse to allow observation of the pool.

This is a work in progress. The imaging is not being done in real time at this point in time. Future work will address this issue. Also, how the image information is to be used to make corrections within the controller is future work.

Closing the loop on GMAW welding will allow robotic automation of welding to proceed to a much broader degree of application.

This paper demonstrates that stereo imaging of out‐of‐position GMAW‐p weld pools is possible and the useful information can be obtained from these images. It also provides insights into the analysis required within the model‐based controller if one is to close the loop on the process specifically with regard to weld pool stability.

The forces and torques associated with friction stir welding (FSW) are discussed as they relate to implementation of the welding process with industrial robots. Experimental results are presented that support the conclusions drawn from models developed by others. It is shown that even with heavy‐duty industrial robots with high stiffness, force feedback is important for successful robotic FSW. Methods of implementing force feedback are reviewed. Attention is paid to stability issues that arise with variations in tool rotation and travel speed. Successful implementations of robotic FSW are cited.

Evaluates the applicability of ultrasonic sensors in a welding environment and reports on experimental measurements carried out with a sensory head containing ultrasonic transducers with different frequencies. Analyses the effects on the sensors of factors such as noise, temperature and shielding gas flow and concludes by suggesting appropriate protective measures for the sensors for them to operate effectively in a welding environment.

The automotive industry has been the principal driver in the development of robotics; however, as car design becomes more sophisticated, demands on robot makers will continue undiminished.