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The recognition and positioning of start welding position (SWP) is the first step and one of the key technologies to realize autonomous robot welding. The purpose of this paper is to describe a method developed to accomplish successful autonomous detection and guiding of SWP.

The images of workpieces are snapped by charge coupled device (CCD) cameras in a relative large range without additional light. The recognized methods of SWP are analyzed according to the given definition. A two‐step method named “coarse‐to‐fine” is proposed to recognize the SWP accurately. The first step is to solve the curve functions of seam and workpieces boundaries by fitting. The intersection point is regarded as initial value of SWP. The second step is to establish a small window that takes the initial value of SWP as centre. Then, the SWP is obtained exactly by corner detection in the window. Both the abundant information of original image and the structured information of recognized image are used according to given rules, which takes full advantage of the image information and improves the recognized precision.

The detected results show that the actual and calculated positions by first step of SWP are identical for regular seam, but different for the irregular curve seam. The exact results can be calculated by the two‐step method in the paper for both regular and irregular seams. The typical planar “S‐shape” and spatial arc curved seams are selected to carry out autonomous guiding of SWP.

The experimental results are given based on the introduction of 3D reconstructed and guided method. The guided precision is less than 1.1 mm, which meets the requirements of practical production. The proposed two‐step method recognizes the SWP rapidly and exactly from coarse to fine.

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.

Some robot makers look for new applications, while others seek overseas partners, but demand for more of the same robots continues.

A report by analysts from Daiwa Securities America, assessing the Japanese robot industry, explains reasons for its growth and prospects for its future.