Abstract
Industrial robots are increasingly applied in the automatic die repair welding via the prevalent wire and arc additive manufacturing (WAAM) technology. However, the precise calibration of work coordinates is indispensable for the off-line programming of robotic welding paths, which often results in positioning error, path deviation, or even tool collisions. The die is pre-heated at about 500 °C before the robotic WAAM processes. Thus, it is challenging to calibrate work coordinates by touch sensing because those points on the X-axis and the Y-axis to determine the location of the part need to be caught by human eyes. In this paper, a camera vision calibration (CVC) method based on stereo vision is developed. Image feature points are extracted by a multi-saliency fusion algorithm based on the human visual attention mechanism. Through stereo vision, 3D information of the feature points is obtained, and the workpiece coordinate system (WCS) is finely calibrated. Compared with the random error of human vision calibration (HVC), the proposed method could improve the workpiece’s calibration accuracy, reduce the unexpected collisions in limited space, and improve the dimensional precision of the welding layer.
Published Version
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