Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process

Visual sensing is an attractive approach to detect the weld position in an arc-welding process, which provides information for seam tracking. However, it is difficult to accurately detect the weld position adjacent to a molten pool because of strong arc disturbances. A novel algorithm based on the weld-pool image centroid is presented to improve the seam-tracking ability. The molten pool images are taken by a camera arranged ahead of the welding torch and the centroid is extracted as a parameter to detect the weld position. It is worth noting that the centroid corresponds to the thermal distribution of the molten pool affected by the offset between the arc and the seam centreline. Therefore the offset between the arc and the seam centreline can be estimated by this centroid. The least square linear regression method is employed to correlate the relationship between the centroid and the offset under different welding conditions. For further analysis of the centroid characteristics, a non-linear neural network is designed with three input variables which are the position, displacement and moving velocity of the centroid, respectively. This neural network model shows higher accuracy of weld detection. In comparison with directly detecting the weld position, the centroid can be measured and computed easily. This algorithm is expected to provide a promising vision model to improve the accuracy of seam tracking in real time, and subsequently to ensure good welding quality.