Weld seam profile extraction using top-down visual attention and fault detection and diagnosis via EWMA for the stable robotic welding process
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Laser vision-sensing technologies are the most widely used to detect weld seam profiles during the intelligentized robotic welding process (IRWP) with thick steel plates, in which the weld seam profile extraction technology plays a crucial role for guiding the welding torch in real time. This paper presents an effective method to extract the weld seam profile from the intense arc background. To emphasize the weld seam profile in images and produce saliency maps at the initial stage, a top-down visual attention model is proposed using the target-driven characteristics of the weld seam profile and splashes. Due to the interference data surviving in the saliency map, a visual attention–based strategy is suggested to gradually discern the larger segments of the weld seam profile through local competition of dynamic saliency based on clustering results. For ineffective weld seam profile extraction resulting from empirical parameters used in the weld seam profile extraction process, the exponentially weighted moving average (EWMA) control chart is employed to implement fault detection and diagnosis (FDD) by monitoring irregular changes of slopes of the extracted weld seam profile. In the final stage, a novel step is arranged to retrieve the possible loss of the weld seam profile. Using the proposed method, validations are carried out using the welding experiments with T-joints and butt joints. Experimental results show that the ratio of successful extraction is over 97% and more stable welding processes with better welds are obtained. This method lays a good foundation for the general weld seam profile extraction process and shows a potential industrial application to the IRWP.
KeywordsWeld seam profile extraction Fault detection and diagnosis Top-down visual attention Exponentially weighted moving average control chart Robotic welding
This work is financially supported by the National Natural Science Foundation of China under the grant no. 51575349, 51665037, and 51575348, and is partly supported by the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System (GZ2016KF002).
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