Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

Abstract

In the flux-cored arc welding process, which is most widely used in shipbuilding, a constantexternal weld bead shape is an important factor in determining proper weld quality; however, thesize of the weld gap is generally not constant, owing to errors generated during the shell formingprocess; moreover, a constant external bead shape for the welding joint is difficult to obtain whenthe weld gap changes. Therefore, this paper presents a method for monitoring the weld gap andcontrolling the weld deposition rate based on a deep neural network (DNN) for the automationof the hull block welding process. Welding experiments were performed with a welding robotsynchronized with the welding machine, and the welding quality was classified according to theexperimental results. Welding current and voltage signals, as the robot passed through the weldseam, were measured using a trigger device and analyzed in the time domain and frequency domain,respectively. From the analyzed data, 24 feature variables were extracted and used as input for theproposed DNN model. Consequently, the offline and online performance verification results for newexperimental data using the proposed DNN model were 93% and 85%, respectively