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
Highlights
Welding is widely applied in heavy industries, such as the automobile and aerospace industries [1,2,3,4]
This study proposes a deep neural network (DNN)-based welding condition monitoring and quality control method using feature variables extracted from the time and frequency domains of the welding signal measured in a hull welding process
The measured welding current and voltage signals were analyzed in the time and frequency domains, respectively, and the feature variables that were most correlated with the weld gap variables were extracted
Summary
Welding is widely applied in heavy industries, such as the automobile and aerospace industries [1,2,3,4]. According to the reviewed literature, charge-coupled-device (CCD) cameras, vision sensors, and artificial intelligence techniques have been applied to successfully monitor the weld defects and weld bead shape during welding, and effective methodologies for tracking curved weld seams have been presented. Based on the advantages of DNNs, such as their capabilities of nonlinear combination, learning between nonlinear variables, and understanding the potential structure of data, excellent results have been achieved in welding monitoring [11,12] To this end, this study proposes a DNN-based welding condition monitoring and quality control method using feature variables extracted from the time and frequency domains of the welding signal measured in a hull welding process. The weld gap detection model was designed using a DNN and learned based on the time and frequency feature variables of the welding signal.
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