Monitoring of root gap change based on electrical signals of flux-cored arc welding using random convolution kernel transform

Abstract

A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals.

A Real-Time Monitoring Method for Droplet Transfer Frequency in Wire-Filled GTAW Based on Arc Sensing

“…During GTAW, the arc voltage value corresponds to the arc length unaffected by other complex disturbances, like arc light, spatter, and fog, so that the change rule of the arc length can be reflected by that of the arc voltage value with great performance stability and in real time [ 28 ]. So arc sensors are widely used for real-time monitoring and control of the welding process because of these characteristics [ 29 , 30 , 31 , 32 ]. Sergio Ríos et al [ 33 ] believed that the arc voltage drops due to the contact between the wire and the pool through the droplet, which changes the pool form and shortens the average arc length; once the wire and pool lose touch with each other, the arc voltage suddenly restores to its original state; the authors pointed out that this discovery can be used in monitoring droplet transfer processing [ 34 ], but they have not proposed a certain monitoring method.…”