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An approach to develop an arc sensor for gap width estimation during automated NG-GMAW with a weaving electrode motion is introduced by combining arc sensor readings with optical measurements of the groove shape to allow precise analyses of the process. The two test specimen welded for this study were designed to feature a variable groove geometry in order to maximize efficiency of the conducted experimental efforts, resulting in 1696 individual weaving cycle records with associated arc sensor measurements, process parameters and groove shape information. Gap width was varied from 18 mm to 25 mm and wire feed rates in the range of 9 m/min to 13 m/min were used in the course of this study. Artificial neural networks were applied as a modelling tool to derive an arc sensor for estimation of gap width suitable for online process control that can adapt to changes in process parameters as well as changes in the weaving motion of the electrode. Wire feed rate, weaving current, sidewall dwell currents and angles were defined as inputs to calculate the gap width. The evaluation of the proposed arc sensor model shows very good estimation capabilities for parameters sufficiently covered during the experiments.
This paper deals with the use of an adaptive control system for compensating the variation in the gap height of a fillet welded lap joint. Gap bridging requires the input of additional filler material and is related to an increased energy input. Hence, the aim was a compensation of the effect of an increased heat input, in order to maintain the weld pool and excessive penetration, which can prevent consequently root reinforcement and burn-through. The findings achieved in this work show possibilities for a real-time controlled adjustments of the welding parameters in automated metal active gas (MAG) welding for compensating a higher heat input, in particular by means of the torch offset relative to the top sheet at the fillet weld on a lap joint.
MAG welding is widely used for thin sheet metal applications such as car body structures due to its ability to tolerate a fair amount of deviation of the components from the ideal shape. In MAG welding, the process window is sufficiently large to accommodate the expected component tolerances. In practice, however, quality control is an issue since most welds are produced with parameters outside of the optimum range, especially in the case of automated MAG welding. To ensure best performance, a robust real-time control law is needed that adapts critical process parameters to the changing conditions, most notably the variation in gap height. Here, the gap-dependent adaptive control algorithm for the deposition of filler material and the related energy input comes into play. With an optical sensor that is mounted in front of the torch, the system measures the actual position of the two components in real-time during the entire welding process and the controller adapts the relevant parameters accordingly using a dynamic process model. This optimization ensures that only the required filler material is used and the associated energy input is tightly controlled to assure best quality even in a fully automated welding process.