A study on the monitoring of weld quality using XGBoost with Particle Swarm Optimization

Abstract

Gas Metal Arc Welding is a joining technique with numerous uses in manufacturing. Since welding process parameters have a considerable impact on the welding quality, online monitoring systems are utilized on production lines to achieve standard welding quality with minimum welding faults. This study presents preliminary work to develop a monitoring system for defect analysis in a Gas Metal Arc Welding process. This study aims to eliminate the need for laboratory tests with a model to reduce welding quality control costs. In this study, welding data such as voltage, current, and wire feeding rate were collected during the welding process in realtime. New features were derived from the gathered data at the preprocessing stage using statistical approaches. The determination of whether the welding process is defective or flawless was made using the Extreme Gradient Boosting Algorithm. The hyperparameters of the algorithm were optimized with Particle Swarm Optimization. The accuracy value was obtained as 93.15% after repeating the conducted experiments ten times. The recall, precision, specificity, and F1 -Score values in these experiments were calculated as 97.22%, 94.75%, 72.35%, and 95.94%, respectively. The mean current value was found to be the most relevant and meaningful feature that describes the welding quality based on intensive experiments. In addition to the proposed algorithm, some other machine -learning algorithms were tested on the welding dataset. With this study, the significance of feature derivation from the acquired welding current data has been discovered to analyze welding quality.