PETRA'23: Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments
Manual metal arc welding is a common and important part of the metal manufacturing industry. Although the capabilities of welding robots improved in the past century special welding tasks can only be performed by human workers. Thus, research about analyzing the welding quality automatically to support workers in real time gained importance during the last years. The welding quality depends on the skills of the worker. Recent studies corroborated the correlation between the performed movements with the arc welder (and the corresponding torch manipulation) and the welder?s skill level. However, related studies reveal a research gap in developing skill level assessment for real-world welding manufacturing processes. An adapted experimental design in this study involving realistic welding tasks addresses this gap. A specialized Weld Monitoring System was used to record the three dimensional movement of the arc welder through an embedded IMU and its current and voltage from the power source synchronously. State-of-the-art deep learning was applied on this data to generate an online skill level assessment of the welder. The classifier?s performance was analyzed on each welding technique separately to optimize the network structure accordingly, based on the revealed differences in the reached accuracies. Finally, using a subset of the data excluding recorded data of certain welding techniques increased the overall performance of the classifier.