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The amount of aluminium sheets in future body-in-white concepts is still on the rise. There is a need for optimizing the joining techniques, caused by the different characteristics compared to the established steel components. Especially the electrode life for resistance spot welding as a reliable and established process needs to be improved. One reason for the short electrode life when welding aluminium is the insulating effect of the aluminium oxide layer. One possibility to reduce the electrode wear is the mechanical destruction of the oxide layer before the welding. This paper describes the influence of a translational and rotational electrode movement on the electrode wear. The oxide layer destruction is detected by resistance measurement. It could be shown that the destruction of the oxide layer already occurs at low movements. However, a homogeneous, large-area destruction is necessary for a wear reduction.

Capacitor discharge welding is an efficient, cost-effective and stable process. It is mostly used for projection welding. Real-time monitoring is desired to ensure quality. Until this point, measured process quantities were evaluated through expert systems. This method is strongly restricted to specific welding tasks and needs deep understanding of the process. Another possibility is quality prediction based on process data with machine learning. This requires classified welding experiments to achieve a high prediction probability. In industrial manufacturing, it is rarely possible to generate big sets classified data. Therefore, semi-supervised learning is investigated to enable model development on small data sets. Supervised learning models on large amounts of data are used as a comparison to the semi-supervised models. A total of 389 classified weld tests were performed. With semi-supervised learning methods, the amount of training data necessary was reduced to 31 classified data sets.