Quality monitoring of projection welding using machine learning with small data sets

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



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.


Seiten (von - bis)323-330
FachzeitschriftScience and Technology of Welding and Joining : a publication of the Institute of Materials
PublikationsstatusVeröffentlicht - 19 Mai 2023

Externe IDs

Scopus 85145503423
WOS 000906969600001
Mendeley 516cfbcd-eafd-3cc1-ac87-9bae7c18a7de



  • Capacitor discharge welding, Machine learning, Process monitoring, Projection welding, Resistance welding, Semi-supervised learning, Supervised learning, capacitor discharge welding, projection welding, supervised learning, machine learning, process monitoring, semi-supervised learning