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

Research output: Contribution to journalResearch articleContributedpeer-review



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.


Original languageEnglish
Pages (from-to)323-330
Number of pages8
JournalScience and Technology of Welding and Joining : a publication of the Institute of Materials
Issue number4
Publication statusPublished - 19 May 2023

External IDs

Scopus 85145503423
WOS 000906969600001



  • Capacitor discharge welding, Machine learning, Process monitoring, Projection welding, Resistance welding, Semi-supervised learning, Supervised learning