Return to search

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

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 takes much time for developing, 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 method can overcome the downsides of expert systems. But it requires classified welding experiments to achieve a high prediction probability. In industrial manufacturing, it is rarely possible to generate big sets of this type of data. Therefore, semi-supervised learning will be investigated to enable model development on small data sets. Supervised learning is used to develop machine learning models on large amounts of data. These models are used as a comparison to the semi-supervised models. The time signals of the process parameters are evaluated in these investigations. 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.

Identiferoai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88992
Date19 January 2024
CreatorsKoal, Johannes, Hertzschuch, Tim, Zschetzsche, Jörg, Füssel, Uwe
PublisherTaylor & Francis
Source SetsHochschulschriftenserver (HSSS) der SLUB Dresden
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/acceptedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text
Rightsinfo:eu-repo/semantics/openAccess
Relation1362-1718, 10.1080/13621718.2022.2162709

Page generated in 0.0025 seconds