An Approach for Adaptive Parameter Setting in Manufacturing Processes

Sonja Straßer, Shailesh Tripathi, Richard Kerschbaumer

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

5 Zitate (Scopus)

Abstract

In traditional manufacturing processes the selection of appropriate process parameters can be a difficult task which relies on rule-based schemes, expertise and domain knowledge of highly skilled workers. Usually the parameter settings remain the same for one production lot, if an acceptable quality is reached. However, each part processed has its own history and slightly different properties. Individual parameter settings for each part can further increase the quality and reduce scrap. Machine learning methods offer the opportunity to generate models based on experimental data, which predict optimal parameters depending on the state of the produced part and its manufacturing conditions. In this paper, we present an approach for selecting variables, building and evaluating models for adaptive parameter settings in manufacturing processes and the application to a real-world use case.

OriginalspracheEnglisch
TitelDATA 2018 - Proceedings of the 7th International Conference on Data Science, Technology and Applications
Redakteure/-innenJorge Bernardino, Christoph Quix
Seiten24-32
Seitenumfang9
ISBN (elektronisch)9789897583186
DOIs
PublikationsstatusVeröffentlicht - 2018
VeranstaltungDATA 2018 - Porto, Portugal
Dauer: 26 Jul 201828 Jul 2018

Publikationsreihe

NameDATA 2018 - Proceedings of the 7th International Conference on Data Science, Technology and Applications

Konferenz

KonferenzDATA 2018
Land/GebietPortugal
OrtPorto
Zeitraum26.07.201828.07.2018

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