Linear vs. Symbolic Regression for Adaptive Parameter Setting in Manufacturing Processes

Sonja Strasser, Jan Zenisek, Shailesh Tripathi, Lukas Schimpelsberger, Herbert Jodlbauer

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parameter setting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical process parameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parameter setting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parameter setting in manufacturing processes.

OriginalspracheEnglisch
TitelData Management Technologies and Applications - 7th International Conference, DATA 2018, Revised Selected Papers
Redakteure/-innenChristoph Quix, Jorge Bernardino
Herausgeber (Verlag)Springer
Seiten50-68
Seitenumfang19
ISBN (Print)9783030266356
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung7th International Conference on Data Science, Technology and Applications, DATA 2018 - Porto, Portugal
Dauer: 26 Jul 201828 Jul 2018

Publikationsreihe

NameCommunications in Computer and Information Science
Band862
ISSN (Print)1865-0929
ISSN (elektronisch)1865-0937

Konferenz

Konferenz7th International Conference on Data Science, Technology and Applications, DATA 2018
Land/GebietPortugal
OrtPorto
Zeitraum26.07.201828.07.2018

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