Approaches to identify relevant process variables in injection moulding using beta regression and SVM

Shailesh Tripathi, Sonja Strasser, Christian Mittermayr, Matthias Dehmer, Herbert Jodlbauer

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

In this paper, we analyze data from an injection moulding process to identify key process variables which influence the quality of the production output. The available data from the injection moulding machines provide information about the run-time, setup parameters of the machines and the measurements of different process variables through sensors. Additionally, we have data about the total output produced and the number of scrap parts. In the first step of the analysis, we preprocessed the data by combining the different sets of data for a whole process. Then we extracted different features, which we used as input variables for modeling the scrap rate. For the predictive modeling, we employed three different models, beta regression with the backward selection, beta boosting with regularization and SVM regression with the radial kernel. All these models provide a set of common key features which affect the scrap rates.

OriginalspracheEnglisch
TitelDATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications
Redakteure/-innenSlimane Hammoudi, Christoph Quix, Jorge Bernardino
Herausgeber (Verlag)SciTePress
Seiten233-242
Seitenumfang10
ISBN (elektronisch)9789897583773
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung8th International Conference on Data Science, Technology and Applications, DATA 2019 - Prague, Tschechische Republik
Dauer: 26 Jul 201928 Jul 2019

Publikationsreihe

NameDATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications

Konferenz

Konferenz8th International Conference on Data Science, Technology and Applications, DATA 2019
Land/GebietTschechische Republik
OrtPrague
Zeitraum26.07.201928.07.2019

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