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

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

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

3 Citations (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.

Original languageEnglish
Title of host publicationDATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications
EditorsSlimane Hammoudi, Christoph Quix, Jorge Bernardino
PublisherSciTePress
Pages233-242
Number of pages10
ISBN (Electronic)9789897583773
DOIs
Publication statusPublished - 2019
Event8th International Conference on Data Science, Technology and Applications, DATA 2019 - Prague, Czech Republic
Duration: 26 Jul 201928 Jul 2019

Publication series

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

Conference

Conference8th International Conference on Data Science, Technology and Applications, DATA 2019
Country/TerritoryCzech Republic
CityPrague
Period26.07.201928.07.2019

Keywords

  • Beta Regression
  • Injection Moulding
  • Scrap Rate Prediction
  • SVM

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