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

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

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

1 Citation (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.

Original languageEnglish
Title of host publicationData Management Technologies and Applications - 7th International Conference, DATA 2018, Revised Selected Papers
EditorsChristoph Quix, Jorge Bernardino
PublisherSpringer Verlag
Pages50-68
Number of pages19
ISBN (Print)9783030266356
DOIs
Publication statusPublished - 2019
Event7th International Conference on Data Science, Technology and Applications, DATA 2018 - Porto, Portugal
Duration: 26 Jul 201828 Jul 2018

Publication series

NameCommunications in Computer and Information Science
Volume862
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference7th International Conference on Data Science, Technology and Applications, DATA 2018
CountryPortugal
CityPorto
Period26.07.201828.07.2018

Keywords

  • Genetic programming
  • Linear regression
  • Manufacturing
  • Process parameter setting
  • Symbolic regression

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