An Approach for Adaptive Parameter Setting in Manufacturing Processes

Sonja Straßer, Shailesh Tripathi, Richard Kerschbaumer

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

4 Citations (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.

Original languageEnglish
Title of host publicationDATA 2018 - Proceedings of the 7th International Conference on Data Science, Technology and Applications
EditorsJorge Bernardino, Christoph Quix
Pages24-32
Number of pages9
ISBN (Electronic)9789897583186
DOIs
Publication statusPublished - 2018
EventDATA 2018 - Porto, Portugal
Duration: 26 Jul 201828 Jul 2018

Publication series

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

Conference

ConferenceDATA 2018
CountryPortugal
CityPorto
Period26.07.201828.07.2018

Keywords

  • Machine learning
  • Manufacturing
  • Model selection
  • Process parameter setting
  • Regression analysis

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