Machine learning and simulation-based surrogate modeling for improved process chain operation

André Hürkamp*, Sebastian Gellrich, Antal Dér, Christoph Herrmann, Klaus Dröder, Sebastian Thiede

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
5 Downloads (Pure)

Abstract

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.

Original languageEnglish
Number of pages11
JournalInternational journal of advanced manufacturing technology
Early online date19 May 2021
DOIs
Publication statusE-pub ahead of print/First online - 19 May 2021

Keywords

  • Cyber physical production systems
  • Digital twin
  • Machine learning
  • Overmolded thermoplastic composites
  • Process chain simulation
  • Production engineering
  • Surrogate modeling

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