Digital Engineering Methods in Practical Use during Mechatronic Design Processes

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • Benjamin Gerschütz - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Christopher Sauer - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Andreas Kormann - , Universität Bayreuth (Autor:in)
  • Simon J. Nicklas - , Universität der Bundeswehr München (Autor:in)
  • Stefan Goetz - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)
  • Matthias Roppel - , Universität Bayreuth (Autor:in)
  • Stephan Tremmel - , Universität Bayreuth (Autor:in)
  • Kristin Paetzold-Byhain - , Professur für Virtuelle Produktentwicklung, Technische Universität Dresden (Autor:in)
  • Sandro Wartzack - , Friedrich-Alexander-Universität Erlangen-Nürnberg (Autor:in)

Abstract

This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

Details

OriginalspracheEnglisch
Aufsatznummer93
FachzeitschriftDesigns : open access engineering design journal
Jahrgang7
Ausgabenummer4
PublikationsstatusVeröffentlicht - Aug. 2023
Peer-Review-StatusJa

Schlagworte

Schlagwörter

  • data mining, data-driven methods, digital engineering, implementation, machine learning, product development, system design, system integration, validation