Digital Engineering Methods in Practical Use during Mechatronic Design Processes

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Benjamin Gerschütz - , Friedrich-Alexander University Erlangen-Nürnberg (Author)
  • Christopher Sauer - , Friedrich-Alexander University Erlangen-Nürnberg (Author)
  • Andreas Kormann - , University of Bayreuth (Author)
  • Simon J. Nicklas - , Bundeswehr University of Munich (Author)
  • Stefan Goetz - , Friedrich-Alexander University Erlangen-Nürnberg (Author)
  • Matthias Roppel - , University of Bayreuth (Author)
  • Stephan Tremmel - , University of Bayreuth (Author)
  • Kristin Paetzold-Byhain - , Chair of Virtual Product Development, TUD Dresden University of Technology (Author)
  • Sandro Wartzack - , Friedrich-Alexander University Erlangen-Nürnberg (Author)

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

Original languageEnglish
Article number93
JournalDesigns : open access engineering design journal
Volume7
Issue number4
Publication statusPublished - Aug 2023
Peer-reviewedYes

Keywords

Keywords

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