Digital twins for machine tools

Research output: Contribution to journal › Research article › Contributed › peer-review

Contributors

  • A. Verl - , University of Stuttgart (Author)
  • S. Ihlenfeldt - , Chair of Machine Tools Development and Adaptive Controls (Author)
  • Ch Okwudire - , University of Michigan, Ann Arbor (Author)
  • E. Ozturk - , University of Sheffield (Author)
  • A. Valente - , University of Applied Sciences and Arts of Southern Switzerland (Author)
  • M. F. Zaeh - , Technical University of Munich (Author)

Abstract

The number of publications on digital twins went from 3.000 in 2017 to over 70.000 in 2024. This paper analyses the use and status of digital twins for machine tools as well as the necessary modelling and identification for enabling optimization, process planning, process control and predictive maintenance on the machine tool level and the fleet level. Recent research on digital twins for machine tools leveraging AI or greybox models is also presented. This paper gives an overview of the application and the technology behind digital twins used for machine tool design, commissioning, and operations.

Details

Original languageEnglish
Pages (from-to)919-942
Number of pages24
JournalCIRP annals
Volume75
Issue number2
Publication statusPublished - Jun 2026
Peer-reviewedYes

Keywords

Keywords

  • Commissioning, Design, Digital Twin, Machine Tool