Digital twins for machine tools

Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung

Beitragende

  • A. Verl - , Universität Stuttgart (Autor:in)
  • S. Ihlenfeldt - , Professur für Werkzeugmaschinenentwicklung und adaptive Steuerungen (Autor:in)
  • Ch Okwudire - , University of Michigan, Ann Arbor (Autor:in)
  • E. Ozturk - , University of Sheffield (Autor:in)
  • A. Valente - , Scuola universitaria professionale della Svizzera italiana (Autor:in)
  • M. F. Zaeh - , Technische Universität München (Autor:in)

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

OriginalspracheEnglisch
Seiten (von - bis)919-942
Seitenumfang24
FachzeitschriftCIRP annals
Jahrgang75
Ausgabenummer2
PublikationsstatusVeröffentlicht - Juni 2026
Peer-Review-StatusJa

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • Commissioning, Design, Digital Twin, Machine Tool