TY - JOUR
T1 - Long living human-machine systems in construction and production enabled by digital twins Exploring applications, challenges, and pathways to sustainability
AU - Vogel-Heuser, Birgit
AU - Hartl, Fandi
AU - Wittemer, Moritz
AU - Zhao, Jingyun
AU - Mayr, Andreas
AU - Fleischer, Martin
AU - Prinz, Theresa
AU - Fischer, Anne
AU - Trauer, Jakob
AU - Schroeder, Philipp
AU - Goldbach, Ann Kathrin
AU - Rothmeyer, Florian
AU - Zimmermann, Markus
AU - Bletzinger, Kai Uwe
AU - Fottner, Johannes
AU - Daub, Rüdiger
AU - Bengler, Klaus
AU - Borrmann, André
AU - Zaeh, Michael F.
AU - Wudy, Katrin
N1 - Publisher Copyright:
© 2024 the author(s), published by De Gruyter, Berlin/Boston.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - In the industrial sector, products evolve significantly over their operational life. A key challenge has been maintaining precise, relevant engineering data. This paper explores the digital twin concept, merging engineering and operational data to enhance product information updates. It examines digital twin applications in construction, material flow, manufacturing and production, citing battery production and additive manufacturing. Digital twins aid in analyzing, experimenting with, and refining a system's design and its operation, offering insights across product and system lifecycles. This includes tackling data management and model-data consistency challenges, as well as the recognition of synergies. This paper emphasizes sustainable, efficient management of engineering information, reflecting shifts in product longevity and documentation in industrial products and machinery.
AB - In the industrial sector, products evolve significantly over their operational life. A key challenge has been maintaining precise, relevant engineering data. This paper explores the digital twin concept, merging engineering and operational data to enhance product information updates. It examines digital twin applications in construction, material flow, manufacturing and production, citing battery production and additive manufacturing. Digital twins aid in analyzing, experimenting with, and refining a system's design and its operation, offering insights across product and system lifecycles. This includes tackling data management and model-data consistency challenges, as well as the recognition of synergies. This paper emphasizes sustainable, efficient management of engineering information, reflecting shifts in product longevity and documentation in industrial products and machinery.
KW - Industry 4.0
KW - construction
KW - digital twin
KW - human machine interaction
KW - production
KW - production and construction engineering
UR - http://www.scopus.com/inward/record.url?scp=85204284509&partnerID=8YFLogxK
U2 - 10.1515/auto-2023-0227
DO - 10.1515/auto-2023-0227
M3 - Article
AN - SCOPUS:85204284509
SN - 0178-2312
VL - 72
SP - 789
EP - 814
JO - At-Automatisierungstechnik
JF - At-Automatisierungstechnik
IS - 9
ER -