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Digital Twin-Native Learning Factory Life-Cycle Management: An Applied Case Study
Conference proceeding   Peer reviewed

Digital Twin-Native Learning Factory Life-Cycle Management: An Applied Case Study

Advancing Learning Factories: Enabling Future-Ready Skills: Proceedings of the 15th Conference on Learning Factories 2025, Volume 2, Vol.1546, pp.342-349
Lecture Notes in Networks and Systems, 1546
15th Conference on Learning Factories CLF2025 (Stellenbosch, 25/03/2025–28/03/2025)
2025
Handle:
https://hdl.handle.net/10863/51674

Abstract

BIM Digital twin Life cycle management Reconfigurability
The concept of Learning Factory has gained traction as an innovative approach to bridging the gap between theoretical education and industrial practice. Considering digitalization as one of the main pivotal concepts in the adoption of Industry 4.0 technologies, holistic Digital Twin applications represent a challenging way to achieve greater effectivity and efficiency of organizations’ processes. Using the project of the new Smart Mini Factory laboratory for Industry 4.0 of the Free University of Bozen/Bolzano as a case study, this paper explores the design and implementation potential and challenges of a Digital Twin-Native Learning Factory, designed from the ground up to enable inborn bidirectional dataflow among its physical and virtual entities. The Digital Twin-Native Learning Factory supports each life-cycle phase of the laboratory, relying on modern processes of point cloud scanning, Building Information Modeling, remote control of Cyber-Physical Production Systems, and advanced automation to emulate real-world industrial environments. The case study highlights the Learning Factory’s design process to foresee seamless integration of Digital Twin applications for new and legacy equipment. The findings from this use case provide valuable insights into how Learning Factories can be effectively created and leveraged to satisfy collaborative and cooperative working principles, maintaining flexibility and reconfigurability capabilities.
url
https://doi.org/10.1007/978-3-031-98883-7_41View

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