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Energy efficiency optimization of manufacturing assets by exploiting digital twins

• The targeted research focuses on the development of digital twins of manufacturing assets, in particular to ensure that their monitored dynamic behaviour is continuously analyzed and interpreted, to guarantee that the digital twins are always representative for the current status of the shop floor while being able to provide reliable forecasting on energy and sustainability aspects. The main goal of this work is therefore to establish this “synchronization” between the different systems composing the production plant and their Digital Twins, through the integration of dedicated stream data analysis and machine learning algorithms. By analysing and elaborating the data flows coming from the physical system I/Os, this “self-learning” functionalities will make the Digital Twins models capable of supporting adequately the dynamic optimization of their physical assents. A key focus is on leveraging physical behaviour models (FEM, MBD, etc.) of the mechanic/mechatronic systems involved.• The research is hosted by the KU Leuven Noise and Vibration Research Group – as part of the Mecha(tro)nic System Dynamics division (LMSD), which currently counts > 100 researchers and is headed by Prof. Wim Desmet (). The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. More information on the research group can be found on the website:

Energy efficiency optimization of manufacturing assets by exploiting digital twins

KU Leuven, Leuven
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