Fraunhofer_IML_Jahresbericht_2021_PDF_WEB

Materialflusssysteme / Material Flow Systems
Kontakt / Contact
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Arnd Ciprina
Informationslogistik und
Assistenzsysteme /
Information Logistics and
Decision Support Systems
Tel. +49 231 9743-243
arnd.ciprina@
iml.fraunhofer.de
Andrej Plenne M. Sc.
Informationslogistik und
Assistenzsysteme /
Information Logistics and
Decision Support Systems
Tel. +49 231 9743-516
andrej.plenne@
iml.fraunhofer.de
Jennifer Wessels M. Sc.
Informationslogistik und
Assistenzsysteme /
Information Logistics and
Decision Support Systems
Tel. +49 231 9743-467
jennifer.wessels@
iml.fraunhofer.de
/ See it All at a Glance with
Digital Twins
/ The aim of the research project “MoProLog“ (Modular
Production Logistics) is to design, test and evaluate modular
systems. It’s part of ENPRO, a collaborative initiative for energy
efficiency and process acceleration in the chemical industry.
Since November 2019, Fraunhofer IML has been working in
collaboration with five consortium partners to study energy-efficient
logistics modules for logistical supply and waste
management in the context of modular production in the
process industry. To test out their concept, the MoProLog
researchers are focusing on a use case involving a modular
layer palletizer with intelligent energy management.
The project has also seen the Fraunhofer IML team develop a
digital twin to ensure a transparent flow of information. This
twin consists of a single process model that systematically
maps all the processes, orders, performance objects, resources
and production events in the value creation system and thus
functions as the central information base for the modules of
the overall system. It is linked to a demonstrator of the modular
layer palletizer, so that it can both supply the modules with
relevant information on production and orders and collect data
(e.g. sensor and energy data, exchanges of information, decisions
made) via the interfaces that have been implemented.
By using the digital twin as a central information base and
conducting intelligent analyses of the historical and current
data through machine learning processes, the researchers can
identify and assess patterns and regularly recurring events. This
lays the foundations for AI-driven predictions and suggestions
for improvements. For example, the AI could recommend
plant modules for maintenance in what is known as predictive
maintenance.
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