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The acquisition of
data and the development of different options in production system and factory
planning requires up to 2/3rds of the total needed time resources. The
digitization of production systems offers the possibility of automated data
acquisition. Nevertheless, approaches concerning fully automated data
acquisition systems are not widely spread among SME (small and medium sized
enterprises).  Furthermore, the
advantages of The Digital Twin are not sufficiently known due to the lack of
competence in SME concerning matters of Industry 4.0. In order to transfer
knowledge about the benefits of digitalization, the development of
demonstrating platforms is crucial. This article introduces a learning factory
based concept to demonstrate the potentials and advantages of real time data
acquisition and subsequent simulation based data processing. Primarily, this
paper proposes methodology for implementation Learning factory based on
Skoltech’s machining equipment .  The
production cell included industrial robot for part handling, loading and
unloading, several milling and lathe machines with installed sensors.  All components had no connection between each
other and information from sensors didn’t bring any sense and use. An
automation upgrade of the cell is proposed, involving Technomatix Plant Simulation
tool, programmable logic controllers, RFID tags.   In this
framework, the digital validation of the real manufacturing system is performed
through Discrete Event Simulation to analyze the performance of the
manufacturing system in terms of productivity and utilization of resources
under different alternative scenarios, avoiding the risks associated with
physical experimentation on the real system. Moreover, Digital twin approach
increase a flexibility of the system  like
Distributed planning, dynamic rescheduling, improved decision support,
automatic planning and execution of offers/orders and wider the range of
products.Finally, working example of Learning factory has been implemented at
the Skoltech’s Masterskaya.  It is empowered
with several features such as Discrete Event Clone gathers information from
sensors, remote control of manufacturing execution, automatic rescheduling
system, individual program of metal treatment or handling operations for each bar.
The test has shown that installed system is capable to execute more variety of
assemblies with better performance compare to ordinary manufacturing system. The
comparison between the Digital Twin and common tools of process optimization,
e.g. VSM, is carried out and shows the benefits of digitalization in a vividly
manner. Benefits of the proposed new approach for the analysis and modification
of production systems can be experienced by participants in practical training
sessions, especially continuous data acquisition, automated derivation of
optimization measures and capturing of motion data. The physical implementation
of the system enabling the Digital Twin based on Discrete event simulation is
the first step to enter Industry 4.0 era. To increase components utilization of
the system and enable It with scalability further researches should be
performed Including optimization methods, machine learning, Genetic algorithm
and etc. 

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