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I-PRAG-5: Simulation and Digital Twinning for Industrial Systems

Experiment Description

The experiment has leveraged the increasing connectivity in Industry 4.0 cyber-physical production systems to unlock new opportunities for data logging, analysis, and processing. Recognizing that some process variables on the shopfloor are challenging or even impossible to measure directly, the experiment focused on soft-sensing these variables by calculating them from other measured variables using simulation models.

A key innovation in this technological experiment was the deployment of simulation software not on industrial PCs (IPCs) at the site, but rather on an industrial site server (on-premise) or into the cloud. This approach significantly saved computational resources in the operational technology (OT) area, reduced the need for multiple licenses, and allowed for parallel calculations of multiple variables within one simulation environment. It also streamlined maintenance processes.

The experiment was implemented in collaboration with Lego at their packing facility near Prague. The designed simulation specifically focused on the energetic behavior of packing machines, showcasing the practical application and benefits of this cloud-based simulation approach in an industrial setting. This implementation not only enhanced the efficiency and effectiveness of the packing process but also demonstrated a scalable and cost-effective solution for Industry 4.0 initiatives.

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The practical implementation and deployment of this experiment have led to notable improvements in production line efficiency. This advancement primarily stems from the cost reductions achieved by avoiding the introduction and purchase of new hardware solutions, such as sensors and PCs onsite. Instead, the experiment adopted a more systematic approach to implementation, which proved to be more efficient and less error-prone compared to traditional, ad-hoc engineering work. This systematic method significantly reduced the time and effort traditionally required in industrial production facilities, as well as lowered the costs associated with purchasing multiple licenses for simulation software by centralizing the simulations under one unified system.

Moreover, the experiment has made a substantial impact in terms of energy consumption and optimization in production systems, which are often energy-intensive. Through simulation modeling of energy demand and optimization techniques, the experiment provided significant support for the operation of Industry 4.0 production systems, especially relevant in the context of rising energy prices. For the partner, this meant gaining a deeper insight into energy consumption patterns and the ability to optimize energy use by operating field devices under more efficient conditions. This not only enhanced operational efficiency but also contributed to more sustainable and cost-effective production processes.

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