The task of the "5GSensPRO" project is to develop a system that adds comprehensive monitoring functionalities to machines and plants: Smart and flexibly deployable sensors and actuators can enable adaptive processes in production machines.
Increasingly complex products and manufacturing processes are already pushing today's production systems to their limits. Only with the help of flexible expandability can many of the existing challenges in the production environment be overcome. The task of the "5GSensPRO" project is therefore to develop a system that adds comprehensive monitoring functionalities to machines and plants: Smart and flexibly deployable sensors and actuators can enable adaptive processes in production machines. In addition to appropriate modules for process monitoring, the communication infrastructure is particularly important.
The new 5G mobile technology provides a basis for meeting the high demands on communication within a network of wirelessly connected machines, servers and smart sensors: Large volumes of data can be transmitted at high speed and with low latencies between cloud and large-scale computing systems to ensure time-critical communication at the field level as well, for example for control applications.
The goal in the "5GSensPRO" project is to develop a system that expands the control of machine tools and production facilities in a modular way and creates a more flexible production environment that meets the requirements of complex components and manufacturing processes. Highly dynamic processes can thus be monitored close to the component and controlled wirelessly. Another goal is to enable the identification of machine and workpiece in order to be able to clearly assign process and logistics data. The 5GSensPRO system is demonstrated in the project using the example of a highly dynamic milling process of components for turbomachinery construction, so-called BLISKs. The overall concept is designed in such a way that it can later be transferred to other manufacturing tasks and processes.
Smart sensors are mounted directly on the component and thus have unprecedented process proximity. This high resolution of the physical phenomena in the form of the process data improves the understanding of the processes and thus also their control, so that further optimizations become possible.
The 5G wireless communication allows the system to be used in any manufacturing strategies with complex relative motion between the component and the machine, which are typical of modern and innovative products. The data rates and latencies of 5G technology open up new possibilities in the real-time control of highly dynamic processes with processing speeds that meet the requirements for the economic efficiency of modern production facilities.
The new system provides plants with a control approach that paves the way for adaptive control, adaptation to changing operating conditions and the ability for long-term self-optimization by additionally including historical data in the analysis.
The 5GSensPRO system is demonstrated in the project using the example of the production of so-called BLISKs for turbomachinery construction. Flexible process monitoring that is as accurate as possible is ensured here with a modular multi-sensor approach in which the sensors are mounted directly on the component. The new 5G mobile communications standard ensures wireless communication of the sensor system with the base station and the machine tool.
The data is stored and aggregated in a cloud system in which the virtual workpiece is provided in the form of a digital twin. It is also planned that the production machine will be able to recognize the current component with the help of a tracking function and thus clearly assign all process and logistics data.
The display of sensor data and process control using smart devices in a virtual production environment offer the user a completely new way of interacting with the overall system.
In the future, the combination of intelligently networked sensors, 5G technology and flexibly expandable cloud systems will help transform conventional machine parks into semi-autonomous systems with flexible processing stations and optimal capacity utilization. In conjunction with cloud-based data pipelines and derived models, companies will then be able to forecast not only production results but also maintenance requirements and downtime risks more reliably than before.
Fraunhofer Institute for Production Technology IPT