The factory of the future has been the subject of many research projects in science and the industry. Future-oriented companies are relying on automation, modularity, artificial intelligence and agility to remain competitive. In order to advance these topics in the industry and research, students, apprentices and members of staff require the corresponding skills and awareness to handle complex issues. This is where the Agile Production Simulation learning concept comes in. It simulates processes such as quality control using artificial intelligence or driverless transport systems on a small scale, recreating automated processes from incoming goods to modular production and quality control. The accompanying educational material translates the hands-on simulated processes into future skills for learners. The model’s digital twin significantly enhances the learning experience.
Agile Production Simulation is a factory consisting of flexible and combinable modules. Starting at the incoming raw goods, the material flow passes through an automated high-bay warehouse, various production stations such as a milling or drilling station and AI quality control to the outgoing goods. A driverless transport system (DTS) with omniwheels transports workpieces between the individual stations, ensuring an agile production process tailored to customer needs. The DTS can be charged at a charging point without changing batteries, if required.
Each workpiece contains an NFC tag where the production data is recorded for digital traceability. The factory produces workpieces in different colours with various processing features. Agile production enables product-specific manufacturing steps within a lean process without the need for set-up time.
Agile Production Simulation is controlled by a central control system (Raspberry Pi 4 Model B) connected to the controllers of the individual factory modules, SPS Siemens S7 1200. Various communication protocols, such as the MQTT protocol (Message Queuing Telemetry Transport), allow the modules to connect and work together.
Cloud-based real-time monitoring provides dashboards to control processes and visualise the factory status. It can also determine KPIs such as cycle times and overall equipment effectiveness (OEE). An online store simulates the customer’s workpiece order process. A moveable camera can be controlled via the dashboard to simulate the principle of remote maintenance.
The Quality Control with AI module makes the complex topic of machine learning tangible while demonstrating its uses in a production environment. Based on the concept of supervised learning, the machine learning modules are implemented with the AI tool Tensorflow.
Agile Production Simulation was developed in cooperation with experts from the Karlsruhe Institute of Technology (KIT), the software company OMM Solutions GmbH and the University of Stuttgart. The model will soon be available for anyone who wants to get to grips with the factory of the future.
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