The digitalization of value chains and manufacturing processes offers new approaches for implementing energy, material, and substance flow analyses, thereby unlocking potential to enhance resource efficiency. Technologies such as the Industrial Internet of Things (IIoT) enable the implementation of cyber-physical production systems (CP(P)S) and digital twins (DT), facilitating real-time data collection.
Through statistical analysis and AI methods, these data can be utilized to address economic and ecological questions, analyzing, controlling, and optimizing the sustainability of products and manufacturing processes in terms of energy, material, and substance flows. This allows, for example, the early detection of increased material and energy consumption, waste, or defect patterns in production. Additionally, the ecological footprint of product and component concepts can be proactively assessed for product development based on real-world data.
The research group SEM focuses on practical and scientific questions related to small-batch and single-unit production within the context of existing manufacturing and production environments (Brownfield Approach). In doing so, we collaborate with colleagues in production technology and logistics, artificial intelligence for technical systems, and production and manufacturing technology in the wood and wood-based materials industry, as well as plastics technology. At the proto_lab of TH Rosenheim, various colleagues work together to research diverse aspects of manufacturing digitalization and to explore and further develop solutions with students through internships and thesis projects.