Intelligent Process Control in Paperboard Compression-Drawing Using Digital Twins and Machine Learning

Disposable plastic packaging is widespread due to its low cost, efficiency, and ease of production. On the contrary, its slow biodegradation contributes to long-term environmental pollution. This favors paperboard as a more sustainable and recyclable alternative. However, to effectively control the compression-drawing process for producing paperboard cups, it is essential to manage the interactions between the material and various process parameters (e.g., blankholder force, tool temperature), as well as ambient conditions. Targeted moistening of the material has been shown to stabilize the process, further increasing the overall complexity. The main challenge is coordinating all these interdependent parameters during each production cycle to ensure a stable paperboard production process. As part of a joint project between Fraunhofer IWU and TU Dresden, an intelligent process control system was developed and implemented on a paperboard cup compression-drawing demonstrator. This paper highlights the development and deployment of this control system, emphasizing the integration of digital twin technology and machine learning to adaptively regulate process parameters of paperboard compression-drawing. The result of such control system is a robust manufacturing process and consistent cup quality, paving the way for more intelligent and sustainable packaging processes.