Demonstrator-based implementation of an infrastructure for event data acquisition in disassembly material flows

Sustainable manufacturing underscores the importance of efficient disassembly processes within the Circular Economy. Disassembly systems face significant challenges due to material flows that are dependent on the quality of products, assemblies and parts. The resulting material flow variations and fuctuating process times hinder standardization, resulting in a low degree of transparency in disassembly systems and the reliance on manual decision-making. This contribution proposes an infrastructure to acquire event data in diverging disassembly material flows, thereby improving system transparency and supporting decision-making. The proposed infrastructure can adapt to changes in disassembly layouts and depths while capturing quality-dependent material flow variations, such as fuctuations in process times for individual components. The infrastructure is implemented as a demonstrator using industrial-grade technologies as part of an automotive use case in a learning factory. Future research areas, such as the extension to a digital twin, are suggested.