Synchronized Data Model to Enable Digital Twins for Reactive Disassembly Planning

Although disassembly is essential for enabling a circular economy, it remains challenging due to the uncertain and instance-specific conditions of end-of-life products. Reactive disassembly planning addresses this challenge by adapting disassembly plans in real time when actual conditions deviate from those assumed in predictive disassembly planning. However, existing approaches to reactive disassembly planning lack integration with real-time data sources. This work presents a synchronized data model that provides such real-time data to enable digital twins supporting data-driven reactive disassembly planning approaches. The data model covers all general elements of disassembly systems and is connected to a synchronization module enabling automated physical-to-virtual communication. To ensure compatibility with predictive disassembly planning, established data modeling methods, such as Petri nets, are applied. The synchronized data model was practically implemented and validated in a lab environment through the disassembly of multiple products under varying condition scenarios. The results confirm the successful real-time synchronization and demonstrate that the data model meets the information requirements of reactive disassembly planning. While this work provides the database required for data-driven reactive disassembly planning, future research can integrate the results into a digital twin and utilize the data in behavioral models, such as heuristics and simulation.