Preserving Dependencies in Partitioned Digital Twin Models for Enabling Modular Validation
Ashkan Zare, Sanja Lazarova‐Molnar · 2025
Leveraging Digital Twins, as near real-time replicas of physical systems, can help identify inefficiencies and optimize production in manufacturing systems. Digital Twins' effectiveness, however, relies on continuous validation of the underlying models to ensure accuracy and reliability, which is particularly challenging for complex, multi-component systems where different components evolve at varying rates. Modular validation mitigates this challenge by decomposing models into smaller sub-models, allowing for tailored validation strategies. A key difficulty in this approach is preserving the interactions and dependencies among the sub-models while validating them individually; isolated validation may yield individually valid sub-models while failing to ensure overall model consistency. To address this, we build on our previously proposed modular validation framework and introduce an approach that enables submodel validation while maintaining interdependencies. By ensuring that the validation process reflects these dependencies, our method enhances the effectiveness of Digital Twins in dynamic manufacturing environments.