Knowledge-Enhanced Digital Twin for Industrial Production Process

The manufacturing domain relies on Digital Twins (DTs) to mirror physical systems digitally, facilitating simulation, monitoring, and optimization. However, existing DTs may fail to capture the rich contextual knowledge essential for decision-making in complex manufacturing processes. The evolution to knowledge-enhanced DTs is essential, as it integrates domain-specific knowledge models, enabling a profound understanding of processes. To address this gap, this research introduces a knowledge-enhanced DT framework for the production process. This framework utilizes the ontology-based approach to aid the knowledge integration with the manufacturing DTs. The designed framework consists of three essential layers: The source layer, the Streaming data and knowledge coupling layer, and the Service layer. The proposed framework was further implemented in a lab-scale manufacturing setting and validated through several tests. The results demonstrated the seamless in