Towards Human-Centric Manufacturing: Leveraging Digital Twin for Enhanced Industrial Processes

Human-centric industrial processes, such as logistics, inspection, maintenance, and complex assembly, heavily rely on human expertise and judgment. In today’s dynamic and complex manufacturing environments, enhancing operator perception is crucial for timely and accurate decision-making. To facilitate effective communication between human workers and the complex factory ecosystem, this research proposes a system framework leveraging Digital Twin (DT) and semantic technologies to manage industrial heterogeneous data and provide operators with real-time insights. The system architecture comprises three primary layers: the Field Layer, the Information and Service Layer, and the Application Layer. The Information Layer integrates four core engines: Knowledge Engine for managing process-specific knowledge, Data Engine for handling streaming data, Artificial Intelligence (AI) Engine for incorporating advanced machine learning models, and 3D Engine for virtual representation and simulation. This paper presents a detailed implementation of the proposed system framework and validates it through a practical in-plant logistics transport use case. Results demonstrate the framework’s effectiveness in enhancing operator perception and decision-making by providing intuitive interfaces and timely insights.