Abstract
Digital twin-driven discrete manufacturing workshops exhibit the 3Vs characteristics of manufacturing data: volume, variety, and velocity. While extensive research has addressed data analytics and decision-making, the seamless integration of workshop manufacturing data with applications remains a significant challenge, leading to information silos. Current data interoperability solutions primarily focus on basic data collection and transmission, lacking comprehensive capabilities in heterogeneous resource integration, multi-source information modeling, massive data management, and value extraction. This paper presents an advanced plug-and-play Industrial Internet of Things (IIoT) gateway solution that addresses three critical challenges through key innovations: (1) unified data interaction mechanisms supporting heterogeneous manufacturing equipment and protocols, enabling efficient visual-physical integration; (2) edge-layer data preprocessing and model encapsulation capabilities that transform unstructured terminal data into standardized formats for cloud twins; and (3) intelligent edge computing functions that enable real-time anomaly detection and response in production processes, reducing reliance on centralized cloud computing. Implementation in a digital twin mold manufacturing workshop demonstrates that the gateway solution reduces information encapsulation time by 57.3%, improves anomaly response time by 44%, and decreases memory utilization by 33.5% compared to traditional approaches, validating its effectiveness for MDTMS operations.
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