Modeling and optimization of manufacturing service collaboration based on digital twins

The article discusses the evolution of manufacturing modes, highlighting the shift towards intelligent, green, and service-oriented production systems. It emphasizes the limitations of traditional single manufacturing services and the need for collaborative manufacturing to meet complex, personalized production demands. The integration of digital twin technology is presented as an innovative solution, enabling real-time monitoring, simulation, and optimization of manufacturing processes. The article delves into the creation of virtual models that accurately represent physical equipment, allowing for dynamic optimization and improved manufacturing efficiency. It also explores the challenges faced by traditional manufacturing service collaboration (MSC) and proposes a digital twin-driven approach to address these issues. The proposed method involves static encapsulation and dynamic sensing to describe and map manufacturing resources, ensuring consistency and reliability in the collaboration process. The article further discusses the implementation of a three-level collaboration architecture, consisting of an information description layer, a basic model layer, and a state monitoring layer. This architecture enables real-time data interaction, synchronization, and dynamic adjustment, enhancing the overall efficiency and flexibility of manufacturing operations. A case study on 3D printing service collaboration is provided to illustrate the practical application of the proposed methods, demonstrating the advantages of digital twin-driven MSC in terms of cost reduction, production efficiency, and resource utilization. The article concludes by highlighting the potential of digital twin technology in improving the reliability and scalability of manufacturing service collaboration, paving the way for more intelligent and efficient production systems.