Data Twin-Driven Cyber-Physical Factory for Smart Manufacturing

• Main Authors: Jwo, J.-S (Author), Lee, C.-H (Author), Lin, C.-S (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Aircraft; Complex production; Cybe-physical factory; Cyber Physical System; Cyber physicals; cyber-physical factory; cyber-physical systems; Data handling; data twin; Data twin; digital manufacturing; Digital manufacturing; Digital Twin; E-learning; Embedded systems; High-fidelity; Industry 4.0; machine learning; Machine learning; Production process; Production technology; Simulation platform; Smart manufacturing; Virtual models
• Online Access: View Fulltext in Publisher

 Because of the complex production processes and technology-intensive operations that take place in the aerospace and defense industry, introducing Industry 4.0 into the manufacturing processes of aircraft composite materials is inevitable. Digital Twin and Cyber-Physical Systems in Industry 4.0 are key techniques to develop digital manufacturing. Since it is very difficult to create high-fidelity virtual models, the development of digital manufacturing for aircraft manufacturers is challenging. In this study, we provide a view from a data simulation perspective and adopt machine learning approaches to simplify the high-fidelity virtual models in Digital Twin. The novel concept is called Data Twin, and the deployable service to support the simulation is known as the Data Twin Service (DTS). Relying on the DTS, we also propose a microservice software architecture, Cyber-Physical Factory (CPF), to simulate the shop floor environment. Additionally, there are two war rooms in the CPF that can be used to establish a collaborative platform: one is the Physical War Room, used to integrate real data, and the other is the Cyber War Room for handling simulation data and the results of the CPF. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.