Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing

Digital twin (DT) is gaining popularity due to its significant impacts on bridging the gap between the physical and cyber worlds. As reported by Grand View Research, Inc. , the global market of DT is expected to reach 26. 07 billion by 2025 with a Compound Annual Growth Rate of 38. 2%. The growing adoption of cyber‐physical system (CPS), Internet of Things, big data analytics, and cloud computing in manufacturing sector has paved the way for low cost and systematic implementation of DT, with promising impacts on (a) product design and development, (b) machine and equipment health monitoring, and (c) product support and services. Successful implementation of DT would increase transparency, cooperation, flexibility, resilience, production speed, scalability, and manufacturing efficiency. Realisation of smart manufacturing requires collaborative and autonomous interactions between sensing, networking, and computational resources across manufacturing assets where data is gathered from physical systems is utilised for the extraction of actionable insights and provision of predictive services. In this study, a reference architecture based on deep learning, DT, and 5C‐CPS is proposed to facilitate the transformation towards smart manufacturing and Industry 4. 0.