Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing. Issue 1 (9th March 2020)

• Record Type: Journal Article
• Authors: Lee, Jay; Azamfar, Moslem; Singh, Jaskaran; Siahpour, Shahin
• Is Part Of: IET collaborative intelligent manufacturing. Volume 2:Issue 1(2020)
• Page Start: 34
• Page End: 36
• Publication Date: 2020-03-09
• Subjects: Internet of Things -- cloud computing -- condition monitoring -- data analysis -- Big Data -- production engineering computing -- cyber‐physical systems; industry 4.0 -- machine health monitoring -- Internet of Things -- grand view research -- deep learning -- product support -- equipment health monitoring -- product design -- cloud computing -- big data analytics -- cyber‐physical system -- compound annual growth rate -- digital twin -- smart manufacturing; Production management -- Periodicals; Production engineering -- Periodicals; Production management; Production engineering; Electronic journals; Periodicals; 658.5
• DOI: 10.1049/iet-cim.2020.0009
• Languages: English
• ISSNs: 2516-8398

Abstract

Abstract : 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.