Real-time vision-based multiple object tracking of a production process: Industrial digital twin case study. (September 2021)

• Record Type: Journal Article
• Authors: Ward, Robert; Soulatiantork, Payam; Finneran, Shaun; Hughes, Ruby; Tiwari, Ashutosh
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 11(2021)
• Page Start: 1861
• Page End: 1872
• Publication Date: 2021-09
• Subjects: Digital twin -- cyber-physical production systems -- multiple object tracking -- real time vision tracking -- smart manufacturing -- industry 4.0; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/09544054211002464
• Languages: English
• ISSNs: 0954-4054

Abstract

The adoption of Industry 4.0 technologies within the manufacturing and process industries is widely accepted to have benefits for production cycles, increase system flexibility and give production managers more options on the production line through reconfigurable systems. A key enabler in Industry 4.0 technology is the rise in Cyber-Physical Systems (CPS) and Digital Twins (DTs). Both technologies connect the physical to the cyber world in order to generate smart manufacturing capabilities. State of the art research accurately describes the frameworks, challenges and advantages surrounding these technologies but fails to deliver on testbeds and case studies that can be used for development and validation. This research demonstrates a novel proof of concept Industry 4.0 production system which lays the foundations for future research in DT technologies, process optimisation and manufacturing data analytics. Using a connected system of commercial off-the-shelf cameras to retrofit a standard programmable logic controlled production process, a digital simulation is updated in real time to create the DT. The system can identify and accurately track the product through the production cycle whilst updating the DT in real-time. The implemented system is a lightweight, low cost, customable and scalable design solution which provides a testbed for practical Industry 4.0 research both for academic and industrial research purposes.