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A digital twin model for enhancing performance measurement in assembly lines

Papanagnou, C

Authors

C Papanagnou



Contributors

M Farsi
Editor

A Daneshkhah
Editor

A Hosseinian-Far
Editor

H Jahankhani
Editor

Abstract

Dynamic manufacturing processes are characterized by a lack of coordination, complexity and sheer volumes of data. Digital transformation technologies offer the manufacturers the capability to better monitor and control both assets and production. This provides also an ever-improving ability to investigate new products and production concepts in the virtual world while optimizing future production with IoT-captured data from different devices and shop floor machine centres. In this study, a digital twin is presented for an assembly line, where IoT-captured data is fed back into the digital twin enabling manufacturers to interface, analyse and measure the performance in real-time of a manufacturing process. The digital twin concept is then applied to an assembly production plan found in the automotive industry, where actual data is considered to analyse how the digital duplicate can be used to review activities and improve productivity within all production shifts.

Citation

Papanagnou, C. (2019). A digital twin model for enhancing performance measurement in assembly lines. In M. Farsi, A. Daneshkhah, A. Hosseinian-Far, & H. Jahankhani (Eds.), Digital Twin Technologies and Smart Cities (53-66). Springer. https://doi.org/10.1007/978-3-030-18732-3_4

Online Publication Date Jul 23, 2019
Publication Date Aug 1, 2019
Deposit Date Feb 24, 2020
Publicly Available Date Aug 1, 2021
Publisher Springer
Pages 53-66
Series Title Internet of Things
Book Title Digital Twin Technologies and Smart Cities
ISBN 9783030187316-(print);-9783030187323-(ebk.)
DOI https://doi.org/10.1007/978-3-030-18732-3_4
Publisher URL https://doi.org/10.1007/978-3-030-18732-3_4
Related Public URLs https://link.springer.com/chapter/10.1007/978-3-030-18732-3_4

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