Modelling and online training method for digital twin workshop

Abstract

Aiming at the difficulties in modelling, simulation and verification in digital twin workshop, a modelling and online training method for digital twin workshop is proposed. This paper describes a multi-level digital twin aggregate modelling method, including the status attributes, the static performance attributes and the fluctuation performance attributes, and designs a digital twin organisation system, namely, digital twin graph. According to the data demand for digital twin aggregates, a spatio-temporal data model is constructed. The digital twin model training method using truncated normal distribution is presented. Furthermore, a verification method based on real-virtual error for a digital twin model is proposed. The effectiveness of real-time status monitoring, online model training and simulation for production is verified by a case.

KEYWORDS:

• Digital twin modelling
• digital twin aggregate
• online training
• model validation
• spatio-temporal data model
• digital twin workshop

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available within the article; supplementary data are available from the corresponding author (Yu Guo) upon reasonable request.

Additional information

Funding

This work was supported in part by National Defense Basic Scientific Research Program of China (CN) [grants number JCKY2018203A001] and Natural Science Foundation of Jiangsu Province [grants number BK20202007].

Notes on contributors

Litong Zhang

Litong Zhang received the B.S. degree in Mechanical and Electronic Engineering from Shandong Agricultural University, Tai’an, China, in 2017, the M.S. degree in Mechanical manufacturing and automation from Kunming University of Science and Technology, Kunming, China, in 2020. He is currently pursuing the Ph.D. degree in Aerospace Manufacturing Engineering Nanjing University of Aeronautics and Aeronautics (NUAA). His research interest includes digital twin, discrete event simulation and intelligent manufacturing system.

Yu Guo

Yu Guo received the B.S. degree in mechanical engineering from Chang’an University, Xi’an, China, in 1993, the M.S. degree in mechatronic engineering from Xinjiang University, Urumchi, China, in 1999 and the Ph. D. degree in electrical automation from Huazhong University of Science and Technology, in 2003. In 2007, he worked as a visiting scholar at the Royal Polytechnic University of Melbourne. He is a Full Professor in the College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics. He has authored or co-authored over 100 papers in scientific journals and international conferences in the related areas. His research interest includes the Internet of Things, industrial big data, digital twin shop-floor and modelling and optimisation of the manufacturing system.

Weiwei Qian

Weiwei Qian received the B.S. degree in school of mechanical and electrical engineering from Jiangxi University of science and technology, Ganzhou, China, in 2015, the M.S. degrees in college of aeronautical manufacturing engineering from Nanchang Hangkong University, Nanchang, China, in 2019. He is currently pursuing the Ph.D. degree in Aerospace manufacturing engineering at Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China. His research interest includes digital twin workshop, cyber physical systems and production process optimisation.

Weili Wang

Weili Wang received the B.S. degree in Mechanical manufacturing and automation from Yangzhou University, Yangzhou, China, in 2019. She is currently pursuing the M.S degree in Mechanical Engineering from Nanjing University of Aeronautics and Aeronautics (NUAA). Her research interest includes big data technology, discrete event simulation and intelligent manufacturing technology.

Daoyuan Liu

Daoyuan Liu received the B.E. degree in aircraft manufacturing engineering, and the M.E degree in aeronautics and aeronautics manufacturing engineering from Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2017 and 2020, respectively. He is currently pursuing the Ph.D. degree at NUAA. His research interests include industrial big data and production process optimisation.

Sai Liu

Sai Liu received the B.S. degree in Mechanical Engineering from Nanjing Normal University, Nanjing, China, in 2020. She is currently pursuing the M.S. degree in Aerospace Manufacturing Engineering at Nanjing University of Aeronautics and Aeronautics (NUAA). Her research interest includes digital twin, discrete event simulation and intelligent manufacturing system.