A multi-level modelling and fidelity evaluation method of digital twins for creating smart production equipment in Industry 4.0

Abstract

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE.

KEYWORDS:

• Digital twin
• smart production equipment
• Industry 4.0
• multi-level modelling
• fidelity evaluation

Disclosure statement

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

Data availability statement

The data used to support the findings of this study are included in the manuscript. We also provide an open digital twin manufacturing cell platform, which is available at http://120.48.54.211:8081/DTMC/.

Additional information

Funding

This work was supported by National Key Research and Development Program of China [grant number 2018YFB1702400]; National Natural Science Foundation of China [grant number 52105530 and 51975463]; China Postdoctoral Science Foundation [grant number 2021M692556].

Notes on contributors

Chao Zhang

Chao Zhang received the B.E. degree in mechanical engineering and automation from Sichuan University, Chengdu, China, in 2015, and PhD degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2020. He is currently the Assistant Professor with the School of Mechanical Engineering, Xi’an Jiaotong University. He was selected into the National Postdoctoral Program for Innovative Talents of China, and the Young Talent fund of University Association for Science and Technology in Shaanxi. His research interests include digital twins, big data-driven intelligent manufacturing, and decision-making support systems. He has published 2 books and authored in excess of 50 scientific publications in IEEE TII, IEEE IoT, KBS, JCP, IJPR, etc. He is a Senior Member of CMES, Member of IEEE, and serves as Editor/Reviewer for several high impact international journals in the intelligent manufacturing field.

Jingjing Li

Jingjing Li is currently pursuing the PhD degree in mechanical engineering with the School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China. From 2022 to 2023, she is a visiting student at the Department of Manufacturing and Production Systems, Politecnico di Milano. Her main research interests are related to digital twin-driven intelligent process planning, knowledge driven decision-making, and intelligent manufacturing systems.

Guanghui Zhou

Guanghui Zhou received the B.E., M.E. and PhD degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 1996, 1999 and 2003. From 2010 to 2011, he was a visiting scholar with the Department of Industrial and Manufacturing Systems Engineering, Florida State University. He is currently the Professor with the School of Mechanical Engineering, Xi’an Jiaotong University. His research interests include service-oriented networked digital manufacturing, low-carbon manufacturing, intelligent manufacturing and knowledge-based manufacturing systems. He is the author of two monographs, more than 120 journal and conference papers, and 30 inventions. His research results won the first prize of the Natural Science of the Ministry of Education of China.

Qian Huang

Qian Huang received the B.E. degree in Environmental Science and engineering from Xi’an Jiaotong University, Xi’an, China, in 2011 and the PhD degree in Environmental Science and engineering from Tsinghua University, Beijing, China, in 2019. She is a senior research engineer of China Nuclear Power Engineering Co. Ltd, Beijing, China. Her research interests include intelligent maintenance of equipment in nuclear power plant, optimised operation control of Nuclear power cooling system.

Min Zhang

Min Zhang received the B.E degree in Nuclear engineering and Nuclear Technology from Tsinghua University, M.E. degree in Nuclear Science and Engineering from China Institute of Nuclear Power Research and Design, and PhD degree in Nuclear Science and Engineering from Harbin Engineering University, in 2008, 2011 and 2019. From 2015 to 2016, she was a visiting scholar with UCLA. She is currently a senior engineer at China Nuclear Power Engineering Co., Ltd, Beijing, China. Her research interests include Nuclear safety, Nuclear Intelligence, Prognostics and Health Management. She is the author of more than 10 journal and conference papers, and 3 inventions.

Yifan Zhi

Yifan Zhi received the B.S. degree in environment engineering from the University of Science and Technology Beijing, Beijing, China, in 2015 and the M.S. degree in environment engineering from RWTH Aachen University, Germany, in 2019. He is a senior research engineer of China Nuclear Power Engineering Co. Ltd, Beijing, China. His research interests include intelligent maintenance of equipment in nuclear power plant, optimised operation control of Nuclear power cooling system.

Zhibo Wei

Zhibo Wei received the M.E. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2022. He is currently working at United Imaging Healthcare Technology Group Co., Ltd., ST BU in Wuhan, China. His research interests include digital twins and system testing of medical surgical robots.