Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework

Abstract

Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many advantages such as low cost of adaptation and convenience for local database, some methods are hard to manage variable data sources and discover proper range of data for smart decision-making. Therefore, Dataspace is considered in this article to be a feasible and effective method. From the relation-defined perspective of utilisation of industrial Big Data, the contribution is a novel industrial Dataspace design with static structure and working flow paths for smart manufacturing. In design, the industrial Dataspace platform has been proposed to accommodate smart manufacturing characteristics with the intelligence of pay-as-you-go, like harnessing distributed heterogenous data from industrial enterprises, understanding industrial data by ontology or knowledge, corelating the data with smart applications, and enabling related decisions. A further analytical case in Surface Mounting Technology manufacturing of welding procedure is provided to illustrate the execution of customisation, focused and related decision support, and system evolution within industrial Dataspace.

KEYWORDS:

• Dataspace
• industrial Big Data
• smart decision-making
• data analytics
• smart manufacturing

Disclosure statement

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

Additional information

Funding

This work is partly supported by National Key R&D Program of China [grant number 2019YFB1705402], Beijing Science Fund for Distinguished Young Scholars [grant number JQ19011], National Natural Science Foundation of China [grant number 52005024], and Fundamental Research Funds for the Central Universities [grant number YWF-21-BJ-J-1182].

Notes on contributors

Jingwei Guo

Jingwei Guo received the B.S. degree in Automation from Beihang University, Beijing, China, in 2021 and is currently pursuing the Master’s degree in Department of Electrical and Electronic Engineering, Imperial College London, London, UK. His research interests cover Dataspace in smart manufacturing and industrial automation of control.

Ying Cheng

Ying Cheng received the B.S. degree in Mechanical Engineering from Wuhan University of Technology, Wuhan, China, in 2010, and received the Ph.D. degree in Control Science and Engineering from Beihang University, Beijing, China, in 2016. She is currently an Associate Professor with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. Her research interests include service-oriented smart manufacturing, manufacturing services supply-demand matching and collaboration. She has authored over 40 journal or conference papers in the above areas.

Dongxu Wang

Dongxu Wang received the B.S. degree in Aircraft quality and reliability from Beihang University, Beijing, China, in 2019, and is currently pursuing the M.S. degree in School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. His research interests include manufacturing service collaboration optimisation and Dataspace.

Fei Tao

Fei Tao received the B.S. and Ph.D. degrees in Mechanical Engineering from Wuhan University of Technology, Wuhan, China, in 2003 and 2008. He is currently a Professor with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. His research interests include service-oriented smart manufacturing, manufacturing service management, and digital twin driven product design/manufacturing/service. He has authored five monographs and over 100 journal papers in the above areas.

Stefan Pickl

Stefan Pickl Studied mathematics, electrical engineering, and philosophy at TU Darmstadt and EPFL Lausanne 1987-93. Dipl.-Ing. ‘93, Doctorate 1998 with award. Assistant Professor at Cologne University (Dr. habil. 2005; venia legendi Changes: “Mathematics’). Since 2005 chair for Operations Research ( – Management Science – ) at UBw Munich. Visiting Professor at University of New Mexico (U.S.A.), University Graz (Austria), University of California at Berkeley, Naval Postgraduate School NPS Monterey (U.S.A.). Chair of the Advisory Board of the German Society for Operations Research (GOR). Vice-President of German Committee for Disaster Reduction DKKV. Honorary Professor University of Nottingham at Malaysia Campus. Fellow European Academy of Industrial Management.