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

Author

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  • Jingwei Guo
  • Ying Cheng
  • Dongxu Wang
  • Fei Tao
  • Stefan Pickl

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.

Suggested Citation

  • Jingwei Guo & Ying Cheng & Dongxu Wang & Fei Tao & Stefan Pickl, 2023. "Industrial Dataspace for smart manufacturing: connotation, key technologies, and framework," International Journal of Production Research, Taylor & Francis Journals, vol. 61(12), pages 3868-3883, June.
    • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:12:p:3868-3883
    DOI: 10.1080/00207543.2021.1955996
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