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A linked data approach for the connection of manufacturing processes with production simulation models
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-1679-3319
University of Skövde, School of Engineering Science.
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-9690-890X
2018 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 70, p. 440-445Article in journal (Refereed) Published
Abstract [en]

This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation. 

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 70, p. 440-445
Keywords [en]
Smart Factory, Digital Twin, Linked Data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16005DOI: 10.1016/j.procir.2018.03.243ISI: 000437126800074Scopus ID: 2-s2.0-85051264447OAI: oai:DiVA.org:his-16005DiVA, id: diva2:1234024
Conference
28th CIRP Design Conference, Nantes, France, May 23-25, 2018
Projects
TWIN
Funder
Knowledge Foundation, 20160297
Note

CC BY-NC-ND 3.0

Edited by Florent Laroche, Alain Bernard

This paper is based on work performed within the research projects TWIN [20160297] sponsored by the Swedish Knowledge Foundation.

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2022-07-15Bibliographically approved

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Senington, RichardNg, AmosOscarsson, Jan

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