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Feature-based Function Block Control Framework for Manufacturing Equipment in Cloud Environments
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-1265-8451
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden. (Produktion och Automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-8679-8049
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och Automatiseringsteknik, Production and Automation Engineering)
2018 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

The ability to adaptively control manufacturing equipment in cloud environments is becoming increasingly more important. Industry 4.0, supported by Cyber Physical Systems and the concept of on-demand, scalable and pay-for-usage resource-sharing in cloud environments offers many promises regarding effective and flexible manufacturing. For implementing the concept of manufacturing services in a cloud environment, a cloud control approach for the sharing and control of networked manufacturing resources is required. This paper presents a cloud service-based control approach which has a product perspective and builds on the combination of event-driven IEC 61499 Function Blocks and product manufacturing features. Distributed control is realised through the use of a networked control structure of such Function Blocks as decision modules, enabling an adaptive run-time behaviour. The control approach has been developed and implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. An application scenario is presented to demonstrate the applicability of the control approach. In this scenario, Assembly Feature-Function Blocks for adaptive control of robotic assembly tasks have been used.

Place, publisher, year, edition, pages
Taylor & Francis, 2018.
Keywords [en]
manufacturing feature, function block, cloud, adaptive, control framework
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-16525DOI: 10.1080/00207543.2018.1542178OAI: oai:DiVA.org:his-16525DiVA, id: diva2:1273460
Available from: 2018-12-21 Created: 2018-12-21 Last updated: 2019-02-11Bibliographically approved

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Adamson, GöranWang, LihuiMoore, Philip

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