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Feature-Based Adaptive Manufacturing Equipment Control for 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. 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)ORCID iD: 0000-0002-1699-3778
Falmouth University, Cornwall, United Kingdom.
2016 (English)In: Proceedings of the ASME 11th International Manufacturing Science and Engineering Conference, 2016, vol 2, American Society of Mechanical Engineers (ASME) , 2016, UNSP V002T04A019Conference paper (Refereed)
Abstract [en]

The ideas of on-demand, scalable and pay-for-usage resource-sharing in Cloud Manufacturing are steadily attracting more interest. For implementing the concept of Manufacturing as-a-Service in a cloud environment, description models and implementation language for resources and their capabilities are required. A standardized approach for systemived virtualization, servisilisation, retrieval, selection and composition into higher levels of functionality is necessary. For the collaborative sharing and use of networked manufacturing resources there is also a need for a control approach for distributed manufacturing equipment. In this paper, the technological perspective for an adaptive cloud service-based control approach is described, and a supporting information model for its implementation. The control is realized through the use of a network of intelligent and distributable Function Block decision modules, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system's integration to the cloud service management functionality is described, as well as a feature-level capability model and the use of ontologies and the Semantic Web.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME) , 2016. UNSP V002T04A019
Keyword [en]
Cloud manufacturing, adaptive robot control, ontology
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13213DOI: 10.1115/MSEC2016-8771ISI: 000388159400054ScopusID: 2-s2.0-84991619741ISBN: 978-0-7918-4990-3OAI: oai:DiVA.org:his-13213DiVA: diva2:1054584
Conference
ASME 2016 11th International Manufacturing Science and Engineering Conference (MSEC 2016), Blacksburg, USA, June 27–July 1, 2016
Available from: 2016-12-08 Created: 2016-12-08 Last updated: 2017-01-03Bibliographically approved

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Adamson, GöranWang, LihuiHolm, Magnus
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