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FEATURE-BASED ADAPTIVE MANUFACTURING EQUIPMENT CONTROL FOR CLOUD ENVIRONMENTS
KTH, Skolan för industriell teknik och management (ITM), Industriell produktion.ORCID-id: 0000-0001-8679-8049
2016 (engelsk)Inngår i: PROCEEDINGS OF THE ASME 11TH INTERNATIONAL MANUFACTURING SCIENCE AND ENGINEERING CONFERENCE, 2016, VOL 2, AMER SOC MECHANICAL ENGINEERS , 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
AMER SOC MECHANICAL ENGINEERS , 2016.
Emneord [en]
Cloud manufacturing, adaptive robot control, ontology
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-197843ISI: 000388159400054Scopus ID: 2-s2.0-84991619741OAI: oai:DiVA.org:kth-197843DiVA, id: diva2:1059497
Konferanse
ASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016; Blacksburg; United States; 27 June 2016 through 1 July 2016
Merknad

QC 20161222

Tilgjengelig fra: 2016-12-22 Laget: 2016-12-08 Sist oppdatert: 2016-12-22bibliografisk kontrollert

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Wang, Lihui

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