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Cloud-based adaptive process planning considering availability and capabilities of machine tools
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0002-3517-3636
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2016 (English)In: Journal of manufacturing systems, ISSN 0278-6125, E-ISSN 1878-6642, Vol. 39, 1-8 p.Article in journal (Refereed) Published
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Abstract [en]

Disturbances on manufacturing shop-floors and the increasing number of product variants necessitate adaptive and flexible process planning methods. This paper proposes a service-oriented Cloud-based software framework comprising two services. The first service generates non-linear process plans using event-driven function blocks and a genetic algorithm. The second service, gathers data from shop-floor machine tools through sensors, input from operators, and machine schedules. An information fusion technique processes the monitoring data in order to feed the process planning service with the status, specifications, and availability time windows of machine tools. The methodology is validated in a case study of a machining SME.

Place, publisher, year, edition, pages
2016. Vol. 39, 1-8 p.
Keyword [en]
Computer programming, Floors, Genetic algorithms, Process planning, Adaptive process, Flexible process planning, Information fusion techniques, Machine schedules, Nonlinear process, Product variants, Service Oriented, Software frameworks, Machine tools
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-188345DOI: 10.1016/j.jmsy.2016.01.003Scopus ID: 2-s2.0-84957886155OAI: oai:DiVA.org:kth-188345DiVA: diva2:934730
Note

QC 20160609

Available from: 2016-06-09 Created: 2016-06-09 Last updated: 2016-12-22Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
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  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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