Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61852
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dc.contributor.authorShafiq, Syed Imranen
dc.contributor.authorSzczerbicki, Edwarden
dc.contributor.authorSanin, Cesaren
dc.date.accessioned2024-07-29T03:04:03Z-
dc.date.available2024-07-29T03:04:03Z-
dc.date.issued2018-
dc.identifier.citationCybernetics and Systems, 49(5-6), p. 280-295en
dc.identifier.issn1087-6553en
dc.identifier.issn0196-9722en
dc.identifier.urihttps://hdl.handle.net/1959.11/61852-
dc.description.abstract<p>Computer integrated manufacturing (CIM) has enormous benefits as it increases the rate of production, reduces errors and production waste, and streamlines manufacturing sub-systems. However, there are some new challenges related to CIM operating in the Internet of Things/Internet of Data (IoT/IoD) scenarios associated with Industry 4.0 and cyber-physical systems. The main challenge is to deal with the massive volume of data flowing between various CIM components functioning in virtual settings of IoT. This paper proposes decisional DNA-based knowledge representation framework to manage the storage, analysis, and processing of data, information, and knowledge of a typical CIM. The framework utilizes the concept of virtual engineering object and virtual engineering process for developing knowledge models of various CIM components such as automatic storage and retrieval systems, automatic guided vehicles, robots, and numerically controlled machines. The proposed model is capable of capturing in real time the manufacturing data, information and knowledge at every stage of production, that is, at the object level, the process level, and at the factory level. The significance of this study is that it will support decision-making by reusing the experience, which will not only help in effective real-time data monitoring and processing, but also make CIM system intelligent and ready to function in the virtual Industry 4.0 environment.</p>en
dc.languageenen
dc.publisherTaylor & Francis Incen
dc.relation.ispartofCybernetics and Systemsen
dc.titleManufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenarioen
dc.typeJournal Articleen
dc.identifier.doi10.1080/01969722.2017.1418265en
local.contributor.firstnameSyed Imranen
local.contributor.firstnameEdwarden
local.contributor.firstnameCesaren
local.profile.schoolSchool of Science and Technologyen
local.profile.emailcmaldon3@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited States of Americaen
local.format.startpage280en
local.format.endpage295en
local.peerreviewedYesen
local.identifier.volume49en
local.identifier.issue5-6en
local.contributor.lastnameShafiqen
local.contributor.lastnameSzczerbickien
local.contributor.lastnameSaninen
dc.identifier.staffune-id:cmaldon3en
local.profile.orcid0000-0001-8515-417Xen
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61852en
local.date.onlineversion2018-02-01-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleManufacturing Data Analysis in Internet of Things/Internet of Data (IoT/IoD) Scenarioen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorShafiq, Syed Imranen
local.search.authorSzczerbicki, Edwarden
local.search.authorSanin, Cesaren
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2018en
local.year.published2018en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/263f193e-eec8-4b84-a2d0-15fa96c0a375en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-01en
Appears in Collections:Journal Article
School of Science and Technology
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