Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61813
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dc.contributor.authorShafiq, Syed Imranen
dc.contributor.authorSzczerbicki, Edwarden
dc.contributor.authorSanin, Cesaren
dc.date.accessioned2024-07-25T09:17:10Z-
dc.date.available2024-07-25T09:17:10Z-
dc.date.issued2019-
dc.identifier.citationCybernetics and Systems, 50(2), p. 154-164en
dc.identifier.issn1087-6553en
dc.identifier.issn0196-9722en
dc.identifier.urihttps://hdl.handle.net/1959.11/61813-
dc.description.abstract<p>In order to allocate resources effectively according to the production plan and to reduce disturbances, a framework for smart production performance analysis is proposed in this article. Decisional DNA based knowledge models of engineering objects, processes and factory are developed within the proposed framework. These models are the virtual representation of manufacturing resources, and with help of Internet of Things, are capable of capturing the past experience and formal decisions. A case study for the smart tool performance analysis is presented in which information of key tool parameters like tool life, surface integrity, tool forces and chip formation can be sensed in real-time, and predictions can be made according to specific requirements. This framework is capable of creating a cyber-physical conjoining of the bottom-level manufacturing resources and thus can work as a technological basis for smart factories and Industry 4.0.</p>en
dc.languageenen
dc.publisherTaylor & Francis Incen
dc.relation.ispartofCybernetics and Systemsen
dc.titleDecisional-DNA Based Smart Production Performance Analysis Modelen
dc.typeJournal Articleen
dc.identifier.doi10.1080/01969722.2019.1565122en
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.startpage154en
local.format.endpage164en
local.peerreviewedYesen
local.identifier.volume50en
local.identifier.issue2en
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:1959.11/61813en
local.date.onlineversion2019-02-07-
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleDecisional-DNA Based Smart Production Performance Analysis Modelen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorShafiq, Syed Imranen
local.search.authorSzczerbicki, Edwarden
local.search.authorSanin, Cesaren
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/d764723e-b8d2-41bf-8fae-de1ec053bcd4en
local.uneassociationNoen
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.available2019en
local.year.published2019en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/d764723e-b8d2-41bf-8fae-de1ec053bcd4en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/d764723e-b8d2-41bf-8fae-de1ec053bcd4en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-01en
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School of Science and Technology
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