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DC poleHodnotaJazyk
dc.contributor.authorČech, Martin
dc.contributor.authorBeltman, Arend-Jan
dc.contributor.authorOzols, Kaspars
dc.date.accessioned2023-02-13T11:00:20Z-
dc.date.available2023-02-13T11:00:20Z-
dc.date.issued2022
dc.identifier.citationČECH, M. BELTMAN, A. OZOLS, K. Digital Twins and AI in Smart Motion Control Applications. In IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022. New York: IEEE, 2022. s. nestránkováno. ISBN: 978-1-66549-996-5 , ISSN: 1946-0740cs
dc.identifier.isbn978-1-66549-996-5
dc.identifier.issn1946-0740
dc.identifier.uri2-s2.0-85141412206
dc.identifier.urihttp://hdl.handle.net/11025/51461
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleDigital Twins and AI in Smart Motion Control Applicationsen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedRecently, smart system integration was identified as a key competence for optimizing machines and robots. However, when one wants to ’tune’ the entire production process a step further is necessary. We should evaluate performance indicators (e.g. energy and material consumption) over the whole machine life cycle in order to align the production with circular economy principles. To reach that target MBSE (model-based system engineering) should be covered by advanced digital twin approaches which allow continuous monitoring of machine performance, predict the failures and maintenance. Moreover, artificial intelligence and machine learning must be used to process big data sets gathered from the production lines. This paper identifies a common set of technologies and building blocks suitable to solve above mentioned problems for a large variety of industrial domains (semiconductor production, health-care robotics, CNC 1 machining, high-speed packaging and others). It presents the first results of the large-scale IMOCO4.E 2 project and shows the pathways for application of the technology on specific machines (so-called pilots). The authors believe the ideas presented could be inspiring also in other domains.en
dc.subject.translatedsmart system integrationen
dc.subject.translatedmechatronicsen
dc.subject.translatedmotion controlen
dc.subject.translateddigital twinen
dc.subject.translatedelectronics systemsen
dc.subject.translatedwireless communicationen
dc.subject.translatedsmart sensorsen
dc.subject.translatedroboticsen
dc.subject.translatedembedded systemsen
dc.subject.translatedmachine learningen
dc.subject.translatedartificial intelligenceen
dc.subject.translatedcyber-physical systemsen
dc.identifier.doi10.1109/ETFA52439.2022.9921533
dc.type.statusPeer-revieweden
dc.identifier.obd43937084
Vyskytuje se v kolekcích:Konferenční příspěvky / Conference papers (NTIS)
Konferenční příspěvky / Conference Papers (KKY)
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