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DC poleHodnotaJazyk
dc.contributor.authorKhalaj, Omid
dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorHassas, Parsa
dc.contributor.authorHosseininezhad, Marziyeh
dc.contributor.authorMašek, Bohuslav
dc.contributor.authorŠtádler, Ctibor
dc.contributor.authorSvoboda, Jiří
dc.date.accessioned2023-05-29T10:00:15Z-
dc.date.available2023-05-29T10:00:15Z-
dc.date.issued2023
dc.identifier.citationKHALAJ, O. JAMSHIDI, M. HASSAS, P. HOSSEININEZHAD, M. MAŠEK, B. ŠTÁDLER, C. SVOBODA, J. Metaverse and AI Digital Twinning of 42SiCr Steel Alloys. Mathematics, 2023, roč. 11, č. 1, s. nestránkováno. ISSN: 2227-7390cs
dc.identifier.issn2227-7390
dc.identifier.uri2-s2.0-85145906268
dc.identifier.urihttp://hdl.handle.net/11025/51905
dc.format23 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofseriesMathematicsen
dc.rights© The Author(s)en
dc.titleMetaverse and AI Digital Twinning of 42SiCr Steel Alloysen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedDigital twins are the most important parts of Cyber-Physical Systems (CPSs), and play a crucial role in the realization of the Metaverse. Therefore, two important factors: flexibility and adaptability, need to be focused on digital twinning systems. From a virtual perspective, constructing buildings, structures, and mechanisms in the Metaverse requires digital materials and components. Hence, accurate and reliable digital models can guarantee the success of implementation, particularly when it comes to completing physical twins in the real world. Accordingly, four Machine Learning (ML) methods to make digital twins of an advanced 42SiCr alloy considering all of its uncertainties and non-linearities have been employed in this paper. These ML methods accelerate the digitalization of the proposed alloy and allow users to employ them for a wide range of similar metals. Based on this technique, producers can borrow these virtual materials and build their structures in the Metaverse. This way, if the properties of the materials were satisfactory, they might buy them and start manufacturing their products. As a case study, we focus on digital twining of an 42SiCr steel with some influential factors in its mechanical properties, making the nature of the alloy complex. Processes, including heat treatment, may restore the material’s deformability; however, Quenching and Partitioning (Q&P) not only eliminates the impact of cold forming but also provides advanced high-strength steel (AHSS) properties. In this research, the combined impacts of different Q&P treatments were investigated on the mechanical properties of 42SiCr steel alloy. The results have shown the acceptability and accuracy of the proposed ML methods in realizing the digital twins of this complex alloy.en
dc.subject.translatedsmart manufacturingen
dc.subject.translatedmetaverseen
dc.subject.translateddigital twinen
dc.subject.translatedmachine learningen
dc.subject.translatedcyber-physical systemsen
dc.subject.translated42SiCr steelen
dc.subject.translatedQ& P treatmenten
dc.subject.translatedartificial intelligenceen
dc.identifier.doi10.3390/math11010004
dc.type.statusPeer-revieweden
dc.identifier.document-number909248900001
dc.identifier.obd43939164
dc.project.IDGX21-02203X/Vylepšení vlastností současných špičkových slitincs
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