Metaverse and AI Digital Twinning of 42SiCr Steel Alloys

Khalaj, O; Jamshidi, M; Hassas, P; Hosseininezhad, M; Mašek, B; Štadler, C; Svoboda, J

Metaverse and AI Digital Twinning of 42SiCr Steel Alloys

Khalaj, O Jamshidi, M

Hassas, P Hosseininezhad, M Mašek, B Štadler, C Svoboda, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Mathematics, 2023, 11, (1)
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Khalaj, O
dc.contributor.author	Jamshidi, M https://orcid.org/0000-0002-5312-6497
dc.contributor.author	Hassas, P
dc.contributor.author	Hosseininezhad, M
dc.contributor.author	Mašek, B
dc.contributor.author	Štadler, C
dc.contributor.author	Svoboda, J
dc.date.accessioned	2024-05-03T04:11:37Z
dc.date.available	2024-05-03T04:11:37Z
dc.date.issued	2023-01-01
dc.identifier.citation	Mathematics, 2023, 11, (1)
dc.identifier.issn	2227-7390
dc.identifier.issn	2227-7390
dc.identifier.uri	http://hdl.handle.net/10453/178601
dc.description.abstract	Digital 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.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Mathematics
dc.relation.isbasedon	10.3390/math11010004
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	49 Mathematical sciences
dc.title	Metaverse and AI Digital Twinning of 42SiCr Steel Alloys
dc.type	Journal Article
utslib.citation.volume	11
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-03T04:11:27Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	1

Abstract:

Digital 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.

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http://hdl.handle.net/10453/178601