Metaverse and AI Digital Twinning of 42SiCr Steel Alloys

Omid Khalaj, Mohammad (Behdad) Jamshidi (), Parsa Hassas, Marziyeh Hosseininezhad, Bohuslav Mašek, Ctibor Štadler and Jiří Svoboda
Additional contact information
Omid Khalaj: Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 306 14 Pilsen, Czech Republic
Mohammad (Behdad) Jamshidi: Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 306 14 Pilsen, Czech Republic
Parsa Hassas: Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 306 14 Pilsen, Czech Republic
Marziyeh Hosseininezhad: Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Sciences, Tehran 19857-17443, Iran
Bohuslav Mašek: Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 306 14 Pilsen, Czech Republic
Ctibor Štadler: Faculty of Electrical Engineering, University of West Bohemia, Univerzitní 22, 306 14 Pilsen, Czech Republic
Jiří Svoboda: Institute of Physics of Materials, Academy of Sciences of the Czech Republic, Žižkova 22, 616 62 Brno, Czech Republic

Mathematics, 2022, vol. 11, issue 1, 1-23

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.

Keywords: smart manufacturing; metaverse; digital twin; machine learning; cyber-physical systems; 42SiCr steel; Q& P treatment; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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