Metaverse and AI Digital Twinning of 42SiCr Steel Alloys

Digital twins are the most important parts of Cyber-Physical Systems (CPSs), and play a crucial role in realization Metaverse. Therefore, two factors: flexibility adaptability, need to be focused on digital twinning systems. From virtual perspective, constructing buildings, structures, mechanisms Metaverse requires materials components. Hence, accurate reliable models can guarantee success implementation, particularly when it comes completing physical real world. Accordingly, four Machine Learning (ML) methods make an advanced 42SiCr alloy considering all its uncertainties non-linearities have been employed this paper. These ML accelerate digitalization proposed allow users employ them for wide range similar metals. Based technique, producers borrow these build their structures This way, if properties were satisfactory, they might buy start manufacturing products. As case study, we focus twining steel with some influential factors mechanical properties, making nature complex. Processes, including heat treatment, may restore material’s deformability; however, Quenching Partitioning (Q&P) not only eliminates impact cold forming but also provides high-strength (AHSS) properties. In research, combined impacts different Q&P treatments investigated alloy. The results shown acceptability accuracy realizing complex