A survey of electromagnetic metal casting computation designs, present approaches, future possibilities, and practical issues
School of Information Technology & Engineering (SITE), Vellore Institute of Technology (VIT), 632014, Vellore, Tamilnadu, India
2 Department of Manufacturing Engineering, School of Mechanical Engineering (SMEC), Vellore Institute of Technology (VIT), 632014, Vellore, Tamilnadu, India
3 School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), 632014, Vellore, Tamilnadu, India
Accepted: 21 June 2021
Published online: 30 June 2021
Electromagnetic metal casting (EMC) is a casting technique that uses electromagnetic energy to heat metal powders. It is a faster, cleaner, and less time-consuming operation. Solid metals create issues in electromagnetics since they reflect the electromagnetic radiation rather than consume it—electromagnetic energy processing results in sounded pieces with higher-ranking material properties and a more excellent microstructure solution. For the physical production of the electromagnetic casting process, knowledge of electromagnetic material interaction is critical. Even where the heated material is an excellent electromagnetic absorber, the total heating quality is sometimes insufficient. Numerical modelling works on finding the proper coupled effects between properties to bring out the most effective operation. The main parameters influencing the quality of output of the EMC process are: power dissipated per unit volume into the material, penetration depth of electromagnetics, complex magnetic permeability and complex dielectric permittivity. The contact mechanism and interference pattern also, in turn, determines the quality of the process. Only a few parameters, such as the environment's temperature, the interference pattern, and the rate of metal solidification, can be controlled by AI models. Neural networks are used to achieve exact outcomes by stimulating the neurons in the human brain. Additive manufacturing (AM) is used to design mold and cores for metal casting. The models outperformed the traditional DFA optimization approach, which is susceptible to local minima. The system works only offline, so real-time analysis and corrections are not yet possible.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2021