This work presents the development of a computational algorithm applied to improve the thermal behaviour in the secondary cooling zone of steel billets and blooms produced by continuous casting. A mathematical solidification heat transfer model works integrated with a neural network based algorithm (NNBA) connected to a knowledge base of boundary conditions of operational parameters and metallurgical constraints. The improved strategy selects a set of cooling conditions (in the secondary cooling zone) and metallurgical criteria established to attain high product quality, which are related to a more homogeneous thermal behaviour during solidification. Initially, the results of simulations performed by using the mathematical model are validated against experimental industrial data, and good agreement is observed, in any case examined, permitting the determination of nominal heat transfer conditions by the inverse heat conduction method. By using the numerical model linked to a NNBA results have been produced determining a set of casting conditions, which has permitted better strand surface temperature profile and metallurgical length to be attained during the continuous casting of SAE 1007 billets and SAE 1025 blooms.
Work in REF suggests a shallow neural network-based method for representing the relationship between temperature and secondary cooling conditions in a continuous casting process.
A solidification heat transfer model and a neural network based algorithm applied to the continuous casting of steel billets and blooms
Modelling and Simulation in Materials Science and Engineering
Materials Science