The existing models and methods used to determine the melting temperature of the mold powders used in the continuous casting process remain inaccurate in the case of equations reported in current literature or consider for commercial software only an equilibrium state. In this work, a new approach has been implemented using neural networks, which will act as a "black box" to predict the melting temperature of a mold powder with a given composition within an acceptable range. The proposed neural network will be working as a regression neural model whose inputs will be the composition of each of the chemical species contained within the powder. A database provided by a research net comprising multiple countries' research institutes will be fed as a training set for network learning. Such data comes from experimental measurements performed mainly through the high-temperature microscope test. The correct implementation and training of the network should provide a new alternative to develop new products and verify existing products' melting properties. In future models, further considerations should be made towards a better understanding of these phenomena, which should consider factors such as the formation of mineral phases, interaction among some specific components of the powder, or even the parameters used at the time of experimental measurement.

Neuromelt model for estimating mold flux melting behaviour

Mapelli C.;
2022-01-01

Abstract

The existing models and methods used to determine the melting temperature of the mold powders used in the continuous casting process remain inaccurate in the case of equations reported in current literature or consider for commercial software only an equilibrium state. In this work, a new approach has been implemented using neural networks, which will act as a "black box" to predict the melting temperature of a mold powder with a given composition within an acceptable range. The proposed neural network will be working as a regression neural model whose inputs will be the composition of each of the chemical species contained within the powder. A database provided by a research net comprising multiple countries' research institutes will be fed as a training set for network learning. Such data comes from experimental measurements performed mainly through the high-temperature microscope test. The correct implementation and training of the network should provide a new alternative to develop new products and verify existing products' melting properties. In future models, further considerations should be made towards a better understanding of these phenomena, which should consider factors such as the formation of mineral phases, interaction among some specific components of the powder, or even the parameters used at the time of experimental measurement.
2022
LIQUIDUS TEMPERATURE
MOLD POWDER
NEURAL NETWORK
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1221099
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