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Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Cast Shop Technology
Presentation Title Assessment of a neural network based approach for application as a digital twin in billet DC casting.
Author(s) Kjerstin Ellingsen, Mohammed M'Hamdi, Knut Omdal Tveito
On-Site Speaker (Planned) Kjerstin Ellingsen
Abstract Scope Hot-tearing is a severe defect limiting the productivity of DC-casting of aluminum billets. Hot-tearing indicators integrated in process FEM models have been shown to be valuable for assessing hot-tearing tendencies for different casting conditions. However, these FEM tools are not efficient enough for a quick screening and selection of process parameters before casting or adjustments during casting to avoid hot-tears. In this work, we assess the possibility of using an artificial neural network (ANN) to predict sump depth and hot-tearing tendency in the center of billets. A FEM DC-casting model is used to build a database for training and testing the neural network. Configuration of the ANN model is discussed as well as assessment for application as a digital twin in billet DC-casting.
Proceedings Inclusion? Planned: Light Metals
Keywords Aluminum, Modeling and Simulation, Machine Learning

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