ABSTRACT An artificial neural network model is used to predict high-temperature ductility of steels from their composition and thermal history. The model used literature data on reduction of area (RA). In most types of steel, RA has a U-shaped or V-shaped function of temperature; this shape was represented using a Gaussian fit. The predictive model considers conditions, including alloy composition and thermal conditions. The predicted values agreed well with most experimental values. This model can predict ductility trough for a wider composition range and thermal history than previous studies have achieved. The model also presents how fine components such as titanium (Ti) and nitrogen (N) affect changes in the hot-ductility trough. This model can be used to set steel-casting operating conditions to ensure that steel is not at the temperature in which ductility is low when the slab passes through the bending/unbending area of a continuous caster.

中文翻译：

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通过深度神经网络从元素组成和热历史预测钢的热延展性

摘要 人工神经网络模型用于根据钢的成分和热历史预测钢的高温延展性。该模型使用了关于减少面积 (RA) 的文献数据。在大多数类型的钢中，RA 具有 U 形或 V 形温度函数；该形状使用高斯拟合表示。预测模型考虑条件，包括合金成分和热条件。预测值与大多数实验值一致。与之前的研究相比，该模型可以预测更广泛的成分范围和热历史的延展性谷值。该模型还展示了钛 (Ti) 和氮 (N) 等精细成分如何影响热延展性槽的变化。